An introduction to research data management for historians

1. History and data
management
Chris Awre
History and data management – necessary bedfellows?
12 December 2013

2. History and data
• There is a lot of data in History research, e.g.,
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Transatlantic slave database
History of marine animal populations (HMAP)
UK economic statistics 1066 – present
Biographical detail of officers in the German armed forces
in the 17th and 18th centuries
• Holding this for the research requires some level of
management
• Digital Curation Centre – “because good research needs
good data”
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3. Starting points
• Management of research data happens
– Existing activity is acknowledged
• Research data management has a much higher profile now
• Initiatives are based on three trends
– The amount of data is growing
– Data management is required by multiple disciplines
– Increasing perception of the value of data
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4. Research Data Management for History
• Why?
• What?
• Where?
• When?
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5. RDM and History – Why?
• Data as research output
– Data itself can be a valid (REF) research output and needs to be
well managed for presentation and assessment
• Transparency of research
– Good data management allows the process of research to be
transparent, adding validity and integrity
• Data security and accuracy
– Data management is not just for outputs, but can support
research practice itself
• Data sharing
– Foster collaboration and increase the value of the data through
making it available for others to use
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6. RDM and History – What?
• Research data takes many forms, e.g., to name a few,
– Computer-generated data from experiments
– Survey data
– Compilations of historical facts
• The scope of research data encompasses the materials
and/or information that are created or gathered to underpin
research analysis
• Completed work / work in progress
– Data management can, and probably should, encompass
both
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7. RDM and History – Where?
• Local research data management does not necessarily mean
local provision of storage
– Local management, making use of services as required
wherever they may be
• Options for storage
– Local
– Cloud
– Disciplinary, national or international data centre
– Publishers (traditional journals, but also data journals)
• All have associated services
– What criteria determine which option(s)?
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8. Data management and storage
• Many people have their own approaches to managing the
data they generate
– USB drives, to CDs/DVDs, to spare hard disks, etc.
– Occasionally use of formal data centres or robust local
storage
• Key is to know what you plan to do, don’t just decide as you
go along
– Data can be the lifeblood of your research – look after it!
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9. Storage and backup
• http://www.youtube.com/watch?v=n_1XFKYAcoI&list=PL2
XF5RiVI7GOLsLiInXnvT8L6XHC02byD
• Make sure it doesn’t happen to you!
• Storage as an active process, not a passive one
– Consciously thinking of how you are recording your data,
and where
• Aim to always know where files are
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10. RDM and History – When?
• When to manage data?
– At all stages of the research data lifecycle
• Key to this is making it easy to embed
– Minimise effort, whilst demonstrating benefits of effort
undertaken
• Timetable?
– Your decision
– Starting with data management at the start of a research
process alleviates issues in the future
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11. Post-research archiving
• Hydra is the University of Hull’s digital repository for
managing, archiving and sharing (where appropriate)
research outputs, including data
• History datasets managed
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Doomsday Book
Historical Marine Animal Populations (HMAP)
Privateering and piracy
Waleran II
…others to come
• http://hydra.hull.ac.uk
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12. Can do / can’t do
• It is important that you recognise what you can do to
manage your data, and what you can’t do
• Identify areas where you may need assistance, and ask
• Use the experience of your course to build up your
experience and knowledge of what works well for you, and
where you find value in getting help
– Record all this in your data management plan over time
• Research data management can be, or appear to be, complex
– Good RDM is about learning how to simplify that
complexity
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13. Thank you
Questions…?
c.awre@hull.ac.uk