The COVID-19 response illustrates that traditional academic reward structures and metrics do not reflect crucial contributions to modern science.

The COVID-19 pandemic has motivated many open and collaborative analytical research projects with real-world impact. However, despite their value, such activities are generally overlooked by traditional academic metrics. Science is ultimately improved by analytical work, whether ensuring reproducible and well-documented code to accompany papers, developing and maintaining flexible tools, sharing and curating data, or disseminating analysis to wider audiences. To increase the impact and sustainability of modern science, it will be crucial to ensure these analytical activities-and the people who do them-are valued in academia.

Europe PMC in 2020.

Europe PMC (https://europepmc.org) is a database of research articles, including peer reviewed full text articles and abstracts, and preprints - all freely available for use via website, APIs and bulk download. This article outlines new developments since 2017 where work has focussed on three key areas: (i) Europe PMC has added to its core content to include life science preprint abstracts and a special collection of full text of COVID-19-related preprints. Europe PMC is unique as an aggregator of biomedical preprints alongside peer-reviewed articles, with over 180 000 preprints available to search. (ii) Europe PMC has significantly expanded its links to content related to the publications, such as links to Unpaywall, providing wider access to full text, preprint peer-review platforms, all major curated data resources in the life sciences, and experimental protocols. The redesigned Europe PMC website features the PubMed abstract and corresponding PMC full text merged into one article page; there is more evident and user-friendly navigation within articles and to related content, plus a figure browse feature. (iii) The expanded annotations platform offers ∼1.3 billion text mined biological terms and concepts sourced from 10 providers and over 40 global data resources.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

MAGPIE: Simplifying access and execution of computational models in the life sciences.

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.

How do physicians become medical experts? A test of three competing theories: distinct domains, independent influence and encapsulation models.

The distinction between basic sciences and clinical knowledge which has led to a theoretical debate on how medical expertise is developed has implications for medical school and lifelong medical education. This longitudinal, population based observational study was conducted to test the fit of three theories-knowledge encapsulation, independent influence, distinct domains-of the development of medical expertise employing structural equation modelling. Data were collected from 548 physicians (292 men-53.3%; 256 women-46.7%; mean age = 24.2 years on admission) who had graduated from medical school 2009-2014. They included (1) Admissions data of undergraduate grade point average and Medical College Admission Test sub-test scores, (2) Course performance data from years 1, 2, and 3 of medical school, and (3) Performance on the NBME exams (i.e., Step 1, Step 2 CK, and Step 3). Statistical fit indices (Goodness of Fit Index-GFI; standardized root mean squared residual-SRMR; root mean squared error of approximation-RSMEA) and comparative fit [Formula: see text] of three theories of cognitive development of medical expertise were used to assess model fit. There is support for the knowledge encapsulation three factor model of clinical competency (GFI = 0.973, SRMR = 0.043, RSMEA = 0.063) which had superior fit indices to both the independent influence and distinct domains theories ([Formula: see text] vs [Formula: see text] [[Formula: see text]] vs [Formula: see text] [[Formula: see text]], respectively). The findings support a theory where basic sciences and medical aptitude are direct, correlated influences on clinical competency that encapsulates basic knowledge.

Open data and digital morphology.

Over the past two decades, the development of methods for visualizing and analysing specimens digitally, in three and even four dimensions, has transformed the study of living and fossil organisms. However, the initial promise that the widespread application of such methods would facilitate access to the underlying digital data has not been fully achieved. The underlying datasets for many published studies are not readily or freely available, introducing a barrier to verification and reproducibility, and the reuse of data. There is no current agreement or policy on the amount and type of data that should be made available alongside studies that use, and in some cases are wholly reliant on, digital morphology. Here, we propose a set of recommendations for minimum standards and additional best practice for three-dimensional digital data publication, and review the issues around data storage, management and accessibility.

Gender representation in the vision sciences: A longitudinal study.

Understanding the current status and historical trends of gender representation within a research field is an important component of fostering a diverse and inclusive scientific community. Here, we report on the gender representation of a large sample of the vision science research community--the attendees of the Annual Meeting of the Vision Sciences Society (VSS). Our analysis shows that the majority of scientists at all career levels in our sample are male. This imbalance is most pronounced for the senior scientists, whereas predoctoral students are nearly balanced between the genders. Historically, the gender imbalance was larger than it is at present, and it has followed a slow-but-steady trend toward gender parity over the past decade. A longitudinal analysis based on tracking individual attendees shows a larger dropout rate for female than male predoctoral trainees. However, among the trainees who continue in the vision science field after graduate school, evidence suggests that career advancement is quite similar between the genders. In an additional analysis, we found that the VSS Young Investigator awardees and the abstract review committee members reflect substantial gender imbalances, suggesting that these recognitions have yet to catch up with the greater gender balance of the rising generation of junior vision scientists. We hope that this report will encourage awareness of issues of diversity in the scientific community and further promote the development of a research field in which all talented scientists are supported to succeed.

Comparison of health risk behavior, awareness, and health benefit beliefs of health science and non-health science students: An international study.

This study determines the differences in health risk behavior, knowledge, and health benefit beliefs between health science and non-health science university students in 17 low and middle income countries. Anonymous questionnaire data were collected in a cross-sectional survey of 13,042 undergraduate university students (4,981 health science and 8,061 non-health science students) from 17 universities in 17 countries across Asia, Africa, and the Americas. Results indicate that overall, health science students had the same mean number of health risk behaviors as non-health science university students. Regarding addictive risk behavior, fewer health science students used tobacco, were binge drinkers, or gambled once a week or more. Health science students also had a greater awareness of health behavior risks (5.5) than non-health science students (4.6). Linear regression analysis found a strong association with poor or weak health benefit beliefs and the health risk behavior index. There was no association between risk awareness and health risk behavior among health science students and an inverse association among non-health science students.