Community consensus on core open science practices to monitor in biomedicine.

The state of open science needs to be monitored to track changes over time and identify areas to create interventions to drive improvements. In order to monitor open science practices, they first need to be well defined and operationalized. To reach consensus on what open science practices to monitor at biomedical research institutions, we conducted a modified 3-round Delphi study.

Evolution of the thematic structure and main producers of physical therapy interventions research: A bibliometric analysis (1986 to 2017).

Background: Bibliometric studies are used to analyse and map scientific areas, and study the scientific output and impact of institutes and countries.

Objectives: Describe the thematic structure and evolution of the field of physical therapy interventions using articles indexed in Physiotherapy Evidence Database (PEDro). Also, identify and compare the main producers (countries, institutions) over time (research output, citation impact).

Mapping the field of physical therapy and identification of the leading active producers. A bibliometric analysis of the period 2000- 2018.

Objectives: The objectives of the study were: 1) Describe the thematic structure and evolution of the field of physical therapy; 2) identify the main research producers (i.e. countries and institutions); and 3) compare their research output and citation impact.

An agenda-setting paper on data sharing platforms: euCanSHare workshop.

Various data sharing platforms are being developed to enhance the sharing of cohort data by addressing the fragmented state of data storage and access systems. However, policy challenges in several domains remain unresolved. The euCanSHare workshop was organized to identify and discuss these challenges and to set the future research agenda.

A scientometric overview of CORD-19.

As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has been made available alongside calls to help mine the information it contains and to create tools to search it more effectively. We analyse the delineation of the publications included in CORD-19 from a scientometric perspective.

Task specialization across research careers.

Research careers are typically envisioned as a single path in which a scientist starts as a member of a team working under the guidance of one or more experienced scientists and, if they are successful, ends with the individual leading their own research group and training future generations of scientists. Here we study the author contribution statements of published research papers in order to explore possible biases and disparities in career trajectories in science. We used Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLoS journals, which included 347,136 distinct authors and their associated contribution statements.

An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics.

Sufficient data presence is one of the key preconditions for applying metrics in practice. Based on both Altmetric.com data and Mendeley data collected up to 2019, this paper presents a state-of-the-art analysis of the presence of 12 kinds of altmetric events for nearly 12.

Open Access uptake by universities worldwide.

The implementation of policies promoting the adoption of an open science (OS) culture must be accompanied by indicators that allow monitoring the uptake of such policies and their potential effects on research publishing and sharing practices. This study presents indicators of open access (OA) at the institutional level for universities worldwide. By combining data from Web of Science, Unpaywall and the Leiden Ranking disambiguation of institutions, we track OA coverage of universities' output for 963 institutions.

Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science.

'Social media metrics' are bursting into science studies as emerging new measures of impact related to scholarly activities. However, their meaning and scope as scholarly metrics is still far from being grasped. This research seeks to shift focus from the consideration of social media metrics around science as mere indicators confined to the analysis of the use and visibility of publications on social media to their consideration as metrics of interaction and circulation of scientific knowledge across different communities of attention, and particularly as metrics that can also be used to characterize these communities.

General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators.

The data collection and reporting approaches of four major altmetric data aggregators are studied. The main aim of this study is to understand how differences in social media tracking and data collection methodologies can have effects on the analytical use of altmetric data. For this purpose, discrepancies in the metrics across aggregators have been studied in order to understand how the methodological choices adopted by these aggregators can explain the discrepancies found.

Reflections around 'the cautionary use' of the h-index: response to Teixeira da Silva and Dobránszki.

In a recent Letter to the Editor Teixeira da Silva and Dobránszki (2018) present a discussion of the issues regarding the h-index as an indicator for the evaluation of individual scholars, particularly in the current landscape of the proliferation of online sources that provide individual level bibliometric indicators. From our point of view, the issues surrounding the h-index go far beyond the problems mentioned by TSD. In this letter we provide some overview of this, mostly by expanding TSD's original argument and discussing more conceptual and global issues related to the indicator, particularly in the outlook of a strong proliferation of online sources providing individual researcher indicators.

Testing Hypotheses on Risk Factors for Scientific Misconduct via Matched-Control Analysis of Papers Containing Problematic Image Duplications.

It is commonly hypothesized that scientists are more likely to engage in data falsification and fabrication when they are subject to pressures to publish, when they are not restrained by forms of social control, when they work in countries lacking policies to tackle scientific misconduct, and when they are male. Evidence to test these hypotheses, however, is inconclusive due to the difficulties of obtaining unbiased data. Here we report a pre-registered test of these four hypotheses, conducted on papers that were identified in a previous study as containing problematic image duplications through a systematic screening of the journal PLoS ONE.

Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences.

For the past 50 years, acknowledgments have been studied as important paratextual traces of research practices, collaboration, and infrastructure in science. Since 2008, funding acknowledgments have been indexed by Web of Science, supporting large-scale analyses of research funding. Applying advanced linguistic methods as well as Correspondence Analysis to more than one million acknowledgments from research articles and reviews published in 2015, this paper aims to go beyond funding disclosure and study the main types of contributions found in acknowledgments on a large scale and through disciplinary comparisons.

The unbearable emptiness of tweeting-About journal articles.

Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature.

Meta-assessment of bias in science.

Numerous biases are believed to affect the scientific literature, but their actual prevalence across disciplines is unknown. To gain a comprehensive picture of the potential imprint of bias in science, we probed for the most commonly postulated bias-related patterns and risk factors, in a large random sample of meta-analyses taken from all disciplines. The magnitude of these biases varied widely across fields and was overall relatively small.

Increasing The Genetic Admixture of Available Lines of Human Pluripotent Stem Cells.

Human pluripotent stem cells (hPSCs) may significantly improve drug development pipeline, serving as an in vitro system for the identification of novel leads, and for testing drug toxicity. Furthermore, these cells may be used to address the issue of differential drug response, a phenomenon greatly influenced by genetic factors. This application depends on the availability of hPSC lines from populations with diverse ancestries.

How Many Is Too Many? On the Relationship between Research Productivity and Impact.

Over the last few decades, the institutionalisation of quantitative research evaluations has created incentives for scholars to publish as many papers as possible. This paper assesses the effects of such incentives on individual researchers' scientific impact, by analysing the relationship between their number of articles and their proportion of highly cited papers. In other words, does the share of an author's top 1% most cited papers increase, remain stable, or decrease as his/her total number of papers increase? Using a large dataset of disambiguated researchers (N = 28,078,476) over the 1980-2013 period, this paper shows that, on average, the higher the number of papers a researcher publishes, the higher the proportion of these papers are amongst the most cited.

A Euploid Line of Human Embryonic Stem Cells Derived from a 43,XX,dup(9q),+12,-14,-15,-18,-21 Embryo.

Aneuploid embryos diagnosed by FISH-based preimplantation genetic screening (PGS) have been shown to yield euploid lines of human embryonic stem cells (hESCs) with a relatively high frequency. Given that the diagnostic procedure is usually based on the analysis of 1-2 blastomeres of 5 to 10-cell cleavage-stage embryos, mosaicism has been a likely explanation for the phenomena. However, FISH-based PGS can have a significant rate of misdiagnosis, and therefore some of those lines may have been derived from euploid embryos misdiagnosed as aneuploid.

Misconduct Policies, Academic Culture and Career Stage, Not Gender or Pressures to Publish, Affect Scientific Integrity.

The honesty and integrity of scientists is widely believed to be threatened by pressures to publish, unsupportive research environments, and other structural, sociological and psychological factors. Belief in the importance of these factors has inspired major policy initiatives, but evidence to support them is either non-existent or derived from self-reports and other sources that have known limitations. We used a retrospective study design to verify whether risk factors for scientific misconduct could predict the occurrence of retractions, which are usually the consequence of research misconduct, or corrections, which are honest rectifications of minor mistakes.

Searching for naïve human pluripotent stem cells.

Normal mouse pluripotent stem cells were originally derived from the inner cell mass (ICM) of blastocysts and shown to be the in vitro equivalent of those pre-implantation embryonic cells, and thus were called embryonic stem cells (ESCs). More than a decade later, pluripotent cells were isolated from the ICM of human blastocysts. Despite being called human ESCs, these cells differ significantly from mouse ESCs, including different morphology and mechanisms of control of pluripotency, suggesting distinct embryonic origins of ESCs from the two species.

Characterizing social media metrics of scholarly papers: the effect of document properties and collaboration patterns.

A number of new metrics based on social media platforms--grouped under the term "altmetrics"--have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.

'Seed + expand': a general methodology for detecting publication oeuvres of individual researchers.

The study of science at the individual scholar level requires the disambiguation of author names. The creation of author's publication oeuvres involves matching the list of unique author names to names used in publication databases. Despite recent progress in the development of unique author identifiers, e.