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Data management training: making the most of limited resources

Learn how to effectively manage research data with limited resources to enhance credibility, secure funding, and facilitate collaborations.

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Data management training: making the most of limited resources

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  1. Data management training: making the most of limited resources Joy Davidson DCC, University of Glasgow j.davidson@hatii.arts.gla.ac.uk

  2. The challenge Well managed, high quality research data is valuable and your institution’s research credibility and reputation depend on it (e.g., climate data crisis). Evidence of sound data management planning is increasingly becoming a requirement by Research Councils and funding bodies Responsibility for data management and longer-term curation now shifting more to HEIs and less to places of deposit (e.g., AHDS)

  3. The potential benefits Scholarly communication/access to data Re-purposing and re-use of data Stimulating new networks/collaborations and new research Knowledge transfer to industry Verification of research/research integrity Fulfilling mandate(s), fulfil grant obligations Re-purposing data for new audiences Secure storage for data intensive research Availability of data underpinning journal articles Increased visibility/citation Keeping Research Data Safe Factsheet http://www.beagrie.com/KRDS_Factsheet_0910.pdf

  4. Recent drivers for data management & data sharing declaration data are a public good and should be openly available Code of good research conduct data should be preserved and accessible for 10 years + Your HEI’s Code of Conduct E Funders’ data policies…

  5. Funders’ data policies http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies

  6. Research Income • Familiarise yourself with what research councils and funding bodies require in terms of data management (DCC policy table offers an at-a-glance reference) • Make sure that senior managers in your institution are made aware of these requirements and the fact that grant income may be lost or withheld if data management requirements are not met • Work with your Research and Enterprise department to make sure that they are aware of these requirements and are able to provide initial advice (triage) during the grant application stage and point to further local sources of support • Make use of TRAC: Investigate Resource Allocation Models used by your institution to find out how indirect costs are re-distrbuted

  7. Institutional expectations Review existing institutional aims and polices to make sure you understand your data management responsibilities (e.g., research aims, ethics policies, code of good research practice) In many cases, such policies make statements that implicitly depend upon good data management; use these to argue that your institution has already made a commitment to manage their data that must be backed up (especially useful in making the case to senior managers) Make sure that you are aware of these policies and that you - through your employment – have agreed to adhere to? Seek assistance from existing working groups and committees wherever possible (ethics boards, data integrity)

  8. Example: GU Code of Good Research Practice Applies to all staff … the University expects researchers to observe the standards of practice set out in guidelines published by funding bodies, scientific societies and other relevant professional bodies. The receipt of funding from these agencies requires the University to confirm compliance with their terms and conditions; it is therefore essential that all researchers are aware of their responsibility to observe these standards during their work. This code of good practice in research is intended for all staff, including persons with honorary positions, and students carrying out research at, or on behalf of, the University.

  9. Example: GU Code of Good Research Practice Not just publications! Throughout their work, researchers are required to keep clear and accurate records of the procedures followed and of the results obtained, including interim results. This is necessary not only as a means of demonstrating proper research practice, but also in case questions are subsequently asked about either the conduct of the research or the results obtained. For similar reasons, data generated in the course of research must be kept securely in paper and/or an appropriateelectronic format. The University expects such data to be securely held for a period of ten years after the completion of a research project, unless otherwise specified by the research funder or sponsor.

  10. Example: GU Code of Good Research Practice Remind your institution of their documented commitment. It is the responsibility of the University to ensure that there are adequateprovisions for training and development to enable research staff to attain necessary skills for their current role, and to support their future career development.

  11. Selection and Appraisal Management and curation require time and money, so you’ll want to make sure that you are investing in the data that is most important to you.

  12. What do we mean by selection and appraisal?

  13. Why do we need to undertake selection and appraisal?

  14. What can help to guide the selection and appraisal process?

  15. Potential for redistribution • Non-replicability • Economic case • Full documentation • Relevance to mission • Scientific or historic value • Uniqueness

  16. But, what bits of the research outputs do you need to keep?

  17. The challenge

  18. ThanksAny questions? Joy Davidson – j.davidson@hatii.arts.gla.ac.uk http://www.dcc.ac.uk

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