1. 11 Researcher practice in data management Margaret Henty

2. 2 2 Data management – what is it?  Data management is a broad catch-all term used by different people in different contexts. It can be used to describe a variety of activities such as:  Data storage,  Data curation,  Data preservation,  Database design,  Data modeling and more.  Sometimes it can be used to refer to data management policy and sometimes to the practice of data management.

3. 3 3 Data management for the researcher  All those activities which a researcher can undertake  to organise and manage their data  to facilitate their own research, and  to provide a foundation for the longer-term sustainability of the data

4. 4 Archiving – different views  The archivist’s view  not all materials are kept in perpetuity, but may be subject to disposal according to defined protocols and schedules  The sociologist’s view  the collection, storage, and preservation of data  The computer science view  A repository for information that the user wishes to retain, but without requiring immediate access

5. Do you have a research data management plan? Investigating Data Management Practices in Australian Universities – Margaret Henty, Belinda Weaver, Stephanie Bradbury and Simon Porter, APSR, 2008. http://www.apsr.edu.au/orca/investigating_data_management.pdf

6. 6 Sharing data I readily share data with colleagues or students working on same or related project on an informal case by case basis. I would like to have access to an area where I could put data files for access and download by colleagues. Sending large files via email is not really possible, and sending data on CDs is also very time-consuming, especially with large files. I would very much welcome a solution to this problem that doesn't cost an arm and a leg to the researcher or school.

7. 7 What is digital curation? “The activity of managing the use of data from its point of creation to ensure it is available for discovery and re-use in the future.” (UK Digital Curation Centre)

8. 8 Important curation boundaries  Data management plan marks beginning of data life cycle  Collaboration—sharing data with other researchers  Publication—releasing data into the public domain  Potential for cross-disciplinary use  Need for additional metadata

9. 9 Data curation continuum  Initially, much data, little metadata, managed privately by researcher (ideally in accordance with data management plan)  Gradual change process  Finally, selected data collections, well described and managed in institutional repositories  Researcher has critical role

10. 10 In summary  Researcher curation decisions affect long term access to data and re-use of data  Help with these decisions and metadata creation needed all through data lifecycle, not just at the end

11. 11 Why bother with a data management plan?  meeting obligations  efficiency  protection  quality  exposure  avoiding catastrophe

12. 12 Creating a data management plan The introduction  About the project  Owners and stakeholders  The aims of the research

13. 13 Re-using and creating data  Survey of existing data you might use  Can you get access?  Under what conditions?  Data to be created  Describe in detail the different forms

14. 14 Copyright, IP and other legal issues  Copyright and IP  usually defined by your employer  will the data be created in Australia or overseas?  will the research project involve partners from other institutions, Australian or otherwise?  who will own the data?  how will the data be licensed?  Ethical and policy issues

15. 15 Access and security  Will others have access to the data during the life of the project?  Inside your institution or externally?  As part of the research team?  On open access?  How will their access by yourself and others be controlled?  Internally and externally

16. 16 File formats  What’s the problem?  Durability  Proprietary vs. open access  Degradation  Compression  Standards, within your discipline and more broadly  Compatibility with collaborators  Choosing the right one  Planning implications  Get it sorted before you start

17. 17 Documentation & record keeping  Data asset inventory  File naming protocols  Metadata standards  Contextual information  Protocols used  Criteria for quality assurance  Software used  Lab notebooks

18. 18 Metadata (1)  The term metadata refers to information used to describe items and groups of items.  It is data about data.  It can be used to describe physical items as well as digital items.  It provides meaning and context.

19. 19 Metadata (2)  Types  Descriptive  Technical  Access or rights  Preservation  Standards  Disciplinary and other  Vocabularies  Ontologies

20. 20 Data organisation  How will you name your data files?  How will you organise your data into folders?  How will you manage transfers and synchronisation of data between different machines?  How will you manage collaborative writing with your colleagues?  How will you keep track of the different versions of your data files and documents?

21. 21 Bibliography management  What bibliographic management tools will you use?  How will you share references with the other members of your group?

22. 22 Short term storage and backup  Where to store?  Know your institution  Know your faculty/school  How to back up?  How much is enough?  Don’t forget your non-digital data

23. 23 Retention and disposal  Longer-term retention  ‘The institutional policy on the secure and safe disposal of primary materials research data must be followed’ (Code)  Is recommended by ANDS in order to encourage data sharing and re-use  Is recommended where the data has intrinsic properties which make it unique  May be affected if data is not properly managed from the outset

24. 24 Responsibilities  Who is responsible for each part of the plan?  Who is responsible for  Approval of the plan?  Modification and review?  Storage of the plan?

25. 25 Budget Once the plan is complete you are in a position to estimate any costs for which you may be responsible  Documentation and metadata  Equipment and software  Storage  …?

26. 26 Thank you!