Fostering scientists’ data sharing behaviors via data repositories, journal supplements, and personal communication methods

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.

Sharing social media data: The role of past experiences, attitudes, norms, and perceived behavioral control

Social media data (SMD) have become an important data source in the social sciences. The purpose of this paper is to investigate the experiences and practices of researchers working with SMD in their research and gain insights into researchers' sharing behavior and influencing factors for their decisions. To achieve these aims, we conducted a survey study among researchers working with SMD. The questionnaire covered different topics related to accessing, (re)using, and sharing SMD. To examine attitudes toward data sharing, perceived subjective norms, and perceived behavioral control, we used questions based on the Theory of Planned Behavior (TPB). We employed a combination of qualitative and quantitative analyses. The results of the qualitative analysis show that the main reasons for not sharing SMD were that sharing was not considered or needed, as well as legal and ethical challenges. The quantitative analyses reveal that there are differences in the relative importance of past sharing and reuse experiences, experienced challenges, attitudes, subjective norms, and perceived behavioral control as predictors of future SMD sharing intentions, depending on the way the data should be shared (publicly, with restricted access, or upon personal request). Importantly, the TPB variables have predictive power for all types of SMD sharing.

How often do cancer researchers make their data and code available and what factors are associated with sharing?

BACKGROUND

Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing.

METHODS

A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021.

RESULTS

One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15-24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12-20%), and then to less than 1% (1/306, 95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research.

CONCLUSIONS

Data and code sharing in oncology occurs infrequently, and at a lower rate than would be expected given the prevalence of mandatory sharing policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community-accepted guidelines when archiving the products of their research.

Data sharing practices across knowledge domains: A dynamic examination of data availability statements in PLOS ONE publications

As the importance of research data gradually grows in sciences, data sharing has come to be encouraged and even mandated by journals and funders in recent years. Following this trend, the data availability statement has been increasingly embraced by academic communities as a means of sharing research data as part of research articles. This article presents a quantitative study of which mechanisms and repositories are used to share research data in PLOS ONE articles. We offer a dynamic examination of this topic from the disciplinary and temporal perspectives based on all statements in English-language research articles published between 2014 and 2020 in the journal. We find a slow yet steady growth in the use of data repositories to share data over time, as opposed to sharing data in the article and/or supplementary materials; this indicates improved compliance with the journal’s data sharing policies. We also find that multidisciplinary data repositories have been increasingly used over time, whereas some disciplinary repositories show a decreasing trend. Our findings can help academic publishers and funders to improve their data sharing policies and serve as an important baseline dataset for future studies on data sharing activities.

Reputation, trust, and norms as mechanisms forming academic reciprocity in data sharing: an empirical test of theory of collective action

Purpose

This research investigated how biological scientists' perceived academic reputation, community trust, and norms all influence their perceived academic reciprocity, which eventually leads to their data sharing intentions.

Design/methodology/approach

A research model was developed based on the theory of collective action, and the research model was empirically evaluated by using the Structural Equation Modeling method based on a total of 649 survey responses.

Findings

The results suggest that perceived academic reputation significantly increases perceived community trust, norm of data sharing, and academic reciprocity. Also, both perceived community trust and norm of data sharing significantly increases biological scientists' perceived academic reciprocity, which significantly affect their data sharing intentions. In addition, both perceived community trust and norm of data sharing significantly affect the relationship between perceived academic reciprocity and data sharing intention.

Research limitations/implications

This research shows that the theory of collective action provides a new theoretical lens for understanding scientists' data sharing behaviors based on the mechanisms of reputation, trust, norm, and reciprocity within a research community.

Practical implications

This research offers several practical implications for facilitating scientists' data sharing behaviors within a research community by increasing scientists' perceived academic reciprocity through the mechanisms of reputation, trust, and norm of data sharing.

Originality/value

The collective action perspective in data sharing has been newly proposed in this research; the research sheds light on how scientists' perceived academic reciprocity and data sharing intention can be encouraged by building trust, reputation, and norm in a research community.

Using current research information systems to investigate data acquisition and data sharing practices of computer scientists

Without sufficient information about research data practices occurring in a particular research organisation, there is a risk of mismatching research data service efforts with the needs of its researchers. This study describes how data acquiring and data sharing occurring within a particular research organisation can be investigated by using current research information system publication data. The case study organisation’s current research information system was used to identify the sample of investigated articles. A sample of 193 journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a classification of the main study types was developed to accommodate the multidisciplinary nature of the case department’s research agenda. Furthermore, a coding framework was developed to capture the key elements of data acquiring and data sharing. The articles representing life sciences and computational research relatively frequently reused open data, whereas data acquisition of experimental research, human interaction studies and human intervention studies often relied on collecting original data. Data sharing also differed between the computationally intensive study types of life sciences and computational research and the study types relying on collection of original data. Research data were not available for reuse in only a minority of life science ( n = 2; 7%) and computational research ( n = 15; 14%) studies. The study types of experimental research, human interaction studies and human intervention studies less frequently made their data available for reuse. The findings suggest that research organisations representing computer sciences may include different subfields that have their own cultures of data sharing. This study demonstrates that analyses of publications listed in current research information systems provide detailed descriptions how the affiliated researchers acquire and share research data.

Characterizing the research data management practices of NIH biomedical researchers indicates the need for better support at laboratory level

BACKGROUND

The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines.

OBJECTIVES

This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services.

METHODS

Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices.

RESULTS

The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM.

DISCUSSION

Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation.

CONCLUSION

Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.

Incentivising research data sharing: a scoping review

Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research.

Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles.

Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives.

Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories.

Incentivising research data sharing: a scoping review

Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories.

An empirical study of research ethics and their role in psychologists’ data sharing intentions using consequentialism theory of ethics

The purpose of this study is to examine how different ethical dimensions of egoism, utilitarianism, and deontology all help in the formation of psychologists’ research ethics for data sharing, and how the research ethics eventually affect psychologists making decisions regarding whether to engage in data sharing. This research utilized consequentialism theory of ethics as its theoretical framework to develop its research model of psychologists’ data sharing as mediated by research ethics. It conducted an online survey with psychologists in US academic institutions and collected a total of 362 valid responses. Then, it employed the structural equation modeling technique to evaluate the research model and related hypotheses of psychologists’ data sharing intentions as mediated by the profession’s research ethics. This research found that perceived career benefit, perceived community benefit, and norm of data sharing all significantly contribute to the formation of psychologists’ research ethics for data sharing, and then these research ethics, along with perceived community benefit and norm of data sharing, significantly influence psychologists’ data sharing intentions. This study suggests that the consequentialism theory of ethics nicely explains psychologists’ formation of their research ethics for data sharing and their decision to engage in data sharing. The study also suggests that research communities can better promote researchers’ data sharing behaviors by stimulating their research ethics through different ethical dimensions, including egoism (career benefit), utilitarianism (community benefit), and deontology (norm of data sharing).

Improvements for research data repositories: The case of text spam

Current research has evolved in such a way scientists must not only adequately describe the algorithms they introduce and the results of their application, but also ensure the possibility of reproducing the results and comparing them with those obtained through other approximations. In this context, public data sets (sometimes shared through repositories) are one of the most important elements for the development of experimental protocols and test benches. This study has analysed a significant number of CS/ML ( Computer Science/ Machine Learning) research data repositories and data sets and detected some limitations that hamper their utility. Particularly, we identify and discuss the following demanding functionalities for repositories: (1) building customised data sets for specific research tasks, (2) facilitating the comparison of different techniques using dissimilar pre-processing methods, (3) ensuring the availability of software applications to reproduce the pre-processing steps without using the repository functionalities and (4) providing protection mechanisms for licencing issues and user rights. To show the introduced functionality, we created STRep (Spam Text Repository) web application which implements our recommendations adapted to the field of spam text repositories. In addition, we launched an instance of STRep in the URL https://rdata.4spam.group to facilitate understanding of this study.

PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews

BACKGROUND

Literature searches underlie the foundations of systematic reviews and related review types. Yet, the literature searching component of systematic reviews and related review types is often poorly reported. Guidance for literature search reporting has been diverse, and, in many cases, does not offer enough detail to authors who need more specific information about reporting search methods and information sources in a clear, reproducible way. This document presents the PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) checklist, and explanation and elaboration.

METHODS

The checklist was developed using a 3-stage Delphi survey process, followed by a consensus conference and public review process.

RESULTS

The final checklist includes 16 reporting items, each of which is detailed with exemplar reporting and rationale.

CONCLUSIONS

The intent of PRISMA-S is to complement the PRISMA Statement and its extensions by providing a checklist that could be used by interdisciplinary authors, editors, and peer reviewers to verify that each component of a search is completely reported and therefore reproducible.

Expanding the actions of Open Government in higher education sector: From web transparency to Open Science

Universities have been pressured by governments to change their way of acting and to be more responsible with the requirements of social development to face the challenges of globalization. To this end, universities must use the principles of Open Science, to allow them to be more transparent regarding the dissemination of scientific results. The purpose of this paper is firstly, to determine the progress made in Open Access policies made by the best-ranked universities regarding ARWU. Secondly, to examine influencing factors that enhance the level of openness in researching, in particular, "transparency", "reputation", "participation", "funding", "foundation" and "size". The main results show that those private and older universities, best-ranked in terms of excellence researching and those that have been gradually adopting Open Government policies concerning the dissemination of information through institutional web pages and social participation, are the most interested with complying the recommendations established by the authorities of the Open Science projects.

Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide

BACKGROUND

Shifts in data sharing policy have increased researchers' access to individual participant data (IPD) from clinical studies. Simultaneously the number of IPD meta-analyses (IPDMAs) is increasing. However, rates of data retrieval have not improved. Our goal was to describe the challenges of retrieving IPD for an IPDMA and provide practical guidance on obtaining and managing datasets based on a review of the literature and practical examples and observations.

METHODS

We systematically searched MEDLINE, Embase, and the Cochrane Library, until January 2019, to identify publications focused on strategies to obtain IPD. In addition, we searched pharmaceutical websites and contacted industry organizations for supplemental information pertaining to recent advances in industry policy and practice. Finally, we documented setbacks and solutions encountered while completing a comprehensive IPDMA and drew on previous experiences related to seeking and using IPD.

RESULTS

Our scoping review identified 16 articles directly relevant for the conduct of IPDMAs. We present short descriptions of these articles alongside overviews of IPD sharing policies and procedures of pharmaceutical companies which display certification of Principles for Responsible Clinical Trial Data Sharing via Pharmaceutical Research and Manufacturers of America or European Federation of Pharmaceutical Industries and Associations websites. Advances in data sharing policy and practice affected the way in which data is requested, obtained, stored and analyzed. For our IPDMA it took 6.5 years to collect and analyze relevant IPD and navigate additional administrative barriers. Delays in obtaining data were largely due to challenges in communication with study sponsors, frequent changes in data sharing policies of study sponsors, and the requirement for a diverse skillset related to research, administrative, statistical and legal issues.

CONCLUSIONS

Knowledge of current data sharing practices and platforms as well as anticipation of necessary tasks and potential obstacles may reduce time and resources required for obtaining and managing data for an IPDMA. Sufficient project funding and timeline flexibility are pre-requisites for successful collection and analysis of IPD. IPDMA researchers must acknowledge the additional and unexpected responsibility they are placing on corresponding study authors or data sharing administrators and should offer assistance in readying data for sharing.

Investigation of challenges in academic institutional repositories

Purpose

The purpose of this paper is to explore the breadth of the challenges and issues facing institutional repositories in academic libraries, based on a survey of academic librarians. Particularly, this study covers the challenges and barriers related to data management facing institutional repositories.

Design/methodology/approach

The study uses a survey method to identify the relative significance of major challenges facing institutional repositories across six dimensions, including: data, metadata, technological requirements, user needs, ethical concerns and administrative challenges.

Findings

The results of the survey reveal that academic librarians identify limited resources, including insufficient budget and staff, as the major factor preventing the development and/or deployment of services in institutional repositories. The study also highlights crucial challenges in different dimensions of institutional repositories, including the sheer amount of data, institutional support for metadata creation and the sensitivity of data.

Originality/value

This study is one of a few studies that comprehensively identified the variety of challenges that institutional repositories face in operating academic libraries with a focus on data management in institutional repositories. In this study, 37 types of challenges were identified in six dimensions of institutional repositories. More importantly, the significance of those challenges was assessed from the perspective of academic librarians involved in institutional repository services.

The formation of research ethics for data sharing by biological scientists: an empirical analysis

Purpose

The purpose of this paper is to investigate how biological scientists form research ethics for data sharing, and what the major factors affecting biological scientists’ formation of research ethics for data sharing are.

Design/methodology/approach

A research model for data sharing was developed based on the consequential theorists’ perspective of ethics. An online survey of 577 participants was administered, and the proposed research model was validated with a structural equation modeling technique.

Findings

The results show that egoism factors (perceived reputation, perceived risk, perceived effort), utilitarianism factors (perceived community benefit and perceived reciprocity) and norm of practice factors (perceived pressure by funding agency, perceived pressure by journal and norm of data sharing) all contribute to the formation of research ethics for data sharing.

Research limitations/implications

This research employed the consequentialist perspective of ethics for its research model development, and the proposed research model nicely explained how egoism, utilitarianism and norm of practice factors influence biological scientists’ research ethics for data sharing, which eventually leads to their data sharing intentions.

Practical implications

This research provides important practical implications for examining scientists’ data sharing behaviors from the perspective of research ethics. This research suggests that scientists’ data sharing behaviors can be better facilitated by emphasizing their egoism, utilitarianism and normative factors involved in research ethics for data sharing.

Originality/value

The ethical perspectives in data sharing research has been under-studied; this research sheds light on biological scientists’ formation of research ethics for data sharing, which can be applied in promoting scientists’ data sharing behaviors across different disciplines.