To what extent is researchers' data-sharing motivated by formal mechanisms of recognition and credit?

Normative challenges in data governance: insights from global health research

Many important questions in health professions education require datasets that are built from several sources, in some cases using data collected for a different purpose. In building and maintaining these datasets, project leaders will need to make decisions about the data. While such decisions are often construed as technical, there are several normative concerns, such as who should have access, how the data will be used, how products resulting from the data will be shared, and how to ensure privacy of the individuals the data is about is respected, etc. Establishing a framework for data governance can help project leaders in avoiding problems, related to such matters, that could limit what can be learned from the data or that might put the project (or future projects) at risk. In this paper, we highlight several normative challenges to be addressed when determining a data governance framework. Drawing from lessons in global health, we illustrate three kinds of normative challenges for projects that rely on data from multiple sources or involved partnerships across institutions or jurisdictions: (1) legal and regulatory requirements, (2) consent, and (3) equitable sharing and fair distribution.

What are the key factors influencing scientific data sharing? A combined application of grounded theory and fuzzy-DEMATEL approach

Scientific data sharing (SDS) has become essential for scientific progress, technological innovation and socioeconomic development. Identifying the key influencing factors of SDS can effectively promote SDS programmes and give full play to the critical role of scientific data. This study used grounded theory and information ecology theory to construct an SDS influencing factor model that encompassed five dimensions and 28 influencing factors and followed the fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) approach to measure and analyse the degree of influence of each influencing factor and identify the key factors. The results show that (1) there are interactions and mutual interactions between the various influencing factors of SDS, which can form a complex network system. (2) 16 influencing factors, such as data-sharing policies, data-sharing regulations and data-sharing standards, comprise the key influencing factors in SDS. (3) The optimisation path of SDS is 'Scientific Researchers' → 'Scientific Data' → 'Policy Environment' → 'Research Organisations → 'Information Technologies'. In this regard, we proposed the following management suggestions to promote the development of SDS programmes in China: focusing on researchers' subjective willingness to share, enhancing the integrated governance of scientific data, fulfilling the role of policy support and guidance, strengthening the support of research organisations and improving SDS platforms with information technology.

Dissecting the tension of open science standards implementation in management and organization journals

Growing concerns about the credibility of scientific findings have sparked a debate on new transparency and openness standards in research. Management and organization studies scholars generally support the new standards, while emphasizing the unique challenges associated with their implementation in this paradigmatically diverse discipline. In this study, I analyze the costs to authors and journals associated with the implementation of new transparency and openness standards, and provide a progress report on the implementation level thus far. Drawing on an analysis of the submission guidelines of 60 empirical management journals, I find that the call for greater transparency was received, but resulted in implementations that were limited in scope and depth. Even standards that could have been easily adopted were left unimplemented, producing a paradoxical situation in which research designs that need transparency standards the most are not exposed to any, likely because the standards are irrelevant to other research designs.

Using preregistration as a tool for transparent fNIRS study design

Significance

The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design and analytical decisions that researchers have to make. Several recent efforts have developed guidelines for preprocessing, analyzing, and reporting practices. For the planning stage of fNIRS studies, similar guidance is desirable. Study preregistration helps researchers to transparently document study protocols before conducting the study, including materials, methods, and analyses, and thus, others to verify, understand, and reproduce a study. Preregistration can thus serve as a useful tool for transparent, careful, and comprehensive fNIRS study design.

Aim

We aim to create a guide on the design and analysis steps involved in fNIRS studies and to provide a preregistration template specified for fNIRS studies.

Approach

The presented preregistration guide has a strong focus on fNIRS specific requirements, and the associated template provides examples based on continuous-wave (CW) fNIRS studies conducted in humans. These can, however, be extended to other types of fNIRS studies.

Results

On a step-by-step basis, we walk the fNIRS user through key methodological and analysis-related aspects central to a comprehensive fNIRS study design. These include items specific to the design of CW, task-based fNIRS studies, but also sections that are of general importance, including an in-depth elaboration on sample size planning.

Conclusions

Our guide introduces these open science tools to the fNIRS community, providing researchers with an overview of key design aspects and specification recommendations for comprehensive study planning. As such it can be used as a template to preregister fNIRS studies or merely as a tool for transparent fNIRS study design.

Data sharing and reuse practices: disciplinary differences and improvements needed

Purpose

This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse.

Design/methodology/approach

The first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse.

Findings

From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance.

Originality/value

This study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423.

Open data and data sharing in articles about COVID-19 published in preprint servers medRxiv and bioRxiv

This study aimed to analyze the content of data availability statements (DAS) and the actual sharing of raw data in preprint articles about COVID-19. The study combined a bibliometric analysis and a cross-sectional survey. We analyzed preprint articles on COVID-19 published on medRxiv and bioRxiv from January 1, 2020 to March 30, 2020. We extracted data sharing statements, tried to locate raw data when authors indicated they were available, and surveyed authors. The authors were surveyed in 2020–2021. We surveyed authors whose articles did not include DAS, who indicated that data are available on request, or their manuscript reported that raw data are available in the manuscript, but raw data were not found. Raw data collected in this study are published on Open Science Framework (https://osf.io/6ztec/). We analyzed 897 preprint articles. There were 699 (78%) articles with Data/Code field present on the website of a preprint server. In 234 (26%) preprints, data/code sharing statement was reported within the manuscript. For 283 preprints that reported that data were accessible, we found raw data/code for 133 (47%) of those 283 preprints (15% of all analyzed preprint articles). Most commonly, authors indicated that data were available on GitHub or another clearly specified web location, on (reasonable) request, in the manuscript or its supplementary files. In conclusion, preprint servers should require authors to provide data sharing statements that will be included both on the website and in the manuscript. Education of researchers about the meaning of data sharing is needed.