Toward a more scientific science

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

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.

SciSciNet: A large-scale open data lake for the science of science research

The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.

Data, measurement and empirical methods in the science of science

The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

Understanding the onset of hot streaks across artistic, cultural, and scientific careers

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs. We find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins. Crucially, hot streaks appear to be associated with neither exploration nor exploitation behavior in isolation, but a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results may have implications for identifying and nurturing talents across a wide range of creative domains.

The dominance of big teams in China’s scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams.

'If I had a hedge fund, I would cure diabetes': endogenous mechanisms for creating public goods

We consider the problem of organizing capital to produce public goods with broad societal value. We review why market corrections via government subsidies or philanthropic initiatives are inadequate, in addition to considering the paradox of patents. Our proposed mechanism (an Ever-growing Prize and a Patent Repository) directs capital towards two innovation problems routinely overlooked: (1) problems for which the reward is insufficient even with established mechanisms (e.g. patents or academic prestige), and (2) problems for which the reward is large, but the effort risk is incalculable. The proposed hedge fund mechanism facilitates crowdsourcing, addressing the challenge of determining problems with broad societal interest; the ever-growing prize allows for an emergent rather than predetermined reward; the patent repository turns private intellectual property into a public good for target problems while circumventing the inventors' threat of patent expiration. We guide this discussion by considering two problems: treating Cystic Fibrosis and curing Diabetes.

EnrichrBot: Twitter bot tracking tweets about human genes

MOTIVATION

Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets.

RESULTS

EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter.

AVAILABILITY AND IMPLEMENTATION

https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Perception and Conflict in Conservation: The Rashomon Effect

Conflict is a common feature in conservation and resource management. Environmental conflicts are frequently attributed to differences in values; however, variability in the perception of facts, rooted in social and cultural differences also underlies conflicts. Such differences in perception have been termed the Rashomon effect after the Kurosawa film. In the present article, we explore a conservation Rashomon effect—a phenomenon that results from a combination of differences in perspective, plausible alternative perspectives of a conservation issue, and the absence of evidence to elevate one perspective above others. As a remedy to the Rashomon effect, policy-makers have turned to scientists as honest brokers who share a common environmental reality. We evaluate this supposition and suggest that scientists, themselves, display Rashomon effects. We suggest that Rashomon effects can be reduced by acknowledging the plurality of reality, embracing epistemic pluralism, and prioritizing an inclusive process of resource management.

Improving data access democratizes and diversifies science

The foundation of the scientific method rests on access to data, and yet such access is often restricted or costly. We investigate how improved data access shifts the quantity, quality, and diversity of scientific research. We examine the impact of reductions in cost and sharing restrictions for satellite imagery data from NASA's Landsat program (the longest record of remote-sensing observations of the Earth) on academic science using a sample of about 24,000 Landsat publications by over 34,000 authors matched to almost 3,000 unique study locations. Analyses show that improved access had a substantial and positive effect on the quantity and quality of Landsat-enabled science. Improved data access also democratizes science by disproportionately helping scientists from the developing world and lower-ranked institutions to publish using Landsat data. This democratization in turn increases the geographic and topical diversity of Landsat-enabled research. Scientists who start using Landsat data after access is improved tend to focus on previously understudied regions close to their home location and introduce novel research topics. These findings suggest that policies that improve access to valuable scientific data may promote scientific progress, reduce inequality among scientists, and increase the diversity of scientific research.

Scientific elite revisited: patterns of productivity, collaboration, authorship and impact

Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns to those of ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel Prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel Prize. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science.

Early-career setback and future career impact

Setbacks are an integral part of a scientific career, yet little is known about their long-term effects. Here we examine junior scientists applying for National Institutes of Health R01 grants. By focusing on proposals fell just below and just above the funding threshold, we compare near-miss with narrow-win applicants, and find that an early-career setback has powerful, opposing effects. On the one hand, it significantly increases attrition, predicting more than a 10% chance of disappearing permanently from the NIH system. Yet, despite an early setback, individuals with near misses systematically outperform those with narrow wins in the longer run. Moreover, this performance advantage seems to go beyond a screening mechanism, suggesting early-career setback appears to cause a performance improvement among those who persevere. Overall, these findings are consistent with the concept that “what doesn’t kill me makes me stronger,” which may have broad implications for identifying, training and nurturing junior scientists.

Little is known about the long-term effects of early-career setback. Here, the authors compare junior scientists who were awarded a NIH grant to those with similar track records, who were not, and find that individuals with the early setback systematically performed better in the longer term.

A dataset of publication records for Nobel laureates

A central question in the science of science concerns how to develop a quantitative understanding of the evolution and impact of individual careers. Over the course of history, a relatively small fraction of individuals have made disproportionate, profound, and lasting impacts on science and society. Despite a long-standing interest in the careers of scientific elites across diverse disciplines, it remains difficult to collect large-scale career histories that could serve as training sets for systematic empirical and theoretical studies. Here, by combining unstructured data collected from CVs, university websites, and Wikipedia, together with the publication and citation database from Microsoft Academic Graph (MAG), we reconstructed publication histories of nearly all Nobel prize winners from the past century, through both manual curation and algorithmic disambiguation procedures. Data validation shows that the collected dataset presents among the most comprehensive collection of publication records for Nobel laureates currently available. As our quantitative understanding of science deepens, this dataset is expected to have increasing value. It will not only allow us to quantitatively probe novel patterns of productivity, collaboration, and impact governing successful scientific careers, it may also help us unearth the fundamental principles underlying creativity and the genesis of scientific breakthroughs.

Who Is (Likely) Peer-Reviewing Your Papers? A Partial Insight into the World’s Top Reviewers

Scientific publishing is experiencing unprecedented growth in terms of outputs across all fields. Inevitably this creates pressure throughout the system on a number of entities. One key element is represented by peer-reviewers, whose demand increases at an even higher pace than that of publications, since more than one reviewer per paper is needed and not all papers that get reviewed get published. The relatively recent Publons platform allows for unprecedented insight into the usual ‘blindness’ of the peer-review system. At a time where the world’s top peer-reviewers are announced and celebrated, we have taken a step back in order to attempt a partial mapping of their profiles to identify trends and key dimensions of this community of ‘super-reviewers’. This commentary focuses necessarily on a limited sample due to manual processing of data, which needs to be done within a single day for the type of information we seek. In investigating the numbers of performed reviews vs. academic citations, our analysis suggests that most reviews are carried out by relatively inexperienced academics. For some of these early career academics, peer-reviewing seems to be the only activity they engage with, given the high number of reviews performed (e.g., three manuscripts per day) and the lack of outputs (zero academic papers and citations in some cases). Additionally, the world’s top researchers (i.e., highly-cited researchers) are understandably busy with research activities and therefore far less active in peer-reviewing. Lastly, there seems to be an uneven distribution at a national level between scientific outputs (e.g., publications) and reviews performed. Our analysis contributes to the ongoing global discourse on the health of scientific peer-review, and it raises some important questions for further discussion.