A 2 hour introductory session presented to PhD students at the University of Melbourne, 13 September 2012. Given by Steve Bennett (VeRSI) and Jeff Christiansen (ANDS).

1. Research Data Planning
...for the Sciences
MSGR UpSkills Program
Jeff Christiansen & Steve Bennett
13 September 2012
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2.  Why data management
 What data
 Where you store it
 Who owns it
 How you manage it
Bonus: start work on a data management plan!
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3. Intro – who we are
 Dr Jeff Christiansen jeff.christiansen@ands.org.au
 Australian National Data Service
 Previously researcher in molecular genetics
 Steve Bennett: steve.bennett@versi.edu.au
 Victorian e-Research Strategic Initiative
 Helps researchers with systems for digital data
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4.  Why data management
 What data
 Where you store it
 Who owns it
 How you manage it
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5. Becoming aware of data
management in research
 BSc (Hons)
Experiment 1
?
Experiment 2
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6. Becoming aware of data
management in research
 PhD
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7. Becoming aware of data
management in research
 PhD
CCACGCGTCCGGTGTGAGCTCTCCTTCAGCTGCTGCAGGCATTACACTCAGCTCTGCTGT
CCAAGCTGCTCATGTGATTGCCCTCTAATCCATTCAGGCAAAGTGAGCTAGACTTGTTTA
AGCTGCAGGTCTTATTTTGATTGTAGCAGGCTAGTGAACAGTCACAGAAGTGGTTCAAGT
ATTGTGCCCCTTGGAGCTGTTATCTTTGAAAATGTGGCCGTGGCTGGAAAAGGATGCATC
TGCACCAATGGCACAGTGACCAGCCAGTTGCTTAGGGGCTTAGCTGGTGGATTTGGACCT
GTCTTCTGCAACCTGGGGAAAGCATAATCTACTGTGTTATTTGATAATGGAAGCGCCGTG
ATCAGATCCATCCCTCTGCTTTGAATTTTCAAACAAATAATCAAGAATTTGGCTCGTGTT
AAAAAAAAAAAAAAAA
17/09/2012 7

8. Becoming aware of data
management in research
 PhD
17/09/2012 8

9. Becoming aware of data
management in research
 PhD
CCACGCGTCCGGTGTGAGCTCTCCTTCAGCTGCTGCAGGCATTACACTCAGCTCTGCTGT
CCAAGCTGCTCATGTGATTGCCCTCTAATCCATTCAGGCAAAGTGAGCTAGACTTGTTTA
AGCTGCAGGTCTTATTTTGATTGTAGCAGGCTAGTGAACAGTCACAGAAGTGGTTCAAGT
ATTGTGCCCCTTGGAGCTGTTATCTTTGAAAATGTGGCCGTGGCTGGAAAAGGATGCATC
TGCACCAATGGCACAGTGACCAGCCAGTTGCTTAGGGGCTTAGCTGGTGGATTTGGACCT
GTCTTCTGCAACCTGGGGAAAGCATAATCTACTGTGTTATTTGATAATGGAAGCGCCGTG
ATCAGATCCATCCCTCTGCTTTGAATTTTCAAACAAATAATCAAGAATTTGGCTCGTGTT
AAAAAAAAAAAAAAAA
17/09/2012 9

10. Becoming aware of data
management in research
 PhD
CCACGCGTCCGGTGTGAGCTCTCCTTCAGCTGCTGCAGGCATTACACTCAGCTCTGCTGT
CCAAGCTGCTCATGTGATTGCCCTCTAATCCATTCAGGCAAAGTGAGCTAGACTTGTTTA
AGCTGCAGGTCTTATTTTGATTGTAGCAGGCTAGTGAACAGTCACAGAAGTGGTTCAAGT
ATTGTGCCCCTTGGAGCTGTTATCTTTGAAAATGTGGCCGTGGCTGGAAAAGGATGCATC
TGCACCAATGGCACAGTGACCAGCCAGTTGCTTAGGGGCTTAGCTGGTGGATTTGGACCT
GTCTTCTGCAACCTGGGGAAAGCATAATCTACTGTGTTATTTGATAATGGAAGCGCCGTG
ATCAGATCCATCCCTCTGCTTTGAATTTTCAAACAAATAATCAAGAATTTGGCTCGTGTT
AAAAAAAAAAAAAAAA
17/09/2012 10

11. Becoming aware of data
management in research
 PhD
CCACGCGTCCGGTGTGAGCTCTCCTTCAGCTGCTGCAGGCATTACACTCAGCTCTGCTGT
CCAAGCTGCTCATGTGATTGCCCTCTAATCCATTCAGGCAAAGTGAGCTAGACTTGTTTA
AGCTGCAGGTCTTATTTTGATTGTAGCAGGCTAGTGAACAGTCACAGAAGTGGTTCAAGT
ATTGTGCCCCTTGGAGCTGTTATCTTTGAAAATGTGGCCGTGGCTGGAAAAGGATGCATC
TGCACCAATGGCACAGTGACCAGCCAGTTGCTTAGGGGCTTAGCTGGTGGATTTGGACCT
GTCTTCTGCAACCTGGGGAAAGCATAATCTACTGTGTTATTTGATAATGGAAGCGCCGTG
ATCAGATCCATCCCTCTGCTTTGAATTTTCAAACAAATAATCAAGAATTTGGCTCGTGTT
AAAAAAAAAAAAAAAA
17/09/2012 11

12. Becoming aware of data
management in research
 PhD
CCACGCGTCCGGTGTGAGCTCTCCTTCAGCTGCTGCAGGCATTACACTCAGCTCTGCTGT
CCAAGCTGCTCATGTGATTGCCCTCTAATCCATTCAGGCAAAGTGAGCTAGACTTGTTTA
AGCTGCAGGTCTTATTTTGATTGTAGCAGGCTAGTGAACAGTCACAGAAGTGGTTCAAGT
ATTGTGCCCCTTGGAGCTGTTATCTTTGAAAATGTGGCCGTGGCTGGAAAAGGATGCATC
TGCACCAATGGCACAGTGACCAGCCAGTTGCTTAGGGGCTTAGCTGGTGGATTTGGACCT
GTCTTCTGCAACCTGGGGAAAGCATAATCTACTGTGTTATTTGATAATGGAAGCGCCGTG
ATCAGATCCATCCCTCTGCTTTGAATTTTCAAACAAATAATCAAGAATTTGGCTCGTGTT
AAAAAAAAAAAAAAAA
17/09/2012 12

13. Becoming aware of data
management in research
 Postdoc

14. Becoming aware of data
management in research
 EMAGE Database Project Manager

15. Becoming aware of data
management in research
 EMAGE Database Project Manager

16. Becoming aware of data
management in research
 EMAGE Database Project Manager

17. Becoming aware of data
management in research
 EMAGE Database Project Manager

18. Becoming aware of data
management in research
 EMAGE Database Project Manager
 Cross DB queries need to use appropriate descriptors, not just free text
 E.g. Gene name identifiers

19. Becoming aware of data
management in research
 Being organised, having systems in place and adopting
community standards are all helpful in data
management.
 Think about what you will be required to do when
publishing.
 There are obligations for having data available for others
post publication.
 It’s useful to have your data organised so you can
collaborate with others easily.
 What will happen to your data when you leave the lab?
Your supervisor would like to know what’s what/where.

20. Data Planning & Managing
Motivators
 #1 Meet your obligations
 legal, ethical, funding requirements; uni, department, group policies
 Find out now – avoid hassle later (ask research-data@unimelb.edu.au)
 #2 Make your life easier
 a data management system to make your research work
 a data management plan to save time
 keeping data, finding stuff again, labelling, security
 sharing & collaborating
 #3 Helping your career
 being a professional researcher
 data – your assets and records – finding, understanding data in years to come
 contributing to global research community
 manage your data now, help your future self.
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21.  Why data management
 What data
 Where you store it
 Who owns it
 How you manage it
Ask: research-data@unimelb.edu.au
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22. What is data?
 Observational data
 Sensor readings, telemetry (non-reproducible)
 Experimental data
 Gene sequences, chromatograms (reproducible,
but expensive)
 Simulation data
 Climate models (model the most important thing)
 Derived/compiled data
 Compiled database (reproducible but expensive)
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23. What else is data?
 Social sciences
 Surveys, statistical data
 Humanities
 Cultural artefacts (video, photos, sound…)
 Physical samples
 Soil, biological, water, archeological…
 Does anyone here not have data?
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24. The University’s definitions
 Research Data
 laboratory notebooks; field notebooks; primary research data (hardcopy or
in computer); questionnaires; audiotapes; videotapes; models; photographs;
films; test responses; slides; artefacts; specimens; samples
 Research Records
 Includes correspondence (electronic mail and paper-based correspondence);
project files; grant applications; ethics applications; technical reports; research
reports; master lists; signed consent forms; and information sheets for research
participants
 Administrative Records (Research Office, Central Records)
 Includes contracts and agreements, patents, licences, grants, intellectual property
and trademarks, policies, ethics, research project files, reports, publications
 What is often included as “Research Data”:
= data + records + copies (physical & digital)
= stuff you used and/or created
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25. Group activity (15 mins)
 Form groups of similar discipline
 Earth sciences/forestry/botany/agriculture
 Health/medical biology/physio/social work
 Engineering/computer science/linguistics
 Discuss:
 What kind of data do you collect?
 How do you get it?
 Your data management checklist:
 Section 1.1
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26.  Why data management
 What data
 Where you store it
 Who owns it
 How you manage it
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27. Research trends
 Research Data is increasing in size
 Protein crystallography 100 GB/experiment
 Gene sequencing 1,000 GB/day
 High-energy physics 10,000,000s GB/year
 Astronomy (SKA) 1,000,000,000 GB/day
 Research Collaborations are increasing
 Human Genome project (1990-2003)
 113 people, 20 orgs
 Belle collaboration (1994-..)
 ~370 people, 60 inst., 14 countries
 ATLAS collaboration @ LHC CERN (1994-2020+)
 ~2500 people, 169 inst., 37 countries
 Research Data is increasingly digital
 Wonderful opportunities for reuse,
sharing, collaboration, analysis
 Data science (4th paradigm)
 “eResearch”!
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28. Research trends
 Large scale data intensive science
 “A totally new way of doing research”
 New research methods, new skills,
therefore new training needed
 New skills...
 Specialists – in both technology and
research
 Informatics – dealing with data from
collection through analysis
 Data Management and Planning –
collecting, maintaining, sharing data
Everyone!
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29. How big?
1mb 10 Gb 1Tb
(spreadsheets) (numerical, (simulations,
synchrotron) 1Pb
video)
Easy! Awkward Easy?
(Probably already solved)
Limit of Google
Drive, DropBox…
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30. Where to keep it?
 Possibilities:
 Research group storage
 Ask!
 Local computer
 Backups crucial. Sharing hard. Disaster looms.
 Cloud (Dropbox, Google Drive)
 Check security, legals. How to archive?
 Ask research-data@unimelb.edu.au
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31. Sharing
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32. 17/09/2012 32

33. Group activity #2 (15 mins)
 Discuss
 How much data will you have?
 Where will you store it?
 What data formats?
 Data management checklist
 Complete section 2.3 & 2.4
 If non-digital: 2.1, 2.2
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34.  Why data management
 What data
 Where you store it
 Who owns it
 How you manage it
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35.  In collaborations, get IP right early.
 Find out:
 Does the University own your data?
 Can you still share it?
 Restrictions?
 Licences?
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36.  IP – who claims to own it
 Copyright – who has legal backing
 (not all data can be copyright)
 Ethics – more rules you agreed to
 Must you keep the data private?
 Must you share it?
 Privacy – can you de-identify the data?
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37. Group activity #3 (15 mins)
 Discuss
 Who owns your data?
 What data can you share? With whom?
 How will you protect confidential information?
 Data management checklist
 Complete section 1.3
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38.  Why data management
 What data
 Where you store it
 Who owns it
 How you manage it
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39. University Code of Conduct for
Research
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40. University Policy on Management of
Research Data and Records
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41. Starting your system
 Consider your goals – what do you want to
get out of managing your data?
 Figure out your criteria for keeping data
 Picture your data three years from now
 Consider the metadata you want to collect
to document your datasets
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42. Benefits
 Find your data 3 years from now
 Get more papers out of your data
 Save time and stress – get organised
 Share with collaborators
 Some journals require data submission
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43. Being more professional...
 Not rocket science!
 Stop and think about what data you have, what you’re doing, what you should be
doing
 Some scary facts:
 Microfilm, non-acidic paper last 100+ years
 magnetic media lasts 10+ years
 optical media lasts 20+ years
 2-10% of hard drives fail every year
 software & hardware can outdate quickly
 Scary stories:
 US study 100’s charges “research misconduct”
40% avoided by better data management!
 UniMelb ~20 cases research misconduct 2008.
Most involved students. All needed good records!
 Climategate scandal, UK – FOI
Burroughs 1977 – B 9495
 Proper Planning & Management is needed!!! Magnetic Tape Subsystem
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44. High level view
Your data management system needs to cover:
(Use, Transform, Update)
Create, Keep,
Capture, Transfer,
Describe Destroy
Store, Secure,
Preserve
(National Archives)
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45. A simple Data Man. System
 Identify key data in your context, important stuff to keep (your Data Assets)
 Find secure places to keep physical & digital Records + Data (filing cabinet, department
shared drive) – backups are essential
 Where and when should there be checks on your data (sanity checks, quality control,
standards)
 File your data and records into logical divisions, say activities, projects, or pieces of work
 eg. folders /DeptShare/johnsmith/Records/ProteinABC Investigation
 Don’t break things down too much, makes things harder to find!
 Have a consistent file naming convention:
 perhaps: ActivityOrContents-LocationOrPerson-CreateDate-Id-Description.ext
 eg. “ProteinABC-LJW-20100409-0001 Raw data from instrument.dat”
 Keep good metadata (notes, records) on how you captured your data, particularly for
physical records
 Descriptions of collections or files – Structured text files good enough
 eg. FileOrCollectionName-metadata.txt
 On other things, entities that are not files – Structured text files or spreadsheets
 Have a good labeling/ID/coding system
 Perhaps keep a registry (spreadsheet will do; IDs, names, location, basic metadata)
 Find the right balance in digitising physical stuff (easy and quick)
 Digital is easy to keep/transfer/search if stored properly. However, digitising/scanning everything
can be time consuming and without good descriptions may not be useful.
 Link digital notes/metadata to physical stuff (IDs, names, labels, codes, location)
 Have some basic digital representations or notes of important physical stuff
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46. Free Tools
 jEdit – text file editor (private notes, metadata and records)
 local disk + file share + Cobian Backup (private project records, data)
 Google Desktop (file and email search)
 Zotero (reference material) (EndNote is Uni default)
 EVO & Skype & Google chat (video/tele/chat communication)
 http://evo.arcs.org.au/
 Sakai@Melbourne (project workspace)
 https://sakai.unimelb.edu.au/ see Info Skills classes
 Google docs + Sites (collaborative editing) on EndNote,
 Google groups (email list) UpSkills 29 June on VC
 research data storage, a tricky one…
 use local storage in preference, ask around
 DropBox, Google Drive, Microsoft SkyDrive, box.com…
 too many others to list, heaps on the web…
 See Digital Research Tools (DiRT) wiki for a huge list
http://digitalresearchtools.pbworks.com/
 Check with your supervisor,
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47. Data Security
 2 aspects to security
 Safety from damage or loss
 How important is the data to you?
 Safety from incorrect use
 What are the possible consequences?
 Safety from damage or loss (unintended and intentional)…
 What’sacceptable loss (safety can cost, use up time)
 Backups (data, software, system)
 How often (hourly, daily, weekly, monthly, manually, automated)?
 How many and where (onsite, offsite, both, multiple)?
 Departmental storage? Probably backed up already!
 Disaster Recovery
 Quality hardware, multiple/spare servers, spare disk drives,
 Operating System and Applications image backups
 (talk with someone technical, your local IT guys)
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48. Data Security
 Safety from damage or loss (continued)…
 Make sure Backup is occurring
 Essential data and records... “Your Archive”
 Frequency should depend on how often your data changes
 Incremental backups are essential. Replication IS NOT SAFE!!!
 Keep some copies (one?) offsite.
 Database backups should use database tools (mysqldump, pg_dump etc.)
 Departmental storage is best... probably backed up already!
 Worst case... DIY, use external hard drives or remote storage
 Seek advice on software
 for Windows I use... Cobian Backup, DriveImage XML
 for Linux I use... rsync (see http://rsync.samba.org/examples.html )
 for Mac there is... Time Machine
 (talk with someone technical, your local IT guys)
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49. Data Security
 Safety from incorrect use (unintended and malicious)…
 PCI DSS - a recommendation (Payment Card Industry Data Security Standard)
 eg. google for: “nacubo.org payment card data security”
 12 requirements that are good practice (first 10 are the basics)
 10 IT basics…
 Firewall servers
 Do not use default usernames/password
 Physically protected stored data (lock up servers, disk, tape, source material)
 Use encrypted transmission over internet (VPN, SSL, SSH, GridFTP, S/MIME email)
 Update antivirus/antimalware software regularly
 Use secure and trusted applications
 Restrict access to sensitive data (tighter control, or put it somewhere else)
 Assign unique IDs for each user
 Record and monitor all access to data
 Plus some good practice…
 Don’t retain sensitive data
 Or encrypt sensitive information
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50. Read up!
 Google: research data toolkit
 http://researchdata.unimelb.edu.au
 ANDS guides
 To consider: identifiers, DOIs, archival,
security, licensing, metadata formats,
ontologies, controlled vocabularies,
definition of “collection”, data reuse,
metadata stores…!
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51. Group activity #4 (15 mins)
 Data management checklist
 Complete section 3.1
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52. Questions?
research-data@unimelb.edu.au
researchdata.unimelb.edu.au
17/09/2012 Copyright (c) 2012, VeRSI Consortium, Lyle Winton , Steve Bennett, Jeff Christiansen 52