Advancing data sharing and reuse: Barriers, solutions, and recommendations from a data workshop

Iratxe Puebla

doi:https://doi.org/10.54900/jeet1-vab53

Open data has been a topic widely discussed among researchers and research-supporting organizations over the last decade. Much progress has been made in data sharing, and we now have more datasets openly available than ever before. At the same time, while in some circles the discussion about practices for data sharing and reuse may have seemed ubiquitous, the reality is that adoption still varies substantially, across research communities and across sectors.

This year’s Researcher to Reader conference provided an opportunity to take stock of progress made to date, and to explore what areas still need attention in the research data ecosystem. As part of the conference, we facilitated a workshop focused on the current barriers and potential solutions to advance research data sharing and reuse.

The workshop brought together representatives of publishers, librarians and research ecosystem vendors. Importantly, the workshop also provided dedicated time to hear insights from a couple of researchers on their perspective and practices with data sharing and reuse: Shozeb Haider (University College London) and Mauricio Contreras (The Sainsbury’s Lab). Both researchers viewed data sharing as integral to their research practice, and highlighted the crucial role of open data in increasing reproducibility and trust in research.

🚩 The data lifecycle

The workshop kicked off with a discussion around the data lifecycle and where research data can be shared and reused by different stakeholders (i.e researchers, funders, institutions, publishers, etc.) during the research process. Drawing on the perspectives shared by the researchers and the attendees’ broader experiences with research data, the discussion highlighted the importance of data management plans as a key item to support best practices in data sharing and policy compliance. Turning to data reuse, metadata and contextual information were noted as critical to enable replication and inclusion of others’ data in new research projects. The need for incentives also received attention, with calls for improvement to the connections between datasets and other outputs at all stages of the research process, in order to facilitate attribution and credit for the researchers who share their data.

The workshop then moved to a deeper exploration of current barriers for the adoption of data sharing and data reuse practices. The discussion brought about a diverse set of potential barriers, some related to items highlighted in the initial conversations, along with additional nuanced considerations related to peer or community expectations and legal or privacy restrictions. The different items raised clustered into three broader themes:

Lack of credit or incentives for sharing or reusing data, and fear of scooping (i.e., the risk of another group making use of shared data, and publishing results based on it before the producers of the data do so)
Lack of consistent community standards
Need for greater support for researchers, in the form of training, dedicated expertise in data management, and funds to support costs associated with data management and sharing

Figure 1. Word cloud highlighting barriers for data sharing and reuse identified by the workshop participants.

🛣️ The path ahead

Having an understanding of the existing challenges, we moved into a solutions-oriented discussion where we asked attendees to propose solutions to the main themes identified. As part of the exploration of the different proposed solutions, we categorized ideas according to the perceived potential impact and effort involved in driving that solution forward.

The group identified a couple of areas as having potential impact but requiring lower effort, likely reflecting their level of development in the open data ecosystem. The first area related to the infrastructure for data sharing and reuse, as well as that required for collecting data metrics and relational metadata between datasets and other outputs. Infrastructure for research data has developed substantially in recent years, with a range of repositories available to host, curate, preserve (and where necessary manage access) for research datasets. Tools and services are also available to collect information on data usage, e.g. DataCite provides services to connect datasets to other outputs and aggregate data usage metrics, and the Make Data Count initiative has developed guidance to consistently report measures of data usage. The second topic with similar categorization of the required effort focused on support for researchers, in the form of training and dedicated roles that support data management and stewardship. This is also an area that has advanced over the last decade, with several institutions (e.g. TU Delft) implementing data steward programs and data-management support for researchers.

On the other hand, a number of solutions raised during the discussion were perceived as bringing high impact but also requiring substantial effort. These involved: 1) updates to research assessment frameworks to recognize open data practices and create incentives for data sharing and data reuse, and 2) greater consistency in standards and community expectations for data sharing, while accounting for the specific needs of individual communities. Overall, the group saw value in exploring additional ways to support the community in their diverse journeys toward adoption of open data practices.

Drawing on the discussions at the workshop, we developed recommendations for how different actors in research data can drive forward data sharing and reuse, summarized in the table below. We welcome feedback on these recommendations, as well as additional examples of groups, institutions or initiatives who are already driving these areas forward.

The recommendations are multiple and varied, and we recognize that some communities may have already completed or may be actively working on some areas, while others will be at earlier stages of implementation. It is not too late to join the journey. We invite you to consider the recommendations, identify one key area that you can work on, and champion its adoption within your organization and your community. Every step counts!

You can read the full white paper summarizing the discussions at
the workshop at https://doi.org/10.5281/zenodo.13306964

Recommended actions	Examples
FUNDERS
Require data management plans and provide information on what is expected in these Encourage researchers to deposit datasets at repositories that assign persistent identifiers (PIDs) Encourage researchers to cite datasets they have used – their own and others’ – in their research outputs Ask researches to report the datasets they shared and their reuse in grant applications and reports Ensure that data contributions are considered as part of the grant application review, by having a dedicated review of data outputs, including data expertise in review panels	ASAP’s Open Access Policy NIH Data Management and Sharing Policy Horizon 2020 Data Management Guidance Supporting the Implementation of the 2023 NIH Data Management and Sharing Policy: What is Still Needed
INSTITUTIONS
Have designated data stewards, who can provide training and support for researchers across different groups and departments Support initiatives to incorporate open science practices, including open data, in research assessment Ask researchers to report the datasets they shared and their reuse in job applications, and in tenure and promotion packets Ensure a dedicated review of data contributions as part of tenure and promotion, including clear guidelines and dedicated reviewers with expertise in data Participate in the development of metrics which support assessment of the value and impact of datasets	TU Delft Data Stewardship programme OpenAire RDM Task Force EMBL guidelines for research assessment (which mention data) Puebla et al., Ten simple rules for recognizing data and software contributions in hiring, promotion, and tenure Coalitions for reforming research assessment: CoARA, HELIOS Open
REPOSITORIES
Implement open metadata and PIDs to enable interoperability with systems and platforms used by other stakeholders (e.g., those used for research evaluation) Support contextual information to accompany the data to promote interpretability and reuse (e.g., a README file) Foster adherence to a universal set of principles for data stewardship (e.g., FAIR) Implement processes that support the long-term preservation of datasets Engage in dialogue with specific disciplinary and regional communities (e.g., via scholarly societies) Implement processes to enable visibility and assessment of the usage of published datasets (e.g., data citations and usage metrics) Collaborate with other data repositories to streamline the data sharing and reuse experience for researchers	FAIR principles Dryad’s best practices for creating reusable data publications NIH Generalist Repository Ecosystem Initiative Dataverse documentation to enable the set up of the Make Data Count metrics in data repositories Make Data Count recommendations, including the COUNTER Code of Practice for Research Data for normalized reports of data usage
PUBLISHERS
Collaborate with other stakeholders and contribute to developing standard workflows and guidelines for authors Develop and implement journal data policies, in consultation with individual editorial boards (to ensure each policy is domain-appropriate), then regularly review and update journal instructions for authors Provide clear guidance on Data Availability Statements, with examples authors can easily utilize, and ensure these statements are machine-readable Articulate guidelines for data citation for authors, reviewers and editors Work with vendors to ensure metadata for data citations is preserved throughout the production process and deposited with Crossref	Cousijn et al., A data citation roadmap for scientific publishers Updates to eLife’s data sharing policies Springer Nature’s Data Availability Statements guidance Muench A., The Roles of Data Editors in Astronomy STM, DataCite, and Crossref Joint Statement on Research Data: Best Practices in Research Data Sharing
SCHOLARLY SOCIETIES (may also be publishers, in which case see above)
Identify and/or develop domain specific, practicable standards for data collection and management Consider instigating data reuse and data rescue programmes or prizes to showcase what value data can provide to the field Work with community leaders who have an interest in data sharing and reuse to demonstrate high-status behavior Encourage data sharing/reuse streams at key conferences, and articles and special issues in key domain publications Promote data sharing and reuse educational sessions and resources	FASEB DataWorks! (provides data prizes, educational webinars and helpdesk support) Research Parasites awards for rigorous secondary data analysis AGU Data Leadership programme Special Issue: Secondary analysis of large quantitative datasets at the Journal of Intellectual Disability Research