Research Data Management: obstacles faced by the novice data manager
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This presentation outlines some of the major obstacles faced by a novice research data manager at a research institute.
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Research Data Management: obstacles faced by the novice data manager
- 1. CSIR Research Data Management: the way forward Louise Patterton CSIRIS September 2013
- 2. The future of research data in the CSIR • Definition • Global trends • Current situation • Problems • Solving the problems • Plan of action • Policy • Summary……………..
- 4. OBSTACLE The concept of Research Data Management (RDM) new! OBSTACLE CSIR Research Data status quo UNKNOWN! OBSTACLE CSIR Research Data policy NON-EXISTENT! OBSTACLE Global trends WE ARE FALLING BEHIND!
- 5. Excuse me sir......do you have a minute to talk about…. carrots?
- 6. Dear Colleague, We will be repeating the NeDICC workshop for rookie data managers in Pretoria on 28 August. This very successful workshop was launched at the 5th African Conference for Digital Scholarship and Curation during June and due to demand we have decided to make it available in Gauteng as well. Space is limited so unfortunately we can accommodate no more than 50 attendees. Venue: CSIR Knowledge Commons Date: 28 August 2013 Time: 09:00 - 14:00 Price: R399 VAT included A light lunch will be served at 13:00. The workshop will provide those who are starting out on the data management journey the opportunity to hear how other rookie data managers are coping with the new challenges, where they find their information and who they talk to. Delegates will also have the opportunity gain advice from those who have already engaged with researchers and those who are providing their research clients with appropriate training. They will also have the opportunity to hear from one institution where data management has become part of the way in which things get done. OBSTACLE #1: Unfamiliarity with ‘Research Data Management‛ concept.........
- 8. Welcome to the Carrot Cake Factory!
- 9. Welcome to the CSIR Carrot Cake Factory!
- 10. WE PRESENT (PROUDLY) : Carrot cake Carrot salad
- 11. However, clients/competitors now require:
- 12. Carrots required! Carrot audit required!
- 13. Excuse me sir......do you have a minute to talk about…. carrots?
- 14. carrot origin, carrot harvesting, carrot organisation.......
- 15. carrot quality
- 16. carrot growth, carrot phases, carrot versions
- 18. carrot storage
- 19. carrot storage
- 20. carrot quality
- 24. Sharing: how?
- 25. Establishing risks of data sharing • misuse • misinterpretation
- 26. Establishing disposal policies, disposal methods
- 27. ??? CSIR libraria n OBSTACLE #2 : What research data do we have in the CSIR? (and that’s just the start……)
- 29. OBSTACLE #3: Many scientists, no research data management policy…limited grasp of RDM benefits
- 30. BENEFITS OF RDM PLANNING:
- 31. Benefits with regards to data access:
- 32. Benefits with regards to sharing
- 33. Benefits with regards to research integrity
- 34. Benefits with regards to research efficiency
- 35. Obstacle #4: Global trends............way ahead • Training tools: (courses, degrees) * DMTpsych (psychology) * Mantra (wide coverage) * Cairo (creative arts) * DATUM for Health (health studies) * DataTrain (archaeology) • Data Archives/Data Repositories/Data banks * UK Data Archive (soc science in UK) * National Space Science Data Center (space) • Funder requirements * DMP is essential
- 36. • UK: Legal requirements…all Research Councils now have research data management policies, based on a set of common principles formulated by Research Councils UK • USA: National Institutes of Health: Data Sharing Policy: Supports the sharing of research data and expects researchers funded at $500,000 or more to include a data sharing plan in their grant proposals • USA: National Science Foundation (NSF): Dissemination and Sharing of Research Results: Beginning January 18, 2011, NSF will require grant proposals to include a supplementary data management plan of no more than 2 pages. This requirement is a new implementation of the long-standing NSF Data Sharing Policy • Australia: Monash University Policy Bank: The purpose of this policy is to ensure that research data is stored, retained, made accessible for use and reuse, and/or disposed of, according to legal, statutory, ethical and funding bodies’ Global Research Data Management Policy trends
- 37. Research Funders % elements National Science Foundation (NSF) 53% NSF Basic Research to Enable Agricultural Development (BREAD) 59% NSF Division of Earth Sciences (EAR) 65% NSF Division of Ocean Sciences 59% NSF Integrated Ocean Drilling Program 47% NSF Ocean Acidification Research 59% DOE Atmospheric Radiation Measurement Program (ARM) 76% National Aeronautics and Space Administration (NASA) - Earth Sciences 65% NIH - National Human Genome Research Institute 88% NIH - Genome-Wide Association Studies (GWAS) 76% American Heart Association 0% Issues in Science and Technology Librarianship: Percent of total data elements addressed by policy (Dietrich et al, 2012)
- 38. Research Funders Outputs Data Time limits Dataplan Access/ sharing Longterm curation Monitoring Guidance Repositor y Data centre Costs AHRC - - BBSRC CRUK - - EPSRC - - ESRC MRC - - NERC STFC Wellcom e Trust UK Funder requirements for data management and sharing (DCC) Source: http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies
- 39. • ‚Homeless‛ data quickly become no data at all: curation NB • There is no economic ‚magic bullet‛ that does not require someone, somewhere, to pay: funding required • What happens to valuable data when project funding ends: long term planning required • Additionally: * infrastructure * policy/guidelines/training * team • Data management planning does not happen in a vacuum Some final points to ponder on….
- 40. So, in a nutshell..........
- 41. THE WAY FORWARD: Step 1: • survey/audit/inventory • aim: Research Data Management Practices • questionnaire edited, refined • ethics clearance • target sample chosen: Research Group Leaders • audio recording…transcribed • all units, all Research Group Leaders • confidentiality • benchmark against similar studies
- 42. THE WAY FORWARD: Step 2: Analysis……………………………………………………………………………………… ………………………………………………………..…..…………………………….. Step 3: Recommendations: • personnel • infrastructure • cost Step 99: • CSIR Research Data Policy • Training/Guidelines • Data Repository • Sharing
- 43. Excuse me sir......do you have a minute to talk about…. carrots?
Editor's Notes
- This was my initial presentation outline
- Then changed me mind……….
- This is such a new field that one cannot but really focus on obstacles blocking the way……
- So…have decided to rename my presention: “Excuse me sir, do you have a minute to talk about carrots”.Yes, it might not make sense now, but it should….in a minute or two.
- This a cut and paste from the ad we emailed to the SA Online User Group as well as other Library-and Information Science groups. The natire of replies received back from the professional community indicate that research data management is at the moment still a very confusing subject, or field.
- For this reason, I am going back to basics, and will explain the concept of research data management in the simplest possible way.
- This is a carrot cake factory.
- The CSIR carrot cake factory, to be more precise.
- Our products are not new research, articles, conference papers, or technology demonstrators….but carrot cake…and carrot salad. It is liked and loved and very popular….we are a national household name and our products have even made a name globally.
- For many years now, clients have been happy with the carrot cake….when suddenly things changed. They now…in addition to carrot cake….would like to buy the carrots and make their own products!
- Which brings us to the following dilemma: do we even know what is going on with our carrots? Can we supply something that we have not really paid attention to?This is the crux of my analogy: carrots are the datasets. Carrot cake and salad…..the articles and discoveries and scientific breakthroughs. So what is needed now is a detailed inventory or audit into the carrots to establish what the current situation is like.
- (I hope the analogy is by now clear to all……?)
- In this audit, we need to establish the origins and harvesting of the carrots (how data was collected, or generated, how deciding on the data was done)
- We need to establish the quality of the carrots…..
- The various growth stages it goes through (data versions, rate of growth)
- How the data is sorted, file formats used…..
- Where do you store the carrots? In a dilapidated storeroom or archive……
- Or a modern state-of-the-art warehouse (this is actually a real farm warehouse…..)
- Is the data protected from corruption or damage? Is there a data disaster recovery plan in place?
- How is the data grouped? What are the naming conventions used? How are the various versions named? How about renaming…how is that done? How is data retrieved when searched for?
- Is data documentation done? Are codebooks, data dictionaries, instrument calibration and other procedures or aspects crucial to data understanding, documented?
- Will data be shared? Is access restricted…and how is access controlled? Are embargoes ever required?
- When data is shared, how will it be done? Will a web-browser be used, or is FTP the preferred method?
- What about data misuse, or misinterpretation? What are the dangers of another researcher tarnishing the original data collector’s reputation?
- Finally….data destruction? Will data ever need to be destroyed, and if so, what are the procedures/methods to be used?
- Impact, co-authorship, sharing resources (financial too), teaching, integrity