Incentivising research data sharing: a scoping review

Abstract

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.

Identifying and addressing data asymmetries so as to enable (better) science

“…There exists an increasing recognition that for data stewardship and sharing to create public and scientific value to achieve success, “it must be a truly multi-professional endeavor,” equipped with a human infrastructure that possesses the mandate and capacity to “create spaces for different types of data to be curated, shared, discoverable, and reusable in an ethical and timely way (Woods and Pinfield, 2021 ).”…”

“…Establishing this human infrastructure lies “at the very basis of the needed revolution in scientific methods” as the move toward more open and effective science in the data era faces “many more socio-cultural hurdles… than technical ones (Mons et al, 2020 ).” A landscape analysis of open science studies performed by Woods and Pinfield ( 2022 ) found that “disciplinary data champions [need] to model good practice and drive cultural change (Woods and Pinfield, 2021 ).”…”

“…In addition to these questions of relationships and connections, researchers seeking to use private sector data may face legal obstacles. Data-sharing agreements, licenses and other legal frameworks are often necessary for unlocking access to private sector data for research, potentially creating barriers to entry for some (Woods and Pinfield, 2021 ).…”

“…To help address reproducibility and reuse challenges, new incentive and performance structures will be needed to open up data, including “data level metrics to credit authors for each reuse, such as downloading, data citations and so on (Woods and Pinfield, 2021 ).” Such a demonstration of uptake and influence is essential for researchers, but “naming and faming” in this way can also act as a key, and often missing, incentive for private sector engagement in scientific initiatives.…”

Enabling Co-Innovation for A Successful Digital Transformation in Wind Energy Using the WeDoWind Ecosystem and A Fault Detection Case Study

“…For example, a data scientist might be a strong supporter of sharing data and knowledge in order to learn from others, but may work for a company who prevents them from doing so due to legal, structural or policy reasons. Some recent initiatives to incentivise data sharing include data marketplaces such as the Greenbyte marketplace for wind data 7 and the IntelStor Market Intelligence Ecosystem 8 , data discovery and sharing platforms such as the Sharewind metadata catalogue 9 and the US DOE Data Archive & Portal 10 , comparison and benchmarking activities such as IEA Wind Task 31 11 , IEA Wind Task 30 (OC6) WP3 Benchmark 12 and CREYAP: Comparison of Resource and Energy Yield Assessment Procedures [11], and challenge-based platforms such as Kaggle 13 , Knowledge Pit [12] and the EDP Open Data Platform 14 . To the authors' knowledge, there is no scientific literature that compares or evaluates these different initiatives.…”