CURE-FAIR working group

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27 Nov 2019

CURE-FAIR working group

Group leading the application: 
Meeting agenda: 

You can join the groups to stay involved with their work if you are an RDA member:

Log in to the RDA Web site with your RDA userid/password, go to the group you wish to join (CURE-FAIR WG and/or Reproducible Health Data Services WG)  and press the "Join group" button on the right (near the top of the page).

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  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
Short Group Status: 

We held a BoF at the 14th RDA Plenary in Helsinki (see collaborative notes: ttps:// ) and are currently taking steps to build a member list. A small group including the co-chairs is currently drafting a working group case statement and is engaged in conversation with aligned groups at RDA. This working group will be a focal point within RDA for working on guidelines and standards for curating for reproducible and FAIR data and code and engaging the RDA community on the issue.

Brief introduction describing the activities and scope of the group(s): 

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.

CURE-FAIR stands for Curating for reproducible and FAIR data and code.

Type of Meeting: 
Working meeting
Avoid conflict with the following group (1): 
Avoid conflict with the following group (2):