## **RDA 15th Plenary Meeting** #### **Melbourne, Australia (Virtual Sessions)| 1 April 2020, 2 April 2020** --- #### **Joint session: CURE-FAIR WG + Reproducible Health Data Services WG** ##### **Anthony Juehne, RDA-US** ##### **Florio Arguillas, Cornell Institute for Social and Economic Research (CISER), Cornell University** ##### **Thu-Mai Christian, Odum Institute, University of North Carolina at Chapel Hill** ##### **Limor Peer, Institution for Social And Policy Studies (ISPS), Yale University** --- **CURE-FAIR stands for CUrating for REproducible and FAIR data and code.** Scientific reproducibility provides a common purpose and language for data professionals and researchers. For data professionals, reproducibility can be a framework to hone and justify curation actions and decisions, and for researchers it offers a rationale for inserting best practices early into the research lifecycle. Curating for reproducibility (CURE) includes activities that ensure that statistical and analytic claims about given data can be reproduced with that data. Academic libraries and data archives have been stepping up to provide systems and standards for making research materials publicly accessible, but the datasets housed in repositories rarely meet the quality standards required by the scientific community. Even as data sharing becomes normative practice in the research community, there is growing awareness that access to data alone – even well-curated data – is not sufficient to guarantee the reproducibility of published research findings. Computational reproducibility, the ability to recreate computational results from the data and code used by the original researcher, is a key requirement to enable researchers to reap the benefits of data sharing, but one that recent reports suggest is not being met. Data curation workflows that enable data access often fall short when research reproducibility is the ultimate goal. Code review and result verification are required in order to confirm the integrity of the scientific record, to build upon previous work to discover, and to develop innovations. Several initiatives confirm that the scientific community is embracing these ideas. For example, the CURE Consortium has been implementing practices and developing workflows and tools that support curating for reproducibility in the social sciences. ##### **MEETING AGENDA** 1. Brief introduction of previous work, and discussion of the draft CURE-FAIR WG case statement 2. A group discussion of WG management and next steps within RDA 3. Group activity on CURE-FAIR practices, gaps, and opportunities for collaboration and integration ##### **MEETING OBJECTIVES** 1. Inform attendees of the WG prior work and activities and elicit input on plans within RDA 2. Identify existing relevant guidelines, policies, practices, and workflows, with a focus on perspectives from a variety of disciplines 3. Identify potential use cases to enrich the WG output 4. Continue to build WG engagement and membership **[Session Slides][1]** **[Session Notes][2]** **[Draft CURE-FAIR WG Case Statement][3]** [1]: https://osf.io/s3eqm/ [2]: https://bit.ly/39sQwFa [3]: https://bit.ly/2wM5Uzo