At the Third Plenary for the Research Data Alliance, held from March 26 to 28 in Dublin, the Community Capability Model Interest Group presented its development and progress since the last plenary, described current challenges, and investigated the next steps to take. With the objective of assessing capability to do data-intensive research in a broad range of disciplines, the group will continue to engage communities of researchers to complete the Community Capability Model Framework profile. “Domain champions” from within RDA can give valuable support for this endeavour by helping to adapt the tool to their disciplines and by acting as a link to their communities.
The data-intensive research lifecycle comprises other stages besides data collection, data processing, or data documentation. In fact, activities such as developing a research concept, preparing a manuscript for publication or even searching and discovering research data are integral parts of the process. The Community Capability Model Framework (CCMF) is based on the assumption that all these factors need to be considered in data curation. And since all the processes in the data-intensive research lifecycle are influenced by the context of the communities that they are embedded in, the CCMF has a disciplinary focus. In a nutshell, it is a tool that allows for assessment of a community’s readiness or capability to do data-intensive research. This capability is dependent on a range of issues; human, technical and environmental that the CCMF substantiates in eight factors, as depicted in figure 1.
Figure 1 The CCMF (communitymodel.sharepoint.com)
In the current context, a community is a group of principal investigators from one discipline. By identifying areas for change and investment, the tool contributes to the assessment and enhancement of a community’s capabilities. Its general applicability makes the model an aid for decision making and planning for different stakeholders. Furthermore, in the light of the RDA’s mission of building bridges, it provides deep insights in the social, technical and organisational structures of research data management in each investigated discipline. These insights are intended to form a productive resource for all present and future Working and Interest Groups of the Alliance.
At the 3rd RDA Plenary the Community Capability Model Interest Group (CCM IG) presented their work done since the 2nd plenary and discussed further development. In the beginning the chairs highlighted areas of particular attention in the development and employment of the profile tool, in particular: legal, ethical and commercial issues; gaining informed consent for reuse and repurposing; appraisal and quality control; trustworthiness; scale and complexity of data; publication and sharing; citation attribution and accreditation in scholarly communications. All these issues are, of course, topics at the heart of many discussions within RDA as a whole. Consequently, by including all these areas in their work, the CCM IG members are concerned with a whole range of pressing issues that are of interest for RDA members. And indeed, it is the core purpose of the IG to do this work for the benefit of the whole Alliance.
Since its first Meeting at the 2nd RDA Plenary the CCM IG has improved the profile tool, held two workshops at international conferences and completed two case studies. The CCMF profile tool is now implemented as an MS Excel spread sheet, containing separate worksheets for each of the eight CCMF factors. The community-specific characteristics of these factors can be assessed with a scorecard tool, see figure 2.
Figure 2 CCMF profile tool, implemented as MS Excel spread sheet
Workshops held during ESIF in January 2014 and IDCC in February 2014 led to amendments that made the tool more (sub)discipline specific in terms of the language and examples used. The profile tool was then applied in two case studies, one with the Data Observation Network for Earth (DataONE) and a second one with Agronomists from Purdue University. These cases differ with respect to the definition of the term community, and therefore show how adaptable the CCMF and the profile tool are. In the case of DataONE, the profile was completed by the senior management team on behalf of the whole organisation. The community, in this case, was an organisation in the field of environment science, whereas, at Purdue University, where three individual agronomists completed the profile independently, the community was a group of principal investigators in a discipline. An issue that became apparent in both case studies concerns the widespread dissemination of completed profiles, which is not possible at the moment due to pending IRB approval at DataONE as well as Purdue University. Closely linked to this issue are concerns about anonymity that might prevent communities or researchers working with the profile. In order to solve these issues, the CCM IG decided to offer the possibility to publish the completed profiles anonymously.
Both cases, however, showed that the tool is employable in general, whereas that the scorecards gained significantly from discipline-oriented specification of terminology and examples. Furthermore, the results revealed that, when focusing on a community’s scorecard separately, actual areas of improvement in data-intensive research can indeed be identified. Not only the first interpretation and visualisation of the individual results, but also the actual process of completing and discussing the score cards led to interesting insights. Based on this experience the CCM IG decided to now pursue a strategy of recruiting “domain champions” to customise the CCMF profile tool for their own disciplines, in order to further improve it and thus facilitate completion by further communities. At the 3rd Plenary Meeting, several RDA members indicated their interest in participating in this process. With growing participation not only of domain champions, but of actual communities who are completing the CCMF profile, the next step of developing visualisations of the results will be undertaken.
The CCM IG invites all RDA members to join this process and, especially, to play a part as “domain champions” to help enrich the profile tool and liaise with communities for the profit of their own disciplines. Interested RDA members are invited to subscribe to the CCM IG mailing list, to contact the IG chairs, and to join the group at the next plenary meeting.
 More background information on the framework is available in a white paper: http://communitymodel.sharepoint.com/Documents/CCMDIRWhitePaper-24042012.pdf.
 The adaptions made by the agronomists at Purdue University are documented here: http://communitymodel.sharepoint.com/Documents/CCMF_Agronomy_Purdue-Brandt20140317.xlsx.