Using DAF as a Data Scoping Tool, by Sarah Jones


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This presentation describes the Data Asset Framework (DAF) as a tool for scoping data content for institutional repositories. It was given as part of module 1 of a 5-module course on digital preservation tools for repository managers, presented by the JISC KeepIt project. For more on this and other presentations in this course look for the tag 'KeepIt course' in the project blog

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  • - I’ll start off with some background context / an overview to DAF - Harry will then explain how it’s been used at Southampton - then we’ll do a group exercise.
  • DAF established in response to a recommendation in the Dealing with Data report. This recognised a lack of awareness as to what data were held within HE institutions and how they were being managed. How can unis make the most of their research data when it is unclear: what there is; where these data are; how they’re being managed; options for reuse etc DAF tries to help users find these things out. Can be a useful tool for repositories to identify data for ingest, or to see what the requirements for support are from researchers left curating data without the necessary resources / skills.
  • 5 projects funded by JISC over a 6 month period in 2008 Development project to come up with the methodology and develop an online tool Implementation projects to test this out and investigate the research data challenge
  • The methodology has four incremental stages, one for planning, one for wrap up and two main audit stages. Stages 2 & 3 pick up directly on the two aspects in the original recommendation i.e. what data exist (inventory stage) and what’s happening to them (assessment stage). Planning: define scope / expected outcomes of the survey; conduct preliminary research; set up interviews / questionnaires. Identifying data: collect basic information (name, description, creator, location); broad mapping to get feel for the extent of data holdings; classification helps refine scope of next stage. Assessing data: look into a few datasets / collections in more depth to identify weaknesses in data management and risks; consider the whole lifecycle. Reporting: collate and analyse information collected; make recommendations on how to improve data management. Information was typically collected by a mix of questionnaires and interviews.
  • Themes covered all activities in the data lifecycle. Some found this model useful as a way to guide discussion Across all themes there was a tendency to unpick issues and concerns
  • Pilots were in a mixture of disciplines and sizes of organisation (research group, departments, schools etc). Focus of implementations differed slightly too. Some were more repository based e.g. Imperial College more concerned with capacity planning so asked questions about data size, growth rates, planned retention, formats… DataShare examples were undertaken to identify suitable data for ingest in light of a lack of voluntary deposits
  • - Lots of data – often complex: survey data and 3D visualisations, CAD drawings. - Didn’t come across any many policies – very ad hoc. - People didn’t know what to do – wanted support – but also unaware of where they could turn e.g. to repository. - Often nowhere for data to go – didn’t always have data centres in their subject area, or the ability to deposit their data in the institutional repositories. Researchers wanted to keep and reuse data but didn’t have time or skills to do it themselves – need for data curation infrastructure. Role for IRs here.
  • We had a workshop in 2009 to collate lessons from pilots and decide next steps for DAF. These were the three main recommendations made. Most institutions were still in the early stages of developing infrastructure so the approach was more useful for gathering requirements than identifying data to manage. DAF has been suggested as a tool for new JISC data management infrastructure projects to use for scoping requirements. The exercise today will focus on this usage too – scoping data & gathering requirements for the repository’s role in data management 2. Lessons / approaches from the pilots have been brought together to help others – see the implementation guide. 3. Some new work has been funded (JISC IDMP project) to see how DAF and other tools can be brought together to help institutions develop their data management strategy.
  • Using DAF as a Data Scoping Tool, by Sarah Jones

    1. 1. Using DAF as a data scoping tool for institutional repositories Sarah Jones DCC, University of Glasgow [email_address]
    2. 2. Background to DAF project <ul><li>“ JISC should develop a Data Audit Framework to enable all universities and colleges to carry out an audit of departmental data collections, awareness, policies and practice for data curation and preservation” </li></ul><ul><li>Liz Lyon, Dealing with Data: Roles, Rights, </li></ul><ul><li>Responsibilities and Relationships, (2007) </li></ul>
    3. 3. Scope of work <ul><li>DAF Development (DAFD) Project </li></ul><ul><li>(University of Glasgow; King’s College London; University of Edinburgh; UKOLN, University of Bath) </li></ul><ul><li>Four pilot implementation projects </li></ul><ul><ul><li>University of Edinburgh </li></ul></ul><ul><ul><li>King’s College London </li></ul></ul><ul><ul><li>Imperial College London </li></ul></ul><ul><ul><li>University College London </li></ul></ul>
    4. 4. The methodology
    5. 5. Themes addressed in DAF surveys <ul><li>Data : type / format, volume, description, creator, funder </li></ul><ul><li>Creation : policy, naming, versioning, metadata & documentation </li></ul><ul><li>Management : storage, backup, roles and responsibilities, planning </li></ul><ul><li>Access: restrictions, rights, security, frequency, ease of retrieval, publish </li></ul><ul><li>Sharing: collaborators, requirements to share, methods, concerns </li></ul><ul><li>Preservation : selection / retention, repository services, obsolescence </li></ul><ul><li>Gaps / needs : services, advice, support, infrastructure </li></ul>
    6. 6. Subject areas of DAF pilots <ul><li>DAFD test cases : GeoSciences; Archaeology; Mechanical Engineering; Humanities </li></ul><ul><li>University of Edinburgh </li></ul><ul><li>Physiology; Divinity; History; Brain Imaging; Astronomy </li></ul><ul><li>University College London </li></ul><ul><li>Archaeology; Scandinavian Studies; Physics & Astronomy; Life & Medical Sciences </li></ul><ul><li>Imperial College London </li></ul><ul><li>Chemical Engineering; Physics; Business School </li></ul><ul><li>King’s College London </li></ul><ul><li>Geography; Psychiatry; Environmental Research; Biomedical And Health Sciences </li></ul><ul><li>DataShare examples </li></ul><ul><li>Cardiac group; Dept of International Development; Social Sciences </li></ul>
    7. 7. Generalised findings <ul><li>Lots of data were created </li></ul><ul><li>Few policies for data creation, storage and management </li></ul><ul><li>Researchers unsure where to begin and were often unaware of available support </li></ul><ul><li>Often no place of deposit or funds for preservation </li></ul><ul><li>Pilot implementation findings </li></ul><ul><li>IJDC paper </li></ul>
    8. 8. Workshop on next steps for DAF <ul><li>Many of the pilots found the actual process of gathering information on data management was more valuable than the asset register. The DAF approach was felt to be useful for defining requirements to improve data management. (JISC funded DMI projects) </li></ul><ul><li>A suggestion was made to enhance DAF with practical examples / guidance from the pilot studies. (Implementation Guide) </li></ul><ul><li>Align the DAF process with other data management planning tools. (IDMP project between AIDA, DAF, DRAMBORA, LIFE) </li></ul>