Archives des mots-clés : data sharing

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We searched the published and unpublished literature for studies on data sharing by researchers in academic institutions.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

Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

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Authors : Jihyun Kim, Soon Kim, Hye-Min Cho, Jae Hwa Chang, Soo Young Kim

Background

Many scholarly journals have established their own data-related policies, which specify their enforcement of data sharing, the types of data to be submitted, and their procedures for making data available.

However, except for the journal impact factor and the subject area, the factors associated with the overall strength of the data sharing policies of scholarly journals remain unknown.

This study examines how factors, including impact factor, subject area, type of journal publisher, and geographical location of the publisher are related to the strength of the data sharing policy.

Methods

From each of the 178 categories of the Web of Science’s 2017 edition of Journal Citation Reports, the top journals in each quartile (Q1, Q2, Q3, and Q4) were selected in December 2018. Of the resulting 709 journals (5%), 700 in the fields of life, health, and physical sciences were selected for analysis.

Four of the authors independently reviewed the results of the journal website searches, categorized the journals’ data sharing policies, and extracted the characteristics of individual journals.

Univariable multinomial logistic regression analyses were initially conducted to determine whether there was a relationship between each factor and the strength of the data sharing policy.

Based on the univariable analyses, a multivariable model was performed to further investigate the factors related to the presence and/or strength of the policy.

Results

Of the 700 journals, 308 (44.0%) had no data sharing policy, 125 (17.9%) had a weak policy, and 267 (38.1%) had a strong policy (expecting or mandating data sharing). The impact factor quartile was positively associated with the strength of the data sharing policies.

Physical science journals were less likely to have a strong policy relative to a weak policy than Life science journals (relative risk ratio [RRR], 0.36; 95% CI [0.17–0.78]). Life science journals had a greater probability of having a weak policy relative to no policy than health science journals (RRR, 2.73; 95% CI [1.05–7.14]).

Commercial publishers were more likely to have a weak policy relative to no policy than non-commercial publishers (RRR, 7.87; 95% CI, [3.98–15.57]). Journals by publishers in Europe, including the majority of those located in the United Kingdom and the Netherlands, were more likely to have a strong data sharing policy than a weak policy (RRR, 2.99; 95% CI [1.85–4.81]).

Conclusions

These findings may account for the increase in commercial publishers’ engagement in data sharing and indicate that European national initiatives that encourage and mandate data sharing may influence the presence of a strong policy in the associated journals.

Future research needs to explore the factors associated with varied degrees in the strength of a data sharing policy as well as more diverse characteristics of journals related to the policy strength.

URL : Data sharing policies of journals in life, health, and physical sciences indexed in Journal Citation Reports

DOI : https://doi.org/10.7717/peerj.9924

Research transparency promotion by surgical journals publishing randomised controlled trials: a survey

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Authors : Nicolas Lombard, A. Gasmi, L. Sulpice, K. Boudjema, Damien Bergeat

Objective

To describe surgical journals’ position statements on data-sharing policies (primary objective) and to describe key features of their research transparency promotion.

Methods

Only “SURGICAL” journals with an impact factor higher than 2 (Web of Science) were eligible for the study. They were included, if there were explicit instructions for clinical trial publication in the official instructions for authors (OIA) or if they had published randomised controlled trial (RCT) between 1 January 2016 and 31 December 2018.

The primary outcome was the existence of a data-sharing policy included in the instructions for authors. Data-sharing policies were grouped into 3 categories, inclusion of data-sharing policy mandatory, optional, or not available.

Details on research transparency promotion were also collected, namely the existence of a “prospective registration of clinical trials requirement policy”, a conflict of interests (COIs) disclosure requirement, and a specific reference to reporting guidelines, such as CONSORT for RCT.

Results

Among the 87 surgical journals identified, 82 were included in the study: 67 (82%) had explicit instructions for RCT and the remaining 15 (18%) had published at least one RCT. The median impact factor was 2.98 [IQR = 2.48–3.77], and in 2016 and 2017, the journals published a median of 11.5 RCT [IQR = 5–20.75].

The OIA of four journals (5%) stated that the inclusion of a data-sharing statement was mandatory, optional in 45% (n = 37), and not included in 50% (n = 41).

No association was found between journal characteristics and the existence of data-sharing policies (mandatory or optional). A “prospective registration of clinical trials requirement” was associated with International Committee of Medical Journal Editors (ICMJE) allusion or affiliation and higher impact factors.

Journals with specific RCT instructions in their OIA and journals referenced on the ICMJE website more frequently mandated the use of CONSORT guidelines.

Conclusion

Research transparency promotion is still limited in surgical journals. Standardisation of journal requirements according to ICMJE guidelines could be a first step forward for research transparency promotion in surgery.

URL : Research transparency promotion by surgical journals publishing randomised controlled trials: a survey

DOI : https://doi.org/10.1186/s13063-020-04756-7

Towards FAIR protocols and workflows: the OpenPREDICT use case

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Authors : Remzi Celebi, Joao Rebelo Moreira, Ahmed A. Hassan, Sandeep Ayyar, Lars Ridder, Tobias Kuhn, Michel Dumontier

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others.

The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data.

We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces.

We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN.

This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

URL : Towards FAIR protocols and workflows: the OpenPREDICT use case

DOI : https://doi.org/10.7717/peerj-cs.281

What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption

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Authors : Anneke Zuiderwijk, Rhythima Shinde, Wei Jeng

Both sharing and using open research data have the revolutionary potentials for forwarding scientific advancement. Although previous research gives insight into researchers’ drivers and inhibitors for sharing and using open research data, both these drivers and inhibitors have not yet been integrated via a thematic analysis and a theoretical argument is lacking.

This study’s purpose is to systematically review the literature on individual researchers’ drivers and inhibitors for sharing and using open research data. This study systematically analyzed 32 open data studies (published between 2004 and 2019 inclusively) and elicited drivers plus inhibitors for both open research data sharing and use in eleven categories total that are: ‘the researcher’s background’, ‘requirements and formal obligations’, ‘personal drivers and intrinsic motivations’, ‘facilitating conditions’, ‘trust’, ‘expected performance’, ‘social influence and affiliation’, ‘effort’, ‘the researcher’s experience and skills’, ‘legislation and regulation’, and ‘data characteristics.’

This study extensively discusses these categories, along with argues how such categories and factors are connected using a thematic analysis. Also, this study discusses several opportunities for altogether applying, extending, using, and testing theories in open research data studies.

With such discussions, an overview of identified categories and factors can be further applied to examine both researchers’ drivers and inhibitors in different research disciplines, such as those with low rates of data sharing and use versus disciplines with high rates of data sharing plus use. What’s more, this study serves as a first vital step towards developing effective incentives for both open data sharing and use behavior.

URL : What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption

DOI : https://doi.org/10.1371/journal.pone.0239283