leading researcher

February 11, 2022

How to Be a Great Leader in Science

Building a positive research environment requires intention, support and a belief that kindness isn’t weakness

By Alison L. Antes

FG Trade/Getty Images

There is a common narrative, in academia and beyond, that says, “You have to be a jerk to be successful.” As a scientist who studies what makes a great leader, it is disheartening how often research trainees and junior faculty in the sciences ask me if this is true.

So, it’s been an especially eye-opening week for science, as academics reflect on Eric Lander’s resignation from his roles as the director the Office of Science and Technology Policy and White House science adviser. A whistleblower investigation found that he had bullied and mistreated his staff , and seemed to be especially abusive toward women who worked with him.

Scientific research demands creative and complex work. Such creativity and technical skill thrive in workplaces that are psychologically safe and supportive . I can tell you that for every abusive supervisor, there are multitudes who inspire, nurture and respect the people who work for them. Leadership is a big responsibility, and in a field rife with setbacks, failure and pressure, it can be hard to get it right all the time. But we have to; we are losing talented people who either drop out of academic life because they have been bullied or abused, or are worried about it. These losses are a detriment to academia, science and society.

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Here are some tips on positive leadership and bolstering academics in developing and sustaining good leadership practices.

Know who you want to be.

Great leaders know what character traits they want to define their leadership. For example, integrity, honesty, kindness, optimism, passion, determination and fairness. They reflect on the traits that already define who they are and those they need to cultivate. They seek out positive role models and mentors, always seeking to learn and grow . They ask for input on their leadership from their peers, staff and supervisors and take criticism well. They are not afraid to show vulnerability and don’t feign perfection. They learn from their mistakes and couple self-confidence with humility. Great leaders strive to take care of themselves by managing their own stress and external pressure.

Know what you value in a work environment.

Great leaders know what qualities they want to define their work environments. For instance, you can value trust, openness, integrity, respect, support, accountability, cooperation, creativity, excellence, engagement, learning and inclusion. Great leaders recognize that their own behavior sets the example. They tell their team members what they value and prioritize these qualities in their decisions and actions. They bring in newcomers who are committed to these qualities and expect respectful conduct. Great leaders look for cues and ask for input to assess the status of the work environment.

Be intentional. Budget the time.

Positive leaders make creating a supportive and affirming environment an explicit priority. They make time to build trust and develop healthy working relationships. They communicate openly, listen effectively and show empathy. They treat people with respect to bring out their best.

Know and develop your people.

A powerful way that a leader can build relationships and foster effective performance is by understanding the interests and career goals of their staff, especially trainees. A great leader mentors and coaches staff, sets high expectations and provides the guidance and resources to achieve expectations. They also celebrate the small successes along the way.

Create space for everyone.

Great leaders ensure everyone gets a chance to speak, and they acknowledge everyone’s contributions. They create space for new ideas, encourage collaboration and insist that team members communicate respectfully. They establish norms for handling mistakes that makes it acceptable to mess up. They embrace productive conflict and engage in difficult conversations. They welcome different personalities, perspectives and backgrounds .

Cultivate a collaborative spirit.

A great leader supports team members in building their own trusting, healthy workplace relationships . A successful scientist once told me he tells his group they may be competing with other labs, but inside their laboratory, they are a team. Research is competitive, but ambition need not squeeze out respect, kindness, and cooperation .

Foster a safe environment.

In a safe environment, people feel respected and engage fully without fear of ridicule or judgment. They ask for help and clarification, offer solutions to problems, admit errors, raise concerns, disagree, and give and receive feedback. These behaviors are essential to conducting rigorous, trustworthy research. Leading by intimidation creates fear and anxiety. It breeds distrust, secrecy and misbehavior. It causes people to quit and impacts their mental health .

Promote a top-down positive culture.

Becoming a great leader also requires support from the top down. Institutions need to provide leadership development. They need to reward positive behavior. They need not only to have no-tolerance policies for bullying and abuse, but also to enforce those policies. Both institutions and the broader academic systems need to celebrate the success stories of great leadership and create space to discuss failures. All of these steps are critical for creating a true and lasting shift in the narrative and culture.

Might the circumstances today be different if Lander had heeded this leadership advice ? How might providing leadership training and coaching to all researchers across their careers transform academia? What if it were the norm for researchers to talk about people skills? What if research trainees and staff could count on being safe and supported in research environments? What if they felt empowered to speak up about abusive behavior?

Academics are exceptional at learning, problem-solving, and rising to a challenge. I think that we are ready to create these needed changes, and I believe that we must.

The Hot List

The Reuters Hot List

This is the Reuters list of the world’s top climate scientists. To build it, we created a system of identifying and ranking 1,000 climate academics according to how influential they are.

By MAURICE TAMMAN

Filed April 20, 2021, 11 a.m. GMT

This series tells the stories of the scientists who are having the biggest impact on the climate-change debate – their lives, their work and their influence on other scientists, the public, activists and political leaders.

To identify the 1,000 most influential scientists, we created the Hot List, which is a combination of three rankings. Those rankings are based on how many research papers scientists have published on topics related to climate change; how often those papers are cited by other scientists in similar fields of study, such as biology, chemistry or physics; and how often those papers are referenced in the lay press, social media, policy papers and other outlets.

The data is provided through Dimensions , the academic research portal of the British-based technology company Digital Science. Its database contains hundreds of thousands of papers related to climate science published by many thousands of scholars, the vast majority published since 1988.

The list combines three rankings:

For the first ranking , we selected researchers based on the number of papers published under their names through December 2020, as indexed in the Dimensions system. We screened for climate-related work by examining the papers’ titles or abstracts – brief descriptions of the research – for phrases closely connected to climate change, such as “climate change” itself, global warming, greenhouse gases and other related terms. These are papers that explicitly focus on climate change rather than mention it in passing. To be included in our count, a paper had to be cited by at least one other scientist at least once.

The first ranking is based on how many papers meet that criteria for each scientist. A rank of one was given to the scientist with the most papers, and 1,000 to the scholar with the fewest.

The second ranking  is based on what Dimensions describes as a “Field Citation Ratio.”  For each paper, a ratio is calculated “by dividing the number of citations a paper has received by the average number received by documents published in the same year and in the same Fields of Research category,” according to Dimensions . This ranking is meant to measure the influence of scientists’ work among their peers.

For example, atmospheric sciences, a subset of earth sciences, is a field of research , as is zoology, which belongs to the biological group of sciences. A zoology-related paper with a ratio of 1.0 means it was cited at the average rate compared to other zoology papers; a paper with a score of 2.0 means it was cited at twice the rate of the average zoology paper. Climate change is a multidisciplinary science, and this approach accounts for differing citation rates in differing fields.

For the Hot List, we calculated an average citation ratio for each scientist’s climate-change papers, then we ranked the ratios of all the scholars on our list. A rank of one was assigned to the scholar with the highest average ratio, and 1,000 to the researcher with the lowest.

The third ranking  is based on Digital Science’s Altmetric Attention Score , a measure of a research paper’s public reach. Most papers receive a score based on references in a variety of publications, including the mainstream media, Wikipedia, public policy papers and social media sites such as Twitter and Facebook. The ranking is meant to measure the influence of scientists’ work in the lay world.

For the Hot List, we assigned a median Altmetric  score to each scientist’s papers and then ranked those scores, with a rank of one going to the highest score and 1,000 to the lowest.

The final score  for each scientist is based on the sum of each ranking – the lower the score, the greater the scholar’s overall influence, and thus the higher he or she ranks on the Hot List.

For example, Keywan Riahi, the head of Austria’s International Institute for Applied Systems Analysis, is the highest-ranking scientist on the Hot List. He ranks 47th for papers published, 10th for his Field Citation Ratio and 30th for his Altmetric Attention Score, for a score of 87.

Riahi, who studies energy systems, said his ranking is probably the result of IIASA’s openness to sharing data and models with other scientists. “That creates long-standing collaborations, and, of course, when we innovate, we pass innovation on, and all that’s important for the scientific network,” he said.

Some notes of caution. First, the Hot List doesn’t claim to be a rank of the “best” or “most important” climate scientists in the world. It’s a measure of influence.

Second, the Hot List has some limitations inherent in our methodology. For instance, our analysis targeted the titles and abstracts of papers, not the full texts, so we may have missed some studies that do touch on climate change. The Altmetric score can be skewed upward if one or a few of a scientist’s papers have particularly high scores and their remaining papers have comparatively low scores.

Also, the Hot List favors the prolific. The first of our three metrics ranks scientists based on the number of papers published. The other two metrics – for citation ratios and public reach – are designed to compensate for this possible bias, but they might not fully do so.

Additionally, the underlying database can sometimes conflate the work of two or more scientists into a single Dimensions profile or assign an individual scientist’s work to more than one profile. While such occurrences are rare, they could affect a scientist's ranking on the list or their inclusion on the list.

Note to readers: To update the information on the Hot List, please contact [email protected] .

* This person, who has used different versions of his name, has two unique identification numbers in the data set, and so appears twice in the list.

By Maurice Tamman

Data analysis: Maurice Tamman

Data provider: Dimensions, part of Digital Science

Graphics: Maryanne Murray

Design: Maryanne Murray, Troy Dunkley and Pete Hausler

Edited by Kari Howard

• Follow Reuters Investigates

Other Reuters investigations

Research profiles descriptors

New research profiles descriptors.

This new draft classification aims to communicate the various characteristics that researchers may have throughout their career. It describes four broad profiles that apply to all researchers, independent of where they work in the private or public sector: in companies, NGOs, research institutes, research universities or universities of applied sciences. Regardless of any particular profession one can outline broad profiles that describe the different characteristics researchers may possess.

First Stage Researcher (R1) (Up to the point of PhD)

This profile includes individuals doing research under supervision in industry, research institutes or universities. It includes doctoral candidates.

Researchers with this profile will:

• Carry out research under supervision;
• Have the ambition to develop knowledge of research methodologies and discipline;
• Have demonstrated a good understanding of a field of study;
• Have demonstrated the ability to produce data under supervision;
• Be capable of critical analysis, evaluation and synthesis of new and complex ideas;
• Be able to explain the outcome of research (and value thereof) to research colleagues.

Desirable competences

• Develops integrated language, communication and environment skills, especially in an international context.

Recognised Researcher (R2) (PhD holders or equivalent who are not yet fully independent)

Here we are including:

• Doctorate degree (PhD) holders who have not yet established a significant level of independence;
• Researchers with an equivalent level of experience and competence.

Necessary competences

All competences of 'First Stage Researcher' plus:

• Has demonstrated a systematic understanding of a field of study and mastery of research associated with that field;
• Has demonstrated the ability to conceive, design, implement and adapt a substantial programme of research with integrity;
• Has made a contribution through original research that extends the frontier of knowledge by developing a substantial body of work, innovation or application. This could merit national or international refereed publication or patent;
• Demonstrates critical analysis, evaluation and synthesis of new and complex ideas;
• Can communicate with their peers peers - be able to explain the outcome of their research (and value thereof) to the research community
• Takes ownership for and manages own career progression, sets realistic and achievable career goals, identifies and develops ways to improve employability;
• Co-authors papers at workshop and conferences
• Understands the agenda of industry and other related employment sectors
• Understands the value of their research work in the context of products and services from industry and other related employment sectors
• Can communicate with the wider community, and with society generally, about their areas of expertise
• Can be expected to promote, within professional contexts, technological, social or cultural advancement in a knowledge based society
• Can mentor First Stage Researchers, helping them to be more effective and successful in their R&D trajectory.

R3 - Established Researcher (Researchers who have developed a level of independence)

This describes researchers who have developed a level of independence.

All necessary and most desirable competences of 'Recognised Researcher' plus:

• Has an established reputation based on research excellence in their field;
• Makes a positive contribution to the development of knowledge, research and development through co-operations and collaborations;
• Identifies research problems and opportunities within their area of expertise;
• Identifies appropriate research methodologies and approaches;
• Conducts research independently which advances a research agenda;
• Can take the lead in executing collaborative research projects in cooperation with colleagues and project partners;
• Publishes papers as lead author, organises workshop or conference sessions
• Establishes collaborative relationships with relevant industry research or development groups
• Communicates their research effectively to the research community and wider society
• Is innovative in their approach to research
• Can form research consortia and secure research funding / budgets / resources from research councils or industry
• Is committed to professional development of his/her own career and acts as mentor for others.

R4 - Leading Researcher (Researchers leading their research area or field)

This is a researcher leading his/her research area or field. It would include the team leader of a research group or head of an industry R&D laboratory. In particular disciplines as an exception, leading researchers may include individuals who operate as lone researchers.

All necessary and most desirable competences of 'Established Researcher' plus:

• Has an international reputation based on research excellence in their field;
• Demonstrates critical judgment in the identification and execution of research activities;
• Makes a substantial contribution (breakthroughs) to their research field or spanning multiple areas;
• Develops a strategic vision on the future of the research field
• Recognises the broader implications and applications of their research;
• Publishes and presents influential papers and books, serves on workshop and conference organising committees and delivers invited talks
• Is an expert at managing and leading research projects
• Is skilled at managing and developing others
• Has a proven record in securing significant research funding / budgets / resources
• Beyond team building and collaboration, focusing on long-term team planning (e.g. career paths for the researchers and securing funding for the team positions)
• Is an excellent communicator and networker within and outside the research community [creating networks]
• Is able to create an innovative and creative environment for research
• Acts as a professional development role model for others

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Course goals.

• Understand the different factors that contribute to managing, leading, and maintaining a successful research team. 
• Understand the roles that each member of a team contributes to the overall success and advancement of a project. 
• Understand the overarching concepts that contribute to a researcher’s success within their career. 
• Understand how being an effective leader will contribute to future success.

Leadership Strategies for the Researcher helps prepare clinical and translational investigators as they face the challenges inherent in establishing a research program. This one-day, in-person course features both interactive and didactic sessions, with a focus on best practices in leading and managing a team, and navigating a career path in research.

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April 25, 2024 | 8:00am – 5:00pm

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This is a one-day course that will take place in person at Harvard Medical School.

Emerging clinician-researchers and principal investigators interested in building their leadership and management skills. 

We believe that the research community is strengthened by understanding how a number of factors including gender identity, sexual orientation, race and ethnicity, socioeconomic status, culture, religion, national origin, language, disability, and age shape the environment in which we live and work, affect each of our personal identities, and impacts all areas of human health.

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The application form has closed. Please check back for future opportunities.

Clarivate Reveals World’s Influential Researchers in Highly Cited Researchers 2023 List

Concentration of top talent with 10 countries/regions representing over 80% of list

London, U.K. November 15, 2023 . Clarivate Plc (NYSE:CLVT), a global leader in connecting people and organizations to intelligence they can trust to transform their world, today revealed its 2023 list of Highly Cited Researchers ™ – influential researchers at universities, research institutes and commercial organizations around the world who have demonstrated significant and broad influence in their field(s) of research.

Analysts at the Institute for Scientific Information (ISI)™ have recognized 6,849 Highly Cited Researchers in 2023, from more than 1,300 institutions in 67 nations and regions. The evaluation and selection process draws on data from the Web of Science™ citation index, together with analysis performed by bibliometric experts and data scientists at the ISI at Clarivate™.

Bar Veinstein, President of Academia & Government at Clarivate said: “We celebrate the Highly Cited Researchers whose contributions transform our world by helping to make it healthier, more sustainable and more secure. Recognition of Highly Cited Researchers not only validates research excellence but also enhances reputation, fosters collaboration, and informs resource allocation, acting as a beacon for academic institutions and commercial organizations.”

This year the evaluation and selection process for Highly Cited Researchers has evolved as we respond to a rise in threats to research integrity. ISI analysts first reviewed Highly Cited Papers™ from the last decade to create a list of preliminary candidates. Enhanced qualitative filters were then used to identify publication anomalies including extreme levels of hyper-authorship, excessive self-citation, or unusual patterns of group citation activity, which warrant exclusion from the list.

David Pendlebury, Head of Research Analysis at the Institute for Scientific Information at Clarivate said: “As the need for high-quality data from rigorously selected sources is becoming ever more important, we have adapted and responded to technological advances and changes in the publishing landscape. Just as we have applied stringent standards and transparent selection criteria to identify trusted journals, we have evolved our evaluation and selection policies for our annual Highly Cited Researchers program to address the challenges of an increasingly complex and polluted scholarly record.”

The key findings for 2023 show:

• 6,849 individual researchers from institutions in 67 countries/regions have been named this year, but 83.8% are based in just 10 countries and 72.7% in the top five, a remarkable concentration of top talent.
• Some extraordinary researchers are recognized in multiple Essential Science Indicators™ (ESI) research fields: 238 named in two fields, 21 named in three fields, four named in four fields, and one named in five fields. Their achievements are truly exceptional and indicate broad, multidisciplinary influence among their peers.
• This year 2,669 Highly Cited Researcher designations were given to researchers at institutions in the United States , which amounts to 37.5% of the group, down from 43.3% in 2018. While the slow downward loss of share continues for U.S.-based Highly Cited Researchers, the U.S. clearly still leads the world in research influence.
• Mainland China is second this year, as it has been for several years, with 1,275 Highly Cited Researcher designations, or 17.9%, up from 7.9% in 2018. In five years, Mainland China has more than doubled its world share of the Highly Cited Researcher population. This reflects a transformational rebalancing of scientific and scholarly contributions at the top level through the globalization of research.
• Among all institutions, including governmental and other types of research organizations, the Chinese Academy of Sciences heads the list with 270 Highly Cited Researcher recognitions, up from 228 last year.Other top-ranked governmental or non-university institutions include the U.S. National Institutes of Health (NIH) (105), Max Planck Society (59), Memorial Sloan Kettering Cancer Center (49) and the Broad Institute (27).

Figure 1: Highly Cited Researcher 2023 designations by country/region

Figure 2: Highly Cited Researcher 2023 designations by institution

The full Highly Cited Researchers 2023 list, analysis and evaluation and selection policy are available here .

Follow us on social media: @ClarivateAG / Clarivate for Academia & Government / #HighlyCited2023.

Our evaluation and selection process for Highly Cited Researchers 2023

The Highly Cited Researchers list by Clarivate is an annual recognition of influential scientists and social scientists worldwide who have made significant contributions to their fields. This year, 7,125 Highly Cited Researcher 2023 designations were issued to 6,849 individuals. The number of awards exceeds the number of unique individuals because some researchers are recognized in more than one Essential Science Indicators (ESI) field of research. Our analysis of countries/regions and institutions counts designated awards and is thus based on the total of 7,125. The 2023 list features 3,793 Highly Cited Researcher recognitions across 20 fields, with an additional 3,332 recognitions for outstanding performance in multiple fields (cross-field). These individuals represent just 1 in 1,000 researchers globally. The selection process involves rigorous evaluation and curation of data from Highly Cited Papers in trusted science and social sciences journals indexed in the Science Citation Index Expanded ™ and Social Sciences Citation Index ™ during the 11-year period 2012 to 2022 and involves the review of 188,500 Highly Cited Papers across various fields.

About Clarivate

Clarivate is a leading global information services provider. We connect people and organizations to intelligence they can trust to transform their perspective, their work and our world. Our subscription and technology-based solutions are coupled with deep domain expertise and cover the areas of Academia & Government, Life Sciences & Healthcare and Intellectual Property. For more information, please visit clarivate.com

Media contact:

Rebecca Krahenbuhl, External Communications Manager, Academia & Government

[email protected]

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• Open access
• Published: 01 November 2023

Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields

• Alexander Krauss 1 , 2 ,
• Lluís Danús 3 &
• Marta Sales-Pardo 3  

Scientific Reports volume  13 , Article number:  18794 ( 2023 ) Cite this article

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Can we help predict the future impact of researchers using early-career factors? We analyze early-career factors of the world’s 100 most prominent researchers across 8 scientific fields and identify four key drivers in researchers’ initial career: working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile (high-impact) journals and co-authoring with other prominent researchers in their field. We find that over 95% of prominent researchers across multiple fields had at least one of these four features in the first 5 years of their career. We find that the most prominent scientists who had an early career advantage in terms of citations and h-index are more likely to have had all four features, and that this advantage persists throughout their career after 10, 15 and 20 years. Our findings show that these few early-career factors help predict researchers’ impact later in their careers. Our research thus points to the need to enhance fairness and career mobility among scientists who have not had a jump start early on.

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Introduction.

What drives high-impact science and how do scientists gain prominence? Can we help predict scientific success and especially the success of young researchers? And what would be the best metrics to do so? These are important questions in the science of science but that we still do not fully understand 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 . These questions are of interest for hiring committees, funding bodies and university departments who make decisions by trying to predict the scientific trajectories of researchers often using limited information. The use of common bibliometric indicators, such as number of publications, journal impact factors and citations, as metrics for assessing research impact has been put into question by some researchers 10 , 11 . Other metrics such as open access publications and altmetrics have been proposed as complements or alternatives for improving the way we assess research 10 , 11 , 12 . Yet any measure of scientific impact and prominence faces constraints. A necessary step in identifying ways to evaluate research more fairly is to apply predictive models that help identify inherent biases to science’s current incentive and evaluation system. To this end, we comprehensively analyze the careers of prominent scientists to identify to what extent early-career factors help predict the success of researchers later on in their career.

Most studies on the drivers of high-impact science focus on the role of an individual factor in isolation, such as the prestige and ranking of researchers’ university 13 , 14 , 15 , 16 , ranking of published papers in journals 17 , 18 , 19 , and collaborations 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 . Total citation counts and h-index of the world’s prominent scientists capture only past accomplishments, but not what has driven those achievements. Rarely are there studies conducted to identify the factors driving the production of high-impact research over time 7 , 8 , 27 , 30 , 31 , combining the different key factors in a single study to understand the relative importance of each factor 13 , 14 , 15 , 16 , 17 , 18 and studying fields across the natural, behavioural and social sciences simultaneously 6 , 28 , 29 . Here, we do so by conducting a comparative analysis of these key factors to shed light on how early-career choices and factors shape the path to later become prominent researchers. To this end, we collected data on the scientific careers of the 100 most prominent scientists in eight different fields across science (genetics, development economics, cognitive psychology, network science, social inequalities in public health, network ecology, metabolomics, and philosophy of science) to which we apply a set of descriptive statistics, as well as classification and regression analyses (Data and Methods sections). Specifically, we examine four key early-career factors (researchers’ university prestige, journal ranking of their top publication, collaboration with other prominent researchers, and overall impact of their early research) which we find capture the scientific achievements during the first 5 years of the career. We then assess how these key factors are related to their h-index later on in their career, while controlling for factors like their geographic location 32 , 33 , 34 , gender 35 and scientific field 23 , 35 (Fig.  1 ).

We find that top researchers across fields have, in the first five years of their career, an advantage compared to the average researchers – the comparison group – that lasts throughout the rest of their career: they are more likely to research at one of the top 25 ranked universities worldwide, publish a paper in a top 5 ranked journal in their field, publish most papers in top quartile journals, and collaborate with other prominent researchers. Indeed, this trend holds for prominent researchers across scientific fields: the prominent researchers at the top of their field early on in their career (compared to their peers) are consistently at the top as their career progresses. Our results highlight how an early-career jump-start drives researchers to prominence, i.e. what a researcher does early on has a very strong impact on how they will perform in the future. The implications of our findings are vast and can provide young researchers with a means to evaluate their own expected career trajectories. Yet because these four attributes of ultra-successful scientists are predictable, the findings also suggest how closed the scientific system already is. The results also point to shortcomings in using the common and highly-influential indicators of success, namely citation and h-index metrics. This is because early career advantages–measured using these metrics–are so strong that they predefine ‘highly-successful scientists’ without further information about the content or social and policy impact of their research.

Conceptual map of the study. We compiled a list of the 800 most prominent scientists across 8 research fields. We obtained for each researcher a full publication list, history of citations of the publications as well as their affiliation records over time from Scopus. Using this information, we obtained data on early-career factors (within the first 5 years after their first publication): being at a top 25 university, publishing in a top 5 journal or most papers in Q1 journals within a specific area of knowledge (according to Journal Citation Reports), and coauthoring with other prominent researchers. We then study the subsequent career of the researchers and measure the evolution of their number of citations and h-index over 5, 10, 15 and 20 years since their first publication.

We collected data for several early-career factors, by building on a dataset we previously compiled that identified the 100 researchers with the highest h-index across eight fields that span across the natural, behavioural and social sciences (see 33 ). These eight fields include genetics, development economics, cognitive psychology, network science, social inequalities in public health, network ecology, metabolomics, and philosophy of science. We extracted all data for this study - publications, bibliometric data, university affiliations etc. for each author - using Scopus database in 2021 (the largest database of peer-reviewed journals), with two exceptions–data for university rankings using QS World University Rankings 2021 36 and for journal rankings using Journal Citation Reports (JCR) 2021 37 . All data used are publicly available via Scopus, QS World University Rankings and Journal Citation Reports (JCR).

To overcome shortcomings of studies with cross-sectional research designs (with data collected at one specific timepoint) we adopt a longitudinal research design by collecting data over the entire scientific career of the 100 prominent researchers across these fields. We use the h-index as a metric for prominence as it is designed to capture the quantity and quality of researchers’ output 38 . For each researcher we set the start of their academic career as the year of their first publication. We then collect data for the first 5 years of researchers’ scientific career, including their early-career university ranking, publication records and journal ranking, and collaborations. Table 1 provides a list of all main variables we study and the descriptive statistics for the variables that are disaggregated by scientific field. Some of these variables we collected are highly correlated, so they were discarded for the analysis we later perform (see Supplementary Figs. S26 ).

All data presented throughout the paper reflect only the first 5 years of prominent researchers’ careers since their first publication, unless explicitly stated otherwise - i.e. with the exception of the number of accumulated citations and the h-index at 10, 15 and 20 years after the first publication of each scientist. All data are presented at the researcher level, i.e. only one aggregate value for each of the 100 prominent researchers across eight fields is provided for each variable. The 100 prominent researchers across these 8 fields have an average h-index of 64, meaning that researchers each have an average of 64 publications that have each received at least 64 citations. The median h-index is 49. In contrast, the average global h-index is approximated at 27 - 32 (median 14–25) as an upper bound estimate (see Table 1 for field-level data) 39 , 40 .

Moreover, as nearly all of today’s prominent researchers were based in Europe and North America in the first five years of their career and to allow for cross-regional comparison, we focus the analysis on Europe and North America—excluding about 6% of other prominent researchers not based there. Among the prominent researchers across each of the eight fields, 21 researchers were at a university outside of Europe or North America at the time of their first publication (largely in Australia, New Zealand and Japan), while most moved within the first five years to a university in Europe or North America to which they have been classified.

Four early-career factors related to early-on prominence and research impact

Early-career factors of prominent scientists.

We analyse the first 5 years of the academic career (starting at the first publication) of the 100 prominent researchers across these eight scientific fields, and we find overall that 47% were at a top 25 ranked university, 77% published a paper in a top 5 ranked journal in their field, 59% of their papers were published in top quartile (Q1) journals and 27% co-authored a paper with another prominent researcher in their field (Table 1 ). These shares are significantly higher than for the comparison group of average researchers (see Methods for calculations for the global averages for researchers and Table 1 for all factors we analyzed): less than 1% of all researchers worldwide—an estimated 0.6%—are at one of the top 25 universities; an estimated 3–14% of all researchers worldwide have published a paper ranked in the top 5 journals in their field; about one third of all articles worldwide are published in top quartile (Q1) journals; 41 , 42 and, about 14% of junior researchers on average have co-authored a paper with a senior researcher in journals across scientific fields, including top multidisciplinary journals.

Furthemore, 92% of all prominent researchers had at least one or more of these four features, with the share increasing to at least 95% for those in genetics, development economics, cognitive psychology and metabolomics. Moreover, more than half of all prominent researchers placed a paper within a top 5 ranked journal in their field in the first 5 years, with the highest shares at 93% for researchers in genetics, 86% in metabolomics and 82% in cognitive psychology (Fig.  2 ). The majority of prominent researchers publish more than half of their papers in top quartile journals (except for philosophy of science) (Fig.  2 ). As we will show later, this initial prominence is often not just a ‘hot streak’ but consistently characterises the impact of researchers’ over their career.

Early-career factors of prominent researchers across fields. Fraction of researchers by field for the four key variables in the first 5 years since the first publication: TOP5 represents whether a researcher published in a top 5 ranked journal in their field. Q1 represents whether a researcher published most of their papers in a top quartile journal. TOP25 represents whether a researcher was affiliated to one of the top 25 universities worldwide. Collab represents whether a researcher co-authored a paper with another prominent researcher in their field.

A researcher’s early institution is also strongly correlated with scientific prominence across a number of fields 13 , 14 , 15 , 16 . Indeed, we find that over 50% of researchers in development economics, cognitive psychology, and genetics were at one of the top 25 ranked universities worldwide in the first 5 years of their career. However, this is not the case in younger scientific fields such as network science, network ecology or metabolomics, suggesting that the role of institutional prominence seems to be more important in well-established, more traditional fields. Being at a top university is the factor, among the four early-career factors, that illustrates the strongest difference between newer and older fields. Another factor that highlights differences between fields is the collaboration network that prominent researchers establish. Network science is the most collaborative field (in which 42% of prominent researchers have co-authored a publication with another prominent researcher) while philosophy of science stands out as the least collaborative (in which 17% of prominent researchers have done so) (Fig.  2 ).

In terms of geographic differences, we find that prominent European researchers are, in their early career, overall more likely to have top publications and to have been at a top 25 ranked university across all fields (Supplementary Fig. S1 ), even though North America has a larger concentration of top universities whose graduates occupy the majority of faculty positions in US universities 43 . Prominent European researchers are, however, less likely to have co-authored a paper with another of these top 100 researchers in their field, except in development economics and cognitive psychology (Supplementary Fig. S1 ) 33 .

In terms of gender differences, our results confirm that the gender gap is even more exacerbated among the scientific elite: females account for 15% of all prominent researchers across fields, ranging from 29% in inequalities in public health to only 6% in genetics 35 . In the first five years, prominent female researchers have a similar (or even higher) share of papers in the top quartile as males across fields, except in genetics. They are also more likely to have researched at a top 25 university than males across fields, except in network science and philosophy of science, and a larger fraction of women has also coauthored a paper with another prominent researcher (Supplementary Fig. S2 ).

Early-career factors are correlated with early-on research output

To understand the relationship of early-career factors to early performance, we disaggregate researchers into four quartiles of increasing number of citations they received during the first five years (i.e. researchers in quartile 1 (QI) are those with the lowest 25% of citations received during the first five years, while researchers in quartile 4 (QIV) – the top cited quartile – are those with the highest 25% of citations). We find that there is a strong correlation between the four early-career drivers and the impact of research output early on in researchers’ career. The fraction of prominent researchers in the top citation quartile in the first five years are, in general, more likely than expected by chance to have any of the four early-career features than other prominent researchers in lower citation quartiles (Fig.  3 ).

Early-career factors of prominent researchers disaggregated by citation quartiles. Fraction of researchers by field and quartile in the first five years who have: ( A ) Publications with other prominent researchers in their field. ( B ) Affiliation in one of the top 25 universities. ( C ) A paper published in a top 5 ranked journal in their field. ( D ) Most of their papers published in a top quartile journal in their field. Grey points represent the values and the 95% confidence interval expected when radomizing the citation quartiles within each field.

The role of publishing with other prominent researchers

Collaboration among scientists has been recognised as a source for innovation and creativity leading to increased research output 20 , 21 . Our analysis is consistent with these findings: co-authorship is strongly correlated with higher citations across all fields, and the relationship is particularly strong in the natural sciences including genetics and network science (Supplementary Fig. S3 ).

Remarkably, the effect of co-authoring with prominent researchers is even greater. We find that only 27% of prominent researchers co-authored at least one paper (and overall 11% of their papers) with another prominent researcher in the first 5 years of their career. The papers co-authored by two (or more) prominent researchers have a much higher number of citations than other papers. The effect, intensity and size of collaborations, however, is not homogeneous across geographic locations 33 nor across fields (Supplementary Fig. S1 , S4 D and S5 ). Furthermore, the disaggregated data by citation quartiles reveal that researchers in the lowest citation quartile have very low shares of co-authorship in their early career across fields with other prominent researchers in their field compared to an average of 56% for those in the top citation quartile (Fig.   3 A). This finding suggests that co-authorship with other prominent researchers early on can have a large return across all fields. Indeed, already during the first five years of the career of scientists in our study, papers with other prominent scientists have overall received more than twice the number of citations than those not co-authored with other prominent scientists in their field (Supplementary Figs. S4 D and S5 ).

Our findings are thus in line with previous studies that analyzed the advantages of co-authoring with leading researchers in one’s field. Working under leading researchers can boost career development through greater citations and mentorship 44 , and provides visibility early on in a scientist’s career 26 . In fact, junior scientists at less recognised universities are most likely to benefit from co-authorship with leading researchers 26 . Young scientists can also apply what they learn from high-impact, established researchers in their own career 27 , 28 , 45 , providing them with a competitive advantage relative to their peers 46 .

The role of prestige of researchers’ institution

Researchers at top universities have a qualitative advantage with respect to researchers in other institutions. They enjoy a high-quality research environment, generally with access to greater resources. Additionally, researchers at prestigious institutions are sought for collaboration as a way to boost the academic careers of researchers at lower tier institutions 22 . Here, we assess the relationship between being at a top university and early-career impact. The share of researchers who have spent part of their early career in such institutions is not homogeneous across fields, with traditional disciplines having much larger shares, as outlined earlier. Not surprisingly, we find that for these disciplines – genetics, development economics and cognitive psychology – being at a top university is strongly correlated with early-on research impact. Nonetheless, across most fields we find that researchers most cited early on in their career are more likely to be in a top institution (Fig.   3 B; Supplementary Fig. S4 C).

Researchers at prestigious universities also have a comparative advantage on other indicators. Among these prominent researchers at a top 25 university in the first 5 years of their career, 79% published a paper in a top 5 journal (compared to 76% at a non-top university), 72% published more than half of all papers in top quartile journals (compared to 64%) and 29% co-authored with another prominent researcher (compared to 25%) (Supplementary Fig. S6 ).

The role of publishing in highly-ranked journals

Publishing in high impact journals early on is correlated with an increase in later impact – by increasing citations it benefits researchers’ career opportunities, increases their prestige and recognition, and helps promotion 18 . Nearly all prominent researchers across fields placed their best paper in their early career within a highly ranked journal, which thus appears to be a necessary condition for becoming a prominent researcher. In fact, publishing in highly-ranked journals is strongly correlated with greater early-career impact, more so than just publishing within journals in Q1 (Fig.   3 C, D). Interestingly, these two early-career factors (publishing in a top 5 journal, versus publishing the majority of articles in Q1 journals) are not highly correlated with each other (Supplementary Fig. S26 ), thus showing that these two variables characerise two different aspects of early career performance: the former characterises the big hits, while the later represents consistency in output quality, and therefore are distinct early-career factors.

Early-career performance is a strong indicator of performance throughout later career stages

As the scientific career of researchers progresses, the number of publications and citations accrued increases and so does the h-index of each researcher (Supplementary Fig. S7 ). We find disparities between fields in terms of the evolution of h-indices over time which reflect differences in the rate of publications, collaboration structures and the size of each field (Supplementary Fig.  S8 ) .

Researcher mobility across quartiles. H-index quartile at five years compared to h-index quartile at 10, 15 and 20 years (first row, panels A – C ), and citation quartile at five years compared to citation quartile at 10, 15 and 20 years (second row, panels D – F ). The darker the region, the stronger the coincidence between the quartile at 10, 15 and 20 years relative to the quartile at the first 5 years. The results reflect the aggregated and normalized data for all fields.

To assess whether early-career performance translates into a sustained advantage over time, we analyse the evolution of h-indices and citations over time for all researchers (pooled together across the eight fields) (Fig.  4 ). To this end, we divide researchers into quartiles based on the normalized h-index and the normalized number of citations at 5, 10, 15 and 20 years since the first publication (see Methods). We then look at the probability of transition over time between quartiles using the 5-year mark as the reference point (Fig.  4 ). We observe that the initial advantage in the first 5 years is still present at 20 years of researchers’ career. Figure 4 C and F shows that 90% of researchers that started their career in the two top citation quartiles (QIII and QIV) have maintained this prominent position over time. Conversely, we observe the same situation for those scientists who were in the lower two quartiles (QI and QII). Both findings are consistent, whether we look at quartiles defined by h-index (Fig.  4 first row) or by citations (Fig.  4 second row) and across fields (Supplementary Figs. S9 – S10 ). Although some fields display greater mobility from lower to upper quartiles, such as in network science and metabolomics, researchers are very unlikely to transition from the top-two to the bottom-two quartiles. This suggests that the initial advantage consistently remains throughout researchers’ career.

Factors driving citations and h-index in researchers’ early career

So far our results in Figs. 3 and   4 show that there is a clear relationship between key early-career factors and the early-on impact of research output, and between early-on impact of research output and impact at later career stages. Here, we want to assess the extent to which early-career factors can explain the evolution in the impact of research output during the career of prominent scientists. To this end, we perform a prediction experiment in which we consider the h-index/citation quartile of a researcher (which we obtain by pooling together normalized h-indices of the 8 fields) at Y (=10, 15 or 20) years after the start of their scientific career as our dependent variable, and different combinations of early-career factors as well as researchers attributes such as gender or current geographic location as our independent variables (see Supplementary Fig. S26 for evidence of lack of co-linearity among independent variables). Specifically, we train a Random Forest classifier for different sets of independent variables, called Models (see Methods for a description of the models—Model 1, 2, 3 and Q5) (Fig.  5 ). In Fig.   5 , we show the prediction results of two classifiers for two different Models (sets of independent variables). First, we train a classifier in which we use binary independent variables that account for the four key early-career factors we study—working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile journals, and co-authoring with other prominent researchers—as well as two common background factors, namely researchers’ geographic location and their gender, called Model 2 (see Methods and Supplementary Material for other models we analyse). Second, we train a classifier in which the only independent varible we considere is the h-index quartile after 5 years of the first publication (Model Q5).

Our classification analysis reveals that assessing the h-index quartile at 5 years (Model Q5), the classifier is more accurate than if we only include the early-career factors. Nonetheless, the classifier for Model 2 is still able to correctly predict overall 40% of the researchers that fall into the lowest quartile (QI) and 38% who fall into the top quartile (QIV) at 20 years from the start of their career—significantly higher than the expected 25% for random quartile assignment. Our results show that the early-career factors we study can explain h-index quartiles as early as 5 years after the first publication (Supplementary Figs. S11 – S12 ) as well as trends in the share of researchers who remain in the same h-index quartile over their career (Fig.   5 ; see also Figs. S13 – S14 for an equivalent analysis for citations). We also observe that for both classifiers, missclassification tends to happen between neighboring quartiles, so that the fraction of lower quartile researchers are seldom classified as QIV researchers and vice versa. Indeed, f1 scores highlight precisely that in Model2 and Q5 the performance for Q1 and QIV is better than for QII and QIII (Fig.  5 H and G; see Supplementary Fig. S15 for precision and recall for the same models). This indicates that early-career features (Model 2) capture a substantial part (but not all) of the information captured by the h-index (Model Q5). Nonetheless, our results show that early-career researchers who are already prominent among their peers are very likely to sustain their advantage 15-20 years later (i.e. researchers in QIV). We find consistent results when we analyse fields individually (Supplementary Figs. S16 – S19 for h-index quartile prediction; Supplementary Figs. S21 – S24 for citation quartile prediction).

Prediction of h-index quartile based on early-career factors. Predicted h-index quartile at five years compared to observed h-index quartile at 10, 15 and 20 years (first, second and third columns). ( A )–( C ) illustrate the prediction results with Model 2 (which takes into account the four early-career factors as well as the geographic location and gender of researchers; see Methods). ( D )–( F ) illustrate the prediction results with Model Q5 (which only takes into account the quartile of the first 5 years). The darker the region, the higher the number of researchers that are correctly classified by the algorithm. The results reflect the aggregated and normalized data for all fields. ( G ) and ( H ) show the f1-score for the predictions of Model 2 and Model Q5, respectively. Grey bars represent the 95% confidence interval expected when predicting randomized citation quartiles.

As a final step, using our trained Random Forest classifiers for Model 2, we analyze the relative importance of the key four early-career factors (Methods). As all variables are binary (0 or 1), this facilitates comparing the relative importance of each factor. Collaborating with other prominent researchers is the most important factor, followed by publishing a paper in a top 5 journal. Working at a top 25 university and publishing more than half of one’s papers in Q1 journals have less explanatory power; and gender and geographic location appear to have little predictive power (Supplementary Figs. S14 , S20 , S25 ). The results illustrate how collaborating with established researchers is perhaps the best strategy for securing a position among the scientific elite. These results are consistent with results from the analysis of citations (Supplementary Fig. S20 ) and with the disaggregated analysis of individual fields (Supplementary Figs. S20 , S25 ). The only exception is philosophy of science for which being at a top institution or publishing in top journals early on are better predictors of h-index and citation quartiles while publishing with other prominent scientists is of much less importance. As a final robustness check, we perform two different regression analyses: an ordinary least squares regression of the h-indices, and a logistic regression of the top tercile of h-indices (see Figs. S27 , S28 , and Supplementary Table S1 ), which confirm the relative importance of variables we obtain using the Random Forest classifier.

Our analysis shows that the future success of a researcher is often determined early on in their career. Indeed, we show that as early as 5 years after the first publication, we can already make accurate predictions of whether a prominent researcher is going to be within the top quartile of leading researchers later on or not. Our study, while limited to prominent scientists, shows that early-career factors also establish a hierarchy within this group of scientists that is sustained over time.

We find four early-career factors that are central drivers for later success across science: working at a highly ranked university, publishing a top 5 journal paper, publishing most papers in top quartile journals and co-authoring with prominent researchers at the early stage of researchers’ career. Most importantly, we find a strong positive correlation between citations during the first five years of their career and the probability to have had any of these central early-career features we identify: researchers in the top quartile of citations are more likely than expected to have the four key features, whereas researchers in the lowest citation quartile are less likely than expected to have these features (but still more likely than the average non-prominent researchers). This finding is very insightful, especially because classification models are able to accurately predict the citation and h-index quartiles after 10, 15 and 20 years for researchers falling into the top and lowest quartiles: what scientists do early on largely determines their impact later on in their careers.

We also find that in traditional areas of science, being at a top-ranked institution can be an important driver, but in younger disciplines it is less important. This finding is especially interesting in light of recent findings about graduates from top-ranked US universities occupying the majority of faculty positions in the US university ecosystem 43 , and raises the question of whether hierarchies in the hiring system pose a threat to innovation and the emergence of new fields of science. Indeed, we also find that in disciplines in which university affiliation is not such an important driver, publishing with other prominent scientists becomes especially important 44 .

Our analysis shows that these four key factors are important as a general strategy for young researchers across science and that an early-career jump start gives scientists an advantage that is sustained throughout their career. At the same time, our results suggest that there are also other factors influencing the h-index at 5 years such as individual, more qualitative or psychological traits of researchers 19 or, in relevant cases, the traits of a PhD advisor 45 that have not been considered here. While it can be a limitation, our results also explain that the success of individual researchers cannot be attributed to a single factor but involve a combined set of early-career factors.

Given that these four attributes of ultra-successful scientists are predictable, the findings suggest that the scientific system is presently relatively closed. The results also illustrate limitations of using highly-influential metrics of success, such as citations and h-index. This is because early career advantages on these metrics are so strong that they predefine ‘highly-prominent scientists’, independent of the content of their research. More generally, the findings point to the need for a reform among the scientific community: As some scientists produce good science but are not successful in the ‘metrics game’, decision makers evaluating the work of researchers should also use additional metrics such as policy and social impact of research, developing new research tools, and the like. Decision makers should thus by no means take this as an opportunity to just use citation and h-index metrics to evaluate scientific prominence.

Overall, our jump-start hypothesis here can, by integrating multiple early-career factors and not focusing on an individual factor in isolation, better explain the Matthew effect in science 47 , namely how the most cited researchers get more cited just because they became highly cited early on in their career. The central implication for researchers is that early-career factors can be fostered through deliberate choices and hard work.

Calculations for the average researchers globally (the comparison group)

The calculations for the average researchers globally—the comparison group—for the four factors analysed here have been made as follows. Firstly, less than 1% of all researchers worldwide—an estimated 0.6%—are at one of the top 25 universities. This share is calculated using UNESCO data on the total number of researchers worldwide at 8,854,288 48 divided by the total number of researchers (university staff) at the same top 25 universities (using QS World University Rankings) at 56,900. For comparison, the top 25 universities account for 1.8% of the total 1396 universities in the Times World University Rankings 49 . Secondly, an estimated 3−14% of all researchers worldwide have published a paper ranked in the top 5% in their field. This share is calculated by using data on the total number of all publications ranked top 5% in researchers’ field at 267,966 publications indexed in Web of Science using the Leiden Ranking 50 divided by the total number of researchers worldwide at 8,854,288 48 or by the total number of researchers (university staff) at 1,914,149 49 that results in a 3% (lower bound) or 14% (upper bound) estimate, respectively. Thirdly, about one third of all articles worldwide (upper bound estimate) are published in top quartile journals indexed in Web of Science; 41 , 42 and as many individual researchers publish multiple articles in quartile 1 journals it is likely that the share is significantly lower for the average researchers to publish at least half of their papers in quartile 1 journals. Fourthly, about 14% of junior researchers on average have co-authored a paper with a senior researcher between 1990 and 2012 in a global study covering about 1000 journals across the sciences (totalling about 6 million publications), with the shares varying across the fields of biology (15%), physics (13%), chemistry (13%), medicine (16%) and mathematics (6%), including the top three multidisciplinary journals (Nature, Science and PNAS) at about 19% for each journal 44 . Fifth, the average h-index using university-level data is estimated at about 27 (median 25) as an upper bound estimate that includes only the top 500 universities 40 . The average h-index using all journal-level data from the Scimago Institutions Ranking 39 via Scopus is estimated at about 32 (median 14). Note that both the mean university-level and journal-level h-indexes are upper bound estimates - i.e. higher than the mean researcher-level h-index given that researchers with lower h-indices are not represented in such estimates. These averages for researchers globally provide the baseline comparisons for our analysis.

Statistical approaches and Models (sets of independent variables)

We use two statistical approaches, a Random Forest classifier and a linear regression, to understand the role that different early-career variables play in the evolution of the h-indices and accumulated citations over the duration of scientists’ career.

Our goal is to assess how well different factors help predict researchers’ h-index/citation counts (the dependent variables). We thereby consider four different groups of independent variables that we denote as Models 1, 2, 3 and Q5. Formally, we will refer to the sets of variables as \(M_1, M_2, M_3, M_{{\rm q}5}.\)

Models 1, 2 and 3

For these three Models, all independent variables are binary (0 or 1). Descriptive data for all variables used in the models are provided in Table 1 . Supplementary Figure  S26 shows that there is no strong correlation between the different variables we consider in what follows.

Model 1 . This model considers as independent variables solely the four key early-career factors we study, namely working at a top 25 ranked university or not ( \(\textrm{topU}\) ) 13 , 14 , 15 , 16 publishing a paper in a top 5 ranked journal or not ( \(\textrm{top5}\) ), publishing most papers in Q1 journals or not ( Q 1) 17 , 18 , 19 and co-authoring with other top 100 researchers or not ( \(\textrm{BS}\) ) 23 , 26 , 27 , 28 , 29 . Therfore \(M_1:=\{\textrm{TOP25},\textrm{TOP5},Q1,\mathrm{Collab.}\}\) .

Model 2 . This model considers the same variables as in Model 1 but also controls for two common background factors: the researchers’ geographic location ( \(\textrm{loc}\) : whether they are based at a university in North America or not) 32 , 33 , 34 , and their gender ( \(\textrm{Gender}\) : whether they are male or not) 35 . These are standard control variables applied in economics and the social sciences. Therefore, \(M_2:=\{\textrm{TOP25},\mathrm{\mathrm TOP5},Q1,\mathrm{Collab.}, \textrm{Firstloc}, \textrm{Gender}\}\) .

Model 3 . This model considers the same variables as in Model 2 but also controls for the average number of co-authors on researchers’ total papers ( \(\textrm{coaut}\) ), so that \(M_2:= \{\textrm{TOP25},\mathrm{\mathrm TOP5},Q1,\mathrm{Collab.}, \textrm{Firstloc}, \textrm{Gender}, \textrm{Avg}\}\) ).

Model Q5 . This model considers only the h-index quartile at 5 years after first publication ( Q 5), \(M_{Q5}:= \{Q5\}\) .

Random forest classifier

In order to quantify the predictive power of the models and the different variables, we performed a classification experiment using a Random Forest Classifier. Our goal was to assess whether we could correctly predict the h-index/citation quartile at 5, 10, 15 and 20 years of career using only indicators from the first 5 years since the first publication.

A Random Forest Classifier (RFC) behaves similarly to a Random Forest Regressor but produces a categorical output instead of a continuous one. In this sense, the classifier iteratively evaluates several decision trees over different parts of the data and averages the resulting outputs.

We evaluated the performance of the classifier with a 10-fold cross validation. In this procedure, the dataset is divided in 10 folds from which one is selected as the test and the others as the training folds iterated several times until each fold has been used as a test. For each one of the models \(M=\{M_1,M_2,M_3,M_{Q5}\}\) , training data for each fold \(F=\{\textrm{training}_F,\textrm{test}_F\}\) corresponds to \(Tr_F (M):=\{ (QY_i, \textbf{x}_i), \,i \in \textrm{training}_F\}\) where \(QY_i\) is the quartile at year Y we want to predict, and \(\textbf{x}_i\) are the feature values or independent variable values \(\textbf{x}_i=\{\left( M \right) _i\}\) for a specific model (and similarly for test data). For each \(Tr_F(M)\) , we train a Random Forest Classifier \(\textrm{RFC}_{F,M}\equiv \textrm{RFC}(Tr_F(M))\) and make predictions for the corresponding test set \(\{\widehat{QY}_j(M)=\textrm{RFC}_{F,M}(\textbf{x}_j), \,j \in \textrm{test}_F\}\) . Since test sets are non-overlapping, in the end we obtain a list of \(\{\widehat{QY}_j(M), \forall j\}\) , which we compared to the real quartiles \(\{QY_j, \, \forall j \}\) to obtain the overall confusion matrices, precision and recall for each model M . We then select the best model from Models 1, 2 and 3 as the one with the best overall precision and recall, in our case Model 2 ( \(M_2\) ).

Note that when performing the classification analysis for the aggregated data comprising all fields, the h-index and citation data are normalized due to high variability among fields (Supplementary Fig.  S8 ).

Feature importance. For each \(\textrm{RFC}_{F,M}\) we obtain permutation feature importances for each independent variable, that is, the feature importance of variable \(\textrm{top5}\) , \(FI_{F,M}(\textrm{top5})\) is the reduction in performance of the Random Forest Classifier when we randomize \(\textrm{top5}\) .

Formally, the feature importance of a variable v is defined as:

where \(s_{k,j}\) is the score function, s the reference score, \(D_{F/ j}\) the new dataset with variable v randomized, k is the repetition, and K the number of repetitions. To obtain the overall feature importance for a variable v , we average over folds \(FI_{M}(v)= \frac{1}{10}\sum _{F=1}^{10} FI_{F,M}(v)\) .

In our case, we selected the f1-score as the score function for the permutation importance and set K = 10 repetitions.

f1-score, precision and recall. The values of performance metrics for the RFC shown in Figure  3 and in Supplementary Fig.  S15 are the results of averaging these metrics over folds in our classification analysis. Black bars in those figures show the 95% confidence interval when assessing the same metrics over a Random Forest Classifier trained with random assignation of quartiles to researchers.

Regression analysis

To assess the predictors of scientific prominence, we analyse which early-career factors influence an increase in citation counts most. We perform ordinary least squares (OLS) and logistic regression analyses.

The OLS results illustrate the mean change in the dependent variable (researchers’ h-index or their total citations in their early career) given a one-unit change in each independent variable (being at a top 25 university or not, being in North America or not etc.). All independent variables are binary (0 or 1). Specifically, the model is \(y_i(M)=a_0+\sum _{i\in M} a_i x_i\) , where the dependent variable y is the normalized h-index/number of citations and \(\textbf{x}_i\) are the independent variables we consider in Model \(M =\{M_1, M_2, M_3\}\) .

We perform OLS regression analysis to assess the predictors of h-index in the first 5, 10, 15 and 20 years for the world’s prominent researchers (see Supplementary Fig. S27 and Supplementary Table S1 for regression coefficients and significance) and to predict the number of citations in the first 5 years (Supplementary Fig. S28 B).

Second, we conduct a logistic regression analysis in which the binary dependent variable \(y_i\) is equal to 1 for the third most-cited top researchers in the first 5 years and \(y_i=0\) for the bottom two-thirds least cited top researchers. These top third researchers reflect the best of the best in their field. We thereby normalise citations by calculating citation terciles for each field individually (Supplementary Fig. S28 A). The model in this case corresponds to \(p(y_i(M))= 1/\left[ 1+\exp \left( -f(M,\textbf{x}_i)\right) \right]\) where \(f(M,\textbf{x}_i)=a_0+\sum _{i\in M} a_i x_i\) . The coefficients \(a_i\) thus express how the probability of \(p(y=1)\) changes when \(x_i=1\) (positive coefficients increase the probability, while negative ones decrease it); \(a_0\) is a coefficient that sets the background probability for \(p(y=1)\) – 1/3 in our case.

Data availability

All data are publicly available, and the lists of prominent researchers and their publications can be provided upon request ([email protected], [email protected]).

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Acknowledgements

AK received funding from the Ministry of Science and Innovation of the Government of Spain (grant RYC2020-029424-I). MS-P acknowledges support from PID2019-106811GB-C31, from MCIN/ AEI/ 10.13039/ 501100011033, and from the Government of Catalonia (2017SGR-896).

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Krauss, A., Danús, L. & Sales-Pardo, M. Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields. Sci Rep 13 , 18794 (2023). https://doi.org/10.1038/s41598-023-46050-x

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Eight NREL Scientists Named to List of Highly Cited Researchers

Joey Luther, pictured here with a solution of all-inorganic perovskite quantum dots showing intense photoluminescence when illuminated with UV light, is one of eight NREL scientists named to the 2021 list of Highly Cited Researchers. Photo by Dennis Schroeder, NREL

Eight researchers affiliated with the National Renewable Energy Laboratory (NREL) are on this year’s list of Highly Cited Researchers, with many familiar names from the 2020 list .

Clarivate PLC, the London-based company that released the list on Nov. 16, selected 6,602 researchers around the world—including 2,622 in the United States—based on the number of highly cited papers they produced between January 2010 and December 2020.

The affiliated researchers from NREL are Matthew Beard, Joe Berry, Keith Emery, Joseph Luther, Michael McGehee, Su-Huai Wei, Mengjin Yang, and Kai Zhu. Michael McGehee, of the University of Colorado-Boulder, is affiliated with NREL and also made the list. Keith Emery is an NREL emeritus researcher, and Su-Huai Wei is a former NREL researcher.

“The impactful research done by these NREL researchers is evidenced in their work being so widely cited by other scientists,” said Bill Tumas, associate laboratory director at NREL. “It is great to see their inclusion on the Highly Cited Researchers list, which clearly demonstrates the importance and relevance of R&D being done at NREL.”

Beard, Emery, Luther, McGehee, Wei, Yang, and Zhu also appeared on last year’s list.

This year marks the first time Berry has been included. A senior research fellow , Berry’s expertise lies in the fast-growing research field of perovskite solar cells.

“I tend to think of science as a team game, and this type of recognition is more a personal one, which doesn’t mean much if your team is not winning—really advancing the technology,” Berry said. “To the extent that it shows that the team is addressing important science and that our work is advancing the field, it is gratifying.”

The list identifies researchers who demonstrated significant influence through the publication of highly cited papers published in journals that rank in the top 1% by citations. Clarivate’s complete list can be found here .

The list recognized researchers in specific fields or for cross-field impact. Emery was singled out for his work in engineering; McGehee, for materials science. Zhu’s listing came in the fields of chemistry and materials science.

Learn more about NREL’s research .

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Avolio ranked #2 most influential researcher in leadership, #3 in organizational behavior

Bruce Avolio

Two new studies establish Bruce Avolio , a professor of management at the University of Washington Foster School of Business, as one of the world’s foremost leaders in the study of leadership and organizational behavior.

A paper in the August 2019 issue of The Leadership Quarterly ranks Avolio the #1 scholar in the world at connecting authors in the field of leadership research, and #2 in productivity and influence of his leadership studies.

A separate article, published earlier this year in the journal Academy of Management Learning and Education , lists Avolio the #3 most influential author —of all time—in the field of organizational behavior.

Avolio is the Mark Pigott Chair in Business Strategic Leadership and founding executive director of the Center for Leadership and Strategic Thinking at the Foster School.

Output and impact

The Leadership Quarterly study—“ A Computerized Approach to Understanding Leadership Research ”—considered the full range of leadership topics published in academic journals over the past three decades.

The authors audited 2,115 leadership articles by 3,190 researchers published in 10 leading academic journals since 1990. Productivity was measured by the number of published papers by each researcher over this period; influence by the number of paper citations in other publications.

In terms of both output and impact of leadership research, Avolio emerged at #2 in the world over the past three decades.

The study also mapped networks of connectivity between scholars in various areas of leadership research. Avolio appeared at the center of everything, the most active hub of activity in the field. He rated #1 in “betweenness centrality,” the authors’ term for connecting otherwise unrelated authors. And he ranked #2 in “degree centrality,” or the total number of co-author collaborations.

Outstanding in OB (and other fields)

The article establishing Avolio as the #3 most influential scholar in organizational behavior arrived at this conclusion by measuring the number of citations in leading OB textbooks of more than 16,000 researchers who were cited at least once. Avolio also was ranked the #30 most-cited author in general management textbooks.

And in 2016, Thompson Reuters recognized him among the 3,000 most influential researchers in the world across all of the sciences .

Prolific and profound

Avolio, the author of more than 150 published articles and 12 books , is recognized as one of the world’s foremost authorities on transformational and authentic leadership.

He has worked directly with a variety of global businesses and organizations —in healthcare, financial services, entrepreneurship and the military , among others—to help them develop authentic leadership and achieve their missions.

Avolio’s long list of academic honors includes the 2013 Eminent Leadership Scholar Award from the Network of Leadership Scholars in the Academy of Management, and fellowships in the Academy of Management, the American Psychological Society, the American Psychological Association and the Society for Industrial & Organizational Psychology, among others.

In 2008, Avolio was named among the world’s 150 most influential management scholars in the Journal of Management (along with fellow Foster faculty Terence Mitchell , professor emeritus of management, and Charles Hill , the Hughes M. and Katherine G. Blake Endowed Professor in Business Administration).

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Researchers     Career stages R1 to R4     Fields of science     Reference countries

Career stages R1 to R4

In order to allow for country comparisons in terms of functions and experience levels, the concept of specific career stages was introduced according to the four career stages outlined and defined in the European Commission’s communication “Towards a European Framework for Research Careers” . Researchers in the MORE3 and MORE4 surveys were asked to self-select into one of these four stages.

These four career stages are:

• R1: First Stage Researcher (up to the point of PhD),
• R2: Recognised Researcher (PhD holders or equivalent who are not yet fully independent),
• R3: Established Researcher  (researchers who have developed a level of independence),
• R4: Leading Researcher  (researchers leading their research area or field).

According to the definitions given in the EC’s communication the different stages are characterized as follows:

A First Stage researcher (R1)  will:

• Carry out research under supervision;
• Have the ambition to develop knowledge of research methodologies and discipline;
• Have demonstrated a good understanding of a field of study;
• Have demonstrated the ability to produce data under supervision;
• Be capable of critical analysis, evaluation and synthesis of new and complex ideas;
• Be able to explain the outcome of research and value thereof to research colleagues.

Recognized researchers (R2)  are PhD holders or researchers with an equivalent level of experience and competence who have not yet established a significant level of independence. In addition to the characteristics assigned to the profile of a first stage researcher a recognized researcher:

• Has demonstrated a systematic understanding of a field of study and mastery of research associated with that field;
• Has demonstrated the ability to conceive, design, implement and adapt a substantial program of research with integrity;
• Has made a contribution through original research that extends the frontier of knowledge by developing a substantial body of work, innovation or application. This could merit national or international refereed publication or patent;
• Demonstrates critical analysis, evaluation and synthesis of new and complex ideas;
• Can communicate with his peers - be able to explain the outcome of his research and value thereof to the research community;
• Takes ownership for and manages own career progression, sets realistic and achievable career goals, identifies and develops ways to improve employability;
• Co-authors papers at workshop and conferences.

An Established researcher (R3)  has developed a level of independence and, in addition to the characteristics assigned to the profile of a recognized researcher:

• Has an established reputation based on research excellence in his field;
• Makes a positive contribution to the development of knowledge, research and development through co-operations and collaborations;
• Identifies research problems and opportunities within his area of expertise Identifies appropriate research methodologies and approaches;
• Conducts research independently which advances a research agenda;
• Can take the lead in executing collaborative research projects in cooperation with colleagues and project partners;
• Publishes papers as lead author, organizes workshops or conference sessions.

A Leading researcher (R4)  leads research in his area or field. He or she leads a team or a research group or is head of an industry R&D laboratory. “In particular disciplines as an exception, leading researchers may include individuals who operate as lone researchers.” A leading researcher, in addition to the characteristics assigned to the profile of an established researcher:

• Has an international reputation based on research excellence in their field.
• Demonstrates critical judgment in the identification and execution of research activities.
• Makes a substantial contribution (breakthroughs) to their research field or spanning multiple areas.
• Develops a strategic vision on the future of the research field.
• Recognizes the broader implications and applications of their research.
• Publishes and presents influential papers and books, serves on workshop and conference organizing committees and delivers invited talks.

How to get started?     Start online indicator tool

Leading the way: UCSB’s NSF graduate fellows span wide spectrum of research fields

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From cell biology to artificial intelligence, the award winners of the annual National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) at UC Santa Barbara reflect the university’s broad span of disciplinary merit in research. This year, 18 UCSB graduate students and five undergraduates received the fellowship. 

“Our students’ success in winning this prestigious fellowship is testimony to the vibrancy of departments across campus,” said Leila J. Rupp, Interim Anne and Michael Towbes Dean of the Graduate Division. “From psychology and ecology to chemical engineering and physics, students are engaged in cutting-edge research that makes a difference in the world.”  

UCSB’s NSF graduate research fellows are Bryce Barbee, Louisa Cornelis, Aled Virgil Cuda, Lauren Nicole Enright, Emily G. Gemmill, Dylan Herman, Hailie E. Kittner, Quinn Tessa Kolt, Haarika Manda, Joyce E. Passananti, Sophia Paul, Sarah Allison Rose Payne, Tyler Nelson, Tachibana Pennebaker, Vade Sandip Shah, Ansh K. Soni, Zsofia Marta Szegletes, Ashley K. Yeh, Rachel Zhang. An additional 16 graduate students received honorable mentions.

The oldest graduate fellowship of its kind, the GRFP recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering and mathematics disciplines who are pursuing research-based master’s and doctoral degrees at accredited U.S. institutions. Fellowships are for a three-year annual stipend of $37,000 with an additional $16,000 cost-of-education allowance for tuition and fees and access to opportunities for professional development.    

Debra Herrick Associate Editorial Director (805) 893-2191 [email protected]

About UC Santa Barbara

The University of California, Santa Barbara is a leading research institution that also provides a comprehensive liberal arts learning experience. Our academic community of faculty, students, and staff is characterized by a culture of interdisciplinary collaboration that is responsive to the needs of our multicultural and global society. All of this takes place within a living and learning environment like no other, as we draw inspiration from the beauty and resources of our extraordinary location at the edge of the Pacific Ocean.

Leila J. Rupp

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The Most Influential Education Researchers (in Charts)

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This is the 11th year that Rick Hess has published the RHSU Edu-Scholar Public Influence Rankings . Who are these education researchers? Where do they teach? Do they hail from red states or blue states? And how often were they cited by newspapers on issues related to COVID-19? These charts offer answers.

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• About AI Leaders
• Top 12 AI Leaders and Researchers
• 1. Andrew Ng
• 2. Fei-Fei Li
• 3. Andrej Karpathy 
• 4. Demis Hassabis 
• 5. Ian Goodfellow 
• 6. Yann LeCun
• 7. Jeremy Howard
• 8. Ruslan Salakhutdinov 
• 9. Geoffrey Hinton 
• 10. Alex Smola 
• 11. Rana el Kaliouby 
• 12. Daphne Koller 

Top 12 AI Leaders and Researchers you Should Know in 2024

About AI Leaders Top 12 AI Leaders and Researchers

Deep learning continues to produce advanced techniques with widespread applications faster than one can keep up with. Dozens of papers get uploaded to arXiv every day, and there are hundreds of scientists and engineers active in the field. To stay in the loop, we’ve put together a list of 12 innovators and researchers in the field that you could follow to know the progress brought by the discipline to science, industry and society. The list includes links to the website, LinkedIn, Twitter account, Facebook profile and Google Scholar Profile of the AI Leaders for you to follow. 

These are the top 12 AI Leaders list to watch in 2022

• Andrej Karpathy
• Demis Hassabis 
• Ian Goodfellow 
• Jeremy Howard
• Ruslan Salakhutdinov
• Geoffrey Hinton
• Alex Smola 
• Rana el Kaliouby
• Daphne Koller 

Founder and CEO of Landing AI, Founder of deeplearning.ai. 

Website: https://www.andrewng.org , Twitter: @AndrewYNg , Facebook: Andrew Ng , Google Scholar . 

Andrew was a co-founder and head of Google Brain. He was also the Chief Scientist at Baidu and led the company’s AI group. He is a pioneer in online education as a co-founder of deeplearning.ai and Coursera – the world’s largest MOOC platform, which started off with more than 100,000 students enrolling for his popular courseCS229A. Dr Ng has touched countless lives through his work as a computer scientist which led to him being named as one of Time magazine’s 100 most influential people in 2012.  

Dr Ng’s research is mainly in fields such as Machine learning, deep learning, computer vision, machine perception and natural language processing. His papers which frequently won the best paper award at academic conferences, eventually made him hugely popular and influential among computer scientists and had a massive influence in the field of AI, robotics and computer vision. 

Some of his most well-known work as one of the top AI Leaders include his Autonomous Helicopter Project at Stanford and the Stanford Artificial Intelligence Robot project, which ended up producing an open-source robotics software platform that is widely used today. The Google brain project, which he founded in 2011, used artificial neural networks that were trained using deep learning. The distributed computer with 16,000 CPU cores learnt how to recognize catches from watching YouTube videos and not being taught what a cat really is. The technology which comes from the project is still used in the speech recognition system of Android Operating Systems. 

Sequoia Professor of Computer Science Stanford University

Stanford Profile , Twitter: @drfeifei , Google Scholar . 

Dr Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University. She is also the Co-Director of the Stanford Institute for human-centred Artificial Intelligence and a Co-Director of the Stanford Vision and Learning Lab. She was the Vice President at Google from Jan 2017 to September 2018 and served as the Chief Scientist in Artificial Intelligence/ Machine Learning at Google Cloud. 

Dr Li currently works in areas such as cognitively inspired AI, deep learning, machine learning, computer vision and Artificial Intelligence in healthcare that focuses on ambient intelligence systems. She has published more than 200 scientific articles in all the major journals and conferences and has also worked on cognitive and computational neuroscience in the past. ImageNet, an invention of Dr Li, is an important massive dataset and benchmarking drive responsible for expanding the latest frontiers of Artificial Intelligence and deep learning. 

Along with her technical contributions to the field, she is also a leading voice at the national level for advocating for diversity in AI and STEM. Dr Li is the chairperson and co-founder of AI4ALL, which is a non-profit focused on diversity and inclusion in AI education. She has received numerous awards and recognition for her work, including the ELLE Magazine’s 2017 Women in Tech, a Global Thinker of 2015 by Foreign Policy and the prestigious “Great Immigrants: The Pride of America” by Carnegie Foundation in 2016. 

Senior Director of Artificial Intelligence at Tesla

Website: https://karpathy.ai , Twitter: @karpathy , Google Scholar . 

Andrej Karpathy leads the team working on the neural networks of the Autopilot in Tesla’s cars. He worked previously at OpenAI as a research scientist on Deep Learning in Computer vision, Reinforcement Learning and Generative Modeling. Andrej worked with Fei-Fei Li for his PhD at Stanford, where he worked on Convolutional/Recurrent Neural Network architectures and their applications in Natural Language Processing and Computer Vision and their intersection. He also interned at Google working on large scale feature learning over YouTube videos. Andrej was the primary instructor at the Stanford class on Convolutional Neural Networks for Visual Recognition (CS231n), which he designed together with Fei-Fei. The Deep Learning Course was a huge success, with the number of students enrolled at 150 in 2015 to 750 in 2017. 

Andrej is active on social media, with 352.4K followers on Twitter. He is an enthusiastic blogger and is the developer of Deep Learning libraries in javascript. He also spends his spare time maintaining Arxiv, which is home to over 100,000 papers on machine learning accumulated over the last six years. 

Co-founder and CEO of Deep Mind 

Website: https://deepmind.com/about#leadership , Twitter: @demishassabis , Google Scholar . 

Demis Hassabis co-founded DeepMind, which is an Artificial Intelligence company inspired by neuroscience. Deep Mind was bought by Google in 2014 in their largest acquisition in Europe to date. Demis is now the Vice President of Engineering at Google DeepMind. He is the lead of all the general Artificial Intelligence efforts at Google and even the program AlphaGo which was the first ever to beat a professional at the game of Go. Deep Mind has contributed significantly to advancements in machine learning and produced several awards winning papers. 

As a former chess prodigy, Demis had an early start in the gaming industry by finishing the simulation game Theme Park at the age of 17. He graduated from Cambridge University in Computer Science and founded Elixir Studios, a pioneering video games company that produced award-winning games. He returned to academia for a PhD after a decade of leading successful technology startups. He completed his PhD in Cognitive neuroscience at University College London and post-doctorates at MIT and Harvard. 

Demis, as one of the top AI Leaders ,   worked in the field of autobiographical memory and amnesia, where he was the co-author of a number of papers that were influential in the field. His work on the episodic memory system, which relates to memory and imagination, received widespread coverage in the media. It was also listed as one of the top 10 breakthroughs of the year by the journal Science. 

Director of Machine Learning at Apple

Website: https://www.iangoodfellow.com/ , Twitter: @goodfellow_ian , Google Scholar . 

Ian is known in the field as a researcher in machine learning. He currently works for Apple as the director of machine learning. He was formerly a research scientist at Google brain and has made a number of contributions to the field of deep learning. 

Ian got his B.S. and M.S. in computer science from Stanford University, where he was under the supervision of Andrew Ng. He earned his PhD in the April of 2014 from the Université de Montréal, where he was under the supervision of Yoshua Bengio and Aaron Courville. After graduating, Ian joined Google, where he worked as part of the research team of Google brain. He then quit Google to join Open Ai, which was then still a new institute. In 2017 Ian returned to Google Research. 

Ian is the lead author of the textbook Deep learning and is most known for the generative adversarial networks that he invented. As part of his work at Google, he developed a system to enable automatic transcription of addresses from photos taken by Street View cars in Google Maps. He also demonstrated vulnerabilities in machine learning systems. The MIT Technology Review cited Ian Goodfellow as one of the 35 Innovators Under 35 in 2017. He was also listed as one of the 100 Global Thinkers by Foreign Policy in 2019. 

Chief AI Scientist at Facebook

Website: https://research.fb.com/people/lecun-yann/ , Twitter: @ylecun , Google Scholar . 

Yann LeCun is a computer scientist primarily known for his work in the field of machine learning, mobile robotics, computer vision, and computational neuroscience. Some of his more popular contributions are in optical character recognition and computer vision that uses convolutional neural networks. He is the founding father of convolutional nets and one of the primary creators of DjVu, an image compression technology. Yann LeCun received the Turing Award in 2018 along with Yoshua Bengio and Geoffrey Hinton for their contribution to Deep Learning. 

Some of LeCun’s well-known works include his machine learning methods. His Convolutional Neural Networks was a biologically inspired image recognition method which he applied to optical character recognition and handwriting recognition. Out of his work came the bank check recognition system that was used by NCR and other companies through which 10% of all checks in the United States passed in the later 1990s and early 2000s.  

LeCun joined AT&T Labs in 1996 as the head of the image processing research department. He worked mostly on the DjVu image compression technology, which would eventually be used by many websites to distribute scanned documents, with the Internet Archive being the most notable site. 

In 2012 LeCun became the founding director of the NYU Center for Data Science, and in 2013 he joined Facebook as the first director of AI research.

Founding Researcher at fas.ai

Website: https://www.fast.ai/about/#jeremy , Twitter: @jeremyphoward

 Jeremy Howard is an entrepreneur, developer, business strategist and educator. He is well known as the founding researcher at fast.ai and a Distinguished Research Scientist at the University of San Francisco.  He was previously the founder and CEO of Enlitic, the first company to apply deep learning to the field of medicine. Its success made it to MIT Tech Review’s world’s top 50 smartest companies for two years in a row. 

Jeremy’s career started as a management consultant at McKinsey & Company, where he remained for eight years before moving on to entrepreneurship. He then became the co-founder of FastMail in 1999, which was sold to Opera Software. FastMail, which was highly successful in Australia, was the first of its kind to allow users to integrate their known desktop clients. Later, he joined the online community of data scientists Kaggle as President and Chief Scientist. The company fast.ai, which he co-founded with Rachael Thomas, is a research institute that is focused on making Deep Learning more accessible to everyone. 

Howard’s company Enlitic was a pioneer in the field of medicine, where it made a medical diagnosis and improved process accuracy and speed by applying machine learning. The deep learning algorithms used by Enlitic can diagnose disease and illnesses, and Howard believes they can be as good or even outperform humans at the task. Jeremy appears regularly on Australia’s news programs and has created numerous tutorials on data science and web development. 

Associate Professor, Carnegie Mellon University 

Website: http://www.cs.cmu.edu/~rsalakhu/ , Twitter: @rsalakhu  

Ruslan Salakhutdinov is a Computer Science professor in the Machine Learning Department at Carnegie Mellon University and has previously held the position of the Director of AI Research at Apple. He specializes in the field of statistical machine learning, and his research interests include deep learning, probabilistic Graphical Models and Large-scale optimization, in which he has published papers. 

Salakhutdinov earned his PhD in machine learning from the University of Toronto in 2009. He spent two years on his postdoctoral at the Artificial Intelligence Lab at Massachusetts Institute of Technology, after which he joined the University of Toronto’s Department of Computer Science and Department of Statistics as an assistant professor.  He has received numerous awards such as the Connaught New Researcher Award, Early Researcher Award, Microsoft Research Faculty Fellowship, Alfred P. Sloan Research Fellowship, Google Faculty Research Award and Fellow of the Canadian Institute for Advanced Research.  

Computer Science Professor at University of Toronto

Website: http://www.cs.toronto.edu/~hinton/ , Twitter: @geoffreyhinton , Google Scholar . 

Geoffrey Hinton is one of the most famous AI Leaders in the world, with his work specializing in machine learning, Neural networks, Artificial intelligence, Cognitive science and Object recognition. Hinton is a cognitive psychologist and a computer scientist who is most known for his work on artificial neural networks. As a leading figure in the deep learning community, Hinton has divided his time working for Google Brain and the University of Toronto since 2013. AlexNet-an image recognition milestone which was designed with collaboration with his students, was a breakthrough in the field of computer vision. In 2018 Hinton received the Turing award along with Yann LeCun and Yoshua Bengio for their work on deep learning. The trip is often referred to as the “Godfathers of Deep Learning” or the “Godfathers of AI”. 

Hinton’s work looks into different ways neural networks can be used for machine learning, symbol processing and memory perception. He has over 200 peers reviewed publications that he has authored or co-authored. What distinguishes his work on artificial neural nets internationally is now they can learn by themselves without a human teacher. The research gives one of the first glimpses into brain-like structures that are truly autonomous and intelligent. Through his work, Hinton has found similarities between broken nets and brain damage which leads to the loss of names and characterization.  His work also examines mental imagery and comes up with puzzles that test creative intelligence and originality. 

Hinton received his Honorary Doctorate from the University of Edinburgh in 2001, and he was also the recipient of the IJCAI Award for Research Excellence lifetime-achievement award in 2005. 

Director, Amazon Web Services

Website: http://alex.smola.org , Twitter: @smolix , Google Scholar . 

Alex Smola has been the director of machine learning at Amazon Web Services since 2016. His work focuses on machine learning, statistical data analysis, computer vision, deep learning and NLP to design tools for data scientists.  Alex is an author for over 200 papers, edited five books and guided many PhD students and researchers. His primary interests are in deep learning, scalability of algorithms, statistical modelling and applications in document analysis, user modelling and many more. 

Alex received his master’s degree at the University of Technology, Munich, in 1996. He earned his doctoral degree at the University of Technology Berlin in computer science in 1998. He worked subsequently as a researcher at the IDA Group and the Australian National University’s Research School for Information Sciences and Engineering. He has also worked with tech giants such as Yahoo and Google as a researcher and taught at Carnegie Mellon University. In 2015 Alex co-founded the Marianas Labs and moved to Amazon Web Services in 2016 to build Artificial Intelligence and Machine Learning tools for the company. 

Alex as an AI leader is always on the lookout for talented interns and team members who are skilled at writing code, working with deep learning, writing efficient algorithms and are familiar with high-performance computer systems. 

CEO and Co-Founder of Affectiva 

Website: https://www.ted.com/speakers/rana_el_kaliouby , Twitter: @kaliouby , Google Scholar . 

Rana el Kaliouby is a pioneer in artificial intelligence and the founder and CEO of Affectiva. Her company which is the spinoff of an MIT media lab aims to integrate emotional intelligence into the digital experiences of users everywhere. She is the head of the emotions analytics team that has worked to develop emotion-sensing algorithms. They have also mined the largest database in the world on emotions and have put together 12 billion data points from videos of 2.9 million volunteers spread across 75 countries. The platform is used by many leading companies around the world to get metrics on consumer engagement. They are pioneering digital apps that are emotion-based for entertainment, enterprise, video communication and online education. 

Rana earned her PhD from Cambridge University, after which she joined as a research scientist at MIT media labs. She was instrumental in the application of emotion recognition technology in a number of different fields, including mental health. She quit MIT to co-found Affectiva, which is a pioneer in the field of Emotion AI. As an AI leader in the domain, it now works with 25% of the Fortune 500 companies. 

Forbes named Rana in their list of America’s Top 50 Women in Tech while she was included in the list of 40 under 40 by Fortune. Rana speaks frequently on the topic of ethics in Artificial intelligence and overcoming biases. Her mission is to integrate artificial emotional intelligence to ‘humanize technology’ and develop deep learning platforms for various aspects of emotions such as facial expressions and tone of voice to understand how users are feeling. 

Co-Founder of Coursera, Founder and CEO of insitro. 

Website: https://ai.stanford.edu/~koller , Twitter: @DaphneKoller , Google Scholar . 

Daphne is a computer scientist and a professor in the Department of Computer Science at Stanford University. She is most popularly known as the co-founder of Coursers, the world’s largest MOOC platform. Her primary research area is artificial intelligence and its application in biomedical sciences. Her work focuses on concepts such as decision making, inference learning and representation in applications pertaining to computer vision and computational biology. 

Daphne launched Coursera together with Andrew Ng in 2012, in which she served as co-CEO along with Ng. She then took up a role as President of Coursera and was recognized as Time magazine’s 100 most influential people in 2012. In 2014 she was also on the Fast Company’s Most Creative People 2014. In 2016 she left Coursera to become the chief computing officer at Calico. She later moved on in 2018 to start Insitro, which is a drug discovery startup. In 2009 she co-authored a textbook on probabilistic graphical models. She later offered the subject as a free online course in February of 2012.  

While this list doesn’t encompass the tremendous contributions of other giants in the field, it is sorted on the basis of the Twitter followers these leaders have online. With that said, there is much depth to be explored in the contributions of these 12 AI leaders, who also happen to have a lot of overlap in their research, work and interactions. 

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Neuroimaging, AI help detect brain changes

Uc researchers co-lead nimh-funded study of children of bipolar parents.

Researchers at the University of Cincinnati and Dell Medical School at the University of Texas at Austin (Dell Med) are leading a study using state-of-the-art neuroimaging techniques and artificial intelligence to identify changes in the brains among children of adults living with bipolar disorder — a debilitating condition that interferes with daily life due to its dramatic mood, energy and activity level shifts. 

Almost 3% of U.S. adults live with bipolar disorder, one of the leading causes of disability worldwide. The highest risk factor for bipolar disorder is having a close family member with the condition.  

The study is co-led by UC’s Melissa DelBello, MD; Jorge Almeida, MD, and Charles Nemeroff, MD, PhD of Dell Med and Indiana University’s Stephen Strakowski, MD. 

Melissa DelBello, MD. Photo/University of Cincinnati.

“We are hoping to clarify how stress and trauma impact brain development in children and adolescents who have a family risk for bipolar disorder,” said DelBello, the Dr. Stanley and Mickey Kaplan Endowed Chair and Professor in the Department of Psychiatry and Behavioral Neuroscience at UC’s College of Medicine. “This work is the next step in studying risk and resilience factors associated with bipolar disorder.”   

The brain’s inability to “hit the brakes” on energy delivered by its reward system results in manic behaviors such as spending too much money, engaging in risky behaviors and sleep loss or disruption, said Almeida, director of the Bipolar Disorder Center at Mulva Clinic for the Neurosciences and associate professor in the Department of Psychiatry and Behavioral Sciences at Dell Med.  

The study is the first of its kind to focus specifically on the progression of the disease over time via neuroimaging in children of parents with bipolar disorder. 

The research leverages AI algorithms to discern variations in participants’ brains, combining imaging data with cognitive, clinical, early life adversity and psychosocial function measures. The result is a precise delineation of brain maturation for each person at risk of developing bipolar disorder. 

This study will increase our understanding of the onset of bipolar disorder and ultimately help us identify effective strategies to intervene early or prevent the onset of the illness.

Melissa DelBello, MD

The five-year longitudinal study uses functional magnetic resonance imaging to identify early signs that the brain is developing bipolar disorder. Participants ages 14-21 — a critical time when mania symptoms often develop — undergo annual brain scans to track changes in the brain. If they become depressed, suicidal or experience mania, the participants undergo additional brain scans to help researchers understand how the damage is unfolding. 

Participants also undergo mood assessments and are required to perform tasks that activate and test the brain’s reward system. 

“This study will increase our understanding of the onset of bipolar disorder and ultimately help us identify effective strategies to intervene early or prevent the onset of the illness,” DelBello said. 

“This study evokes hope for me,” Almeida said. “Hope that we finally have the tools to help this condition and possibly prevent it from ever happening.”  

Impact Lives Here

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In its third year of a five-year grant, the research is funded by the National Institute of Mental Health. Additional investigators on the project at UC include David Fleck, PhD, Kelly Cohen, PhD, Cal Adler, MD, and L Rodrigo Patino Duran, MD.  

A version of this story was first published in the Dell Med newsroom .

Featured photo at top of brain scans. Photo/Nur Ceren Demir/iStock.

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U.S. News & World Report: Metformin may help young patients with bipolar disorder avoid weight gain

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The Charnel-House

From bauhaus to beinhaus.

Selim Khan-Magomedov

With lightning telegrams:

On Anatole Kopp

Representing soviet modernism.

As promised, this post will briefly consider the main theoretical contentions and scholarly contributions of the French-Russian architectural historian Anatole Kopp. My own remarks will be limited to an examination of Kopp’s work on Soviet avant-garde architecture beginning in the 1950s and 1960s. From there, it will seek to ascertain any political implications that result from his dramatic presentation of the modern movement’s adventures in the USSR.

Kopp’s photos of Soviet avant-garde architecture

. With some justice the historiographical claim could be made that, by rediscovering Soviet architectural modernism from the interwar period, Kopp effectively introduced the subject to a whole generation of architects following the Second World War. Scattered accounts remained, of course, from a few celebrated exponents of the “international style” (a phrase that Kopp, like Giedion, never fully accepted). But these had largely been buried beneath these architects’ subsequent achievements, and remained in any case either a source of embarrassment or embitterment that most of them — Le Corbusier , Walter Gropius , Ernst May , Hannes Meyer , Mart Stam, Margarete Schütte-Lihotzky, André Lurçat, Arthur Korn, etc. — preferred to forget.

Hegelian Marxist theorist Henri Lefebvre, 1971

Henri Lefebvre, later one of Kopp’s primary collaborators, drew upon Kopp’s reading of the era while spelling out just how groundbreaking his narrative of the Soviet avant-garde was in the 1960s in  The Urban Revolution :

Between 1920 and 1930, Russia experienced a tremendous spurt of creative activity. Quite amazingly, Russian society, turned upside down through revolution, managed to produce superstructures (out of the depths) of astonishing novelty. This occurred in just about every field of endeavor, including politics, architecture, and urbanism. These super­structures were far in advance of the existing structures (social relations) and base (productive forces). The existing base and superstructures would have had to follow, make up for their delay, and reach the level of the superstructures that had come into existence through the process of revolutionary creativity. This was a key problem for Lenin during his last years. Today, however, it has become painfully obvious that those structures and the “base” did a poor job of catching up. The superstructures produced by revolutionary genius collapsed on top of a base (peasant, backward) that had been badly or inadequately modified. Isn’t this the great drama of our era? Architectural and urbanist thought cannot arise from thought or theory alone (urbanistic, sociological, economic). It came into being during this total phenomenon known as revolution. The creations of the revolutionary period in the Soviet Union quickly disappeared; they were destroyed and then forgotten. So why did it take forty years, why did we have to wait until today (an age that some claim is characterized by speed, acceleration, vertigo) and the work of Anatole Kopp to acknowledge the achievements of architectural and urban thought and practice in the Soviet Union? ( The Urban Revolution , pg. 184).

Kopp’s studies were a revelation not only to Western readers, however, but to many of his comrades in the East as well. Indeed, his archival visits to the USSR roughly overlapped with pioneering investigations in the field by Soviet historians like Selim Khan-Magomedov and Oleg Shvidkovskii. The Soviet modernists’ legacy was unknown even in its country of origin, having been politically suppressed for decades. (Though I’d have to double-check, I seem to recall he even worked in tandem with Khan-Magomedov at one point). Unlike his colleagues/contemporaries, who kept more or less neutral in their appraisal of modern architecture, Kopp assigned it a positively revolutionary value. There is something to this approach, to be sure, though the reasons behind this fact perhaps eluded the historian himself. In the introduction to his seminal treatise, Town and Revolution , he explained some of the motivations for his research. Anticipating potential criticisms, Kopp wrote:

It may be objected that if these buildings and projects, all now more than thirty years old, are technically and formally obsolete, why bother to return to them? Because they constitute an important page of world architectural history and because a knowledge of the history of modem architecture makes it easier to understand and appreciate the architecture of today. Because much current [1966] experimentation and research is merely a continuation of efforts begun during the twenties (when it is not simple plagiarism) and because a knowledge of what was done then could assist modem architecture in escaping from the vicious circle in which it now seems trapped. Because the research undertaken at that time related not only to forms and techniques but also to :first principles and because most of the so-called social programs of today have their origin in that remote period and arc a con­ sequence of precisely the economic, political, and social context that existed then. In my opinion, these reasons are amply sufficient to justify a new look at the Soviet architecture of the twenties. They are, however, only secondary considerations. The principal reason for undertaking such a study lies elsewhere. For the avant-garde of the Soviet architects of the twenties, architecture was a means, a lever to be employed in achieving the highest goal that man can set himself. For them architecture was, above all, a tool for “transforming mankind.” The world had been turned upside down, a new society was being built on the basis of new productive relations between individuals. Soon it would give birth to a new man freed of the prejudices and·habits of the past. This new society, this new man, could not develop in the old human dens fashioned in the image of a discredited social order. A special environment and appropriate structures were indispensable. But this environment was not conceived merely as a reflection, or material “translation,” of the new society; it had to-be-created Immediately, since only by living in it would man as he was become man as he was to be. Thus was established a dialectical conception of the role of the human environment: a reflection of the new society, it was at the same time the mold in which that society was to be cast. To some extent, the new environment, the new architecture, was viewed as a device designed for correcting, transforming, and improving man. In the language of the time architecture was a “social condenser” within which indispensable mutations were to be produced. ( Town and Revolution , pg. 12).

In such passages the logic of Kopp’s argument unfolds magnificently. Here he laid out the case for modern architecture as facilitating, expediting, and even generating social change on its own. Kopp’s own formal training as an architect had come, of course, in the United States, under the supervision of exiled Bauhaus masters such as Walter Gropius and Josef Albers. Returning to France after the war, as Falbel discusses below, Kopp joined the French Communist Party and soon fell into the same circles as the prominent Hegelian Marxist Henri Lefebvre and other leading lights such as Claude Schnaidt. Kopp also came into contact with the well-known French intellectual Paul Virilio, who reminded his interviewer in Crepuscular Dawn that he’d “worked with Anatole Kopp, who published  Town and Revolution .” (Virilio goes on to flatter himself in the course of the interview by insisting that it was he, and not Lefebvre, who’d first coined the idea of an “urban revolution”). Continue reading →

Modernist architecture archive

An update on the Modernist Architecture Archive/Database I discussed a couple posts ago.  I’ve begun work on it, and have uploaded almost half of the documents I intend to include.  Only a few of the Russian ones are up yet, but I’m hoping to post them over the next couple days.  There are many more on the way.

Anyway, anyone interested in taking a look at this archive (arranged as a continuous text) can access it here.

However, this might not be the most convenient way to browse through it all.  For a more manageable overall view of each of the individual articles (detailing the author, title, and year of publication), click here.

The Scientific Research Institute of the Russian Customs Academy

Nadezhda Lipatova

Director of the Institute

NADEZHDA LIPATOVA,

Leading Researcher of the Russian Customs Academy, Candidate of Technical Sciences, Senior Researcher (Associate Professor)

140009, Lyubertsy, Moscow Region

Komsomolskij prospect, 4

Tel.: +7(495) 500-13-90

E-mail:   [email protected]

 General information       

The Research Institute of the Russian Customs Academy was established in 2014. The Institute is a structural unit of the Russian Customs Academy, carrying out scientific and research activities within the framework of the statutory activities of the Academy.

The main activities of the institute are:

• implementation of fundamental, searchable, applied, research and developmental works on customs activity issues;
• participation in scientific and technical programs, competitions, grants and other forms of scientific research conducted by ministries and departments in order to carry out accreditation indicators for the Academy;
• preparation of scientific papers: monographs, articles, reports, textbooks and other types of scientific works;
• formation of issues of the journal «Bulletin of the Russian Customs Academy», – a peer-reviewed scientific periodical edition on economic and legal sciences;
• preparation of information and analytical materials for the leadership of the Academy, for meetings and Boards of the Federal Customs Service of Russia, for the Academy website, for the media.

History reference

Term of Reference

Institute page

Information about the Heads of a Research institute

Additional information

Subdivisions of the Research Institute:

Department of Scientific and Information Research in Customs

Head of Department: Tamara Mikhailenko,

Doctor of Philology, Professor.

Tel .: 8 (495) 500-13-01

Scientific editors of the magazine “Herald of the Russian Customs Academy”

E-mail: [email protected]

Department of Economic and Legal Studies in Customs

Head of Department: Vladimir Novikov,

Doctor of Economics, professor.

Tel .: +7 (498) 602-39-64

Department of the Study of Problems in the Theory and Practice of Customs Control, Trade Nomenclature, Expertise and Trade Restrictions

Head of Department: Kozhuhanov Nikolai

Ph.D., associate professor

Tel. : +7 (498) 602-39-10

E-mail:   [email protected]

Department of the Study of Customs Problems of the Development of Eurasian Integration

Head of department: Gladkov Andrey

Tel.: +7 (498) 602-39-62

E-mail:  [email protected]

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Transformations That Work

• Michael Mankins
• Patrick Litre

More than a third of large organizations have some type of transformation program underway at any given time, and many launch one major change initiative after another. Though they kick off with a lot of fanfare, most of these efforts fail to deliver. Only 12% produce lasting results, and that figure hasn’t budged in the past two decades, despite everything we’ve learned over the years about how to lead change.

Clearly, businesses need a new model for transformation. In this article the authors present one based on research with dozens of leading companies that have defied the odds, such as Ford, Dell, Amgen, T-Mobile, Adobe, and Virgin Australia. The successful programs, the authors found, employed six critical practices: treating transformation as a continuous process; building it into the company’s operating rhythm; explicitly managing organizational energy; using aspirations, not benchmarks, to set goals; driving change from the middle of the organization out; and tapping significant external capital to fund the effort from the start.

Lessons from companies that are defying the odds

Idea in Brief

The problem.

Although companies frequently engage in transformation initiatives, few are actually transformative. Research indicates that only 12% of major change programs produce lasting results.

Why It Happens

Leaders are increasingly content with incremental improvements. As a result, they experience fewer outright failures but equally fewer real transformations.

The Solution

To deliver, change programs must treat transformation as a continuous process, build it into the company’s operating rhythm, explicitly manage organizational energy, state aspirations rather than set targets, drive change from the middle out, and be funded by serious capital investments.

Nearly every major corporation has embarked on some sort of transformation in recent years. By our estimates, at any given time more than a third of large organizations have a transformation program underway. When asked, roughly 50% of CEOs we’ve interviewed report that their company has undertaken two or more major change efforts within the past five years, with nearly 20% reporting three or more.

• Michael Mankins is a leader in Bain’s Organization and Strategy practices and is a partner based in Austin, Texas. He is a coauthor of Time, Talent, Energy: Overcome Organizational Drag and Unleash Your Team’s Productive Power (Harvard Business Review Press, 2017).
• PL Patrick Litre leads Bain’s Global Transformation and Change practice and is a partner based in Atlanta.

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• The Science of Beauty: How Bleeding-Edge Biotech Is Transforming How We Look

From skin care to fragrance, the best grooming products on the market are now informed by years—and sometimes decades—of lab-grade research.

Marisa meltzer, marisa meltzer's most recent stories.

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The most exciting skin care, fragrance, and grooming companies on the market have homed in on these breakthroughs—the kind supported by years of lab development and legitimate clinical trials—to deliver everything from perfume to serums that give our body, hair, and faces real results. 

We talked to three very different companies at the forefront of this trend: SickScience, which develops products based on exosomes; Air Company, which makes a carbon-captured ethanol fragrance; and Neurae, whose neuroscience-based approach targets the effect emotions have on how we look.

SickScience

SickScience—a young line produced by a biotech research company based in Istanbul, Turkey—is turning out some of the most exciting exosome-based products. Its first product, ShapeShift V-Line Jaw Defining Serum , which is clinically proven to reduce the appearance of a double chin, sold out in 3 hours following a Today show appearance and quickly amassed a waiting list of about 3,500 people. Its newest product, PowerCycle Scalp Treatment Serum , is no less exciting—and launched on April 29.

For PowerCycle, that means using plant exosomes—garlic and wheat—and bioengineered biotin, which work at the molecular level to deliver coded messages that naturally increase hair follicle production in as little as 4 weeks. The before and after photos are compelling cases that their native Istanbul might soon need fewer hair transplants.

“The last thing you want to do is market science. Our proposition is, this is not an ingredient story. It’s a technology story,” says Tyler Heiden Jones, a former La Mer executive who co-created SickScience after meeting  Koçak-Denizci and Yildirim-Canpolat at a conference. “People want to know what’s in it for me.” So the researchers have been focusing on products that address hyper-specific concerns. Their next act is body serum that they’re joking is Ozempic in a bottle. Better sign up for the wait list now.

Air Company

Your new favorite fragrance has top notes of orange peel and fig leaf, so it’s zesty and fresh. “But as it develops, a lush floral scent comes forward with the heart notes of jasmine, violet, and azalea. Finally, we wanted the base to be warm and rich—stemming from a blend of velvety musk and smoky tobacco, it has a cozy yet sophisticated overall scent,” says Gregory Constantine, CEO and co-founder of Air Company.

But it’s not just the shape-shifting of the scene that will lure you in. It’s a sophisticated fragrance, yes, but it was designed to educate fragrance lovers about sustainable production. Air Eau de Parfum is the world’s first carbon-negative perfume.

It’s a beautiful proof of concept demonstrating that solutions to climate change exist in practically every industry. “We’re able to develop our perfume by capturing CO2, combining it with green hydrogen to transform it into an alcohol mixture, and distilling the mixture to yield carbon-negative ethanol,” says Constantine. “Then all you need is scent oils that are slowly hand-mixed with water and voilà… the world’s first carbon-negative fragrance.” 

Neuraé

The French beauty company Sisley Paris has been using the power of plants in its ultra-high-end products since the 1970s. But its new line, Neuraé, takes the idea of plant science to a new level. The offering—which includes a serum, a cream, a balm, an emulsion, and three roll-on fragrance oils—is based on using neuroscience to change our moods, so that the way we feel can help change the way we look. Call it fighting emotional aging.

If skin care products of the past focused on defying genetic aging, Neuraé is about how our emotions transform our face. (The name comes from combining the Greek word neuron, a reference to the nervous system and the brain, with AÉ, for Activated by Emotions)

The products have been in the works for 10 years, according to Caroline Bertrand, the brand’s head of active ingredients and scientific communication. Research has shown that the skin and brain use neuromediators, such as dopamine and serotonin, to communicate. So when there are high levels of dopamine and serotonin, the skin looks balanced. But when they’re low, skin looks damaged. For example, tiredness can manifest as heavy eye bags and lack of muscle tone; sadness can look like a dull complexion and mouth lines; stress produces muscle tension and fine lines. Each product is tethered to a routine for a specific targeted emotion. The énergie routine improves skin firmness, Joie was been designed to revive your skin’s glow, and Sérénité can help soften wrinkles.

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In Tight Presidential Race, Voters Are Broadly Critical of Both Biden and Trump

About half of voters say that, if given the chance, they would replace both candidates on the ballot, table of contents.

• The state of the 2024 presidential race
• Other findings: Biden’s job approval ticks up, Trump’s election-related criminal charges
• Educational differences in candidate support
• What are 2020 voters’ preferences today?
• How Biden’s supporters view his personal traits
• How Trump’s supporters view his personal traits
• Views of Biden’s presidency and retrospective evaluations of Trump’s time in office
• Attention to the candidates
• Does it matter who wins?
• What if voters could change the presidential ballot?
• How important is it for the losing candidate to publicly acknowledge the winner?
• 4. Joe Biden’s approval ratings
• Acknowledgments
• The American Trends Panel survey methodology
• Validated voters

Pew Research Center conducted this study to understand voters’ views on the 2024 presidential election, as well how the public views President Joe Biden. For this analysis, we surveyed 8,709 adults – including 7,166 registered voters – from April 8 to April 14, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this report , along with responses, and the survey methodology .

As the 2024 presidential race heats up, American voters face a similar set of choices as they did four years ago – and many are not happy about it.

With the election still more than six months away, a new Pew Research Center survey finds that the presidential race is virtually tied : 49% of registered voters favor Donald Trump or lean toward voting for him, while 48% support or lean toward Joe Biden.

A defining characteristic of the contest is that voters overall have little confidence in either candidate across a range of key traits, including fitness for office, personal ethics and respect for democratic values.

Where Trump has the advantage: More than a third of voters say they are extremely or very confident that Trump has the physical fitness (36%) and mental fitness (38%) needed to do the job of president.

Far fewer say the same of Biden (15% are at least very confident in his physical fitness; 21% are extremely or very confident in his mental fitness). Majorities say they are not too or not at all confident in Biden’s physical and mental fitness.

Where Biden has the advantage: More voters are extremely or very confident in Biden (34%) than in Trump (26%) to act ethically in office. And while 38% say they are at least very confident in Biden to respect the country’s democratic values, fewer (34%) express that level of confidence in Trump. The survey was conducted before the start of Trump’s “hush money” trial in New York City .

( Read more about voters’ views of Biden and Trump in Chapter 2. )

The new Center survey of 8,709 adults – including 7,166 registered voters – conducted April 8-14, 2024, finds large divides in voters’ candidate preference by age, education, and race and ethnicity. As was the case in 2020, younger voters and those with a four-year college degree are more likely to favor Biden than Trump.

Older voters and those with no college degree favor Trump by large margins.

Among racial and ethnic groups:

• White voters favor Trump (56%) over Biden (42%) by a wide margin.
• Roughly three-quarters of Black voters (77%) support Biden, while 18% back Trump.
• Hispanic voters are more evenly divided – 52% favor Biden, while 44% back Trump.
• Asian voters favor Biden (59%) over Trump (36%).

( Read more about voters’ candidate preferences in Chapter 1. )

Most voters who turned out in 2020 favor the same candidate in 2024. Among validated 2020 voters, overwhelming majorities of those who cast ballots for Biden (91%) and Trump (94%) support the same candidate this year. Registered voters who did not vote in 2020 are about evenly divided: 48% back Trump, while 46% support Biden.

A majority of voters say “it really matters who wins” the 2024 race. Today, 69% of voters say it really matters which candidate wins the presidential contest this November. This is somewhat smaller than the share who said this in April 2020 about that year’s election (74%). Nearly identical shares of Biden’s and Trump’s supporters say the outcome of the presidential race really matters.

About half of voters would replace both Biden and Trump on the 2024 ballot

Reflecting their dissatisfaction with the Biden-Trump matchup, nearly half of registered voters (49%) say that, if they had the ability to decide the major party candidates for the 2024 election, they would replace both Biden and Trump on the ballot .

Biden’s supporters are especially likely to say they would replace both candidates if they had the chance. Roughly six-in-ten (62%) express this view, compared with 35% of Trump supporters.

There also are stark age differences in these views: 66% of voters under 30 say they would replace both candidates if they had the chance, compared with 54% of those ages 30 to 49 and fewer than half (43%) of those 50 and older.

( Read more about voters’ feelings toward the upcoming election in Chapter 3. )

Evaluations of the Biden and Trump presidencies

• 42% of voters overall say Trump was a good or great president, while 11% say he was average. This is a modest improvement since March 2021, two months after he left office.
• 28% of voters say Biden is a good or great president, while 21% say he is average. These views are mostly on par with June 2020 assessments of the kind of president Biden would be – but today, a smaller share of voters say he is average.

( Read more about ratings of Biden’s and Trump’s presidencies in Chapter 1. )

• Biden’s approval among the general public: Today, Biden’s approval rating sits at 35% – roughly on par with his rating in January (33%). His job rating has climbed slightly among Democrats over that period, however. Today, 65% of Democrats approve of him – up 4 percentage points since January. ( Read more about Biden’s approval rating in Chapter 4. )
• Conceding the presidential election: A majority of voters say it is important that the losing candidate in November publicly acknowledge the winner as the legitimate president. But Trump’s supporters are far less likely than Biden’s to say it is very important (44% vs. 77%).  ( Read more about voters’ views on election concession in Chapter 3. )

Trump’s criminal charges related to the 2020 election

As Trump faces charges that he sought to overturn the outcome of the 2020 election, 45% of Americans say they think Trump’s actions broke the law. This compares with 38% who say his actions did not break the law – including 15% who say his actions were wrong but not illegal, and 23% who say he did nothing wrong. Nearly two-in-ten are not sure.

Democrats mostly say Trump broke the law; Republicans are more divided. An overwhelming majority of Democrats and Democratic-leaning independents (78%) say Trump’s actions in seeking to change the outcome of the 2020 election broke the law. 

Among Republicans and Republican leaners:

• 49% say Trump did nothing wrong.
• 21% say he did something wrong but did not break the law.
• 9% say Trump broke the law.
• 20% are not sure.

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• Donald Trump
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Voters’ views of Trump and Biden differ sharply by religion

Changing partisan coalitions in a politically divided nation, about 1 in 4 americans have unfavorable views of both biden and trump, 2024 presidential primary season was one of the shortest in the modern political era, americans more upbeat on the economy; biden’s job rating remains very low, most popular, report materials.

• April 2024 Biden Job Approval Detailed Tables

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