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July 2023 - Irene Pasquetto - What is research data “misuse”? And how can it be prevented or mitigated? There are many good reasons to incentivize the sharing and reuse of research data, including reproducing research, making public assets available to the public, leveraging investments in research, and advancing collaboration and innovation in science. Often, the reuse of open data leads to the production of new, urgently needed knowledge. Thus, researchers and funding agencies alike are increasingly expected, and in some cases mandated, to release research data for reuse by others. However, fears of data “misuse” can impede the open sharing and reuse of research products, which in turn slows down scientific progress and lowers public trust in science and science institutions. The development and adoption of a shared framework for defining and preventing data misuse can help policymakers make important decisions about when and how to release crucial research data, evaluate the potential for public consumption of given research objects, and tailor documentation and representation of research data to non-expert communities. Yet, defining data “misuse” broadly remains challenging. Within this presentation, in order to work towards a better understanding of the complexity of data misuse, I will discuss how researchers in a number of fields currently understand and define the practice of misusing research data, including in pen science and data sharing practices, fairness in algorithms and computational data science, information and data literacy, science policy, science skepticism, and history of science. I will end with a discussion on how different domains describe and define data misuse and identify areas of overlap and disagreement. Finally, I propose a definition and a set of mitigation and prevention strategies. --- The Open Science Webinar series is a monthly gathering dedicated to building a broad community around open science. The group is organized by the Earth Science Information Partners (ESIP) Open Science Cluster. Learn more: wiki.esipfed.org/Open_Science_Cluster