Publishing your research: Research Data Management (Introduction) (November 2013) slides. Delivered as part of the Durham University Researcher Development Programme. Further Training available at https://www.dur.ac.uk/library/research/training/

1. Publishing your
Research
Research Data Management
James Bisset james.bisset@durham.ac.uk
Academic Liaison Librarian (Research Support)
Sebastian Pałucha sebastian.palucha@dur.ac.uk
Research Data Manager (CIS/Library)

2. Session outline
- What... is “Research Data”?
- Small group activity
- What... is “Research Data Management” ?
- Data life cycle, existing practice and policy
- Why... Is Research Data Management important?
- Drivers for change, Requirements on & benefits for researchers
- How... to manage and secure research data
- Data Management Planning. Document storage and back-up
- How... To share data
- Benefits of sharing data and tools available

3. Part 1
What is
Research
Data?

4. Via Flickr Creative Commons, by © Stuti Sakhalkar. Original available at
http://www.flickr.com/photos/theblackcanvas/2945878325/
What is
Research Data?

5. Questionnaires
Interview transcripts
Test answers

6. Questionnaires
Interview transcripts
Test answers
artefacts
specimens
photographs
film footage

7. Questionnaires
Interview transcripts
Test answers
artefacts
specimens
photographs
film footage
algorithms Simulation software
models

8. Lab notebooks
Field notebooks
Diaries
correspondence
Questionnaires
Interview transcripts
Test answers
artefacts
specimens
photographs
film footage
algorithms Simulation software
models

9. Lab notebooks
Field notebooks
Diaries
correspondence
Questionnaires
Interview transcripts
Code books
Test answers
artefacts
specimens
photographs
film footageMethodologies & workflows
algorithms Simulation software
models
Grant applications

10. Lab notebooks
Field notebooks
Diaries
correspondence
Questionnaires
Interview transcripts
Code books
Test answers
artefacts
specimens
photographs
film footageMethodologies & workflows
algorithms Simulation software
models
Grant applications
.pdf
.rtf
.docx
.xml
35mm
IX240
.xls
spss
.jpg
.gif

11. Research Data
There is no single or simple definition
of what constitutes „research data‟

12. Research Data
There is no single or simple definition
of what constitutes „research data‟
- it is used to support the production or validation of
original research.

13. Research Data
There is no single or simple definition
of what constitutes „research data‟
- it is used to support the production or validation of
original research.
- it can be „born digital‟, or it can be analogue (and
then digitised)

14. Research Data
There is no single or simple definition
of what constitutes „research data‟
- it is used to support the production or validation of
original research.
- it can be „born digital‟, or it can be analogue (and
then digitised)
- it is situational...

15. Data is situational
Ship logbooks :
- historical record of events
- data to reconstruct weather
patterns
- data on naval personnel
(genealogical / demographic)
- extrapolation of data on
ration provisions etc.

16. Data is situational
CCTV footage:
- data on crime & prevention
- data on foot-fall
- demographic data

17. Data is situational
Data can be used...

18. Data is situational
Data can be used...
... and re-used...

19. Data is situational
Data can be used...
... and re-used...
... for purposes you
may not have thought of...

20. Data is situational
Data can be used...
... and re-used...
... for purposes you
may not have thought of...
... even after you have extracted all
the value you need from it.

21. “ Research data ... is
collected, observed, or
created, for purposes of
analysis to produce original
research results.”
Research Data Explained (2013) Edinburgh University MANTRA
http://datalib.edina.ac.uk/mantra/

22. Via Flickr Creative Commons, by © Stuti Sakhalkar. Original available at
http://www.flickr.com/photos/theblackcanvas/2945878325/
Where is
your data?

23. Where is your data?
JISC RDM Survey
- Russell Group institutions average over
2PB of data

24. Where is your data?
JISC RDM Survey
- Russell Group institutions average over
2PB of data
- significant data storage on external
drives, hard drives etc.

25. Where is your data?
JISC RDM Survey
- Russell Group institutions average over
2PB of data
- significant data storage on external
drives, hard drives etc.
- 23% of institutions had lost research data

26. Where is your data?
JISC RDM Survey
- Russell Group institutions average over
2PB of data
- significant data storage on external
drives, hard drives etc.
- 23% of institutions had lost research data
- how would this impact upon your PhD?

27. Part 2
What is
Research Data
Management?

28. “ Research data management
concerns the organisation of
data, from its entry to the research
cycle through to the dissemination
and archiving of valuable results.”
Whyte, A., Tedds, J. (2011). „Making the Case for Research Data
Management‟. DCC Briefing Papers.
http://www.dcc.ac.uk/resources/briefing-papers/making-case-rdm

29. Data Life-cycle
UK Data Archive www.data-archive.ac.uk/create-manage/life-cycle

30. Durham RDM Policy

31. Part 3
Why manage
your data?

32. Via Flickr Creative Commons, by © Stuti Sakhalkar. Original available at
http://www.flickr.com/photos/theblackcanvas/2945878325/
What are the benefits
of managing your
data effectively?

33. Why manage your
data?

34. … it is a requirement
“Publicly funded research data are a public
good, produced in the public interest, which
should be made openly available with as few
restrictions as possible in a timely and
responsible manner that does not harm
intellectual property.”
RCUK Common Principles on Data Policy
http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx

35. … it is a requirement
The European Commission is developing an
Open Data Pilot to:
“facilitate research data
registration, discovery, access and re-use,”
Horizon 2020 – Outline of a Pilot for Open Research Data
http://www.coar-repositories.org/files/Horizon_2020_Open_Data_Pilot_20130703_final.pdf

36. … it is a requirement

37. … it is a requirement

38. … it is good practice
• Knowing where it is aids retrieval

39. … it is good practice
• Knowing where it is aids retrieval
• You may need to retrieve the data 3 months
or 3 years after you have „created‟ it
- eg when writing up your PhD or article

40. … it is good practice
• Knowing where it is aids retrieval
• You may need to retrieve the data 3 months
or 3 years after you have „created‟ it
- eg when writing up your PhD or article
• To safeguard your data from loss / theft /
corruption or damage / obsolescence

41. … it is good practice
• Project in 1986
• Multiple formats
of data
(image, video, text
) stored on Laser
Disc
• Copyright issues
http://www.bbc.co.uk/news/technology-
13367398
http://en.wikipedia.org/wiki/BBC_Domesd
ay_Project

42. … it is good practice

43. … boosts your profile
“10,555 studies … we found that studies that made
data available in a public repository received 9%
more citations than similar studies for which the
data was not made available.”
Piowar H. & Vision T. (2013) “Data reuse and the open data citation advantage” PeerJ
http://peerj.com/articles/175/

44. … data can be re-used
• You can share something which can be built
upon in ways you might not have imagined
- inter-disciplinary research
- collaboration opportunities

45. … data can be re-used
• You can share something which can be built
upon in ways you might not have imagined
- inter-disciplinary research
- collaboration opportunities
• Data can be tested and replicated
- identify fraud and error
- Fraud in cancer care
- Sir Cyril Burt (1893-1971): Heritability of IQ
- Reinhart-Rogoff revisited

46. Why manage your
data?
• You are increasingly likely to be required to
• It is good research practice
- to defend your research publications
- to secure against loss of data
• It boost your citation potential
• Your data can be re-used and replicated

47. Why manage your
data?
http://youtu.be/N2zK3sAtr-4

48. Part 4
How to
manage and
secure data?

49. Data Management
Plans

50. Data Management
Planning
• The Majority of UK funders ask for a data management
plan as part of a funding application
• Purpose:
- to help you properly manage your data
- to provide a funder with confidence that you are a good
investment.

51. Via Flickr Creative Commons, by © Stuti Sakhalkar. Original available at
http://www.flickr.com/photos/theblackcanvas/2945878325/
What questions
might a data
management plan
need to address?

52. Questions to consider
• What is the story of the data ?
• What form and format are the data in ?
• What is the expected lifespan of the data ?
• How could the date be used, re-used or re-purposed ?
• How large is the data set ? Will it grow ?
• Who are the potential audiences ?
• Who owns the data ?
• Is the data sensitive ?
• What publications are linked to the data ?
• How should the data be made accessible ?
Witt & Carlson (2007) “Conducting a Data Interview” Scientist

53. Who is involved?
• Influences and dependencies…
- Researcher requirements
- Funder and institutional requirements
- Availability and suitability of data storage
- Research Group requirements
- Publisher requirements
- Legal Requirements
(FoI, Copyright, Ethics, Data Protections)

54. Who is involved?
• Actors and interactions
- Researcher & PI
- Research Office
- IT Business Partner for Research / Research Data
Manager
- Librarians / archivist / record managers
(metadata schema, curation)
- FOI officers
- Technical and laboratory staff

55. DMP Online tool
http://dmponline.dcc.ac.uk

56. Create a plan based on template ...

57. ... and answer the questions

58. ... and answer the questions

59. ... and answer the questions

60. ... and collaborate …

61. DM Plan: common themes
• Data collection, what and how
(i.e. volume, format, )
• Documentation, administrative data and
metadata
• Ethics and legal compliance
(the FOI, IPR and DP acts; confidentiality and embargoes)
• Storage and backup
(day to day practices)
• Data sharing and preservation
(where, who and when will have access)

62. Storage and back-
up

63. Organising your data
• plan a hierarchy of files and folders, organised
by…
- type of data (text, image, model, sound, video etc.)
- type of research activity (survey, interview etc.)
- type of material (documentation, publication, etc.)

64. Organising your data
• Be systematic and consistent with naming
conventions and housekeeping from the start…
- files should be sortable by name
- filenames should indicate the „version‟
- filenames should be easily distinguisable

65. Thinking about filenames
• Names should be clear and descriptive
- to both you, and third parties

66. Thinking about filenames
• Consider including elements in filenames…
- Date 2013_12_12
- Project identifier CARD
- Content description RDM_presentation
- Version v1_2
2013_12_12-CARD-RDM_presentation-v1_2.pptx

67. Thinking about filenames
• Consider including elements in filenames…
- Date 2013_12_12
- Project identifier CARD
- Content description RDM_presentation
- Version v1_2
CARD/2013_12_12-RDM_presentation-v1_2.pptx

68. Thinking about filenames
• Pitfalls to avoid
- Whitespace
- Unsupported characters in filenames
- Capitalisation
2013_12_12-RO-RDM_presentation-v1.2.pptx
2013_12_12-RO-rdm_presentation-v1.2.pptx

69. Thinking about filenames
• Discovery right file when needed

70. Keeping track of
data

71. Data about Data
• To keep track of data…
• … and to describe what data is available to a
secondary user
• Spreadsheet?
• Lab notebook?
- electronic / paper?
• Database?

72. Data about Data

73. Data about Data

74. Data about Data
http://etheses.dur.ac.uk/8472/

75. Metadata
• Simple
– readme.txt
– cover page
• Advanced,
domain standards
- DDI; METS; TEI; QDE

76. Data formats

77. Data formats
• Think about what format you are saving your data
in…
Prefer this… … over this
ASCII (human readable)
(.txt, .xml, .csv )
Binary formats
(.exe, .doc, )
Open standard
.odt
.ods
Proprietary
.docx
.xlsx

78. Data back-up and
security

79. Data back-ups
• Are you just digitising / photocopying?
• Are you saving files into in multiple locations
(pendrives, hard drive, external hard drive?)
• Tip for Durham Research Students:-
- (stevens)(j:) your Durham network drive
• Other tools available: SyncToy, Time Machine, Deja
Dup

81. Data security
• Password vault
– Do you use passwords >8+
– Public Key Encryption (PKI) use 128 – 256
• Virtual Encrypted Drive
– TrueCrypt, FileVault

82. Data security
• Secure Interent Protocols
– WiFi: WPA2 but not WEP
– Browser: HTTPS
– Virtual Private Network (VPN), Secure Shell (SSH)
• How to access j: drive off campus
– DU MDS Anywhere
– WinSPC, Macfusion, sftp

83. Part 5
Sharing
your data

84. Sharing your data

85. Accessing shared data

86. Further Reading
• DCC training materials on RDM
- http://www.dcc.ac.uk/training/train-trainer/disciplinary-rdm-
training/conceptualise/conceptualise
• Examples of Research Data plans
- http://relu.data-archive.ac.uk/data-sharing/planning/examples/
- http://www.dcc.ac.uk/resources/data-management-plans/guidance-examples
• Data Management Plan templates
- https://dmponline.dcc.ac.uk/

87. Questions …?

88. [15] Via Flickr Creative Commons, and by L. Whittaker: Available at
http://www.flickr.com/photos/7577311@N06/1490557341
Image Credits
[3] Via Flickr Creative Commons, and by Eric Fischer: Available at
http://www.flickr.com/photos/24431382@N03/4671562937
[31] Via Flickr Creative Commons, and by barks photo stream: Available
at http://www.flickr.com/photos/49503168860@N01/4257136773
[48] Via Flickr Creative Commons, and by Darwin Bell: Available at
http://www.flickr.com/photos/darwinbell/1454251440/
[16] Via Flickr Creative Commons, and by What What: Available at
http://www.flickr.com/photos/99136715@N00/26553280
[27] Via Flickr Creative Commons, and by FutUndBeidl: Available at
http://www.flickr.com/photos/61423903@N06/7369580478

89. Image Credits
[81] Via Flickr Creative Commons, and by binnyva: Available at
http://www.flickr.com/photos/61999649@N00/8600465534
[Slides 63-66] Vitae®, © 2010 Careers Research and Advisory Centre
(CRAC) Limited„ Available at www.vitae.ac.uk/rdf

90. Measuring
Researcher
Development

91. Measuring
Researcher
Development

92. Measuring
Researcher
Development

93. Measuring
Researcher
Development