Leveraging Data Communities to Advance Open Science - Ithaka S+R

"Several recent studies have indicated that large numbers of researchers in many STEM fields now accept the value of openly sharing research data. Yet, the actual practice of sharing data—especially in forms that comply with FAIR principles—remains a challenge for many researchers to integrate into their workflows and prioritize among the demands on their time.[1] In many disciplines and subfields, data sharing is still mostly an ideal, honored more in the breach than in practice.[2] The barriers to open data sharing are numerous.[3] However, sustained funding from federal agencies in the United States including the NSF and NIH and important initiatives in other countries such as Canada’s Tri-Agency Research Data Management Policy and the European Union’s OpenAire, is creating a growing infrastructure for open sharing of research data, albeit one that highlights the tension between scientific research practices that are now regularly multi-national in scope yet exist within funding and regulatory structures determined largely by national entities.[4] In the US context, the most visible fruits of these efforts are the decentralized network of repositories that have become available to researchers in many fields and are now a vital infrastructure for data sharing across many fields. As incentive structures have slowly shifted, the number of researchers taking advantage of these resources has also grown. The existence of these repositories are necessary enabling conditions for data sharing, but their ability to transform researcher’s practices around data depositing and sharing absent changes to incentive structures and the culture of research communities will remain uneven. Furthering the goals of open science requires convincing more researchers of the value of data sharing to themselves and to the community of researchers with whom they most tangibly identify. Creating and encouraging community norms that reward sharing is necessary because data sharing, especially FAIR (findable, accessible, interoperable, and reusable) compliant sharing, is hard work. Absent strong incentive and reward structures, researchers are often reluctant to take on this “extra” labor. Successful data sharing ultimately depends on cultural and social infrastructures as much as on technical infrastructures...."