Archives des mots-clés : research data

fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community

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Authors : René Bernard, Tracey L. Weissgerber, Evgeny Bobrov, Stacey J. Winham, Ulrich Dirnag, Nico Riedel

Statistically significant findings are more likely to be published than non-significant or null findings, leaving scientists and healthcare personnel to make decisions based on distorted scientific evidence.

Continuously expanding ´file drawers’ of unpublished data from well-designed experiments waste resources creates problems for researchers, the scientific community and the public. There is limited awareness of the negative impact that publication bias and selective reporting have on the scientific literature.

Alternative publication formats have recently been introduced that make it easier to publish research that is difficult to publish in traditional peer reviewed journals. These include micropublications, data repositories, data journals, preprints, publishing platforms, and journals focusing on null or neutral results. While these alternative formats have the potential to reduce publication bias, many scientists are unaware that these formats exist and don’t know how to use them.

Our open source file drawer data liberation effort (fiddle) tool (RRID:SCR_017327 available at: http://s-quest.bihealth.org/fiddle/) is a match-making Shiny app designed to help biomedical researchers to identify the most appropriate publication format for their data. Users can search for a publication format that meets their needs, compare and contrast different publication formats, and find links to publishing platforms.

This tool will assist scientists in getting otherwise inaccessible, hidden data out of the file drawer into the scientific community and literature. We briefly highlight essential details that should be included to ensure reporting quality, which will allow others to use and benefit from research published in these new formats.

URL : fiddle: a tool to combat publication bias by getting research out of the file drawer and into the scientific community

DOI : https://doi.org/10.1042/CS20201125

Enforcing public data archiving policies in academic publishing: A study of ecology journals

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Authors : Dan Sholler, Karthik Ram, Carl Boettiger, Daniel S Katz

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success.

Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. The effort to increase data sharing in the sciences is one part of a broader “data revolution” that has prompted discussion about a paradigm shift in scientific research.

Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets.

We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies.

We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions.

Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets.

We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.

URL : Enforcing public data archiving policies in academic publishing: A study of ecology journals

DOI : https://doi.org/10.1177/2053951719836258

Research Data Sharing in Spain: Exploring Determinants, Practices, and Perceptions

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Authors : Rafael Aleixandre-Benavent, Antonio Vidal-Infer, Adolfo Alonso-Arroyo, Fernanda Peset, Antonia Ferrer Sapena

This work provides an overview of a Spanish survey on research data, which was carried out within the framework of the project Datasea at the beginning of 2015. It is covered by the objectives of sustainable development (goal 9) to support the research.

The purpose of the study was to identify the habits and current experiences of Spanish researchers in the health sciences in relation to the management and sharing of raw research data. Method: An electronic questionnaire composed of 40 questions divided into three blocks was designed.

The three Section s contained questions on the following aspects: (A) personal information; (B) creation and reuse of data; and (C) preservation of data. The questionnaire was sent by email to a list of universities in Spain to be distributed among their researchers and professors. A total of 1063 researchers completed the questionnaire.

More than half of the respondents (54.9%) lacked a data management plan; nearly a quarter had storage systems for the research group; 81.5% used personal computers to store data; “Contact with colleagues” was the most frequent means used to locate and access other researchers’ data; and nearly 60% of researchers stated their data were available to the research group and collaborating colleagues.

The main fears about sharing were legal questions (47.9%), misuse or interpretation of data (42.7%), and loss of authorship (28.7%).

The results allow us to understand the state of data sharing among Spanish researchers and can serve as a basis to identify the needs of researchers to share data, optimize existing infrastructure, and promote data sharing among those who do not practice it yet.

URL : Research Data Sharing in Spain: Exploring Determinants, Practices, and Perceptions

DOI : https://doi.org/10.3390/data5020029

Préservation des données de recherche : proposer des services de soutien aux chercheurs du site Uni Arve de l’université de Genève

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Auteur/Author : Manuela Bezzi

Ce travail porte sur les pratiques des chercheurs du site Uni Arve (faculté des sciences) de l’université de Genève concernant la préservation et la réutilisation des données de recherche, et son objectif est d’évaluer les besoins des chercheurs afin de leur proposer des services de soutien appropriés.

La préservation des données de recherche s’inscrit dans le mouvement de l’Open Data dont l’objectif est de rendre les données de recherche publiquement accessibles, intelligibles et réutilisables, en particulier lorsque ces données ont été produites grâce à des recherches financées par des fonds publics.

Pour ce faire, le FNS demande aux chercheurs de déposer leurs données dans des archives publiques répondant aux principes FAIR. Or, depuis juin 2019, l’université de Genève met à disposition de ses chercheurs une archive institutionnelle, Yareta, répondant aux critères du FNS.

Afin de répondre aux mieux aux besoins des chercheurs, une approche en deux temps a été adoptée : (1) une analyse des jeux de données déposés sur Yareta a permis d’identifier les problématiques faisant obstacle à la réutilisation des données. (2) Puis, des entretiens menés avec des chercheurs ont permis d’analyser leurs pratiques de préservation et leurs besoins.

Les informations récoltées par ces deux approches ont permis de faire les propositions suivantes: un guide d’archivage portant sur quatre activités permettant de garantir une bonne préservation : format, contexte, métadonnées, licence, la mise en place de ressources additionnelles (page web ou formation) couvrant des notions peu comprises par les chercheurs, la modification de pages web existantes pour des raisons de cohérence, l’ajout d’information dans l’outil Yareta.

Ces propositions sont des solutions concrètes, basées sur les ressources existantes de l’université de Genève afin de pouvoir être complémentaires aux services de soutien et aux ressources déjà proposés par l’université de Genève.

De plus, ces propositions pourront bénéficier à toute la communauté de l’université de Genève et pas uniquement aux chercheurs du site Uni Arve.

DOI : https://doc.rero.ch/record/329678

Opening Pandora’s Box: Peeking inside Psychology’s data sharing practices, and seven recommendations for change

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Authors : John N. Towse, David A Ellis, Andrea S Towse

Open data-sharing is a valuable practice that ought to enhance the impact, reach, and transparency of a research project.

While widely advocated by many researchers and mandated by some journals and funding agencies, little is known about detailed practices across psychological science. In a pre-registered study, we show that overall, few research papers directly link to available data in many, though not all, journals.

Most importantly, even where open data can be identified, the majority of these lacked completeness and reusability—conclusions that closely mirror those reported outside of Psychology.

Exploring the reasons behind these findings, we offer seven specific recommendations for engineering and incentivizing improved practices, so that the potential of open data can be better realized across psychology and social science more generally.

URL : Opening Pandora’s Box: Peeking inside Psychology’s data sharing practices, and seven recommendations for change

DOI : https://doi.org/10.3758/s13428-020-01486-1

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

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Authors : Laure Perrier, Erik Blondal, Heather MacDonald

Background

Funding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation.

We conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data.

Methods

We searched the published and unpublished literature for studies on data sharing by researchers in academic institutions. Two independent reviewers screened citations and abstracts, then full-text articles.

Data abstraction was performed independently by two investigators. The abstracted data was read and reread in order to generate codes. Key concepts were identified and thematic analysis was used for data synthesis.

Results

We reviewed 2005 records and included 45 studies along with 3 companion reports. The studies were published between 2003 and 2018 and most were conducted in North America (60%) or Europe (17%).

The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories.

Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking.

Conclusion

Researchers lack skills to share data in a manner that is efficient and effective. Improved infrastructure support would allow them to make data available quickly and seamlessly. The lack of incentives for sharing research data with regards to academic appointment, promotion, recognition, and rewards need to be addressed.

URL : The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

DOI : https://doi.org/10.1371/journal.pone.0234275.s002

ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

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Authors: Nico Riedel, Miriam Kip, Evgeny Bobro

Open research data are increasingly recognized as a quality indicator and an important resource to increase transparency, robustness and collaboration in science. However, no standardized way of reporting Open Data in publications exists, making it difficult to find shared datasets and assess the prevalence of Open Data in an automated fashion.

We developed ODDPub (Open Data Detection in Publications), a text-mining algorithm that screens biomedical publications and detects cases of Open Data. Using English-language original research publications from a single biomedical research institution (n = 8689) and randomly selected from PubMed (n = 1500) we iteratively developed a set of derived keyword categories.

ODDPub can detect data sharing through field-specific repositories, general-purpose repositories or the supplement. Additionally, it can detect shared analysis code (Open Code).

To validate ODDPub, we manually screened 792 publications randomly selected from PubMed. On this validation dataset, our algorithm detected Open Data publications with a sensitivity of 0.73 and specificity of 0.97.

Open Data was detected for 11.5% (n = 91) of publications. Open Code was detected for 1.4% (n = 11) of publications with a sensitivity of 0.73 and specificity of 1.00. We compared our results to the linked datasets found in the databases PubMed and Web of Science.

Our algorithm can automatically screen large numbers of publications for Open Data. It can thus be used to assess Open Data sharing rates on the level of subject areas, journals, or institutions. It can also identify individual Open Data publications in a larger publication corpus. ODDPub is published as an R package on GitHub.

URL : ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications

DOI : http://doi.org/10.5334/dsj-2020-042