This two-part course is a collaboration between CU Libraries/Information Services and the Office of Research Compliance & Training. The purpose of this course is to familiarize you with the various aspects of research data management (RDM) Part 1: Why RDM is both recommended and required What research data are Who is responsible for RDM Part 2: When RDM activities occur How you can carry out RDM activities

1. Managing Research Data
Part 2
WHY – WHAT– WHO – WHEN & HOW
Planning Working
Finalizing
Sharing
Data
This work is licensed under a Creative Commons
Attribution 4.0 International License.

2. WHY manage data -
WHAT research data are-
WHO manages research data -
WHEN & HOW data management is done -
Planning Working
Finalizing
Sharing
Data
Managing Research Data
This work is licensed under a Creative Commons
Attribution 4.0 International License.

3. This two-part course is a collaboration between CU Libraries/
Information Services and the Office of Research Compliance &
Training. The purpose of this course is to familiarize you with the
various aspects of research data management (RDM) by taking
3
Managing Research Data
56/
Managing Research Data
This course will guide you through these areas, offering in-depth
details on each of them. Please refer to the top navigation to keep
track of which area you are currently exploring.
• Why RDM is both recommended and required
• What research data are
• Who is responsible for RDM
• When RDM activities occur
• How you can carry out RDM activities
Part 1:
Part 2:

4. Learning objectives:
At the end of this training you will be able to:
• Identify at which research stages data management activities occur
• Understand practical details of research data management such as:
– File naming
– File formats
– Spreadsheet structure
– Data preservation
4
Managing Research Data
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Managing Research Data

5. Links to many of the references and
policies referred to in this course can be
found on the final slides.
Have Fun!
5
Managing Research Data
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Managing Research Data

6. When does Research Data
Management happen?
How is it done?
WHY –WHAT – WHO – WHEN & HOW
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Managing Research Data

7. WHY –WHAT – WHO – WHEN & HOW
Planning Working
Finalizing
Sharing
Data
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Managing Research Data

8. Planning
Planning Working
Finalizing
Sharing
Data
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW

9. When planning to manage
data or writing a data
management plan consider:
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
• What data will be shared?
• Who will have access to the data?
• Where will the data to be shared be located?
• When will the data be shared?
• How will researchers locate and access the data?

10. CONSIDER:
• File format
• File sizes
• Changing rates of data production
• Anticipated size of project data
• Storage & Back-up
• Privacy / security requirements
• Data description
• Retention period
• Sharing requirements
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Plan for the entire data life-cycle.

11. • Non-proprietary
• Open, documented standard
• Standard representation (e.g., ASCII, Unicode)
• Common, or commonly used by the research community (e.g.
FITS, CIF)
• Unencrypted
• Uncompressed
Planning
Some
commonly
recognized
formats
to
avoid
for
storage
include:
Word
[.doc(x)],
SPSS
[.sav],
Excel
[.xls(x)],
STATA
[.dta],
Access
[.mdb,
.accdb],
JPEG
[.jpg],
.gif,
QuickIme
[.mov],
SAS
[.sas]
Some
commonly
recognized
formats
meeIng
these
criteria:
ASCII
[e.g.,
.csv,
.txt],
PDF
[.pdf],
FLAC,
TIFF,
JPEG2000
[.jp2],
MPEG-­‐4
[.mp4],
XML
[.xml,
.odf,
.rdf],
R
[.r]
11
http://www.data-archive.ac.uk/media/2894/managingsharing.pdf
http://www.digitalpreservation.gov/formats/index.shtml?PHPSESSID=c26c5e5101396d5f5ebacedb13cae6e356/
Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Storage file formats should be:
X
✓
Not
sure
about
the
extension?
Check
hYps://
www.naIonalarchives.
gov.uk/PRONOM/
default.htm

12. Storage / Back-ups Planning
Lifespan of Storage Media: http://www.crashplan.com/medialifespan/1256/
Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
No storage medium lasts forever.
Consider the following media life-spans:

13. When choosing storage and back-up options you should:
• Reduce the risk of damage or loss
• Use multiple locations (here, near, far)
• Create a back-up schedule
• Use reliable back-up media
• Test your back-up system (i.e., test file recovery, checksums)
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Storage / Back-ups

14. Remember to:
• Back up data frequently
• Make 3 copies
– Original (here)
– External/local (near)
– External/remote – different geographic area (far)
• Verify recovery is possible
– Confirm that file has not been corrupted, e.g., checksum
validation
– Make sure you can reload the file, i.e., test file restore after
initial set-up
– Check file recovery periodically & systematically thereafter
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Storage / Back-ups

15. Consider physical, network, computer system and file security
for:
• Intellectual Property –Trade secrets, commercial
information, confidential materials, restricted data
• Personally identifying information (PII)
• Personal health information (PHI)
• High-security data
Planning
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WHY –WHAT – WHO – WHEN & HOW
Privacy & Security

16. CU Information Security Charter:
“Users are persons who use Information Resources. Users
are responsible for ensuring that such Resources are used
properly in compliance with the Columbia University
Acceptable Usage of Information Resources Policy
http://policylibrary.columbia.edu/acceptable-usage-
information-resources-policy,
information is not made available to unauthorized persons,
and appropriate security controls are in place.”
Planning
16 http://policylibrary.columbia.edu/information-security-charter56/
Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Privacy & Security

17. (Some) Best practices for handling sensitive data:
• Restrict physical access to computers, offices and storage media
• Encrypt any device (mobile, laptop, desktop, tablet, removable media
[e.g., USB flash drives, CDs, hard drives]) containing sensitive data
• Store lab notebooks, research records, in locked cabinets
• Keep confidential and sensitive data on computers not connected to the
Internet
• Don't send confidential data via e-mail or FTP (use encryption, if you
must)
• Use strong passwords on files and computers
• Sanitize all systems before reusing, disposing, or donating
Planning
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WHY –WHAT – WHO – WHEN & HOW
Privacy & Security

18. • Lab notebooks
• Data descriptions / code book
• File naming
– Consistency: Pick a system, write it down, & stick with it
– Identify necessary elements
– Create brief, understandable names
– Date: YYYY-MM-DD
– Version: v01, v02,…FINAL
In general, try to stay away from spaces in filenames as well as the following
characters:
. / : * ? “ < > | [ ] & $
• File / directory structure
• Sometimes there is a community standard for data formatting &
description for sharing/integration (aka metadata schema) – Find
yours!
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Description / Documentation

19. Plan to keep your data according to:
• CU Data Retention Policy: at least 3 years
• Funder requirements: It varies – check them!
• Regulations
• Contract terms, for industry sponsored research
• The importance of the data, regardless of external
requirements
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Retention Period

20. Do you plan to share your data? Prepare to follow the
requirements of your:
• Funder
• Journal
• Discipline
• Data repository
Planning
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Planning to share

21. Working
Planning Working
Finalizing
Sharing
Data
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW

22. Review the data management plan:
• Are you following it?
• Did it survive first contact with the research? If not,
– Does it need to be revised?
– Take the opportunity to change it as necessary,
documenting the changes
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Collection

23. Revisit your:
• File naming conventions
– Are they written down?
– Does everyone on the project know & follow them?
• File structure / organization / tagging
– Is it easy to understand / logical?
– Is everyone on the project familiar with the organizational
practices so they can store and find files efficiently?
• Back-up processes
– Are they working?
– Are they being followed?
Working
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WHY –WHAT – WHO – WHEN & HOW
Data Collection

24. Are you using someone else’s data as part of your research?
You should probably cite it…
Consider a citation management software to keep track of it:
Working
hYp://
library.columbia.edu/
research/citaIon-­‐
management.html
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Collection

25. Using a spreadsheet for your data? Structure your data so that
it’s easily sortable & usable by other software/machines. Be
consistent with your:
• Labels
• Types
• Formats
• Layout
(Alternatively, consider using a database for easier data management)
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Collection

26. Spreadsheet labels:
Adopt a consistent style that indicates a cell contains a label
rather than a value
Working
Date
Instrument
SoundLevel_R
SoundLevel_L
Amp_Se7ng
2013-­‐12-­‐22
BK-­‐732A
84.6
86.0
3
2013-­‐12-­‐23
BK-­‐732A
115.2
116.4
9
2013-­‐12-­‐24
BK-­‐732A
128.7
130.0
11
Date:
12/22/2013
Instrument
BK732A
Sound
lev
Right
<85
Amplifier
3
(27%)
Lei
86.0
Date:
Dec
23,
2013
Instrument
Amp_Sekng
SoundLevel-­‐R
115
BK_732-­‐A
9
SoundLevel_L
116.4
J
L
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WHY –WHAT – WHO – WHEN & HOW
Data Collection

27. Spreadsheet types:
Don’t mix text & number types in the same column
Working
Date
Instrument
SoundLevel_R
SoundLevel_L
Amp_Se7ng
2013-­‐12-­‐22
BK-­‐732A
84.6
86.0
3
2013-­‐12-­‐23
BK-­‐732A
115.2
116.4
9
2013-­‐12-­‐24
BK-­‐732A
128.7
130.0
11
Date:
12/22/2013
Instrument
BK732A
Sound
lev
Right
<85
Amplifier
3
(27%)
Lei
86.0
Date:
Dec
23,
2013
Instrument
Amp_Sekng
SoundLevel-­‐R
115
BK_732-­‐A
9
SoundLevel_L
116.4
J
L
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WHY –WHAT – WHO – WHEN & HOW
Data Collection

28. Spreadsheet formats:
Do all of your dates or other variable values look the same?
Working
Date
Instrument
SoundLevel_R
SoundLevel_L
Amp_Se7ng
2013-­‐12-­‐22
BK-­‐732A
84.6
86.0
3
2013-­‐12-­‐23
BK-­‐732A
115.2
116.4
9
2013-­‐12-­‐24
BK-­‐732A
128.7
130.0
11
J
L
Date:
12/22/2013
Instrument
BK732A
Sound
lev
Right
<85
Amplifier
3
(27%)
Lei
86.0
Date:
Dec
23,
2013
Instrument
Amp_Sekng
SoundLevel-­‐R
115
BK_732-­‐A
9
SoundLevel_L
116.4
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Collection

29. Spreadsheet layout:
Tables of similar data should be structured similarly
Working
Date
Instrument
SoundLevel_R
SoundLevel_L
Amp_Se7ng
2013-­‐12-­‐22
BK-­‐732A
84.6
86.0
3
2013-­‐12-­‐23
BK-­‐732A
115.2
116.4
9
2013-­‐12-­‐24
BK-­‐732A
128.7
130.0
11
J
L
Date:
12/22/2013
Instrument
BK732A
Sound
lev
Right
<85
Amplifier
3
(27%)
Lei
86.0
Date:
Dec
23,
2013
Instrument
Amp_Sekng
SoundLevel-­‐R
115
BK_732-­‐A
9
SoundLevel_L
116.4
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Collection

30. When it’s not a spreadsheet (it may be a database):
Be consistent!
• Consistent process
• Consistent organization
• Consistent descriptions
AND
• Consistently documenting everything that’s done
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data Collection

31. (Some)Best practices for assuring quality data entry:
• System-limited value entry, i.e., hard code controlled lists of
values
• Check 5-10% of data records manually
• Check out-of-range values
• Check empty values / blank fields
• Consider using a data entry program or double entry keying
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Quality Assurance / Control

32. • Keep an untouched, “raw” copy of the data file – Make it Read
Only
• Save cleaned or analyzed data as new files (with good file
names, as previously described)
– Take extensive notes of the actions taken or scripts used to
“clean” the data
• Use a scripted language (e.g., R, SAS, SPSS) to consistently
process data and create a record of data processing & analysis
• Document scripts / code with comments!
• Write a ReadMe.txt file as you go, rather than trying to
remember what you did later
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Analysis

33. Create a file to document your project. Include:
• What data are being collected & why
• Names of project files (data & analysis)
• Project file naming and file organization conventions
• Data definitions (aka Code Book or Data Dictionary) – more
next slide
• Project standards
• Calibration, precision, accuracy & units of instruments or
measurements
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Analysis - documenting

34. Code books / data dictionaries should include:
• Data codes or coding keys
• Missing value codes
• Field name / Column header / Data label
– Definition e.g., Amp_setting | Dial setting of guitar amplifier
– Values – Possible values e.g., from 0 to 11, whole numbers
– Units – may be included in either Definitions or Values
– Type e.g., string, float, char, date [YYYYMMDD]
Working
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Analysis - documenting

35. Finalizing
Planning Working
Finalizing
Sharing
Data
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW

36. • Check requirements
• Are data useful/usable
• Select data for preservation
• Choose publication path
• Consider publishing negative data – Others may find it useful
• Repositories
Finalizing
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WHY –WHAT – WHO – WHEN & HOW
Preparing data for publication,
sharing, storage, preservation:

37. Have you fulfilled the expectations of your:
• Funder
• Journal
• Discipline
• Repository
• Institution
Finalizing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Check requirements

38. Are data consistently:
• Formatted
• Named
• Organized
• Described / Documented
Are they in a file format that may be easily accessed and reused?
Finalizing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Usability of data

39. You might consider long-term preservation if the answer to any
of these questions is “Yes”
• Do the data support published research?
• Are the data difficult or expensive to regenerate?
• Are the data required for your research but from another source
(i.e. not your original research data)?
– If so, is the future availability of that data from the original source
uncertain?
• Do you plan to share your data, or are you required to per funder
agreement?
• Are the data historically significant?
• Are the data vulnerable to loss, corruption, endangerment, etc.?
Finalizing
https://lib.stanford.edu/data-services/preserve3956/
Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data preservation: Selection

40. Sharing
Planning Working
Finalizing
Sharing
Data
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WHY –WHAT – WHO – WHEN & HOW

41. Honestly, the party most interested in the data you are
producing today is probably:
Your future self
But there are others to consider, too, so…
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Who will you share with?

42. “By ‘final research data,’ we mean recorded factual material
commonly accepted in the scientific community as necessary to
validate research findings.”
NIH FAQ Data Sharing (3/03)
Guidelines will “be determined by the community of interest”
and “may include…data, publications, samples, physical
collections, software and models.”
Data Management and Sharing FAQ (11/10)
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
What to share

43. “Timely release and sharing’ is defined as no later than the
acceptance for publication of the main findings from the final
data set.”
Data Sharing Policy, Section II.8.2.3.1, NIH Grants Policy Statement (10/12)
“The expectation is that all data will be made available after a
reasonable length of time….[which] will be determined by the
community of interest…”
Data Management and Sharing FAQ (11/10)
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
When to share

44. There are many paths to publishing data:
• Data paper / Data journal
• Supplementary material
• Data repositories
Wherever you publish, make sure people can find it, use it, and
give you credit for it! (this usually requires a permanent
identifier e.g., DOI)
Do you have negative data? Others may find it useful – consider
making it available!
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Publishing data

45. Institutional repository
Columbia’s repository accepts materials from faculty, students,
and staff. It offers:
• Long-term preservation strategy
• Multiple back-ups (including off site)
• Quality content descriptions for increased discoverability
• Monthly usage reports
• Permanent URL & doi
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Repositories

46. Disciplinary repository e.g.,
• GenBank
• RCSB Protein Data Bank
• ICPSR
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Repositories

47. Public access repository e.g.,
• Figshare.com
• DataDryad.org
• ResearchCompendia
• Academic Commons
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Repositories

48. • How would you like your data cited
• Licensing
• Privacy/confidentiality/anonymization – Revisit IRB
commitments
• What to share
• When to share
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
When you share your data, consider:

49. • Publish your data, and make sure to cite it in your journal
publication
• When publishing your data, provide a preferred citation
• Did you use someone else’s data?
– Check the license for restrictions
– Provide the following minimum in your work’s citations:
• Title
• Author/Creator name
• Publisher
• Publication year
• Unique identifier e.g., DOI
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Data citation

50. In the USA, facts – which means most datasets – are outside the
scope of copyright protection. Some researchers have adopted
the practice of data licensing because of this.
There are many different license types, with varied provisions
for reuse and attribution. When thinking about licenses keep in
mind:
• Funder requirements
• Institutional requirements
• Scientific and scholarly ethos of extending knowledge
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Licensing

51. Revisit:
• IRB commitments
• Privacy Board requirements (HIPAA)
• Institutional requirements
• Ethical considerations
Consider:
• Have direct identifiers been removed?
• Have indirect identifiers that could reveal identity when
combined been managed?
• Does relational or spatial data have the possibility of
identifying participants?
Maintain the maximum amount of detail possible without
compromising participants confidentiality.
Sharing
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Managing Research Data
WHY –WHAT – WHO – WHEN & HOW
Privacy / Anonymization

52. .
Planning Working
Finalizing
Sharing
Data
• Managing research data
takes place at every stage of
the research and scholarly
process
– Planning
– Working, where you
follow the plan,
collecting and analyzing
data
– Finalizing, where you make sure you followed the plan
– Sharing, where you sigh with relief; it’s so simple, because you
followed your plan!
• Research data management can be complex, but there are resources
available
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WHY –WHAT – WHO – WHEN & HOW
Take-aways
à SEE NEXT PAGE!

53. Resources for Research
Data Management
links located to the left
WHY –WHAT – WHO – WHEN & HOW
5356/
Managing Research Data
Resources for Research Data Management:
WHY –WHAT – WHO – WHEN & HOW
Title
URL
Scholarly Communications Program,
Data Management
http://scholcomm.columbia.edu/data-management/
Research and Data Integrity Program
(ReaDI)
http://www.columbia.edu/cu/compliance/docs/ReaDI_Program/
index.html
Data Management Plan Templates
http://scholcomm.columbia.edu/data-management/data-
management-plan-templates/
CUIT Research Computing Services http://rcs.columbia.edu
Academic Commons Archival Storage http://academiccommons.columbia.edu/about
Citation Management http://library.columbia.edu/research/citation-management.html
Managing Secure Information -
Training
http://columbia.sighttraining.com
Data Security Policies http://policylibrary.columbia.edu/category/computingtechnology
This work is licensed under a Creative Commons
Attribution 4.0 International License.

54. RESOURCES
• CU Data Policies & Procedures:
– Faculty Handbook
– Sponsored Projects Handbook
– Clinical Research Handbook
– Administrative Policy Library, Security Policies
e.g., Electronic Information Resources Security, Data
Classification Policy, Policy on Electronic Data Security Breach
Reporting and Response
• Scholarly Communications Program
• Office of Research Compliance and Training
5456/
Managing Research Data This work is licensed under a Creative Commons
Attribution 4.0 International License.

55. RESOURCES
• Data Management Plans
• CUIT Active Storage options
• Academic Commons archival storage
• Citation management
• Executive Vice President’s Office of Research (EVPR)
• Training on managing Personal Health Information
(PHI)
• Research and Data Integrity Program (ReaDI)
5556/
Managing Research Data This work is licensed under a Creative Commons
Attribution 4.0 International License.

56. REFERENCES
• ScoY,
Mark,
Boardman,
Richard
P.,
Reed,
Philippa
A.S.
and
Cox,
Simon
J.
(2012)
Introducing
research
data.
Southampton,
GB,
Univeristy
of
Southampton,
29pp.
hYp://eprints.soton.ac.uk/338816/
• Responsible
research
data
management
and
the
prevenIon
of
scienIfic
misconduct
www.knaw.nl/Content/Internet_KNAW/publicaIes/pdf/2013569.pdf
• hYp://dmconsult.library.virginia.edu/
5656/
Managing Research Data
Created
by:
Amy
Nurnberger,
2015-­‐05-­‐12
This work is licensed under a Creative Commons
Attribution 4.0 International License.