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The Brown Bag Teacher

Teach the Children. Love the Children. Change the World.

January 12, 2020

Organizing Research in 1st & 2nd Grade

Independent research in 1st and 2nd Grade is not something that just magically happens. Organizing writing is not something that just happens automatically. Both of these skills have to be explicitly modeled and scaffolded for students. The great news? When given the opportunity, students rise. The Common Core Standards ask our 1st and 2nd grade students to “Participate in shared research and writing projects”, as well as, “…gather information from provided sources to answer a question.” Our students are very capable of participating in real-world research with the appropriate scaffolds, supports, and explicit instruction. But how do we get there?

Where Do We Get Our Research in 1st & 2nd Grade?

Initially, research in 1st and 2nd Grade begins with books ( Pebble Go and National Graphic Kids are some of our favorites). I’ll also print articles and books from Reading AZ and Read Works if they are available. (If you have RAZ Kids, then you can just assign the Reading AZ texts to specific students and they can access them online. #savethetrees). Starting with print resources help me better manage the research and allows us to learn basic research skills before integrating technology.

research paper for 1st grade

Then, we slowly branch to ebooks using EPIC . I’m able to create topic specific collections for students and share them directly to their EPIC accounts. From there, we model using videos from YouTube ( SciShow Kids ). Now, the SciShow Kids videos are on Epic , so it’s even safer!! (Note – These are 6 and 7 year olds. In my classroom, they will not have the privilege or responsibility to freely roam the internet or YouTube.)

Finally we branch into online databases (all KY schools have free access to Kentucky Virtual Library) and teacher-chosen websites. I link specific websites students are allowed to visit from Google Classroom. As we explore these online resources, we have frequent conversations about internet safety and internet expectations. When online, our choices should always help us become better readers, writers, and humans.

research paper for 1st grade

Scaffolding research collection in this way allows me the opportunity to model expectations for each resource and how to use it, as well as, ensure students are safe.

Why Organize Research in 1st & 2nd Grade?

Organizing and structuring writing is not a skill that is innate within students. Students have to be explicitly taught executive functioning skills – such as organization. Additionally, when we research I don’t want students just copying down an entire book or webpage. The world’s most random collection of information will not be helpful in sharing our learning down the road. Researching in 1st and 2nd Grade means we invest the time to learn, read, model, practice, and tweak together.

When teaching students to gather and organize information, there are DOZENS of structures for doing it. As a teacher, I typically pick 3-4 different ways that are developmentally appropriate for my 1st and 2nd graders, as well as, lend themselves to the types of research we will be doing.

research paper for 1st grade

Planning of Instruction

Reading and writing are forever connected and they should be. We can leverage each one to ensure that students see both subjects in context, as well as, part of their daily lives. Additionally, as I am preparing for our research unit , we will leverage whatever we are learning in science and/or social studies. This ensures students have the background to do specific research about a topic, rather than “All About Monkeys”.

As new strategies for organizing research are explored we do not abandon all the others. Rather, the strategies we learn are ones that can easily be combined. Sketch noting is the best example of this. It can be a part of a concept map, questions and answers, and/or creating subtopics.

As I introduce ways to organize writing , I will typically do it as a part of our reading or science mini-lessons. The strategy is modeled in the context of content and then, we practice again together during writing. Next, students typically work in partners to try the strategy out and ultimately, they work independently. Some students will need more teacher support in independently researching and that’s okay.

Sketch Noting

Sketch noting is typically the first way students to collect research. It is the most kid-friendly and non-threatening. As a class, we read a text from our science or social studies learning and then, consider the big ideas. (At this point, we haven’t talked about developing a research question, so our information gathering is broad.) We talk about the ideas and what symbols or pictures represent them. Then, we discuss importance of including captions that contain important vocabulary, people, ideas, and numbers. Sketch notes don’t need to be in complete sentences, so it’s fine to write single words, bullets, or fragments.

research paper for 1st grade

Teaching students to create subtopics is a great way to start narrowing the research field. From all-the-random-facts to these-facts-fit-the-subtopics-I-have-chosen, students are to start differentiating between important information and “fun extras”.

The use of subheadings is easily modeled using the table of contents in informational texts. We spend time looking at these texts, noticing what subtopics the author chose to write about, and what types of information he/she included (and didn’t include).

As students choose subtopics, we put each subtopic as a heading on a different page in their writing notebook. Then, research collected for each subtopic is placed on the page specific to the learning. This can be done using bullets or sticky notes. Although expensive, I prefer the sticky-note route. It allows the details to be easily manipulated/moved around and seem less daunting for students who are reluctant writers.

Concept Mapping

Additionally, concept mapping is very similar to creating subtopics. Ultimately, this strategy becomes a little nebulous. Often times I will introduce it before subtopics sometimes after. There is no hard and fast rule. If taught after subtopics, we will create concept maps with ALL the information and then, create subtopics into which to sort the information. If teaching after subtopics, we natural embed subtopics into our mind maps.

research paper for 1st grade

The student sample belows shows a general collection of information with some sketch noting. That’s okay! It is a signal to me, as the teacher, we may need more support in structuring our thinking or we may not be focused on a specific research question.

research paper for 1st grade

Question & Answer

Hands-down the question/answer strategy is THE most effective for helping students explore specific research questions and avoiding the “All About” book filled with lots of random facts.

To begin this strategy, we read an informational text aloud and identify a sentence or idea in the text that we want to learn more about. We write this sentence or details from the text on a sticky note and stick it at the top of a page in our writing journal. From there, we make a bulleted list of questions from that detail. What do we want to know more about? What would our reader want to know more about?

research paper for 1st grade

Now, as we read/listen/write, these become our research questions. This strategy is gold because it means students are driving the inquiry, we are looking at something specific, and the questions will determine which sources we need. Therefore, using multiple information sources become authentic.

research paper for 1st grade

We Have the Information…Now What?

Now that we have completed research on several different topics, questions, and/or questions, we are ready to publish and share our learning. The science or social studies unit our learning aligned with determine how the information is shared. Sometimes we use Google Slides, paragraphs , letters, and sometimes we’ll share our ideas in a speech.

Research in 1st and 2nd Grade is a tough task. There will be missteps – not so great mini-lessons, skipping of steps, moving too fast, hard-to-find-research topics – and that’s okay. All of these things help us, as teachers, and students grow. Research in the real-world is not perfect, and it shouldn’t be in our classrooms either.

So, my challenge to you – offer students real opportunities to learn and research without over scaffolding. Be brave in teaching students’ strategies that allow choice, flexibility, and curiosity to reign. You’ve got this, friends.

research paper for 1st grade

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Getting First Graders Started With Research

Teaching academically honest research skills helps first graders learn how to collect, organize, and interpret information.

Photo of first graders on tablet in classroom

Earlier in my career, I was told two facts that I thought to be false: First graders can’t do research, because they aren’t old enough; and if facts are needed for a nonfiction text, the students can just make them up. Teachers I knew went along with this misinformation, as it seemed to make teaching and learning easier. I always felt differently, and now—having returned to teaching first grade 14 years after beginning my career with that age group—I wanted to prove that first graders can and should learn how to research. 

A lot has changed over the years. Not only has the science of reading given teachers a much better understanding of how to teach reading skills , but we now exist in a culture abundant in information and misinformation. It’s imperative that we teach academically honest research skills to students as early as possible. 

Use a Familiar Resource, and Pair it with a Planned Unit

How soon do you start research in first grade? Certainly not at the start of the year with the summer lapse in skills and knowledge and when new students aren’t yet able to read. By December of this school year, skills had either been recovered or established sufficiently that I thought we could launch into research. This also purposely coincided with a unit of writing on nonfiction—the perfect pairing.

The research needed an age-related focus to make it manageable, so I chose animals. I thought about taking an even safer route and have one whole class topic that we researched together, so that students could compare notes and skills. I referred back to my days working in inquiry-based curriculums (like the International Baccalaureate Primary Years Program) and had students choose which animal to study. Our school librarian recommended that we use Epic because the service has an abundance of excellent nonfiction animal texts of different levels.

Teach the Basics for Organized Research 

I began with a conversation about academic honesty and why we don’t just copy information from books. We can’t say this is our knowledge if we do this; it belongs to the author. Instead, we read and learn. Then, we state what we learned in our own words. Once this concept is understood, I model how to do this by creating a basic step-by-step flowchart taught to me by my wife—a longtime first-grade and kindergarten teacher and firm believer in research skills.

  • Read one sentence at a time.
  • Turn the book over or the iPad around.
  • Think about what you have learned. Can you remember the fact? Is the fact useful? Is it even a fact?
  • If the answer is no, reread the sentence or move onto the next one.
  • If the answer is yes, write the fact in your own words. Don’t worry about spelling. There are new, complex vocabulary words, so use your sounding-out/stretching-out strategies just like you would any other word. Write a whole sentence on a sticky note.
  • Place the sticky note in your graphic organizer. Think about which section it goes in. If you aren’t sure, place it in the “other facts” section.

The key to collecting notes is the challenging skill of categorizing them. I created a graphic organizer that reflected the length and sections of the exemplar nonfiction text from our assessment materials for the writing unit. This meant it had five pages: an introduction, “what” the animal looks like, “where” the animal lives, “how” the animal behaved, and a last page for “other facts” that could become a general conclusion.

Our district’s literacy expert advised me not to hand out my premade graphic organizer too soon in this process because writing notes and categorizing are two different skills. This was my intention, but I forgot the good advice and handed out the organizer right away. This meant dedicating time for examining and organizing notes in each combined writing and reading lesson. A lot of one-on-one feedback was needed for some students, while others flourished and could do this work independently. The result was that the research had a built-in extension for those students who were already confident readers.

Focus on What Students Need to Practice 

Research is an essential academic skill but one that needs to be tackled gradually. I insisted that my students use whole sentences rather than words or phrases because they’re at the stage of understanding what a complete sentence is and need regular practice. In this work, there’s no mention of citation language and vetting sources; in the past, I’ve introduced those concepts to students in fourth grade and used them regularly with my fifth-grade students. Finding texts that span the reading skill range of a first-grade class is a big enough task. 

For some of the key shared scientific vocabulary around science concepts, such as animal groups (mammals, etc.) or eating habits (carnivore, etc.), I created class word lists, having first sounded out the words with the class and then asked students to attempt spelling them in their writing.

The Power of Research Can Facilitate Student Growth 

I was delighted with the results of the research project. In one and a half weeks, every student had a graphic organizer with relevant notes, and many students had numerous notes. With my fourth- and fifth-grade students, I noticed that one of the biggest difficulties for them was taking notes and writing them in a way that showed a logical sequence. Therefore, we concluded our research by numbering the notes in each section to create a sequential order. 

This activity took three lessons and also worked for my first graders. These organized notes created an internal structure that made the next step in the writing process, creating a first draft of their nonfiction teaching books, so much easier. 

The overall result was that first graders were able to truly grasp the power of research and gathering accurate facts. I proved that young children can do this, especially when they work with topics that already fascinate them. Their love of learning motivated them to read higher-level and more sophisticated texts than they or I would normally pick, further proving how interest motivates readers to embrace complexity.

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FREE 1st Grade Research Paper Writing Template

Use curiosity driven learning to introduce your youngest learners to the fun of report writing with this free printable research paper template perfect for first grade, second grade – and even kindergarten.


Wait, research papers in kindergarten & first grade already?

You may not think of assigning your budding student a research paper before they can spell “research,” but learning to love the research process and report on their findings can be a wonderful goal for your child’s education.

The world is teeming with amazing things to discover. You can guide your young ones through the process of discovery and presentation while using their own curiosity to open up the world of exciting education.

Writing a paper does NOT have to be an overwhelming task.

This is one of my main goals in homeschooling my girls. As students climb to higher grades, writing papers becomes an essential part of their grading, application processes, and education overall. I dreaded papers growing up and want to teach my kids early on (yes, VERY early on) that writing research papers even as early as in first grade can be a fun way to present all their hard work in researching a topic that really came alive to them.

This free First Grade Research Paper Writing Template will help kids get excited about learning something new.

Your child can choose any topic they want to learn about for this writing template. This can be used with pre-writers and struggling writers because they can often choose drawing over writing. 

Various writing prompts guide them through the discovery process which include:

  • Draw a picture of what you want to learn about.
  • Write down your topic
  • Find a book about your topic at your local library
  • Draw a picture of the cover of the book you found
  • What letter does your topic start with? Draw as many of that letter as you can in the space below
  • Write or draw about 1 new thing you learned about
  • 3 questions I have about my topic are
  • 3 books I found about my topic
  • New questions you have during your reading
  • Draw or sketch something new you learned from one of your books
  • Write an idea for an activity you can do to learn more about your topic
  • 3 new words you learned during your reading and their meanings
  • Write about someone that you learn about when researching your topic
  • Glue a picture of this person here
  • Write basic facts about this person
  • Write about your experience doing your related activity

7 Pages include a mix of writing a drawing to keep your child interested and proud in presenting what they’ve learned. Use any or all the pages – whichever work best for your child. (report cover not shown)

Mix & Match Different Level Prompts To Fit Your Child’s Ability

My kindergartener chose to learn about flowers. She drew the flowers from the cover of the book, practiced writing the letter F, and drew a picture of the parts of a flower.

My 6 year old chose to learn about gymnastics and completed a report about the subject, including presenting what she learned about a famous gymnast

This free report writing template includes both pages geared more towards kindergarten students and pages more suitable for 1st or 2nd grade students. You can choose the pages that work best for your child’s level and writing ability. This works well for children of different ages as well. Your younger ones can use the pre-writer’s pages while your older students can report on more books, a person they learned about, and an activity they did to learn more about their topic.

I hope you enjoy this research paper writing template for kindergarten, 1st grade and even 2nd grade students!

I create these resources myself free of charge as a gift to other homeschool families. If you like this printable,  please share it on social media. It makes a HUGE difference when people share my printables, so THANK YOU!!

Fun Amazon Finds For This Printable:

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Fun Book Report Templates For Kids


[…] Curiosity Driven Report Writing For Little Ones […]

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I was so glad to see this this morning. I am working on report writing with my wee one right now!

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Oh that makes me so happy! Let me know if you use it and how it went! 🙂

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This is how learning should be! I think my daughter would have chosen gymnastics as a topic, too – it’s her new favorite thing to talk about, lol!

Thanks for commenting! I had her in gymnastics when she was a toddler – before her sisters were born. It was wonderful. But now with three kids it’s so expensive and logistically challenging! We did find some great videos on YouTube of gymnastics competitions so that was a great way to feed her interest. Have a great day!

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This is great! Thanks!

I hope you find a good use for it. Thanks for stopping by! 🙂

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I love this! I’ll have to download it and use it for myself. Thank you so much for sharing and for linking up!

I hope your girls get something good out of it! Thanks for hosting your linkup!

[…] Report Writing for Little Ones from “PK1HomeschoolFun” […]

[…] If you’d like a report template for older kids, try my Curiosity Driven Report Printable. […]

[…] Previous Next […]

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Is this still available for download? When I click on the button, it just takes me back to the sign up page.

Hi, Yes! You’re probably just missing the welcome email with the freebies password. Check your email spam/junk folder for the link 🙂 Feel free to email me if you still have trouble. -Christy

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I would like to print this but can’t find the link

Hi, this is in my Subscriber Freebies Library. Click here: and enter the current password from my emails. Enjoy!

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J. Richard Gentry Ph.D.

5 Research-based Practices for Kindergarten and First Grade

Here's what every kindergarten and first grade teacher should be doing..

Posted June 1, 2017

[Note: In this guest post, two renowned former kindergarten teachers who wrote about the extraordinary literacy successes of children in their classrooms (Feldgus & Cardonick, 1999) show how today’s cutting -edge research in cognitive psychology and neuroscience supports the best practices they and their colleagues have advocated and honed for three decades. What they discovered intuitively and through reflection and collaboration is now cutting-edge best practice in 21st century kindergartens.]

5 Powerful Research-Based Techniques for Exemplary Kindergartens Today

By Eileen Feldgus PhD and Isabell Cardonick MEd

The recent high-profile spotlight on a landmark study in Developmental Psychology has drawn attention to research by Canadian cognitive psychologists Gene Ouellette and Monique Sénéchal (2017). In some respects Ouellette and Sénéchal discovered what we, Eileen and Isabell, have advocated for decades: a powerful connection to improved end-of-first-grade reading scores through the use of early writing and invented spelling. The study’s title, “Invented Spelling in Kindergarten as a Predictor of Reading and Spelling in Grade 1: A New Pathway to Literacy, or Just the Same Road, Less Known?”(Ouellette and Sénéchal, 2017), reflects what exemplary kindergarten teachers have known about the powerful writing/reading connection for years—kid writing is a pathway to reading success. But this work is still not well known or universally practiced. This less-known pathway is the one we traveled. Starting out as passionate kindergarten teachers in the 1960’s and 70’s, we were ardent about creating classrooms that worked for children. We devoured the research of that era and became life-long learners throughout our careers. Early on we discovered better outcomes for children as we focused more on writing, encouraged invented spelling, developed innovative strategies for teaching phonics and eliminated boring worksheets. Today in 2017 five best-practice techniques we discovered in our practice are now wholly supported by research and recommended for today’s kindergartens and first grades.

1. Use Invented Spelling (Ouellette & Sénéchal, 2017). We found invented spelling to be joyful, motivational for our students, and wonderful in terms of providing opportunities for scaffolding and systematically teaching almost all important aspects of the kindergarten literacy curriculum including phonics, phonemic awareness, knowledge of the alphabet, writing conventions, and vocabulary development. But perhaps the most amazing discovery throughout our journey was that kids had remarkable capacities to make meaning if we supported them in the process and allowed their creative juices to flow. Early on we learned as we had read in Don Graves’ research (1983), that kids write best when we step back and allow them to choose their own topics and give them ownership and autonomy. We called our teaching model, “Kid Writing” (1999). Our model fit perfectly with a growing model now called Guided Reading for differentiated reading instruction (Fountas & Pinnell, 2012) and we put in a whole layer underpinning Lucy Calkins work (2003) by showing teachers exactly how to get started and how to move forward with writing workshop and formative assessment. Here are a few samples that illustrate kids’ capacity to grow and flourish as writers in kindergarten:

Hameray (2017) Used with permission.

When I was born my mommy and daddy used to pay a lot of attention to me. But now they don’t pay a lot of attention to me. They pay a lot to Conner.

Hameray (2017) Used with permission.

2. Abandon teaching “Letter of the Week (Reutzel, 1992, 2015). Teaching one letter per week was standard practice in kindergarten when we began teaching. We tried our best to jazz up our teaching of the alphabetic principle because we knew it was essential to breaking the code and reading. Our students sang for the letter, danced for the letter, cooked for the letter, and cut and pasted for the letter. We took elaborate measures to teach the alphabet and sounds because we knew it was important. One fond memory was our “P” Party:” we served foods beginning with P—pizza, pretzels, popcorn, pepperoni, and the like. But with letter of the week the pace was too slow, and as far back as 1992 researchers were noticing the same problem and cautioning teachers to “break the letter-a-week tradition.” (Reutzel, 1992)

So in our classrooms we began to use children’s names on the first day of kindergarten—from Albert to Zoie—and learned to focus on all the sounds and letters from the very beginning. In contrast to when we were using letter of the week our students mastered letters and sounds far sooner.

Today, as reported by Reutzel (2015), “research has identified six evidence- based alphabet letter learning orders through which young children may acquire knowledge of alphabet letter names and sounds (Justice, Pence, Bowles, & Wiggins, 2006 ).” And guess what? “The first learning order is called the own-name effect.” (Reutzel, 2015) We got it right before the research proved it!

3. Use Invented Spelling and a Developmental Writing Scale to monitor progress (Gentry, 2006, 2000). Even before we published the first book on Kid Writing, we were collaborating with Richard Gentry on how to use a developmental spelling/writing assessment along with a developmental rubric to show how young children’s progression through five phases of developmental spelling revealed—among other things—the individual child’s understanding of phonics and his or her invented spellings as evidence of what the child knew or did not know. We found this work to be much more powerful for targeting instruction and monitoring kindergartners’ progress than traditional spelling tests or even measures of phonemic awareness and alphabet knowledge. Progress monitoring by phase observation is now supported by empirical research! (Ouellette & Sénéchal, 2017)

When we started out as neophyte teachers, kids were simply memorizing words that we gave them on a list. We learned to scaffold what they were using in their invented spelling and to show them how English spelling works. Our teaching went from giving lists of what to spell to showing kids how to spell and invented spelling was our vehicle! Without getting into the particulars of an analysis, look at the following samples that show one kindergarten child’s remarkable progress from fall to spring of her kindergarten year.

Hameray (2017) Used with premission.

September sample: It was a sunny day.

June sample: Tuesday my tooth was wiggling. When it was in my mouth, it bled. When it fell out, it stopped bleeding. My mom gently pulled it out with a paper towel and I was happy that it fell out.

4. Let go of worksheets! (Palmer & Invernizzi, 2015). We found that teaching and learning in our classrooms improved when we abandoned worksheets. Remember those nonsensical work sheets where children were to write the letter that the word for each picture would begin with? When we first began teaching we remember students who squirmed with sit-at-the-desk busy worksheets and struggled over the Y is for Yak worksheet wondering why Y was the match for the first sound in “goat” which is the picture they saw on the worksheet.

5. Teach children to stretch though a word with a moving target. (Feldgus, Cardonick, & Gentry, 2017) Research by Ouelette, validated our Stretching Through a Word with a Moving Target teaching methodology. Their research, “confirmed that facilitating invented spelling within a Vygotskian teaching approach can bring about benefits in learning to read and spell, and these benefits go beyond the expansion of alphabetic knowledge and phonological awareness.

research paper for 1st grade

Our stretching through technique, for example, helped kids move from l for lady in Phase 2 to lad in Phase 3 to ladee in syllable chunks in Phase 4 on the way to conventional lady . The stretching through technique met kids where they were and supported them in moving to higher levels of spelling sophistication from phase to phase.

Keep the Faith—Keep the Passion—Keep Your Kids Writing

One thing that hasn’t changed over the years is our passion for literacy-learning classrooms for beginners. Today as staff developers and authors, we continue to encounter kindergarten teachers all over America and beyond who share our passion, devotion to children, and vision for joyful, play-based, academic kindergarten and first grade classrooms. We believe implementing these five research-based strategies surrounding kid writing will be transformational in America. It is the answer to reversing the decades-old trend of flat-lined first grade reading scores!

For details on these five strategies and creating joyful kid-writing classrooms that work, check out our comprehensive guide for kindergarten and grade 1 teachers: Kid Writing in the 21st Century: A Systematic Approach to Phonics, Spelling and Writing Workshop (Hameray, 2017).

Link to Kid Writing to learn more.

Dr. J. Richard Gentry is the author of Raising Confident Readers, How to Teach Your Child to Read and Write–From Baby to Age 7 . Follow him on Facebook , Twitter , and LinkedIn and find out more information about his work on his website .

Calkins, L. M. (2003). The Nuts and Bolts of Teaching Writing. Portsmouth, NH: Heinemann.

Feldgus, E., Cardonick, I. & Gentry, J. R. (2017). Kid Writing in the 21st Century . Las Angeles, CA: Hameray Publishing Group.

Fountas, I. and Pinnell, G.S.(2013). The Reading Teacher. 66 (4) 268-284.

Gentry, R. (2006). Breaking the code: The new science of beginning reading and writing . Portsmouth, NH: Heinemann.

Gentry, R. (2000). A retrospective on invented spelling and a look forward. The Reading Teacher . 54 (3) 318-332.

Graves, D. H. (1983). Writing: Teachers and Children at Work . Portsmouth, NH: Heinemann.

Justice , L.M. , Pence , K. , Bowles , R.B. , & Wiggins , A. ( 2006 ). An investigation of four hypotheses concerning the order by which 4- year- old children learn the alphabet letters. Early Childhood Research Quarterly , 21 ( 3), 374 – 389 .

Ouelette, G. & Sénéchal, M. (2017). Invented spelling in kindergarten as a predictor of reading and spelling in grade 1: A new Pathway to literacy, or just the same road, less known? Developmental Psychology . 53 (1) 77– 88.

Palmer, J. & Invernizzi, M. (2015). No More Phonics and Spelling Worksheets . Portsmouth NH: Heinemann.

Reutzel, D. R. (2015). Early literacy research: Findings primary-grade teachers will want to know. The Reading Teacher . 69, (1), 14–24. DOI: 10.1002/trtr.1387 © 2015 International Literacy Association

Reutzel , D.R. ( 1992 ). Breaking the letter- a- week tradition: Conveying the alphabetic principle to young children. Childhood Education , 69 ( 1 ), 20 – 23 .

J. Richard Gentry Ph.D.

J. Richard Gentry, Ph.D. , is an expert on childhood literacy, reading, and spelling. He is the author of Raising Confident Readers: H ow to Teach Your Child to Read and Write—Baby to Age 7 .

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Researching with 1st Grade-Teaching Good Research Steps

Mar 19, 2015  •   2 Comments

     Teaching good research habits to younger grades is a very important part of being a librarian.  In the past, I have introduced the research steps using a lesson that connected the Big 6 steps to following a recipe for making an apple pie.  You can read about this “Recipe for Research” lesson on my blog here.       Typically, after I do this lesson, we would investigate Non-Fiction books, Text Features, and databases, and then they would do their research later in their classrooms with their teachers.      This year, I wanted to be more involved in helping them learn how to do the actual research part, so I decided to do a mini-research project with my first graders as part of their library lessons.   Step #1      I started off by asking them what they wanted to learn more about.  Most of them answered with some sort of animal, so we decided that our first research project together would be about animals.        This was actually perfect, as w e had just finished reading the book Those Darn Squirrels  by Adam Rubin. (By the way, if you haven’t read this, it’s a FANTASTIC book.  The kids LOVE raising their fists and shouting “Those Darn Squirrels!” whenever Old Man Fookwire yells at the crazy squirrels in his backyard.  Plus, they were super excited to find out that there are 2 more books in the series!)

      I told them that I was going to research Squirrels since we had just read about them, and then  I introduced them to PebbleGo .   This is an absolutely FANTASTIC database by Capstone Digital!  It is ideal for younger grades, and makes it super easy for them to find information. Each topic is divided into 6 main tabs: body, habitat, food, life cycle, fun facts, and related articles.  Each tab allows the student to either read or listen to the information, and most topics have a video they can watch as well.  It is slightly expensive, but well worth the money!  (If you are interested in checking it out, you can get a free 2 week trial by filling out a short form found here .)  I gave them some time to explore the database so they would be familiar with it for our next lesson, and I told them to think about what animal they would like to research.

Step #2      On their next visit, we quickly reviewed the “Recipe for Research” steps again, and  brainstormed a list of “egg” questions to research about their animal.  T hey came up with things like: what does my animal eat, how fast can it run, who is it afraid of, how does it move, where is it’s home, etc.   I created a Research Brochure (aka: note-taking tool) and had them chose 4 questions to write inside each egg.  

  Once their questions were written down, I asked “What do you think the lines underneath each egg are for?”  They correctly guessed it was for their answers to the questions.  I had them look at the lines, and we discussed how they weren’t very long and they would have to make sure they only wrote down the important words.      I pulled up information on Squirrels on PebbleGo, and we read that “Tree Squirrels have bushy tails that are as long as their body.” I had them help me find the “important facts” and I told them the general rule was they could write up to 3 words from a sentence.  (I am trying to start them early on learning how to paraphrase and write notes, and not just copy everything that they see.)  It was fun writing down their ideas, and then they would check to see if they had more than 3 words. After several tries, they came up with “long, bushy tails”. We practiced a few more times together, and then they  spent the rest of their library time on PebbleGo trying to find the answers for their animal. Before they left, I collected their research brochures to keep them safe for next time. S tep #3       The next week in the library, I showed them another database that I love to use with the younger grades,  Facts 4 Me .  It’s super cheap (only $50.00 for an entire year subscription!) You can take a quick tour of the site to learn more here .  It’s developed by former teachers, and the layout is also very friendly.   Each topic starts with a “Quick Facts” section that gives basic information.  On animals, it gives a variety of info such as: type, habitat, diet, weight, height, etc.  Along the left side are photographs, and under the Quick Facts section are short paragraphs giving more information.    

     At  the bottom, it even gives the exact citation to include on your Works Cited page, so I took this opportunity to begin teaching  them how to do a simple Works Cited page.  For the younger grades, I created just a simple ABC form (A=Author or website, B=Book Title or topic title, C=Copyright date).  I told them anytime they used facts from a source, they had to fill out a slip for their Works Cited page.  I had a stack and we just stapled the slip to their brochure so it all stayed together.  I gave them the rest of this library period to finish finding answers to their questions.

Step 4      Now that they had their answers, I showed them how to take their notes and create detailed, complete sentences on notebook paper.  We also talked about how to write a simple paragraphs (one paragraph for each “egg” question that they had answers for.)  When they were done, I had them work in pairs and peer-edit.  They helped each other with spelling, capitals, punctuation, and made sure that all their egg questions were answered.   Step 5 I gave them a variety of formats to choose from for their final presentation:   1) They could write a basic report using the 2-page format. 

2) They could make their own animal book using the brochure format including a Table of Contents and Author page.

3) Those that wanted to create a true Non-Fiction animal book could create one with a Table of Contents, Index, and Works Cited page.

Step #6      For their finale, each student presented their animal reports to the group.  I believe it’s important for students to get practice talking in front of their peers. Next time, I think it would be fun to Skype with another library and let students from each class share.  Technology options:        There are so many different apps that you can use to present their final information as well.  I love Tellagami and Sock Puppets, and both of these are easy to use.  Since we completed these activities toward the end of the year, our normal schedules were interrupted due to state testing, book fair, and end of the year changes.  Exploring those apps were a great way to keep the kids excited about their research project and provided motivation for them to finish.  If you are interested in doing this research activity with your students, you can find it here on my website or at  TpT store .  I’d love to hear how you do research with your students! Sandy

2 Responses to “Researching with 1st Grade-Teaching Good Research Steps”

Clear and easy to follow, yet thorough and precise. Thank you!

You’re welcome Mona! I hope they help! 🙂 Sandy

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How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Table of contents

Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

  • Read it carefully, looking for anything confusing you might need to clarify with your professor.
  • Identify the assignment goal, deadline, length specifications, formatting, and submission method.
  • Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

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research paper for 1st grade

There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

  • A paper following the chronology of World War II would not be original or specific enough.
  • A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

  • Is there anything people seem to overlook in the sources you research?
  • Are there any heated debates you can address?
  • Do you have a unique take on your topic?
  • Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

  • Maintaining forward momentum — write now, perfect later.
  • Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
  • Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

  • topic sentences against the thesis statement;
  • topic sentences against each other, for similarities and logical ordering;
  • and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

  • Offer new arguments or essential information
  • Take up any more space than necessary
  • Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

  • Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
  • Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
  • Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
  • If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

  • Confirm that your paper completes every task specified in your assignment sheet.
  • Check for logical organization and flow of paragraphs.
  • Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

  • each sentence helps support the topic sentence.
  • no unnecessary or irrelevant information is present.
  • all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

Scribbr’s professional editors can help with the revision process with our award-winning proofreading services.

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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Original research article, insights into first grade students' development of conceptual numerical understanding as drawn from progression-based assessments.

research paper for 1st grade

  • 1 Department Humanities, Institute Special Education, Leibniz University Hanover, Hanover, Germany
  • 2 Department Educational Studies, Institute Psychology, University Duisburg-Essen, Essen, Germany
  • 3 Centre for Education Practice Research, University of Johannesburg, Johannesburg, South Africa
  • 4 Department Human Sciences, Institute Inclusive Education, University of Potsdam, Potsdam, Germany

Early numeracy has been found to be one of the strongest predictors for later success in learning. Equipping children with a sound conceptual numerical understanding should therefore be a focus of early primary school mathematics. Assessments that are aligned to empirically validated learning progressions can support teachers to understand their students learning better and target instruction accordingly. This study examines numeracy learning of 101 first grade students over the course of one school year using progression-based assessments. Findings show that the students' performance increased significantly over time and that the initial conceptual numerical understanding had a positive effect on the students' learning progress as well as their end of school year performance. Analyzing the performance data based on the levels of the underlying developmental model uncovered an increasing elaboration of conceptual numerical understanding over time, but also individual differences within this process that need to be addressed through targeted intervention.


Research suggests that children's mathematical knowledge varies quite substantially when commencing formal schooling in Grade 1 ( Bodovski and Farkas, 2007 ; Dowker, 2008 ), and that without appropriate teaching, differences in mathematical performance tend to be consistent over time ( Aunola et al., 2004 ; Morgan et al., 2011 ; Missall et al., 2012 ; Navarro et al., 2012 ). Furthermore, early numeracy concepts were found to be the strongest predictor for later learning ( Duncan et al., 2007 ; Krajewski and Schneider, 2009 ; Romano et al., 2010 ; Claessens and Engel, 2013 ). Early knowledge in numeracy predicted not only success in mathematics, but also success in reading ( Lerkkanen et al., 2005 ; Duncan et al., 2007 ; Romano et al., 2010 ; Purpura et al., 2017 ) and was a stronger predictor for later academic achievement than other developmental skills, such as literacy, attention, and social skills ( Duncan et al., 2007 ). In a similar vein, the initial numeracy skills of children at the transition to school do not only predict later achievement but also the learning growth children are likely to show.

A number of recent studies examining the development of math performance in primary school suggest a cumulative growth pattern ( Aunola et al., 2004 ; Bodovski and Farkas, 2007 ; Morgan et al., 2011 ; Geary et al., 2012 ; Missall et al., 2012 ; Salaschek et al., 2014 ; Hojnoski et al., 2018 ). For a detailed synthesis of early numeracy growth studies, see Salaschek et al. (2014) . Cumulative development, also known as Matthew effect ( Stanovich, 1986 ), is characterized by a gradual accumulation of knowledge and skills over time. Children who start with good skills and sophisticated knowledge increase their performance more than those who start with lower levels of proficiency. This growth pattern was found among unselected populations of primary school students ( Aunola et al., 2004 ; Salaschek et al., 2014 ) as well as for specific groups of students, such as children with learning difficulties ( Geary et al., 2012 ), speech language impairments ( Morgan et al., 2011 ) and disability ( Hojnoski et al., 2018 ). Further, studies examining the effects of learning progress-related predictors found a significant effect of the initial learning status (intercept) as well as the learning growth (slope) on later math performance ( Keller-Margulis et al., 2008 ; Kuhn et al., 2019 ).

In accordance with these research findings, we propose that the acquisition of early numeracy in first grade is exceedingly crucial for children's later performance at school. To support teachers in providing early numeracy instruction that is tailored to the student's individual levels of understanding, we designed a formative assessment tool—hereafter called Learning Progress Assessment (LPA)—based on a learning progression approach. In this study, we used the LPA to shed more light onto first grade students' development of conceptual numerical understanding and simultaneously further investigate the quality of the instrument.

Defining Early Numeracy and Conceptual Knowledge

Mathematics represents a complex construct that is composed of various skills which are usually organized within five domains: numbers and operations, geometry, measurement, algebra, and data analysis ( Clements and Sarama, 2009 ). These domains are also described in the German National Educational Standards ( KMK, 2004 ) which are applied in the mathematics curricula of the different federal states of Germany. A significant amount of research has been conducted in the domain of numbers and operations covering the field of arithmetic. In early primary school (first and second grade), numbers and operations, also referred to as early numeracy ( Aunio and Niemivirta, 2010 ) or symbolic number sense ( Jordan et al., 2010 ), encompasses the skills of number knowledge, verbal counting, basic calculations, and quantity comparison. Early numeracy has been found to be the area of early mathematics most predictive for later success in mathematics ( Mazzocco and Thompson, 2005 ). In this paper the terms numeracy and arithmetic are used interchangeably.

Early numeracy skills, such as basic operations, are underpinned by conceptual knowledge that assigns meaning to the procedures and arithmetic facts. In contrast to procedural knowledge that involves knowing how to perform a calculation, conceptual knowledge involves understanding why arithmetical problems can be solved in a certain way ( Hiebert and Lefevre, 1986 ). Conceptual understanding of early numeracy skills is considered the foundation for developing sound mathematical skills later on ( Gelman and Gallistel, 1978 ). Hence, one objective of the German National Educational Standards is to initiate a change in German mathematics education away from focusing on the pure knowledge of arithmetic facts and the performance of routine procedures toward conceptual understanding ( KMK, 2004 ). However, conceptual knowledge and procedural skills should be considered as iterative, each prompting the learning of the other ( Rittle-Johnson et al., 2001 ).

Learning Progressions in Early Numeracy

Learning progressions, also known as learning trajectories, conceptualize learning as “a development of progressive sophistication in understanding and skills within a domain” ( Heritage, 2008 , p. 4). In other words, learning progressions describe how knowledge, concepts and skills within a certain domain typically develop and what it means to improve in that area of learning. Black et al. (2011) describe learning progression as a pathway, or “road map” (p. 4) that presents knowledge and skill development as sequential in its increase in complexity.

According to Clements and Sarama (2009) learning trajectories consist of three parts: a mathematical goal, often given by the curriculum, a developmental path “along which children develop to reach that goal” (p. 3), and a set of instructional activities matched to each level of the developmental path that help children to develop higher levels of understanding. By applying this 3-fold approach, Clements and Sarama (2009) created and empirically investigated learning progressions for a variety of mathematical areas, including early numeracy skills, such as counting, comparing numbers, composing numbers, and addition and subtraction. Empson (2011) pointed out that the idea of learning progressions as a “series of predictable levels” ( Sarnecka and Carey, 2008 , p. 664) is not new within mathematics research. For example, Gelman and Gallistel's (1978) description of children's acquisition of counting skills and Fuson's (1988) model of children's development of number concepts are well-established and broadly recognized. Such historical approaches built the theoretical foundation for more recent empirically-supported models that aim to describe development through the concept of learning progressions.

For example, in their model of number-knower levels Sarnecka and Carey (2008) describe the developmental process that occurs between being able to recite the counting list while pointing at objects to being able to understand the “cardinal principle” ( Gelman and Gallistel, 1978 ). The number-knower levels framework is supported by studies using the “Give-N” or “Give-A-Number” task ( Le Corre and Carey, 2007 ; Lee and Sarnecka, 2011 ). In this task, children were requested to generate subsets of a particular number from a larger set of objects that is placed in front of them (e.g., “Give me three marbles”).

Fritz et al. (2013) published a model that aims to map the development of early numeracy, with a particular focus on conceptual understanding. This model of conceptual numerical development has been empirically validated in cross-sectional as well as longitudinal studies ( Fritz et al., 2018 ) and builds the theoretical foundation of the LPA introduced in this study. The model describes six successive, hierarchical levels of increasing numerical sophistication, with each level characterized by a central numerical concept. The model emphasizes a conceptual progression, in which less sophisticated concepts build the foundation for more sophisticated concepts and understands development in the sense of “overlapping waves” ( Siegler and Alibali, 2005 ). This means, the levels describe an increasing elaboration of conceptual understanding, rather than distinct, exclusive stages of ability. Table 1 shows the central numerical concept for each level and a short description of the associated skills. For a detailed description of the model, see Fritz et al. (2013) .

Table 1 . Development model of conceptual numerical understanding by Fritz et al. (2013) .

Learning Progression-Based Assessments

Internationally, the learning progression approach has been applied to inform educational standards, national curricula, and large scale assessments, as well as formative assessment practices (e.g., Daro et al., 2011 ; ACARA, 2017 ). To serve these different purposes, learning progressions differ in their scope (i.e., the amount of instructional time and content) and their grain size (i.e., the level of detail provided about changes in student thinking) ( Gotwals, 2018 ). For example, a learning progression with a larger scope and grain size may be more appropriate to inform educational standards than formative assessment practices because standards need to describe students' understanding over a longer period of time. To be useful for formative assessment purposes, learning progressions with a smaller scope and grain size would be more suitable to support teachers in their instructional decision making, as these types of progressions describe “nuances in the shifts in student thinking” ( Gotwals, 2018 , p. 158). Research on the quality of instruction has suggested that formative assessment is important practice for teachers to support their students' learning best ( Black and Wiliam, 1998 ; Wiliam et al., 2004 ; Kingston and Nash, 2011 ). However, in a meta-analysis Stahnke et al. (2016) findings suggest that mathematics teachers tend to struggle with choosing adequate tasks to support their students learning and have difficulties interpreting tasks and identifying their potential for instruction. As learning progressions describe how the development in a certain domain typically looks like, we support the premise of Clements et al. (2008) , which stated that assessments that are aligned to learning progressions are important tools to support formative assessment practices. Such assessments can inform instruction that is targeted to the students' individual levels of understanding.

To our knowledge, however, there are only few progression-based numeracy assessments available in Germany. Even fewer have been empirically validated. Most of the German formative mathematics assessments, which are also psychometrically tested, aim to monitor how well students progress in learning the content specified in a certain year level curriculum, but do not take the developmental perspective into account ( Strathmann and Klauer, 2012 ; Salaschek et al., 2014 ; Gebhardt et al., 2016 ; Kuhn et al., 2018 ).

Rationale of the Study and Research Questions

The present study sought to add to previous research by examining the LPA for use within the German school context which was designed in alignment to the model of conceptual numerical development by Fritz et al. (2013) . The goal of this study was to provide insights into the first grade students' conceptual numerical development as well as to further investigate the quality of the LPA, but not to evaluate the effectiveness of the interventions. Therefor four research questions were addressed.

Research question 1: To what extent does numerical performance (assessed through the LPA) change over time? In line with other mathematical growth studies in early primary school (e.g., Bodovski and Farkas, 2007 ), a significant increase in performance over time was expected.

Research question 2: To what extent does the numerical knowledge prior to school predict change in numerical performance over time? Based on previous studies examining the effects of domain specific predictors on math learning, such as those by Krajewski and Schneider (2009) a positive effect of the numerical pre-knowledge on the children's numerical development was expected. A cumulative growth pattern predicted by the numerical knowledge prior to school was also anticipated, in line with findings by Salaschek et al. (2014) .

Research question 3: To what extent do numerical knowledge prior to school and change in numerical performance over time predict numerical performance at the end of Grade 1? It was hypothesized that the numerical knowledge prior to school would explain a relatively high share of the variance of the numerical performance at the end of the school year. With regard to findings from Kuhn et al. (2019) it was further expected that the LPA would also be a significant predictor for numeracy performance at the end of the school year.

Research question 4: How does the conceptual numerical understanding change over the course of Grade 1? As suggested by findings of Fritz et al. (2018) it was expected that most children would start school at Level III (concept of cardinality). Over the course of Grade 1 the students are expected to gain about one conceptual level, reaching Level IV or V (concept of part-part-whole relations or concept of equidistant number line intervals) by the end of the school year. Given the considerable heterogeneity of mathematical knowledge in German first graders (e.g., Peter-Koop and Kollhoff, 2015 ), it was also postulated that a wide range of levels would be found.

Participants and Procedure

As part of a longitudinal Response-to-Intervention study, a total of 101 (55% female) first grade students ( M Age = 78.24 months, SD = 3.89) from six classes of two German primary schools were examined over the course of one school year. The schools were located in an urban area with a higher socioeconomic status. The data was collected by trained Master students of the Inclusive Education program of the local university.

The first grade students' conceptual numerical knowledge was assessed at the beginning of school using the “Mathematics and arithmetical concepts in preschool age” (MARKO-D; Ricken et al., 2013 ) as a pre-test, and re-assessed at the end of the school year using the “Mathematics and arithmetical concepts of first grade students” (MARKO-D1; Fritz et al., 2017 ) as a post-test. Between the two MARKO-D tests, the Learning progress assessment (LPA) was applied over nine measurement points (LPA t1 to LPA t9 ) starting at about 12 weeks after the beginning of school with ~4 weeks in between each measurement. All students received general teaching according to the requirements of the German curriculum ( Landesinstitut für Schule und Medien Berlin-Brandenburg (LISUM), 2015 ). Some students participated in additional mathematical interventions as part of the Response-to-Intervention study (e.g., Gerlach et al., 2013 ).


Marko-d test series.

The MARKO-D ( Ricken et al., 2013 ) and MARKO-D1 ( Fritz et al., 2017 ) tests are standardized, Rasch scaled diagnostic instruments that have been designed based on the development model of arithmetical concepts by Fritz et al. (2013) described previously (see Table 1 ). Based on the levels of the model, the tests aim to capture children's understanding of arithmetical concepts for different levels and age groups (MARKO-D: Levels I to V, Age: 48–87 months; MARKO-D1: Levels II to VI, Age: 71–119 months). The test items are presented in a randomized order. The MARKO-D tests are linked by 22 anchor items spread over different developmental levels. Both tests are conducted as individual tests in a one-on-one situation and take ~30 min each. The children's performance data can be analyzed quantitatively (based on raw scores, transferred into T-scores and percentile ranks), as well as qualitatively (based on response patterns, transferred into individual conceptual levels).

Learning Progress Assessment (LPA)

The LPA aims to assess the children's performance within the different levels of the same developmental model (see Table 1 ). In contrast to the MARKO-D tests ( Ricken et al., 2013 ; Fritz et al., 2017 ), the LPA, however, does not assess all levels in one test. Instead, it uses shorter tests that are targeted to the student's current numerical understanding and applied formatively within group settings.

In this study the short tests were targeted to the students' current level of proficiency based on their performance on the previous test. For the first measurement point of the LPA, the performance on the MARKO-D test served as decision criterion for assigning the appropriate test version. The short tests of the LPA were conducted in small groups of students at the same developmental level and took ~15 min. The instructions were read aloud by trained Master students whilst the first grade students solved the given assessment tasks in individual test booklets.

Each short test consisted of 15 items covering three levels (five items per level): the student's current level of proficiency, the previous level and the subsequent level. As a result, at each measurement point up to five different test versions were used (Levels I to III, Levels II to IV, Levels III to V, Levels IV to VI, Levels V to VI+). The test items were drawn from a pool of items operationalizing the levels of the model. The item pool consisted of 90 dichotomous items (I:12, II:17, III:10, IV:12, V:14, VI:13, VI+:12). The hypothesized developmental levels of the items had been empirically evaluated in previous cross-sectional and longitudinal studies ( Balt et al., 2017 ). Subsets of five items per level, including one linking item per level, were drawn from the item pool and randomly assigned to each measurement time. In a multi-matrix test booklet design ( Johnson, 1992 ), linking items are items all tests have in common. As such, they provide a reference point for evaluating the difficulty of the remaining items and enable a concurrent scaling of all items within the Rasch model ( von Davier, 2011 ). With the exception of the linking items, there was no repetition of the same items in consecutive measurements to avoid memory effects.

Please note that the twelve VI+ items did not relate to the developmental model. These items were introduced at measurement point seven to cater for students who performed above Level VI at the time. The items were expected to be more difficult as they assessed the concepts of Levels V and VI but within a higher number range.

Data Analysis

The Rasch model was used as the underlying mathematical model to build the progression-based assessments used in this study (MARKO-D tests and LPA). Within the Rasch model item and person parameters are mapped on a joint scale. The numerical performance data of the students obtained through the MARKO-D tests and the LPA were scaled applying simple dichotomous Rasch models ( Rasch, 1960 ). MARKO-D and MARKO-D1 were combined onto one scale and calibrated simultaneously based on linking items, and the short tests of the LPA were mapped on another scale based on their linking items. Thus, numeracy performance was measured on two separate scales (MARKO-D/D1 and LPA t1 to LPA t9 ). The item parameters of the measurement models were fitted using Conditional-Maximum-Likelihood (CML) estimation and person parameters were determined based on the Maximum-Likelihood estimation (ML). The specification of the measurement models as well as its goodness of fit were evaluated using the eRM package ( Mair and Hatzinger, 2007 ) within R software ( R Core Team, 2017 , Version 1.2.1335). The goodness of fit to the Rasch model was assessed through item-fit analyses. The assumption of sample invariance of the LPA scale was tested through Andersen Likelihood Ratio Tests (LRT) ( Andersen, 1973 ). For the LRT tests, the median was used as an internal split criterion to compare the item parameter estimations for students with higher and lower test scores. Gender was used as an external split criterion to determine whether the item parameter estimation differed significantly between male and female students. No systematic difference between different subgroups of the sample should be found if the Rasch model is valid ( van den Wollenberg, 1988 ).

After fitting and testing the Rasch model, the person parameters of both numeracy scales (MARKO-D tests and LPA) were used to investigate the student's numerical development over the course of Grade 1. To account for the characteristics of the longitudinal design of the study (e.g., time as an independent variable), linear Mixed Models were used to address research question 1 (the effect of time on numeracy performance) and research question 2 (the effect of numerical knowledge prior to school on numeracy development in first grade). Competing models were compared using log likelihood tests, with the more complex model retained if that fitted the data significantly better ( Bliese and Ployhart, 2002 ). To address research question 3 (the effect of numerical knowledge prior to school and change in numerical performance over time on numerical performance at the end of the school year), the “Random-Intercept-Random-Slope” model from the previous linear Mixed Models analysis was used to estimate intercept and slope parameters for each student based on the LPA (compare Kuhn et al., 2019 ). The individual intercepts and slopes served as learning progression related predictors for end of school year numeracy performance (MARKO-D1 Post ) analyzed through hierarchical multiple regression. To address research question 4 (change in conceptual numerical understanding), the students' performance on the MARKO-D tests as well as on the LPA tests was allocated to the levels of the developmental model following the reporting standards of the MARKO-D test series. The standards propose that full understanding of the numerical concept of a level can be assumed when at least 75% of the items within this level were solved correctly, given that (a) each test item could be reliably assigned to the theoretically founded developmental levels and their associated underlying numerical concepts, and (b) the hierarchy of the levels was valid ( Ricken et al., 2013 ). For this study we assumed that the items of the LPA met both conditions and therefore the application of the 75% criterion was valid (see also Balt et al., 2017 ).

Item Fit Analysis

Mean square values (MSQ) were used to assess the Rasch model fit on the item level. MSQ values represent the residuals between the Rasch model expectations and the observed responses ( Wu and Adams, 2013 ). Ideally, MSQ scores have a value of 1, however a range between 0.75 ≤ MSQ ≤ 1.30 is considered acceptable ( Bond and Fox, 2007 ). Table 2 shows that the MSQ values of both scales (MARKO-D tests and LPA) were close to 1 on average with a small standard deviation of 0.11 which indicates a reasonable item fit. For single items, the MSQ values were also reasonable as none exceeded the range of an acceptable fit. Please note that the number of items within each scale is reduced due to the common linking items.

Table 2 . Item fit statistics of the measurement instruments.

Person Parameter Analysis

Table 3 shows the descriptive statistics of the person parameters of the two scales (MARKO-D tests and LPA) based on the Rasch model ( Rasch, 1960 ).

Table 3 . Descriptive statistics of person parameters from MARKO-D tests and LPA.

The person parameters significantly increased from the beginning (MARKO-D Pre ) to the end of the school year (MARKO-D1 Post ), t (96) = −19.03, p < 0.001, r = 0.89. The significant increase in performance over time was also captured in between the pre-and the post-test through the LPA tests (LPA t1 -LPA t9 ), t (747) = 23.47, p < 0.001, r = 0.65 (compare Model 2 in Table 5 ).

Item Parameter Analysis

Table 4 shows the descriptive statistics of the item parameters of the LPA item pool for each level of the developmental model (see Table 1 ). The mean difficulty of the items significantly increased from Level I to VI+, F (6, 82) = 52.63, p < 0.001, r = 0.89.

Table 4 . Descriptive statistics of item parameters of the LPA items per developmental level.

Sample Invariance Analysis

Median split.

The Andersen LRT ( Andersen, 1973 ) with median as the internal split criterion showed a non-significant result (χ 2 = 59.86; df = 57; p = 0.37), indicating that the estimations of the item parameters did not differ significantly between students with low and high scores in the LPA.

Gender Split

The Andersen LRT ( Andersen, 1973 ) with gender as the external split criterion showed a significant result (χ 2 = 112.76; df = 69; p = 0.001), indicating that some item parameter estimations differed significantly between male and female students. As recommended by Koller et al. (2012) a Wald test was applied to identify the items that show a bias toward a certain gender. The Wald test found that the statistic item parameter estimations of three items showed significant differences between genders ( p < 0.05). Rerunning the Andersen LRT after excluding these three items from the analysis led to a non-significant result (χ 2 = 79.67; df = 66; p = 0.12).

Growth Analysis

We started the growth model building process with the “Random intercept only” model (Model 1) as the baseline model and subsequently added more complexity. In Model 2, “Time” was added as a fixed effect (independent variable) to model its relationship with the students' performance in the LPA (dependent variable). The first measurement of the LPA was conducted about 12 weeks after the beginning of school. Each step in “Time” represents 4 weeks of schooling. In Model 3, “Random slopes” were introduced to account for possible differences in growth patterns over time. In Model 4, the grand mean centered person parameters ( Enders and Tofighi, 2007 ) of the variable MARKO-D Pre were added to the previous model as a predictor for possible intercept variation. In Model 5 the interaction term “Time x MARKO-D Pre ” was added to test for potential effects of numerical knowledge prior to school on numeracy development.

Table 5 displays the model parameters of the linear Mixed Models. The model comparison suggests that adding the fixed effect of “Time” significantly improved the fit of Model 2 compared to the Baseline model. Introducing “Random slopes” further improved the fit of Model 3. The average growth predicted through the “Random-Intercept-Random-Slope” model (Model 3) was 0.30 ( SE = 0.02, p < 0.01) with a SD of 0.10. The average intercept was 0.79 ( SE = 0.11, p < 0.01) with a SD of 0.98. Adding “MARKO-D Pre ” as a second predictor to the model improved the fit of Model 4 compared to Model 3. However, adding the interaction term “Time × MARKO-D Pre ” in Model 5 did not significantly improve the model fit. The model indices in Table 5 also indicate that model fit and explained variance increase with increasing complexity up to Model 4.

Table 5 . Fixed effects for mixed models predicting LPA performance ( N = 99 a ).

Prediction Analysis

The small intraclass correlation (ICC) of 0.01, estimated as part of the growth model building process, indicates that the hierarchical structure of the data is not likely to affect the subsequent regression analysis. Multicollinearity between the predictors, tested through variance inflation factors, also appeared to be unproblematic as all factors were smaller than 3.5 ( Meyers, 1990 ).

Model 1 in Table 6 shows that the performance in the pre-test (MARKO-D Pre ) explains 43% of the variation in the post-test performance (MARKO-D1 Post ). Introducing the individual intercept and slope parameters from the LPA to the model (Model 2) adds 20% of explained variance. The model parameters indicate that the LPA related predictors make a significant contribution to the model.

Table 6 . Parameters for multiple regression models predicting MARKO-D1 Post ( N = 97 a ).

Level-Based Analysis

Drawing on the 75% criterion (see section Data Analysis of this paper) the students were allocated to the different levels of the developmental model (see Table 1 ). Table 7 displays the relative frequencies of the level allocations based on the students' pre- and post-test performance (MARKO-D tests) and their LPA results over the nine measurement points. Inferential statistics indicate significant differences in level distributions between the pre- and the post-test, χ 2 (6) = 57.41, p < 0.001, r Sp = 0.48, as well as between the LPA tests, χ 2 (56) = 351.37, p < 0.001, r Sp = 0.46.

Table 7 . Level-based analysis of MARKO-D tests and LPA tests (relative frequencies in %).

The results of both measures (MARKO-D tests and LPA) show that the number of students at Levels I to IV decreased, whereas the number of students at Levels V, VI, and above increased as the school year progressed. From pre- to post-test the students gained one conceptual level on average ( Mdn = 1) with 40% of the students gaining even more than one level. The students' conceptual change in numerical understanding over the course of Grade 1 assessed through the LPA is visualized in Figure 1 .

Figure 1 . Level-based analysis of the LPA ( N = 101). a Level 7 is not a level of the developmental model. In this figure, Level 7 includes students who mastered at least 75% of the items at Level VI. b Level 8 is not a level of the developmental model. In this figure, Level 8 includes students who mastered at least 75% of the items at Levels V and VI in a higher number range.

The purpose of the study was to investigate the extent to which progression-based assessments can be used to describe the development of conceptual numerical understanding of children at the transition to school. The overarching goal was to build a progression-based formative assessment tool that is empirically tested and supports teachers in their everyday practice of teaching students at different levels of numerical understanding.

Prior to addressing the research questions, we would like to briefly discuss the results of the Rasch scaling. The item fit analysis, as well as the sample invariance tests, indicate that the Rasch model was valid for the purpose of this study. All items showed an acceptable fit and no systematic difference could be found between high and low performing students within the sample. Three items were identified to have a gender bias, which was resolved by removing these items from the analysis. This procedure may constitute a methodological limitation of this study as the χ 2 statistic is known to be highly sensitive to large df ( Wheaton et al., 1977 ).

This study was guided by four research questions. The first research question considered the effect of time on the students' performance in the LPA to examine the extent to which the assessment is able to detect changes in performance over time. The results of the linear Mixed Models analysis show that the students' performance increased significantly over the course of Grade 1. In accordance with studies using curriculum-based formative assessments ( Salaschek et al., 2014 ; Kuhn et al., 2019 ) these findings suggest that the progression-based LPA, used in this study, was also able to detect changes in performance over time.

The second research question concerned the effect of numerical knowledge prior to formal schooling (assessed through MARKO-D in the pre-test) on numeracy learning over the course of Grade 1 (as assessed through the LPA). In line with recent studies examining early numeracy as predictor for successful numeracy learning (e.g., Krajewski and Schneider, 2009 ; Claessens and Engel, 2013 ; Nguyen et al., 2016 ), this study further supports the positive effect of numerical pre-knowledge on the subsequent acquisition of more sophisticated numeracy skills. However, a cumulative growth pattern, as described in several studies (see Salaschek et al., 2014 ), is not reflected in this study, as shown by the lack of significant interaction of “Time x MARKO-D Pre .” Considering the profound conceptual understanding many of the students in this study's sample showed in the pre-test at the beginning of school (almost 30% on Levels V and VI), this finding is not surprising. The LPA is aligned to a developmental model that covers a clearly defined number of developmental levels. Children who already start at the higher levels of the model, consequently, cannot be reliably assessed beyond the scope of the model as their learning progresses. To reduce this effect, more difficult items were introduced at measurement point seven, but it is still likely that the actual growth of students beyond Level VI was larger than reflected by the LPA. This may have skewed the results as over the course of Grade 1, a larger number of students exceeded the levels described by the model and assessed through the LPA. In future studies it would be interesting to examine whether a cumulative growth pattern could be found in a sample of low and average performing students. Another reason for the lack of interaction may be the context of the data collection, as the LPA data was gained as part of a longitudinal Response-to-Intervention study. Though the assessment has not been linked to a specific type of intervention yet, the intervention the students received within this study may have affected their individual growth. Nonetheless, we assume that this was not problematic for the purpose of this study, as the goal was to investigate the extent to which the LPA can be used to describe numerical development independent of the type of intervention the students received.

The third research question examined the predictive effect of the LPA on numerical performance at the end of the school year (assessed through MARKO-D1 in the post-test). As expected, the student's pre-test MARKO-D performance explained a fair share of the MARKO-D1 post-test performance (43%). However, introducing the LPA parameters into the model significantly increased the explained variation of the student's post-test performance by 20%. This finding may be interpreted as an indicator for the prognostic validity of the LPA. It should, however, be considered that all measures used in this study (MARKO-D tests and LPA) built on the same developmental model. Hence, further evidence should be collected by using different types of assessment to support the assumption of the (prognostic) validity of the LPA.

The fourth research question sought to provide a more detailed picture of how the conceptual change of numerical understanding appears over the course of Grade 1. The use of progression-based assessments in this study, suggested a heterogeneity of children's numerical understanding at the beginning of school. The students did not only differ significantly in their overall test scores, but also in their individual levels of conceptual numerical knowledge. The MARKO-D test (pre-test) indicated that 19% of the students demonstrated a conceptual understanding of the ordinal number line (Level II). Approximately 50% showed an understanding of the concepts of cardinality (Level III) or part-part-whole relations (Level IV), which can be considered average according to the norming sample of the MARKO-D test ( Ricken et al., 2013 ). The pre-test results further revealed that almost 30% of the students demonstrated an extensive informal numerical knowledge at the beginning of school in the form of a conceptual understanding of equidistant number line intervals (Level V) or units in numbers (Level VI). The MARKO-D1 (post-test) indicated that the number of students at the lower levels decreased whereas the number of students at the higher levels increased with an average gain of one conceptual level over the course of the school year. Similar results had been reported by Fritz et al. (2018) .

The analysis of the LPA based on the 75% criterion also showed an increase of the share of students with more sophisticated concepts over time, while the number of students with lower conceptual knowledge decreased. At the first two measurement points, there were 1 to 2% of the children at Level I, who at the time were developing their number word sequence and counting skills. The LPA data suggested that all children of the sample had fully acquired the concept of counting by the third measurement point. 2% of the children, however, spent an extended period of time, up to the sixth measurement point, developing the ordinal number line concept characteristic for Level II. Compared to their classmates the students at Levels I or II were lacking important conceptual prerequisites that build the foundation for the acquisition of more sophisticated numeracy skills. More importantly, these prerequisites are demanded by the curriculum that usually introduces addition and subtraction during the first half of Grade 1. This means, these students may be at risk for developing mathematical learning difficulties, as their conceptual understanding and procedural skills (e.g., error-prone counting strategies) are not likely to be viable for more complex arithmetic problems and the larger number range they will encounter later on in Grade 1 and in Grade 2. To prevent that the gap between their current numerical knowledge and the curriculum expectations gets bigger, these students should immediately receive intervention that is targeted to their individual level of conceptual understanding.

The level-based analysis of the LPA tests further showed that after 12 weeks of schooling (first measurement point of the LPA) 55% of the children were working toward understanding the concept of part-part-whole relations (Level IV) and 25% had an even more sophisticated conceptual understanding associated with Levels V (concept of equidistant number line estimations) or VI (concept of units in numbers). These children demonstrated a sound understanding of the concept of part-part-whole relations (within the number range up to 20) and were able to master arithmetical problems flexibly without necessarily depending on counting strategies. By the end of the school year, 14% of the students were allocated at Levels III or IV, while 86% of the students were working at Level V or above. The latter number was larger based on the LPA test (86%) than based on the MARKO-D1 test (67%). This discrepancy may be explained by differences in the time of the assessment (~6 weeks in between the two measurements), the type of assessment (MARKO-D1 as an individual test vs. the LPA as a group test), and the number of items (MARKO-D1 with 48 items vs. LPA test with 15 items).

These insights into the developmental process of conceptual numerical understanding of first grade students highlight the importance of progression-based assessments to support mathematics teachers. The current version of the LPA was particularly suitable to describe the development of low and average performing students. The development of students beyond Level VI could, however, not be reliably assessed with the LPA due to the limitations of the underlying developmental model. By using this type of assessment, teachers stand to not only gain a deeper insight into their students' learning, but also a better understanding of how numeracy learning typically progresses and a student's location within this learning pathway ( Black et al., 2011 ), enabling teachers to derive and target intervention accordingly. The progression-based assessments used in this study (MARKO-D tests and LPA) come along with an empirically validated pathway description of numeracy learning ( Fritz et al., 2018 ) as well as instructional activities for targeted interventions (e.g., Gerlach et al., 2013 ).

Given the importance of early numeracy for future learning, progression-based assessments seem especially important for early primary school mathematics. For this reason, further empirical research is needed to provide teachers with this approach to assessment, thereby adding to the curriculum-based instruments that are currently available in Germany. This study is one step toward the goal of designing such an instrument.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

MB, AF, and AE designed the tasks and instruments used in this study. MB and AE planned the study design and carried out the data collection. MB performed the computations and drafted the manuscript advised by AF and AE. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: early numeracy, development, assessment, learning progression, primary school

Citation: Balt M, Fritz A and Ehlert A (2020) Insights Into First Grade Students' Development of Conceptual Numerical Understanding as Drawn From Progression-Based Assessments. Front. Educ. 5:80. doi: 10.3389/feduc.2020.00080

Received: 13 October 2019; Accepted: 18 May 2020; Published: 17 June 2020.

Reviewed by:

Copyright © 2020 Balt, Fritz and Ehlert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miriam Balt,

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Research Writing (Grades 1-2)

Our Research Writing lesson plan for grades 1-2 introduces students to the concept of research writing and the importance of being factually accurate in research writing. Students practice doing basic research and writing paragraphs based on this research with their classmates.


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Our Research Writing Lesson Plan for grades 1-2 teaches students about the importance of researching and reporting findings accurately and effectively. Being able to clearly and accurately inform and communicate findings through writing is a valuable skill that students will need in many areas of their lives. Gathering and summarizing key information will also be a powerful tool for academic reading and writing throughout upper grades and higher education. In this lesson, students are asked to use the information they have learned and their collaboration skills to create a group outline for a research paper and do shared research. Students will then work independently to write their own paragraphs based on this group research.

At the end of the lesson, students will be able to participate in shared research and research writing to create an expository paragraph that shares their findings.

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Research Paper: Write a First Draft

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  • introduction
  • let the reader know what the topic is
  • inform the reader about your point of view
  • arouse the reader's curiosity so that he or she will want to read about your topic
  • Limit each paragraph to one main idea. (Don't try to talk about more than one idea per paragraph.)
  • Prove your points continually by using specific examples and quotations from your note cards.
  • Use transition words to ensure a smooth flow of ideas from paragraph to paragraph.
  • summarize your points, leaving out specific examples
  • restate the main idea of the paper

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How to write your first research paper.

Writing a research manuscript is an intimidating process for many novice writers in the sciences. One of the stumbling blocks is the beginning of the process and creating the first draft. This paper presents guidelines on how to initiate the writing process and draft each section of a research manuscript. The paper discusses seven rules that allow the writer to prepare a well-structured and comprehensive manuscript for a publication submission. In addition, the author lists different strategies for successful revision. Each of those strategies represents a step in the revision process and should help the writer improve the quality of the manuscript. The paper could be considered a brief manual for publication.

It is late at night. You have been struggling with your project for a year. You generated an enormous amount of interesting data. Your pipette feels like an extension of your hand, and running western blots has become part of your daily routine, similar to brushing your teeth. Your colleagues think you are ready to write a paper, and your lab mates tease you about your “slow” writing progress. Yet days pass, and you cannot force yourself to sit down to write. You have not written anything for a while (lab reports do not count), and you feel you have lost your stamina. How does the writing process work? How can you fit your writing into a daily schedule packed with experiments? What section should you start with? What distinguishes a good research paper from a bad one? How should you revise your paper? These and many other questions buzz in your head and keep you stressed. As a result, you procrastinate. In this paper, I will discuss the issues related to the writing process of a scientific paper. Specifically, I will focus on the best approaches to start a scientific paper, tips for writing each section, and the best revision strategies.

1. Schedule your writing time in Outlook

Whether you have written 100 papers or you are struggling with your first, starting the process is the most difficult part unless you have a rigid writing schedule. Writing is hard. It is a very difficult process of intense concentration and brain work. As stated in Hayes’ framework for the study of writing: “It is a generative activity requiring motivation, and it is an intellectual activity requiring cognitive processes and memory” [ 1 ]. In his book How to Write a Lot: A Practical Guide to Productive Academic Writing , Paul Silvia says that for some, “it’s easier to embalm the dead than to write an article about it” [ 2 ]. Just as with any type of hard work, you will not succeed unless you practice regularly. If you have not done physical exercises for a year, only regular workouts can get you into good shape again. The same kind of regular exercises, or I call them “writing sessions,” are required to be a productive author. Choose from 1- to 2-hour blocks in your daily work schedule and consider them as non-cancellable appointments. When figuring out which blocks of time will be set for writing, you should select the time that works best for this type of work. For many people, mornings are more productive. One Yale University graduate student spent a semester writing from 8 a.m. to 9 a.m. when her lab was empty. At the end of the semester, she was amazed at how much she accomplished without even interrupting her regular lab hours. In addition, doing the hardest task first thing in the morning contributes to the sense of accomplishment during the rest of the day. This positive feeling spills over into our work and life and has a very positive effect on our overall attitude.

Rule 1: Create regular time blocks for writing as appointments in your calendar and keep these appointments.

2. start with an outline.

Now that you have scheduled time, you need to decide how to start writing. The best strategy is to start with an outline. This will not be an outline that you are used to, with Roman numerals for each section and neat parallel listing of topic sentences and supporting points. This outline will be similar to a template for your paper. Initially, the outline will form a structure for your paper; it will help generate ideas and formulate hypotheses. Following the advice of George M. Whitesides, “. . . start with a blank piece of paper, and write down, in any order, all important ideas that occur to you concerning the paper” [ 3 ]. Use Table 1 as a starting point for your outline. Include your visuals (figures, tables, formulas, equations, and algorithms), and list your findings. These will constitute the first level of your outline, which will eventually expand as you elaborate.

The next stage is to add context and structure. Here you will group all your ideas into sections: Introduction, Methods, Results, and Discussion/Conclusion ( Table 2 ). This step will help add coherence to your work and sift your ideas.

Now that you have expanded your outline, you are ready for the next step: discussing the ideas for your paper with your colleagues and mentor. Many universities have a writing center where graduate students can schedule individual consultations and receive assistance with their paper drafts. Getting feedback during early stages of your draft can save a lot of time. Talking through ideas allows people to conceptualize and organize thoughts to find their direction without wasting time on unnecessary writing. Outlining is the most effective way of communicating your ideas and exchanging thoughts. Moreover, it is also the best stage to decide to which publication you will submit the paper. Many people come up with three choices and discuss them with their mentors and colleagues. Having a list of journal priorities can help you quickly resubmit your paper if your paper is rejected.

Rule 2: Create a detailed outline and discuss it with your mentor and peers.

3. continue with drafts.

After you get enough feedback and decide on the journal you will submit to, the process of real writing begins. Copy your outline into a separate file and expand on each of the points, adding data and elaborating on the details. When you create the first draft, do not succumb to the temptation of editing. Do not slow down to choose a better word or better phrase; do not halt to improve your sentence structure. Pour your ideas into the paper and leave revision and editing for later. As Paul Silvia explains, “Revising while you generate text is like drinking decaffeinated coffee in the early morning: noble idea, wrong time” [ 2 ].

Many students complain that they are not productive writers because they experience writer’s block. Staring at an empty screen is frustrating, but your screen is not really empty: You have a template of your article, and all you need to do is fill in the blanks. Indeed, writer’s block is a logical fallacy for a scientist ― it is just an excuse to procrastinate. When scientists start writing a research paper, they already have their files with data, lab notes with materials and experimental designs, some visuals, and tables with results. All they need to do is scrutinize these pieces and put them together into a comprehensive paper.

3.1. Starting with Materials and Methods

If you still struggle with starting a paper, then write the Materials and Methods section first. Since you have all your notes, it should not be problematic for you to describe the experimental design and procedures. Your most important goal in this section is to be as explicit as possible by providing enough detail and references. In the end, the purpose of this section is to allow other researchers to evaluate and repeat your work. So do not run into the same problems as the writers of the sentences in (1):

1a. Bacteria were pelleted by centrifugation. 1b. To isolate T cells, lymph nodes were collected.

As you can see, crucial pieces of information are missing: the speed of centrifuging your bacteria, the time, and the temperature in (1a); the source of lymph nodes for collection in (b). The sentences can be improved when information is added, as in (2a) and (2b), respectfully:

2a. Bacteria were pelleted by centrifugation at 3000g for 15 min at 25°C. 2b. To isolate T cells, mediastinal and mesenteric lymph nodes from Balb/c mice were collected at day 7 after immunization with ovabumin.

If your method has previously been published and is well-known, then you should provide only the literature reference, as in (3a). If your method is unpublished, then you need to make sure you provide all essential details, as in (3b).

3a. Stem cells were isolated, according to Johnson [23]. 3b. Stem cells were isolated using biotinylated carbon nanotubes coated with anti-CD34 antibodies.

Furthermore, cohesion and fluency are crucial in this section. One of the malpractices resulting in disrupted fluency is switching from passive voice to active and vice versa within the same paragraph, as shown in (4). This switching misleads and distracts the reader.

4. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. We took ratings of enjoyment, mood, and arousal as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness [ 4 ].

The problem with (4) is that the reader has to switch from the point of view of the experiment (passive voice) to the point of view of the experimenter (active voice). This switch causes confusion about the performer of the actions in the first and the third sentences. To improve the coherence and fluency of the paragraph above, you should be consistent in choosing the point of view: first person “we” or passive voice [ 5 ]. Let’s consider two revised examples in (5).

5a. We programmed behavioral computer-based experiments of Study 1 by using E-Prime. We took ratings of enjoyment, mood, and arousal by using Visual Analogue Scales (SI Methods) as the patients listened to preferred pleasant music and unpreferred music. We operationalized the preferred and unpreferred status of the music along a continuum of pleasantness. 5b. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. Ratings of enjoyment, mood, and arousal were taken as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness.

If you choose the point of view of the experimenter, then you may end up with repetitive “we did this” sentences. For many readers, paragraphs with sentences all beginning with “we” may also sound disruptive. So if you choose active sentences, you need to keep the number of “we” subjects to a minimum and vary the beginnings of the sentences [ 6 ].

Interestingly, recent studies have reported that the Materials and Methods section is the only section in research papers in which passive voice predominantly overrides the use of the active voice [ 5 , 7 , 8 , 9 ]. For example, Martínez shows a significant drop in active voice use in the Methods sections based on the corpus of 1 million words of experimental full text research articles in the biological sciences [ 7 ]. According to the author, the active voice patterned with “we” is used only as a tool to reveal personal responsibility for the procedural decisions in designing and performing experimental work. This means that while all other sections of the research paper use active voice, passive voice is still the most predominant in Materials and Methods sections.

Writing Materials and Methods sections is a meticulous and time consuming task requiring extreme accuracy and clarity. This is why when you complete your draft, you should ask for as much feedback from your colleagues as possible. Numerous readers of this section will help you identify the missing links and improve the technical style of this section.

Rule 3: Be meticulous and accurate in describing the Materials and Methods. Do not change the point of view within one paragraph.

3.2. writing results section.

For many authors, writing the Results section is more intimidating than writing the Materials and Methods section . If people are interested in your paper, they are interested in your results. That is why it is vital to use all your writing skills to objectively present your key findings in an orderly and logical sequence using illustrative materials and text.

Your Results should be organized into different segments or subsections where each one presents the purpose of the experiment, your experimental approach, data including text and visuals (tables, figures, schematics, algorithms, and formulas), and data commentary. For most journals, your data commentary will include a meaningful summary of the data presented in the visuals and an explanation of the most significant findings. This data presentation should not repeat the data in the visuals, but rather highlight the most important points. In the “standard” research paper approach, your Results section should exclude data interpretation, leaving it for the Discussion section. However, interpretations gradually and secretly creep into research papers: “Reducing the data, generalizing from the data, and highlighting scientific cases are all highly interpretive processes. It should be clear by now that we do not let the data speak for themselves in research reports; in summarizing our results, we interpret them for the reader” [ 10 ]. As a result, many journals including the Journal of Experimental Medicine and the Journal of Clinical Investigation use joint Results/Discussion sections, where results are immediately followed by interpretations.

Another important aspect of this section is to create a comprehensive and supported argument or a well-researched case. This means that you should be selective in presenting data and choose only those experimental details that are essential for your reader to understand your findings. You might have conducted an experiment 20 times and collected numerous records, but this does not mean that you should present all those records in your paper. You need to distinguish your results from your data and be able to discard excessive experimental details that could distract and confuse the reader. However, creating a picture or an argument should not be confused with data manipulation or falsification, which is a willful distortion of data and results. If some of your findings contradict your ideas, you have to mention this and find a plausible explanation for the contradiction.

In addition, your text should not include irrelevant and peripheral information, including overview sentences, as in (6).

6. To show our results, we first introduce all components of experimental system and then describe the outcome of infections.

Indeed, wordiness convolutes your sentences and conceals your ideas from readers. One common source of wordiness is unnecessary intensifiers. Adverbial intensifiers such as “clearly,” “essential,” “quite,” “basically,” “rather,” “fairly,” “really,” and “virtually” not only add verbosity to your sentences, but also lower your results’ credibility. They appeal to the reader’s emotions but lower objectivity, as in the common examples in (7):

7a. Table 3 clearly shows that … 7b. It is obvious from figure 4 that …

Another source of wordiness is nominalizations, i.e., nouns derived from verbs and adjectives paired with weak verbs including “be,” “have,” “do,” “make,” “cause,” “provide,” and “get” and constructions such as “there is/are.”

8a. We tested the hypothesis that there is a disruption of membrane asymmetry. 8b. In this paper we provide an argument that stem cells repopulate injured organs.

In the sentences above, the abstract nominalizations “disruption” and “argument” do not contribute to the clarity of the sentences, but rather clutter them with useless vocabulary that distracts from the meaning. To improve your sentences, avoid unnecessary nominalizations and change passive verbs and constructions into active and direct sentences.

9a. We tested the hypothesis that the membrane asymmetry is disrupted. 9b. In this paper we argue that stem cells repopulate injured organs.

Your Results section is the heart of your paper, representing a year or more of your daily research. So lead your reader through your story by writing direct, concise, and clear sentences.

Rule 4: Be clear, concise, and objective in describing your Results.

3.3. now it is time for your introduction.

Now that you are almost half through drafting your research paper, it is time to update your outline. While describing your Methods and Results, many of you diverged from the original outline and re-focused your ideas. So before you move on to create your Introduction, re-read your Methods and Results sections and change your outline to match your research focus. The updated outline will help you review the general picture of your paper, the topic, the main idea, and the purpose, which are all important for writing your introduction.

The best way to structure your introduction is to follow the three-move approach shown in Table 3 .

Adapted from Swales and Feak [ 11 ].

The moves and information from your outline can help to create your Introduction efficiently and without missing steps. These moves are traffic signs that lead the reader through the road of your ideas. Each move plays an important role in your paper and should be presented with deep thought and care. When you establish the territory, you place your research in context and highlight the importance of your research topic. By finding the niche, you outline the scope of your research problem and enter the scientific dialogue. The final move, “occupying the niche,” is where you explain your research in a nutshell and highlight your paper’s significance. The three moves allow your readers to evaluate their interest in your paper and play a significant role in the paper review process, determining your paper reviewers.

Some academic writers assume that the reader “should follow the paper” to find the answers about your methodology and your findings. As a result, many novice writers do not present their experimental approach and the major findings, wrongly believing that the reader will locate the necessary information later while reading the subsequent sections [ 5 ]. However, this “suspense” approach is not appropriate for scientific writing. To interest the reader, scientific authors should be direct and straightforward and present informative one-sentence summaries of the results and the approach.

Another problem is that writers understate the significance of the Introduction. Many new researchers mistakenly think that all their readers understand the importance of the research question and omit this part. However, this assumption is faulty because the purpose of the section is not to evaluate the importance of the research question in general. The goal is to present the importance of your research contribution and your findings. Therefore, you should be explicit and clear in describing the benefit of the paper.

The Introduction should not be long. Indeed, for most journals, this is a very brief section of about 250 to 600 words, but it might be the most difficult section due to its importance.

Rule 5: Interest your reader in the Introduction section by signalling all its elements and stating the novelty of the work.

3.4. discussion of the results.

For many scientists, writing a Discussion section is as scary as starting a paper. Most of the fear comes from the variation in the section. Since every paper has its unique results and findings, the Discussion section differs in its length, shape, and structure. However, some general principles of writing this section still exist. Knowing these rules, or “moves,” can change your attitude about this section and help you create a comprehensive interpretation of your results.

The purpose of the Discussion section is to place your findings in the research context and “to explain the meaning of the findings and why they are important, without appearing arrogant, condescending, or patronizing” [ 11 ]. The structure of the first two moves is almost a mirror reflection of the one in the Introduction. In the Introduction, you zoom in from general to specific and from the background to your research question; in the Discussion section, you zoom out from the summary of your findings to the research context, as shown in Table 4 .

Adapted from Swales and Feak and Hess [ 11 , 12 ].

The biggest challenge for many writers is the opening paragraph of the Discussion section. Following the moves in Table 1 , the best choice is to start with the study’s major findings that provide the answer to the research question in your Introduction. The most common starting phrases are “Our findings demonstrate . . .,” or “In this study, we have shown that . . .,” or “Our results suggest . . .” In some cases, however, reminding the reader about the research question or even providing a brief context and then stating the answer would make more sense. This is important in those cases where the researcher presents a number of findings or where more than one research question was presented. Your summary of the study’s major findings should be followed by your presentation of the importance of these findings. One of the most frequent mistakes of the novice writer is to assume the importance of his findings. Even if the importance is clear to you, it may not be obvious to your reader. Digesting the findings and their importance to your reader is as crucial as stating your research question.

Another useful strategy is to be proactive in the first move by predicting and commenting on the alternative explanations of the results. Addressing potential doubts will save you from painful comments about the wrong interpretation of your results and will present you as a thoughtful and considerate researcher. Moreover, the evaluation of the alternative explanations might help you create a logical step to the next move of the discussion section: the research context.

The goal of the research context move is to show how your findings fit into the general picture of the current research and how you contribute to the existing knowledge on the topic. This is also the place to discuss any discrepancies and unexpected findings that may otherwise distort the general picture of your paper. Moreover, outlining the scope of your research by showing the limitations, weaknesses, and assumptions is essential and adds modesty to your image as a scientist. However, make sure that you do not end your paper with the problems that override your findings. Try to suggest feasible explanations and solutions.

If your submission does not require a separate Conclusion section, then adding another paragraph about the “take-home message” is a must. This should be a general statement reiterating your answer to the research question and adding its scientific implications, practical application, or advice.

Just as in all other sections of your paper, the clear and precise language and concise comprehensive sentences are vital. However, in addition to that, your writing should convey confidence and authority. The easiest way to illustrate your tone is to use the active voice and the first person pronouns. Accompanied by clarity and succinctness, these tools are the best to convince your readers of your point and your ideas.

Rule 6: Present the principles, relationships, and generalizations in a concise and convincing tone.

4. choosing the best working revision strategies.

Now that you have created the first draft, your attitude toward your writing should have improved. Moreover, you should feel more confident that you are able to accomplish your project and submit your paper within a reasonable timeframe. You also have worked out your writing schedule and followed it precisely. Do not stop ― you are only at the midpoint from your destination. Just as the best and most precious diamond is no more than an unattractive stone recognized only by trained professionals, your ideas and your results may go unnoticed if they are not polished and brushed. Despite your attempts to present your ideas in a logical and comprehensive way, first drafts are frequently a mess. Use the advice of Paul Silvia: “Your first drafts should sound like they were hastily translated from Icelandic by a non-native speaker” [ 2 ]. The degree of your success will depend on how you are able to revise and edit your paper.

The revision can be done at the macrostructure and the microstructure levels [ 13 ]. The macrostructure revision includes the revision of the organization, content, and flow. The microstructure level includes individual words, sentence structure, grammar, punctuation, and spelling.

The best way to approach the macrostructure revision is through the outline of the ideas in your paper. The last time you updated your outline was before writing the Introduction and the Discussion. Now that you have the beginning and the conclusion, you can take a bird’s-eye view of the whole paper. The outline will allow you to see if the ideas of your paper are coherently structured, if your results are logically built, and if the discussion is linked to the research question in the Introduction. You will be able to see if something is missing in any of the sections or if you need to rearrange your information to make your point.

The next step is to revise each of the sections starting from the beginning. Ideally, you should limit yourself to working on small sections of about five pages at a time [ 14 ]. After these short sections, your eyes get used to your writing and your efficiency in spotting problems decreases. When reading for content and organization, you should control your urge to edit your paper for sentence structure and grammar and focus only on the flow of your ideas and logic of your presentation. Experienced researchers tend to make almost three times the number of changes to meaning than novice writers [ 15 , 16 ]. Revising is a difficult but useful skill, which academic writers obtain with years of practice.

In contrast to the macrostructure revision, which is a linear process and is done usually through a detailed outline and by sections, microstructure revision is a non-linear process. While the goal of the macrostructure revision is to analyze your ideas and their logic, the goal of the microstructure editing is to scrutinize the form of your ideas: your paragraphs, sentences, and words. You do not need and are not recommended to follow the order of the paper to perform this type of revision. You can start from the end or from different sections. You can even revise by reading sentences backward, sentence by sentence and word by word.

One of the microstructure revision strategies frequently used during writing center consultations is to read the paper aloud [ 17 ]. You may read aloud to yourself, to a tape recorder, or to a colleague or friend. When reading and listening to your paper, you are more likely to notice the places where the fluency is disrupted and where you stumble because of a very long and unclear sentence or a wrong connector.

Another revision strategy is to learn your common errors and to do a targeted search for them [ 13 ]. All writers have a set of problems that are specific to them, i.e., their writing idiosyncrasies. Remembering these problems is as important for an academic writer as remembering your friends’ birthdays. Create a list of these idiosyncrasies and run a search for these problems using your word processor. If your problem is demonstrative pronouns without summary words, then search for “this/these/those” in your text and check if you used the word appropriately. If you have a problem with intensifiers, then search for “really” or “very” and delete them from the text. The same targeted search can be done to eliminate wordiness. Searching for “there is/are” or “and” can help you avoid the bulky sentences.

The final strategy is working with a hard copy and a pencil. Print a double space copy with font size 14 and re-read your paper in several steps. Try reading your paper line by line with the rest of the text covered with a piece of paper. When you are forced to see only a small portion of your writing, you are less likely to get distracted and are more likely to notice problems. You will end up spotting more unnecessary words, wrongly worded phrases, or unparallel constructions.

After you apply all these strategies, you are ready to share your writing with your friends, colleagues, and a writing advisor in the writing center. Get as much feedback as you can, especially from non-specialists in your field. Patiently listen to what others say to you ― you are not expected to defend your writing or explain what you wanted to say. You may decide what you want to change and how after you receive the feedback and sort it in your head. Even though some researchers make the revision an endless process and can hardly stop after a 14th draft; having from five to seven drafts of your paper is a norm in the sciences. If you can’t stop revising, then set a deadline for yourself and stick to it. Deadlines always help.

Rule 7: Revise your paper at the macrostructure and the microstructure level using different strategies and techniques. Receive feedback and revise again.

5. it is time to submit.

It is late at night again. You are still in your lab finishing revisions and getting ready to submit your paper. You feel happy ― you have finally finished a year’s worth of work. You will submit your paper tomorrow, and regardless of the outcome, you know that you can do it. If one journal does not take your paper, you will take advantage of the feedback and resubmit again. You will have a publication, and this is the most important achievement.

What is even more important is that you have your scheduled writing time that you are going to keep for your future publications, for reading and taking notes, for writing grants, and for reviewing papers. You are not going to lose stamina this time, and you will become a productive scientist. But for now, let’s celebrate the end of the paper.

  • Hayes JR. In: The Science of Writing: Theories, Methods, Individual Differences, and Applications. Levy CM, Ransdell SE, editors. Mahwah, NJ: Lawrence Erlbaum; 1996. A new framework for understanding cognition and affect in writing; pp. 1–28. [ Google Scholar ]
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Homeschool Help and Curriculum

How to Help T(w)eens Write Their First Research Paper

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We have learned over the years that parents often feel they know how to help teens write their first research paper. Here’s a little encouragement.

How to Help Teens Write Their First Research Paper. It is important to write research papers in high school (and prepare for them in middle school). Here are some non-intimidating tips. #HomeschoolHighSchool #HomeschoolMiddleSchool #ResearchPaperReadiness #ResearchPaperWriting #ResearchPaperWritingHelp

How to Help Teens Write Their First Research Paper

Middle schoolers and early high schoolers often feel intimidated by the thought of writing research papers . (Their parents do, too, sometimes!)

Anyone who has more than one kid has discovered that they are all different:

  • Their personalities
  • Their academic strengths and weaknesses
  • Their needs and interests

One thing I have noticed that is the SAME in most tweens and teens: They need some handholding when they begin learning to write research papers. So how do you start that handholding and writing process?

One way is to start by a parent-led report-style research paper. (Remember the papers you used to write- if you went to traditional school in 5th or 6th grade? Those were reports: You were simply presenting information. You were not digesting the information. You were not worried about format or style.)

7Sisters can help with that report-style research paper, and we can do it for free! Simply download this parent-directed, step-by-step guide to writing a very basic, report-style research paper. It is a simple and fairly quick process that will remove some of the intimidation factor for both you and your homeschoolers!

research paper help for reluctant writers

Their next research paper should be a real research paper…but keep up the hand holding!

The next research-writing project should go beyond the simple report. Homeschoolers should be learning some serious research-paper basic concepts like:

  • What is plagiarism?
  • How do you choose a topic for a research paper?
  • How do you actually do the research?
  • How do you organize your research?
  • How do you create citations?

With this next-step research paper, they do not need to decide on an official style or format for their research paper. They can do that next year.

In the meantime, the concepts listed above are big concepts. Working through the process with a parent will be important.

However, in this real research paper, there will be work that many students will be able to do on their own. To understand what the help/support/let-go process can look like, it is good to have a guide. That’s why we created the writing guide: Research Writing Readiness .

Research Writing Readiness

This guide came about because our local homeschool parents kept asking 7Sister, Allison, to help young people write their very first research paper. (You may have noticed that Allison created our popular MLA Research Paper Writing Guide . Homeschooling parents told her they wanted a prequel!)

After teaching our local beginners, Allison (along with 7Sister Marilyn, who constructed 7Sisters’ Middle School Fairy Tale Writing Guide ) created a new writing guide to help homeschooling parents and their homeschoolers to create that very first research paper. It is a homeschooler/parent, do-it-together guide: Research Writing Readiness: Foundational Skills for Successful Research Papers.

Let me explain how to help teens to write their first research paper using 7Sisters Research Writing Readiness downloadable eworkbook.

Research Writing Readiness: Foundational Skills for Successful Research Papers  is a step -by-step guide. It provides the tools you need to introduce the research-paper writing process in 20 short lessons. The lessons can be done one per day over 20 school days or (for a more relaxed process) the lessons can be spread out to 1 or 2 steps per week.

Research Writing Readiness helps a student work with a parent on a first research paper.

This easy-to-use guide provides the tools you need to introduce the research-paper writing process. That way, when they are in high school, they will be prepared to write their research papers.

The guide is intended for use:

  • in homeschool middle school (7th grade or 8th grade for many students)
  • homeschool high schoolers who lack experience or
  • homeschool high schoolers who struggle with research writing

This 7Sisters guide does not teach a particular style of research paper (i.e., MLA, APA, Chicago). Instead, it provides:

  • Step by step instructions for student from topic choice to final draft
  • Practice assignments that will help them create a first research paper
  • paraphrasing
  • note taking
  • citing sources
  • Including: How-to instructions for the parent/teacher
  • Sample grading rubric

Download Research Writing Readiness for your homeschoolers and help them develop powerful research paper writing skills that they need in high school (and in college)!

Work together for a less-intimidating first research paper. 7Sisters Research Writing Readiness is a learn-together research paper writing guide for late middle school or early high school.

Planning the order of your homeschoolers’ research paper writing? Here’s a post with a suggested order for these papers (although we all know that there’s not ONE right way to homeschool).

For more encouragement for helping your teens with their research paper writing check out these episodes of The Homeschool Highschool Podcast:

  • Writing Research Papers: You CAN Do It!
  • Writing Research Papers, Interview with Kat Patrick

As you look ahead at Language Arts for homeschooling high school, you may get some encouragement on this how-to post from our friend, Betsy at BJ’s Homeschool.

Click here to view an excerpt from Research Writing Readiness: Foundational Skills for Successful Research Papers .

Research paper fun – practical tips for teens.

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Vicki Tillman

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  • Published: 19 February 2024

Genomic data in the All of Us Research Program

The all of us research program genomics investigators.

Nature ( 2024 ) Cite this article

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  • Genetic variation
  • Genome-wide association studies

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 , 2 , 3 , 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Comprehensively identifying genetic variation and cataloguing its contribution to health and disease, in conjunction with environmental and lifestyle factors, is a central goal of human health research 1 , 2 . A key limitation in efforts to build this catalogue has been the historic under-representation of large subsets of individuals in biomedical research including individuals from diverse ancestries, individuals with disabilities and individuals from disadvantaged backgrounds 3 , 4 . The All of Us Research Program (All of Us) aims to address this gap by enrolling and collecting comprehensive health data on at least one million individuals who reflect the diversity across the USA 5 , 6 . An essential component of All of Us is the generation of whole-genome sequence (WGS) and genotyping data on one million participants. All of Us is committed to making this dataset broadly useful—not only by democratizing access to this dataset across the scientific community but also to return value to the participants themselves by returning individual DNA results, such as genetic ancestry, hereditary disease risk and pharmacogenetics according to clinical standards, to those who wish to receive these research results.

Here we describe the release of WGS data from 245,388 All of Us participants and demonstrate the impact of this high-quality data in genetic and health studies. We carried out a series of data harmonization and quality control (QC) procedures and conducted analyses characterizing the properties of the dataset including genetic ancestry and relatedness. We validated the data by replicating well-established genotype–phenotype associations including low-density lipoprotein cholesterol (LDL-C) and 117 additional diseases. These data are available through the All of Us Researcher Workbench, a cloud platform that embodies and enables programme priorities, facilitating equitable data and compute access while ensuring responsible conduct of research and protecting participant privacy through a passport data access model.

The All of Us Research Program

To accelerate health research, All of Us is committed to curating and releasing research data early and often 6 . Less than five years after national enrolment began in 2018, this fifth data release includes data from more than 413,000 All of Us participants. Summary data are made available through a public Data Browser, and individual-level participant data are made available to researchers through the Researcher Workbench (Fig. 1a and Data availability).

figure 1

a , The All of Us Research Hub contains a publicly accessible Data Browser for exploration of summary phenotypic and genomic data. The Researcher Workbench is a secure cloud-based environment of participant-level data in a Controlled Tier that is widely accessible to researchers. b , All of Us participants have rich phenotype data from a combination of physical measurements, survey responses, EHRs, wearables and genomic data. Dots indicate the presence of the specific data type for the given number of participants. c , Overall summary of participants under-represented in biomedical research (UBR) with data available in the Controlled Tier. The All of Us logo in a is reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Participant data include a rich combination of phenotypic and genomic data (Fig. 1b ). Participants are asked to complete consent for research use of data, sharing of electronic health records (EHRs), donation of biospecimens (blood or saliva, and urine), in-person provision of physical measurements (height, weight and blood pressure) and surveys initially covering demographics, lifestyle and overall health 7 . Participants are also consented for recontact. EHR data, harmonized using the Observational Medical Outcomes Partnership Common Data Model 8 ( Methods ), are available for more than 287,000 participants (69.42%) from more than 50 health care provider organizations. The EHR dataset is longitudinal, with a quarter of participants having 10 years of EHR data (Extended Data Fig. 1 ). Data include 245,388 WGSs and genome-wide genotyping on 312,925 participants. Sequenced and genotyped individuals in this data release were not prioritized on the basis of any clinical or phenotypic feature. Notably, 99% of participants with WGS data also have survey data and physical measurements, and 84% also have EHR data. In this data release, 77% of individuals with genomic data identify with groups historically under-represented in biomedical research, including 46% who self-identify with a racial or ethnic minority group (Fig. 1c , Supplementary Table 1 and Supplementary Note ).

Scaling the All of Us infrastructure

The genomic dataset generated from All of Us participants is a resource for research and discovery and serves as the basis for return of individual health-related DNA results to participants. Consequently, the US Food and Drug Administration determined that All of Us met the criteria for a significant risk device study. As such, the entire All of Us genomics effort from sample acquisition to sequencing meets clinical laboratory standards 9 .

All of Us participants were recruited through a national network of partners, starting in 2018, as previously described 5 . Participants may enrol through All of Us - funded health care provider organizations or direct volunteer pathways and all biospecimens, including blood and saliva, are sent to the central All of Us Biobank for processing and storage. Genomics data for this release were generated from blood-derived DNA. The programme began return of actionable genomic results in December 2022. As of April 2023, approximately 51,000 individuals were sent notifications asking whether they wanted to view their results, and approximately half have accepted. Return continues on an ongoing basis.

The All of Us Data and Research Center maintains all participant information and biospecimen ID linkage to ensure that participant confidentiality and coded identifiers (participant and aliquot level) are used to track each sample through the All of Us genomics workflow. This workflow facilitates weekly automated aliquot and plating requests to the Biobank, supplies relevant metadata for the sample shipments to the Genome Centers, and contains a feedback loop to inform action on samples that fail QC at any stage. Further, the consent status of each participant is checked before sample shipment to confirm that they are still active. Although all participants with genomic data are consented for the same general research use category, the programme accommodates different preferences for the return of genomic data to participants and only data for those individuals who have consented for return of individual health-related DNA results are distributed to the All of Us Clinical Validation Labs for further evaluation and health-related clinical reporting. All participants in All of Us that choose to get health-related DNA results have the option to schedule a genetic counselling appointment to discuss their results. Individuals with positive findings who choose to obtain results are required to schedule an appointment with a genetic counsellor to receive those findings.

Genome sequencing

To satisfy the requirements for clinical accuracy, precision and consistency across DNA sample extraction and sequencing, the All of Us Genome Centers and Biobank harmonized laboratory protocols, established standard QC methodologies and metrics, and conducted a series of validation experiments using previously characterized clinical samples and commercially available reference standards 9 . Briefly, PCR-free barcoded WGS libraries were constructed with the Illumina Kapa HyperPrep kit. Libraries were pooled and sequenced on the Illumina NovaSeq 6000 instrument. After demultiplexing, initial QC analysis is performed with the Illumina DRAGEN pipeline (Supplementary Table 2 ) leveraging lane, library, flow cell, barcode and sample level metrics as well as assessing contamination, mapping quality and concordance to genotyping array data independently processed from a different aliquot of DNA. The Genome Centers use these metrics to determine whether each sample meets programme specifications and then submits sequencing data to the Data and Research Center for further QC, joint calling and distribution to the research community ( Methods ).

This effort to harmonize sequencing methods, multi-level QC and use of identical data processing protocols mitigated the variability in sequencing location and protocols that often leads to batch effects in large genomic datasets 9 . As a result, the data are not only of clinical-grade quality, but also consistent in coverage (≥30× mean) and uniformity across Genome Centers (Supplementary Figs. 1 – 5 ).

Joint calling and variant discovery

We carried out joint calling across the entire All of Us WGS dataset (Extended Data Fig. 2 ). Joint calling leverages information across samples to prune artefact variants, which increases sensitivity, and enables flagging samples with potential issues that were missed during single-sample QC 10 (Supplementary Table 3 ). Scaling conventional approaches to whole-genome joint calling beyond 50,000 individuals is a notable computational challenge 11 , 12 . To address this, we developed a new cloud variant storage solution, the Genomic Variant Store (GVS), which is based on a schema designed for querying and rendering variants in which the variants are stored in GVS and rendered to an analysable variant file, as opposed to the variant file being the primary storage mechanism (Code availability). We carried out QC on the joint call set on the basis of the approach developed for gnomAD 3.1 (ref.  13 ). This included flagging samples with outlying values in eight metrics (Supplementary Table 4 , Supplementary Fig. 2 and Methods ).

To calculate the sensitivity and precision of the joint call dataset, we included four well-characterized samples. We sequenced the National Institute of Standards and Technology reference materials (DNA samples) from the Genome in a Bottle consortium 13 and carried out variant calling as described above. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations 14 . The overall sensitivity for single-nucleotide variants was over 98.7% and precision was more than 99.9%. For short insertions or deletions, the sensitivity was over 97% and precision was more than 99.6% (Supplementary Table 5 and Methods ).

The joint call set included more than 1 billion genetic variants. We annotated the joint call dataset on the basis of functional annotation (for example, gene symbol and protein change) using Illumina Nirvana 15 . We defined coding variants as those inducing an amino acid change on a canonical ENSEMBL transcript and found 272,051,104 non-coding and 3,913,722 coding variants that have not been described previously in dbSNP 16 v153 (Extended Data Table 1 ). A total of 3,912,832 (99.98%) of the coding variants are rare (allelic frequency < 0.01) and the remaining 883 (0.02%) are common (allelic frequency > 0.01). Of the coding variants, 454 (0.01%) are common in one or more of the non-European computed ancestries in All of Us, rare among participants of European ancestry, and have an allelic number greater than 1,000 (Extended Data Table 2 and Extended Data Fig. 3 ). The distributions of pathogenic, or likely pathogenic, ClinVar variant counts per participant, stratified by computed ancestry, filtered to only those variants that are found in individuals with an allele count of <40 are shown in Extended Data Fig. 4 . The potential medical implications of these known and new variants with respect to variant pathogenicity by ancestry are highlighted in a companion paper 17 . In particular, we find that the European ancestry subset has the highest rate of pathogenic variation (2.1%), which was twice the rate of pathogenic variation in individuals of East Asian ancestry 17 .The lower frequency of variants in East Asian individuals may be partially explained by the fact the sample size in that group is small and there may be knowledge bias in the variant databases that is reducing the number of findings in some of the less-studied ancestry groups.

Genetic ancestry and relatedness

Genetic ancestry inference confirmed that 51.1% of the All of Us WGS dataset is derived from individuals of non-European ancestry. Briefly, the ancestry categories are based on the same labels used in gnomAD 18 . We trained a classifier on a 16-dimensional principal component analysis (PCA) space of a diverse reference based on 3,202 samples and 151,159 autosomal single-nucleotide polymorphisms. We projected the All of Us samples into the PCA space of the training data, based on the same single-nucleotide polymorphisms from the WGS data, and generated categorical ancestry predictions from the trained classifier ( Methods ). Continuous genetic ancestry fractions for All of Us samples were inferred using the same PCA data, and participants’ patterns of ancestry and admixture were compared to their self-identified race and ethnicity (Fig. 2 and Methods ). Continuous ancestry inference carried out using genome-wide genotypes yields highly concordant estimates.

figure 2

a , b , Uniform manifold approximation and projection (UMAP) representations of All of Us WGS PCA data with self-described race ( a ) and ethnicity ( b ) labels. c , Proportion of genetic ancestry per individual in six distinct and coherent ancestry groups defined by Human Genome Diversity Project and 1000 Genomes samples.

Kinship estimation confirmed that All of Us WGS data consist largely of unrelated individuals with about 85% (215,107) having no first- or second-degree relatives in the dataset (Supplementary Fig. 6 ). As many genomic analyses leverage unrelated individuals, we identified the smallest set of samples that are required to be removed from the remaining individuals that had first- or second-degree relatives and retained one individual from each kindred. This procedure yielded a maximal independent set of 231,442 individuals (about 94%) with genome sequence data in the current release ( Methods ).

Genetic determinants of LDL-C

As a measure of data quality and utility, we carried out a single-variant genome-wide association study (GWAS) for LDL-C, a trait with well-established genomic architecture ( Methods ). Of the 245,388 WGS participants, 91,749 had one or more LDL-C measurements. The All of Us LDL-C GWAS identified 20 well-established genome-wide significant loci, with minimal genomic inflation (Fig. 3 , Extended Data Table 3 and Supplementary Fig. 7 ). We compared the results to those of a recent multi-ethnic LDL-C GWAS in the National Heart, Lung, and Blood Institute (NHLBI) TOPMed study that included 66,329 ancestrally diverse (56% non-European ancestry) individuals 19 . We found a strong correlation between the effect estimates for NHLBI TOPMed genome-wide significant loci and those of All of Us ( R 2  = 0.98, P  < 1.61 × 10 −45 ; Fig. 3 , inset). Notably, the per-locus effect sizes observed in All of Us are decreased compared to those in TOPMed, which is in part due to differences in the underlying statistical model, differences in the ancestral composition of these datasets and differences in laboratory value ascertainment between EHR-derived data and epidemiology studies. A companion manuscript extended this work to identify common and rare genetic associations for three diseases (atrial fibrillation, coronary artery disease and type 2 diabetes) and two quantitative traits (height and LDL-C) in the All of Us dataset and identified very high concordance with previous efforts across all of these diseases and traits 20 .

figure 3

Manhattan plot demonstrating robust replication of 20 well-established LDL-C genetic loci among 91,749 individuals with 1 or more LDL-C measurements. The red horizontal line denotes the genome wide significance threshold of P = 5 × 10 –8 . Inset, effect estimate ( β ) comparison between NHLBI TOPMed LDL-C GWAS ( x  axis) and All of Us LDL-C GWAS ( y  axis) for the subset of 194 independent variants clumped (window 250 kb, r2 0.5) that reached genome-wide significance in NHLBI TOPMed.

Genotype-by-phenotype associations

As another measure of data quality and utility, we tested replication rates of previously reported phenotype–genotype associations in the five predicted genetic ancestry populations present in the Phenotype/Genotype Reference Map (PGRM): AFR, African ancestry; AMR, Latino/admixed American ancestry; EAS, East Asian ancestry; EUR, European ancestry; SAS, South Asian ancestry. The PGRM contains published associations in the GWAS catalogue in these ancestry populations that map to International Classification of Diseases-based phenotype codes 21 . This replication study specifically looked across 4,947 variants, calculating replication rates for powered associations in each ancestry population. The overall replication rates for associations powered at 80% were: 72.0% (18/25) in AFR, 100% (13/13) in AMR, 46.6% (7/15) in EAS, 74.9% (1,064/1,421) in EUR, and 100% (1/1) in SAS. With the exception of the EAS ancestry results, these powered replication rates are comparable to those of the published PGRM analysis where the replication rates of several single-site EHR-linked biobanks ranges from 76% to 85%. These results demonstrate the utility of the data and also highlight opportunities for further work understanding the specifics of the All of Us population and the potential contribution of gene–environment interactions to genotype–phenotype mapping and motivates the development of methods for multi-site EHR phenotype data extraction, harmonization and genetic association studies.

More broadly, the All of Us resource highlights the opportunities to identify genotype–phenotype associations that differ across diverse populations 22 . For example, the Duffy blood group locus ( ACKR1 ) is more prevalent in individuals of AFR ancestry and individuals of AMR ancestry than in individuals of EUR ancestry. Although the phenome-wide association study of this locus highlights the well-established association of the Duffy blood group with lower white blood cell counts both in individuals of AFR and AMR ancestry 23 , 24 , it also revealed genetic-ancestry-specific phenotype patterns, with minimal phenotypic associations in individuals of EAS ancestry and individuals of EUR ancestry (Fig. 4 and Extended Data Table 4 ). Conversely, rs9273363 in the HLA-DQB1 locus is associated with increased risk of type 1 diabetes 25 , 26 and diabetic complications across ancestries, but only associates with increased risk of coeliac disease in individuals of EUR ancestry (Extended Data Fig. 5 ). Similarly, the TCF7L2 locus 27 strongly associates with increased risk of type 2 diabetes and associated complications across several ancestries (Extended Data Fig. 6 ). Association testing results are available in Supplementary Dataset 1 .

figure 4

Results of genetic-ancestry-stratified phenome-wide association analysis among unrelated individuals highlighting ancestry-specific disease associations across the four most common genetic ancestries of participant. Bonferroni-adjusted phenome-wide significance threshold (<2.88 × 10 −5 ) is plotted as a red horizontal line. AFR ( n  = 34,037, minor allele fraction (MAF) 0.82); AMR ( n  = 28,901, MAF 0.10); EAS ( n  = 32,55, MAF 0.003); EUR ( n  = 101,613, MAF 0.007).

The cloud-based Researcher Workbench

All of Us genomic data are available in a secure, access-controlled cloud-based analysis environment: the All of Us Researcher Workbench. Unlike traditional data access models that require per-project approval, access in the Researcher Workbench is governed by a data passport model based on a researcher’s authenticated identity, institutional affiliation, and completion of self-service training and compliance attestation 28 . After gaining access, a researcher may create a new workspace at any time to conduct a study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is regularly audited and made accessible publicly on the All of Us Research Projects Directory. This streamlined access model is guided by the principles that: participants are research partners and maintaining their privacy and data security is paramount; their data should be made as accessible as possible for authorized researchers; and we should continually seek to remove unnecessary barriers to accessing and using All of Us data.

For researchers at institutions with an existing institutional data use agreement, access can be gained as soon as they complete the required verification and compliance steps. As of August 2023, 556 institutions have agreements in place, allowing more than 5,000 approved researchers to actively work on more than 4,400 projects. The median time for a researcher from initial registration to completion of these requirements is 28.6 h (10th percentile: 48 min, 90th percentile: 14.9 days), a fraction of the weeks to months it can take to assemble a project-specific application and have it reviewed by an access board with conventional access models.

Given that the size of the project’s phenotypic and genomic dataset is expected to reach 4.75 PB in 2023, the use of a central data store and cloud analysis tools will save funders an estimated US$16.5 million per year when compared to the typical approach of allowing researchers to download genomic data. Storing one copy per institution of this data at 556 registered institutions would cost about US$1.16 billion per year. By contrast, storing a central cloud copy costs about US$1.14 million per year, a 99.9% saving. Importantly, cloud infrastructure also democratizes data access particularly for researchers who do not have high-performance local compute resources.

Here we present the All of Us Research Program’s approach to generating diverse clinical-grade genomic data at an unprecedented scale. We present the data release of about 245,000 genome sequences as part of a scalable framework that will grow to include genetic information and health data for one million or more people living across the USA. Our observations permit several conclusions.

First, the All of Us programme is making a notable contribution to improving the study of human biology through purposeful inclusion of under-represented individuals at scale 29 , 30 . Of the participants with genomic data in All of Us, 45.92% self-identified as a non-European race or ethnicity. This diversity enabled identification of more than 275 million new genetic variants across the dataset not previously captured by other large-scale genome aggregation efforts with diverse participants that have submitted variation to dbSNP v153, such as NHLBI TOPMed 31 freeze 8 (Extended Data Table 1 ). In contrast to gnomAD, All of Us permits individual-level genotype access with detailed phenotype data for all participants. Furthermore, unlike many genomics resources, All of Us is uniformly consented for general research use and enables researchers to go from initial account creation to individual-level data access in as little as a few hours. The All of Us cohort is significantly more diverse than those of other large contemporary research studies generating WGS data 32 , 33 . This enables a more equitable future for precision medicine (for example, through constructing polygenic risk scores that are appropriately calibrated to diverse populations 34 , 35 as the eMERGE programme has done leveraging All of Us data 36 , 37 ). Developing new tools and regulatory frameworks to enable analyses across multiple biobanks in the cloud to harness the unique strengths of each is an active area of investigation addressed in a companion paper to this work 38 .

Second, the All of Us Researcher Workbench embodies the programme’s design philosophy of open science, reproducible research, equitable access and transparency to researchers and to research participants 26 . Importantly, for research studies, no group of data users should have privileged access to All of Us resources based on anything other than data protection criteria. Although the All of Us Researcher Workbench initially targeted onboarding US academic, health care and non-profit organizations, it has recently expanded to international researchers. We anticipate further genomic and phenotypic data releases at regular intervals with data available to all researcher communities. We also anticipate additional derived data and functionality to be made available, such as reference data, structural variants and a service for array imputation using the All of Us genomic data.

Third, All of Us enables studying human biology at an unprecedented scale. The programmatic goal of sequencing one million or more genomes has required harnessing the output of multiple sequencing centres. Previous work has focused on achieving functional equivalence in data processing and joint calling pipelines 39 . To achieve clinical-grade data equivalence, All of Us required protocol equivalence at both sequencing production level and data processing across the sequencing centres. Furthermore, previous work has demonstrated the value of joint calling at scale 10 , 18 . The new GVS framework developed by the All of Us programme enables joint calling at extreme scales (Code availability). Finally, the provision of data access through cloud-native tools enables scalable and secure access and analysis to researchers while simultaneously enabling the trust of research participants and transparency underlying the All of Us data passport access model.

The clinical-grade sequencing carried out by All of Us enables not only research, but also the return of value to participants through clinically relevant genetic results and health-related traits to those who opt-in to receiving this information. In the years ahead, we anticipate that this partnership with All of Us participants will enable researchers to move beyond large-scale genomic discovery to understanding the consequences of implementing genomic medicine at scale.

The All of Us cohort

All of Us aims to engage a longitudinal cohort of one million or more US participants, with a focus on including populations that have historically been under-represented in biomedical research. Details of the All of Us cohort have been described previously 5 . Briefly, the primary objective is to build a robust research resource that can facilitate the exploration of biological, clinical, social and environmental determinants of health and disease. The programme will collect and curate health-related data and biospecimens, and these data and biospecimens will be made broadly available for research uses. Health data are obtained through the electronic medical record and through participant surveys. Survey templates can be found on our public website: . Adults 18 years and older who have the capacity to consent and reside in the USA or a US territory at present are eligible. Informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, HIPAA Authorization for Research use of EHRs and other external health data, and Consent for Return of Genomic Results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us IRB follows the regulations and guidance of the NIH Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Data accessibility through a ‘data passport’

Authorization for access to participant-level data in All of Us is based on a ‘data passport’ model, through which authorized researchers do not need IRB review for each research project. The data passport is required for gaining data access to the Researcher Workbench and for creating workspaces to carry out research projects using All of Us data. At present, data passports are authorized through a six-step process that includes affiliation with an institution that has signed a Data Use and Registration Agreement, account creation, identity verification, completion of ethics training, and attestation to a data user code of conduct. Results reported follow the All of Us Data and Statistics Dissemination Policy disallowing disclosure of group counts under 20 to protect participant privacy without seeking prior approval 40 .

At present, All of Us gathers EHR data from about 50 health care organizations that are funded to recruit and enrol participants as well as transfer EHR data for those participants who have consented to provide them. Data stewards at each provider organization harmonize their local data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, and then submit it to the All of Us Data and Research Center (DRC) so that it can be linked with other participant data and further curated for research use. OMOP is a common data model standardizing health information from disparate EHRs to common vocabularies and organized into tables according to data domains. EHR data are updated from the recruitment sites and sent to the DRC quarterly. Updated data releases to the research community occur approximately once a year. Supplementary Table 6 outlines the OMOP concepts collected by the DRC quarterly from the recruitment sites.

Biospecimen collection and processing

Participants who consented to participate in All of Us donated fresh whole blood (4 ml EDTA and 10 ml EDTA) as a primary source of DNA. The All of Us Biobank managed by the Mayo Clinic extracted DNA from 4 ml EDTA whole blood, and DNA was stored at −80 °C at an average concentration of 150 ng µl −1 . The buffy coat isolated from 10 ml EDTA whole blood has been used for extracting DNA in the case of initial extraction failure or absence of 4 ml EDTA whole blood. The Biobank plated 2.4 µg DNA with a concentration of 60 ng µl −1 in duplicate for array and WGS samples. The samples are distributed to All of Us Genome Centers weekly, and a negative (empty well) control and National Institute of Standards and Technology controls are incorporated every two months for QC purposes.

Genome Center sample receipt, accession and QC

On receipt of DNA sample shipments, the All of Us Genome Centers carry out an inspection of the packaging and sample containers to ensure that sample integrity has not been compromised during transport and to verify that the sample containers correspond to the shipping manifest. QC of the submitted samples also includes DNA quantification, using routine procedures to confirm volume and concentration (Supplementary Table 7 ). Any issues or discrepancies are recorded, and affected samples are put on hold until resolved. Samples that meet quality thresholds are accessioned in the Laboratory Information Management System, and sample aliquots are prepared for library construction processing (for example, normalized with respect to concentration and volume).

WGS library construction, sequencing and primary data QC

The DNA sample is first sheared using a Covaris sonicator and is then size-selected using AMPure XP beads to restrict the range of library insert sizes. Using the PCR Free Kapa HyperPrep library construction kit, enzymatic steps are completed to repair the jagged ends of DNA fragments, add proper A-base segments, and ligate indexed adapter barcode sequences onto samples. Excess adaptors are removed using AMPure XP beads for a final clean-up. Libraries are quantified using quantitative PCR with the Illumina Kapa DNA Quantification Kit and then normalized and pooled for sequencing (Supplementary Table 7 ).

Pooled libraries are loaded on the Illumina NovaSeq 6000 instrument. The data from the initial sequencing run are used to QC individual libraries and to remove non-conforming samples from the pipeline. The data are also used to calibrate the pooling volume of each individual library and re-pool the libraries for additional NovaSeq sequencing to reach an average coverage of 30×.

After demultiplexing, WGS analysis occurs on the Illumina DRAGEN platform. The DRAGEN pipeline consists of highly optimized algorithms for mapping, aligning, sorting, duplicate marking and haplotype variant calling and makes use of platform features such as compression and BCL conversion. Alignment uses the GRCh38dh reference genome. QC data are collected at every stage of the analysis protocol, providing high-resolution metrics required to ensure data consistency for large-scale multiplexing. The DRAGEN pipeline produces a large number of metrics that cover lane, library, flow cell, barcode and sample-level metrics for all runs as well as assessing contamination and mapping quality. The All of Us Genome Centers use these metrics to determine pass or fail for each sample before submitting the CRAM files to the All of Us DRC. For mapping and variant calling, all Genome Centers have harmonized on a set of DRAGEN parameters, which ensures consistency in processing (Supplementary Table 2 ).

Every step through the WGS procedure is rigorously controlled by predefined QC measures. Various control mechanisms and acceptance criteria were established during WGS assay validation. Specific metrics for reviewing and releasing genome data are: mean coverage (threshold of ≥30×), genome coverage (threshold of ≥90% at 20×), coverage of hereditary disease risk genes (threshold of ≥95% at 20×), aligned Q30 bases (threshold of ≥8 × 10 10 ), contamination (threshold of ≤1%) and concordance to independently processed array data.

Array genotyping

Samples are processed for genotyping at three All of Us Genome Centers (Broad, Johns Hopkins University and University of Washington). DNA samples are received from the Biobank and the process is facilitated by the All of Us genomics workflow described above. All three centres used an identical array product, scanners, resource files and genotype calling software for array processing to reduce batch effects. Each centre has its own Laboratory Information Management System that manages workflow control, sample and reagent tracking, and centre-specific liquid handling robotics.

Samples are processed using the Illumina Global Diversity Array (GDA) with Illumina Infinium LCG chemistry using the automated protocol and scanned on Illumina iSCANs with Automated Array Loaders. Illumina IAAP software converts raw data (IDAT files; 2 per sample) into a single GTC file per sample using the BPM file (defines strand, probe sequences and illumicode address) and the EGT file (defines the relationship between intensities and genotype calls). Files used for this data release are: GDA-8v1-0_A5.bpm, GDA-8v1-0_A1_ClusterFile.egt, gentrain v3, reference hg19 and gencall cutoff 0.15. The GDA array assays a total of 1,914,935 variant positions including 1,790,654 single-nucleotide variants, 44,172 indels, 9,935 intensity-only probes for CNV calling, and 70,174 duplicates (same position, different probes). Picard GtcToVcf is used to convert the GTC files to VCF format. Resulting VCF and IDAT files are submitted to the DRC for ingestion and further processing. The VCF file contains assay name, chromosome, position, genotype calls, quality score, raw and normalized intensities, B allele frequency and log R ratio values. Each genome centre is running the GDA array under Clinical Laboratory Improvement Amendments-compliant protocols. The GTC files are parsed and metrics are uploaded to in-house Laboratory Information Management System systems for QC review.

At batch level (each set of 96-well plates run together in the laboratory at one time), each genome centre includes positive control samples that are required to have >98% call rate and >99% concordance to existing data to approve release of the batch of data. At the sample level, the call rate and sex are the key QC determinants 41 . Contamination is also measured using BAFRegress 42 and reported out as metadata. Any sample with a call rate below 98% is repeated one time in the laboratory. Genotyped sex is determined by plotting normalized x versus normalized y intensity values for a batch of samples. Any sample discordant with ‘sex at birth’ reported by the All of Us participant is flagged for further detailed review and repeated one time in the laboratory. If several sex-discordant samples are clustered on an array or on a 96-well plate, the entire array or plate will have data production repeated. Samples identified with sex chromosome aneuploidies are also reported back as metadata (XXX, XXY, XYY and so on). A final processing status of ‘pass’, ‘fail’ or ‘abandon’ is determined before release of data to the All of Us DRC. An array sample will pass if the call rate is >98% and the genotyped sex and sex at birth are concordant (or the sex at birth is not applicable). An array sample will fail if the genotyped sex and the sex at birth are discordant. An array sample will have the status of abandon if the call rate is <98% after at least two attempts at the genome centre.

Data from the arrays are used for participant return of genetic ancestry and non-health-related traits for those who consent, and they are also used to facilitate additional QC of the matched WGS data. Contamination is assessed in the array data to determine whether DNA re-extraction is required before WGS. Re-extraction is prompted by level of contamination combined with consent status for return of results. The arrays are also used to confirm sample identity between the WGS data and the matched array data by assessing concordance at 100 unique sites. To establish concordance, a fingerprint file of these 100 sites is provided to the Genome Centers to assess concordance with the same sites in the WGS data before CRAM submission.

Genomic data curation

As seen in Extended Data Fig. 2 , we generate a joint call set for all WGS samples and make these data available in their entirety and by sample subsets to researchers. A breakdown of the frequencies, stratified by computed ancestries for which we had more than 10,000 participants can be found in Extended Data Fig. 3 . The joint call set process allows us to leverage information across samples to improve QC and increase accuracy.

Single-sample QC

If a sample fails single-sample QC, it is excluded from the release and is not reported in this document. These tests detect sample swaps, cross-individual contamination and sample preparation errors. In some cases, we carry out these tests twice (at both the Genome Center and the DRC), for two reasons: to confirm internal consistency between sites; and to mark samples as passing (or failing) QC on the basis of the research pipeline criteria. The single-sample QC process accepts a higher contamination rate than the clinical pipeline (0.03 for the research pipeline versus 0.01 for the clinical pipeline), but otherwise uses identical thresholds. The list of specific QC processes, passing criteria, error modes addressed and an overview of the results can be found in Supplementary Table 3 .

Joint call set QC

During joint calling, we carry out additional QC steps using information that is available across samples including hard thresholds, population outliers, allele-specific filters, and sensitivity and precision evaluation. Supplementary Table 4 summarizes both the steps that we took and the results obtained for the WGS data. More detailed information about the methods and specific parameters can be found in the All of Us Genomic Research Data Quality Report 36 .

Batch effect analysis

We analysed cross-sequencing centre batch effects in the joint call set. To quantify the batch effect, we calculated Cohen’s d (ref.  43 ) for four metrics (insertion/deletion ratio, single-nucleotide polymorphism count, indel count and single-nucleotide polymorphism transition/transversion ratio) across the three genome sequencing centres (Baylor College of Medicine, Broad Institute and University of Washington), stratified by computed ancestry and seven regions of the genome (whole genome, high-confidence calling, repetitive, GC content of >0.85, GC content of <0.15, low mappability, the ACMG59 genes and regions of large duplications (>1 kb)). Using random batches as a control set, all comparisons had a Cohen’s d of <0.35. Here we report any Cohen’s d results >0.5, which we chose before this analysis and is conventionally the threshold of a medium effect size 44 .

We found that there was an effect size in indel counts (Cohen’s d of 0.53) in the entire genome, between Broad Institute and University of Washington, but this was being driven by repetitive and low-mappability regions. We found no batch effects with Cohen’s d of >0.5 in the ratio metrics or in any metrics in the high-confidence calling, low or high GC content, or ACMG59 regions. A complete list of the batch effects with Cohen’s d of >0.5 are found in Supplementary Table 8 .

Sensitivity and precision evaluation

To determine sensitivity and precision, we included four well-characterized control samples (four National Institute of Standards and Technology Genome in a Bottle samples (HG-001, HG-003, HG-004 and HG-005). The samples were sequenced with the same protocol as All of Us. Of note, these samples were not included in data released to researchers. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations. We use the high-confidence calling region, defined by Genome in a Bottle v4.2.1, as the source of ground truth. To be called a true positive, a variant must match the chromosome, position, reference allele, alternate allele and zygosity. In cases of sites with multiple alternative alleles, each alternative allele is considered separately. Sensitivity and precision results are reported in Supplementary Table 5 .

Genetic ancestry inference

We computed categorical ancestry for all WGS samples in All of Us and made these available to researchers. These predictions are also the basis for population allele frequency calculations in the Genomic Variants section of the public Data Browser. We used the high-quality set of sites to determine an ancestry label for each sample. The ancestry categories are based on the same labels used in gnomAD 18 , the Human Genome Diversity Project (HGDP) 45 and 1000 Genomes 1 : African (AFR); Latino/admixed American (AMR); East Asian (EAS); Middle Eastern (MID); European (EUR), composed of Finnish (FIN) and Non-Finnish European (NFE); Other (OTH), not belonging to one of the other ancestries or is an admixture; South Asian (SAS).

We trained a random forest classifier 46 on a training set of the HGDP and 1000 Genomes samples variants on the autosome, obtained from gnomAD 11 . We generated the first 16 principal components (PCs) of the training sample genotypes (using the hwe_normalized_pca in Hail) at the high-quality variant sites for use as the feature vector for each training sample. We used the truth labels from the sample metadata, which can be found alongside the VCFs. Note that we do not train the classifier on the samples labelled as Other. We use the label probabilities (‘confidence’) of the classifier on the other ancestries to determine ancestry of Other.

To determine the ancestry of All of Us samples, we project the All of Us samples into the PCA space of the training data and apply the classifier. As a proxy for the accuracy of our All of Us predictions, we look at the concordance between the survey results and the predicted ancestry. The concordance between self-reported ethnicity and the ancestry predictions was 87.7%.

PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups. Rye was run on the first 16 PCs, using the defined reference ancestry groups to assign ancestry group fractions to individual All of Us participant samples.


We calculated the kinship score using the Hail pc_relate function and reported any pairs with a kinship score above 0.1. The kinship score is half of the fraction of the genetic material shared (ranges from 0.0 to 0.5). We determined the maximal independent set 41 for related samples. We identified a maximally unrelated set of 231,442 samples (94%) for kinship scored greater than 0.1.

LDL-C common variant GWAS

The phenotypic data were extracted from the Curated Data Repository (CDR, Control Tier Dataset v7) in the All of Us Researcher Workbench. The All of Us Cohort Builder and Dataset Builder were used to extract all LDL cholesterol measurements from the Lab and Measurements criteria in EHR data for all participants who have WGS data. The most recent measurements were selected as the phenotype and adjusted for statin use 19 , age and sex. A rank-based inverse normal transformation was applied for this continuous trait to increase power and deflate type I error. Analysis was carried out on the Hail MatrixTable representation of the All of Us WGS joint-called data including removing monomorphic variants, variants with a call rate of <95% and variants with extreme Hardy–Weinberg equilibrium values ( P  < 10 −15 ). A linear regression was carried out with REGENIE 48 on variants with a minor allele frequency >5%, further adjusting for relatedness to the first five ancestry PCs. The final analysis included 34,924 participants and 8,589,520 variants.

Genotype-by-phenotype replication

We tested replication rates of known phenotype–genotype associations in three of the four largest populations: EUR, AFR and EAS. The AMR population was not included because they have no registered GWAS. This method is a conceptual extension of the original GWAS × phenome-wide association study, which replicated 66% of powered associations in a single EHR-linked biobank 49 . The PGRM is an expansion of this work by Bastarache et al., based on associations in the GWAS catalogue 50 in June 2020 (ref.  51 ). After directly matching the Experimental Factor Ontology terms to phecodes, the authors identified 8,085 unique loci and 170 unique phecodes that compose the PGRM. They showed replication rates in several EHR-linked biobanks ranging from 76% to 85%. For this analysis, we used the EUR-, and AFR-based maps, considering only catalogue associations that were P  < 5 × 10 −8 significant.

The main tools used were the Python package Hail for data extraction, plink for genomic associations, and the R packages PheWAS and pgrm for further analysis and visualization. The phenotypes, participant-reported sex at birth, and year of birth were extracted from the All of Us CDR (Controlled Tier Dataset v7). These phenotypes were then loaded into a plink-compatible format using the PheWAS package, and related samples were removed by sub-setting to the maximally unrelated dataset ( n  = 231,442). Only samples with EHR data were kept, filtered by selected loci, annotated with demographic and phenotypic information extracted from the CDR and ancestry prediction information provided by All of Us, ultimately resulting in 181,345 participants for downstream analysis. The variants in the PGRM were filtered by a minimum population-specific allele frequency of >1% or population-specific allele count of >100, leaving 4,986 variants. Results for which there were at least 20 cases in the ancestry group were included. Then, a series of Firth logistic regression tests with phecodes as the outcome and variants as the predictor were carried out, adjusting for age, sex (for non-sex-specific phenotypes) and the first three genomic PC features as covariates. The PGRM was annotated with power calculations based on the case counts and reported allele frequencies. Power of 80% or greater was considered powered for this analysis.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The All of Us Research Hub has a tiered data access data passport model with three data access tiers. The Public Tier dataset contains only aggregate data with identifiers removed. These data are available to the public through Data Snapshots ( ) and the public Data Browser ( ). The Registered Tier curated dataset contains individual-level data, available only to approved researchers on the Researcher Workbench. At present, the Registered Tier includes data from EHRs, wearables and surveys, as well as physical measurements taken at the time of participant enrolment. The Controlled Tier dataset contains all data in the Registered Tier and additionally genomic data in the form of WGS and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events. At present, Registered Tier and Controlled Tier data are available to researchers at academic institutions, non-profit institutions, and both non-profit and for-profit health care institutions. Work is underway to begin extending access to additional audiences, including industry-affiliated researchers. Researchers have the option to register for Registered Tier and/or Controlled Tier access by completing the All of Us Researcher Workbench access process, which includes identity verification and All of Us-specific training in research involving human participants ( ). Researchers may create a new workspace at any time to conduct any research study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is made accessible publicly on the All of Us Research Projects Directory at .

Code availability

The GVS code is available at . The LDL GWAS pipeline is available as a demonstration project in the Featured Workspace Library on the Researcher Workbench ( ).

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The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers (OT2 OD026549; OT2 OD026554; OT2 OD026557; OT2 OD026556; OT2 OD026550; OT2 OD 026552; OT2 OD026553; OT2 OD026548; OT2 OD026551; OT2 OD026555); Inter agency agreement AOD 16037; Federally Qualified Health Centers HHSN 263201600085U; Data and Research Center: U2C OD023196; Genome Centers (OT2 OD002748; OT2 OD002750; OT2 OD002751); Biobank: U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: U24 OD023163; Communications and Engagement: OT2 OD023205; OT2 OD023206; and Community Partners (OT2 OD025277; OT2 OD025315; OT2 OD025337; OT2 OD025276). In addition, the All of Us Research Program would not be possible without the partnership of its participants. All of Us and the All of Us logo are service marks of the US Department of Health and Human Services. E.E.E. is an investigator of the Howard Hughes Medical Institute. We acknowledge the foundational contributions of our friend and colleague, the late Deborah A. Nickerson. Debbie’s years of insightful contributions throughout the formation of the All of Us genomics programme are permanently imprinted, and she shares credit for all of the successes of this programme.

Author information

Authors and affiliations.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Alexander G. Bick & Henry R. Condon

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Ginger A. Metcalf, Eric Boerwinkle, Richard A. Gibbs, Donna M. Muzny, Eric Venner, Kimberly Walker, Jianhong Hu, Harsha Doddapaneni, Christie L. Kovar, Mullai Murugan, Shannon Dugan, Ziad Khan & Eric Boerwinkle

Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

Kelsey R. Mayo, Jodell E. Linder, Melissa Basford, Ashley Able, Ashley E. Green, Robert J. Carroll, Jennifer Zhang & Yuanyuan Wang

Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Lee Lichtenstein, Anthony Philippakis, Sophie Schwartz, M. Morgan T. Aster, Kristian Cibulskis, Andrea Haessly, Rebecca Asch, Aurora Cremer, Kylee Degatano, Akum Shergill, Laura D. Gauthier, Samuel K. Lee, Aaron Hatcher, George B. Grant, Genevieve R. Brandt, Miguel Covarrubias, Eric Banks & Wail Baalawi

Verily, South San Francisco, CA, USA

Shimon Rura, David Glazer, Moira K. Dillon & C. H. Albach

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Robert J. Carroll, Paul A. Harris & Dan M. Roden

All of Us Research Program, National Institutes of Health, Bethesda, MD, USA

Anjene Musick, Andrea H. Ramirez, Sokny Lim, Siddhartha Nambiar, Bradley Ozenberger, Anastasia L. Wise, Chris Lunt, Geoffrey S. Ginsburg & Joshua C. Denny

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

I. King Jordan, Shashwat Deepali Nagar & Shivam Sharma

Neuroscience Institute, Institute of Translational Genomic Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Robert Meller

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Mine S. Cicek, Stephen N. Thibodeau & Mine S. Cicek

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kimberly F. Doheny, Michelle Z. Mawhinney, Sean M. L. Griffith, Elvin Hsu, Hua Ling & Marcia K. Adams

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

Evan E. Eichler, Joshua D. Smith, Christian D. Frazar, Colleen P. Davis, Karynne E. Patterson, Marsha M. Wheeler, Sean McGee, Mitzi L. Murray, Valeria Vasta, Dru Leistritz, Matthew A. Richardson, Aparna Radhakrishnan & Brenna W. Ehmen

Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

Evan E. Eichler

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Stacey Gabriel, Heidi L. Rehm, Niall J. Lennon, Christina Austin-Tse, Eric Banks, Michael Gatzen, Namrata Gupta, Katie Larsson, Sheli McDonough, Steven M. Harrison, Christopher Kachulis, Matthew S. Lebo, Seung Hoan Choi & Xin Wang

Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

Gail P. Jarvik & Elisabeth A. Rosenthal

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Dan M. Roden

Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

Center for Individualized Medicine, Biorepository Program, Mayo Clinic, Rochester, MN, USA

Stephen N. Thibodeau, Ashley L. Blegen, Samantha J. Wirkus, Victoria A. Wagner, Jeffrey G. Meyer & Mine S. Cicek

Color Health, Burlingame, CA, USA

Scott Topper, Cynthia L. Neben, Marcie Steeves & Alicia Y. Zhou

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle

Laboratory for Molecular Medicine, Massachusetts General Brigham Personalized Medicine, Cambridge, MA, USA

Christina Austin-Tse, Emma Henricks & Matthew S. Lebo

Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

Christina M. Lockwood, Brian H. Shirts, Colin C. Pritchard, Jillian G. Buchan & Niklas Krumm

Manuscript Writing Group

  • Alexander G. Bick
  • , Ginger A. Metcalf
  • , Kelsey R. Mayo
  • , Lee Lichtenstein
  • , Shimon Rura
  • , Robert J. Carroll
  • , Anjene Musick
  • , Jodell E. Linder
  • , I. King Jordan
  • , Shashwat Deepali Nagar
  • , Shivam Sharma
  •  & Robert Meller

All of Us Research Program Genomics Principal Investigators

  • Melissa Basford
  • , Eric Boerwinkle
  • , Mine S. Cicek
  • , Kimberly F. Doheny
  • , Evan E. Eichler
  • , Stacey Gabriel
  • , Richard A. Gibbs
  • , David Glazer
  • , Paul A. Harris
  • , Gail P. Jarvik
  • , Anthony Philippakis
  • , Heidi L. Rehm
  • , Dan M. Roden
  • , Stephen N. Thibodeau
  •  & Scott Topper

Biobank, Mayo

  • Ashley L. Blegen
  • , Samantha J. Wirkus
  • , Victoria A. Wagner
  • , Jeffrey G. Meyer
  •  & Stephen N. Thibodeau

Genome Center: Baylor-Hopkins Clinical Genome Center

  • Donna M. Muzny
  • , Eric Venner
  • , Michelle Z. Mawhinney
  • , Sean M. L. Griffith
  • , Elvin Hsu
  • , Marcia K. Adams
  • , Kimberly Walker
  • , Jianhong Hu
  • , Harsha Doddapaneni
  • , Christie L. Kovar
  • , Mullai Murugan
  • , Shannon Dugan
  • , Ziad Khan
  •  & Richard A. Gibbs

Genome Center: Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine

  • Niall J. Lennon
  • , Christina Austin-Tse
  • , Eric Banks
  • , Michael Gatzen
  • , Namrata Gupta
  • , Emma Henricks
  • , Katie Larsson
  • , Sheli McDonough
  • , Steven M. Harrison
  • , Christopher Kachulis
  • , Matthew S. Lebo
  • , Cynthia L. Neben
  • , Marcie Steeves
  • , Alicia Y. Zhou
  • , Scott Topper
  •  & Stacey Gabriel

Genome Center: University of Washington

  • Gail P. Jarvik
  • , Joshua D. Smith
  • , Christian D. Frazar
  • , Colleen P. Davis
  • , Karynne E. Patterson
  • , Marsha M. Wheeler
  • , Sean McGee
  • , Christina M. Lockwood
  • , Brian H. Shirts
  • , Colin C. Pritchard
  • , Mitzi L. Murray
  • , Valeria Vasta
  • , Dru Leistritz
  • , Matthew A. Richardson
  • , Jillian G. Buchan
  • , Aparna Radhakrishnan
  • , Niklas Krumm
  •  & Brenna W. Ehmen

Data and Research Center

  • Lee Lichtenstein
  • , Sophie Schwartz
  • , M. Morgan T. Aster
  • , Kristian Cibulskis
  • , Andrea Haessly
  • , Rebecca Asch
  • , Aurora Cremer
  • , Kylee Degatano
  • , Akum Shergill
  • , Laura D. Gauthier
  • , Samuel K. Lee
  • , Aaron Hatcher
  • , George B. Grant
  • , Genevieve R. Brandt
  • , Miguel Covarrubias
  • , Melissa Basford
  • , Alexander G. Bick
  • , Ashley Able
  • , Ashley E. Green
  • , Jennifer Zhang
  • , Henry R. Condon
  • , Yuanyuan Wang
  • , Moira K. Dillon
  • , C. H. Albach
  • , Wail Baalawi
  •  & Dan M. Roden

All of Us Research Demonstration Project Teams

  • Seung Hoan Choi
  • , Elisabeth A. Rosenthal

NIH All of Us Research Program Staff

  • Andrea H. Ramirez
  • , Sokny Lim
  • , Siddhartha Nambiar
  • , Bradley Ozenberger
  • , Anastasia L. Wise
  • , Chris Lunt
  • , Geoffrey S. Ginsburg
  •  & Joshua C. Denny


The All of Us Biobank (Mayo Clinic) collected, stored and plated participant biospecimens. The All of Us Genome Centers (Baylor-Hopkins Clinical Genome Center; Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine; and University of Washington School of Medicine) generated and QCed the whole-genomic data. The All of Us Data and Research Center (Vanderbilt University Medical Center, Broad Institute of MIT and Harvard, and Verily) generated the WGS joint call set, carried out quality assurance and QC analyses and developed the Researcher Workbench. All of Us Research Demonstration Project Teams contributed analyses. The other All of Us Genomics Investigators and NIH All of Us Research Program Staff provided crucial programmatic support. Members of the manuscript writing group (A.G.B., G.A.M., K.R.M., L.L., S.R., R.J.C. and A.M.) wrote the first draft of this manuscript, which was revised with contributions and feedback from all authors.

Corresponding author

Correspondence to Alexander G. Bick .

Ethics declarations

Competing interests.

D.M.M., G.A.M., E.V., K.W., J.H., H.D., C.L.K., M.M., S.D., Z.K., E. Boerwinkle and R.A.G. declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. Eric Venner is affiliated with Codified Genomics, a provider of genetic interpretation. E.E.E. is a scientific advisory board member of Variant Bio, Inc. A.G.B. is a scientific advisory board member of TenSixteen Bio. The remaining authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Timothy Frayling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended data fig. 1 historic availability of ehr records in all of us v7 controlled tier curated data repository (n = 413,457)..

For better visibility, the plot shows growth starting in 2010.

Extended Data Fig. 2 Overview of the Genomic Data Curation Pipeline for WGS samples.

The Data and Research Center (DRC) performs additional single sample quality control (QC) on the data as it arrives from the Genome Centers. The variants from samples that pass this QC are loaded into the Genomic Variant Store (GVS), where we jointly call the variants and apply additional QC. We apply a joint call set QC process, which is stored with the call set. The entire joint call set is rendered as a Hail Variant Dataset (VDS), which can be accessed from the analysis notebooks in the Researcher Workbench. Subsections of the genome are extracted from the VDS and rendered in different formats with all participants. Auxiliary data can also be accessed through the Researcher Workbench. This includes variant functional annotations, joint call set QC results, predicted ancestry, and relatedness. Auxiliary data are derived from GVS (arrow not shown) and the VDS. The Cohort Builder directly queries GVS when researchers request genomic data for subsets of samples. Aligned reads, as cram files, are available in the Researcher Workbench (not shown). The graphics of the dish, gene and computer and the All of Us logo are reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Extended Data Fig. 3 Proportion of allelic frequencies (AF), stratified by computed ancestry with over 10,000 participants.

Bar counts are not cumulative (eg, “pop AF < 0.01” does not include “pop AF < 0.001”).

Extended Data Fig. 4 Distribution of pathogenic, and likely pathogenic ClinVar variants.

Stratified by ancestry filtered to only those variants that are found in allele count (AC) < 40 individuals for 245,388 short read WGS samples.

Extended Data Fig. 5 Ancestry specific HLA-DQB1 ( rs9273363 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight ancestry specific consequences across ancestries.

Extended Data Fig. 6 Ancestry specific TCF7L2 ( rs7903146 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight diabetic consequences across ancestries.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Tables 1–8 and Note.

Reporting Summary

Supplementary dataset 1.

Associations of ACKR1, HLA-DQB1 and TCF7L2 loci with all Phecodes stratified by genetic ancestry.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit .

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The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature (2024).

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Accepted : 08 December 2023

Published : 19 February 2024


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