1. Practical Data Management
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Research Data Management
Workshop 2.2

2. Learning outcomes
• By the end of this session you will be able to:
– Describe and apply practical principles of data
management
– Select appropriate messages about practical data
management for particular audiences (in terms of
discipline and seniority)
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

3. Session 2.2 overview
• The importance of good data management
• Risk assessment
• Data quality
• Data security
• Teaching data management
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

4. Practical data management
• One of the key ways to motivate researchers
for RDM is to consider the inherent
importance of data quality management and
the consequences of bad management
• Practical data management includes:
– Data quality
– Metadata quality, e.g. file naming
– Backing up data / data security
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

5. The importance of good data
management
• The range of different arguments for RDM include
data quality issues
• A good “starting point” for libraries in engaging
with RDM is raising PhD researchers’ awareness
• Information professionals should already
understand the principles of good data
management
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

6. Activity Practical messages for PhD
students
• Brainstorm what you think might be key
practical messages for PhD students about
how to manage their data.
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

7. Activity: Using stories about what can
go wrong
• If you wanted to use one of these stories to
inform an audience of early career
researchers, which would you pick and why?
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

8. Risk assessment
• Data practices should undergo risk assessment
• This implies categorising risks, in terms of their
severity and their likelihood, then determining
possible stances (from toleration to terminating
the activity)
• A risk log is a project management tool for of
monitoring risks
• The key to managing risk is often said to be
– Planning early and continuing to update plan
– Apportioning responsibility clearly
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

9. Risk assessment
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Based on DATUM in Action (2012 b) and JISC project guidelines.
Low severity Medium
severity
High severity
Low probability Tolerate Tolerate
Treat
Treat
Medium
probability
Tolerate
Treat
Treat Treat
Transfer
High
probability
Treat Treat
Transfer
Treat
Transfer
Terminate

10. Risk logs
Risk
description
(Condition/Cau
se/consequenc
e)
Probability
(P)
1 – 5
(1 = low
5 = high)
Severity
(S)
1 – 5
(1 = low
5 = high)
Risk
Score
(PxS)
Timescale Owner Detail of
action to
be taken
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Based on JISC infoNet (2012).

11. Data threats
• Theft or loss of device
• Corruption of back up material
• Hard drive failures
• Difficulty locating data files
– Difficulty finding relevant version
• Colleagues move on, taking files with them so they cannot
be consulted or leaving data without explanations of their
source
• Files over-written
• Poor metadata
• Not enough information about context is supplied to
understand the data
• Obsolescence of file types
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

12. High risk data (DATUM, 2012b)
• “Details relating to identifiable individuals the contents of which, if
compromised, have the potential to cause damage or distress
• Any set of data relating to an identifiable individual’s sensitive
personal details
• Data concerning any vulnerable individual
• Large data sets relating to 1,000 or more identifiable individuals
• Research recommendations, before the decision was officially
announced
• Data that, if compromised, would affect contracts with commercial
or other partners, or confidentiality and non-disclosure agreements
• Information that would compromise patent applications
• Any data that is the result of an un-repeatable study”
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

13. Data quality (Gordon, 2007)
• Completeness
• Correctness
• Enterprise awareness
• Input validation
• Integrity
• Currency
• Duplication
• Inconsistency
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

14. Quality controls in the research
context
• Instrument calibration
• Taking multiple measurements
• Following protocols in taking measurements
• Validation rules
• Using controlled vocabularies
• Expert validation
• Statistical tests to identify anomalous values
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

15. Metadata quality: file naming
conventions
• “Data files are distinguishable from each
other within their containing folder
• Data file naming prevents confusion when
multiple people are working on shared
files
• Data files are easier to locate and browse
• Data files can be retrieved not only by the
creator but by other users
• Data files can be sorted in logical
sequence
• Data files are not accidentally overwritten
or deleted
• Different versions of data files can be
identified
• If data files are moved to other storage
platform their names will retain useful
context”
(EDINA and Data Library, n.d.)
• Simplicity
• Avoid special characters, spaces
• Appropriate word order
• Rules about version control
(DATUM in Action, 2012a)
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

16. Data security: storage and back up
• Choice of media
• Frequency of back up
• How long are back ups stored?
• Security, if sensitive data
• Issues with cloud based services, such as
Dropbox, e.g. procedures for restoring files,
reliability
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

17. REFERENCES
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

18. References
• DATUM in Action. (2012 a). Folders and files – guidance Newcastle:
Northumbria University School of Computing, Engineering &
Information Sciences. Retrieved from
http://www.northumbria.ac.uk/static/5007/ceispdf/filenameguide
.pdf
• DATUM in Action. (2012 b). Information security guidance.
Newcastle: Northumbria University School of Computing,
Engineering & Information Sciences. Retrieved from
http://www.northumbria.ac.uk/static/5007/ceispdf/infosecurity.p
df
• EDINA and Data Library, University of Edinburgh. (n.d.). Research
Data MANTRA. Retrieved from http://datalib.edina.ac.uk/mantra/
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose

19. References
• Gordon, K. (2007). Principles of Data Management: Facilitating
Information Sharing. Swindon: British Computing Society.
• JISC infoNet (2012). The Risk Log. Newcastle upon Tyne. Retrieved
from http://www.jiscinfonet.ac.uk/infokits/risk-
management/identifying-risk/risk-log
• UK Data Archive (2011). Managing and Sharing Data: Best Practice
for Researchers (3d ed., fully revised). Colchester: University of
Essex. Retrieved from http://www.data-
archive.ac.uk/media/2894/managingsharing.pdf [This includes a
useful checklist as an appendix.]
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose