Introduction to research data management

Slides provided by DaMaRO Project, University of Oxford

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WHAT IS RESEARCH DATA MANAGEMENT?

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What is data?

“A reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing.” Digital Curation Centre

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Slide adapted from the PrePARe Project Introduction to research data management Page 3

What is data?

Any information you use in your research

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In small groups…

 Introduce yourself  What sort of data do you use?  Where does it come from?

 Are you creating new data?

 Are you working with pre-existing data?

 Where is your data stored?

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What is data management?

  Data management is a general term covering how you organize, structure, store, and care for the information used or generated during a research project It includes:  How you deal with information on a day-to-day basis over the lifetime of a project  What happens to data in the longer term – what you do with it after the project concludes

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Why spend time and effort on this?

 So you can work efficiently and effectively  Save time and reduce frustration  Highlight patterns or connections that might otherwise be missed  Because your data is precious   To enable data re-use and sharing To meet funders’ and institutional requirements

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University of Oxford policy

Introduced July 2012

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University of Oxford policy

   The full policy can be viewed on the University of Oxford Research Data Management website Research data is the information needed outputs’ ‘to support or validate a research project’s observations, findings or Research data should be:  Accurate, complete, identifiable, retrievable, and securely stored  Able to be made available to others

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University of Oxford policy

 Research data should be retained for ‘as long as they are of continuing value to the researcher and the wider research community’ – but a

minimum

of three years   Specific requirements from funders take precedence Researchers are responsible for:  Developing and documenting clear data management procedures  Planning for the ongoing custodianship of their data  Ensuring that legal, ethical, and funding body requirements are met  Policy applies to University staff and doctoral students  Depositing relevant research data may ultimately become a condition of award for doctorates

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Funders’ requirements

 Funding bodies are taking an increasing interest in what happens to research data  You may be required to make your data publicly available at the end of a project  Check the small print in your grant conditions   Many funders require a data management plan as part of grant applications Oxford’s RDM website provides a summary of requirements

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DAY-TO-DAY DATA MANAGEMENT

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Can you find what you need, when you need it?

‘What a mess’ by .pst, via Flickr: http://www.flickr.com/photos/psteichen/3915657914 / .

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Questions to ask

 Are you using the most appropriate software or other tools to store and analyse your data?

 Do you have a system in place for dealing with new data when you acquire it?

 If so, is it realistic?

 Are you recording all the necessary contextual information?

 Are you using helpful, consistent file naming conventions? Is your file structure clear?

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File naming

 Aim for concise but informative names  Ideally, you should be able to tell what’s in a file without opening it  Think about the ordering of elements within a filename  YYYY-MM-DD dates allow chronological sorting  You can force an order by adding a number at the beginning of the name  Consider including version information

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File naming strategies – examples

 Order by date:  Order by type: Interview-recording_MBD_2012-12-15.mp3

 Interview-recording_THD_2013-04-12.mp3

the newspaper articles I downloaded… I named the Interview-transcript_THD_2013-04-12.docx

mentioning the name of the periodical and the year Order by subject:  Forced order with numbering: of publication, which would have been very useful MBD_interview-recording_2012-12-15.mp3

01_THD_interview-recording_2013-04-12.mp3

later, when I began writing the thesis. MBD_interview-transcript_2012-12-15.docx

02_THD_interview-transcript_2013-04-12.docx

THD_interview-transcript_2013-04-12.docx

03_MBD_interview-recording_2012-12-15.mp3

04_MBD_interview-transcript_2012-12-15.docx

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Research Skills Toolkit

 Website and hands on workshops  A guide to software, University services, and other tools and resources for research  Requires SSO login

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http://www.skillstoolkit.ox.ac.uk/ Introduction to research data management Page 17

IT Learning Programme

 Over 200 different IT courses   Covering software, skills, and new technologies http://www.oucs.ox.ac.uk/itlp/ ITLP Portfolio offers course materials and other resources

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http://portfolio.it.ox.ac.uk/ Introduction to research data management Page 18

ORDS – Online Research Database Service

      Specifically designed for academic research data Cloud-hosted and automatically backed up Web interface makes collaboration straightforward If desired, databases can easily be made public Designed to permit easy archiving Currently recruiting early adopters – free use of system in return for feedback  Contact [email protected]

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KEEPING YOUR DATA SAFE

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http://blogs.ch.cam.ac.uk/pmr/2011/08/01/why-you-need-a-data-management-plan/

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Slide adapted from the PrePARe Project Introduction to research data management Page 21

What would happen to your data if there was a fire in your office, department or home?

‘Fire’ by andrewmalone, via Flickr: http://www.flickr.com/photos/andrewmalone/2032844649/

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Make multiple copies… …and keep them in different places Automate the process if you can

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Example back-up plan

 Back-up strategy for a recent postdoc research project:  Working data stored on personal laptop  Weekly back-up to external hard drive, and to two memory sticks  Key files also sent as email attachments, or saved to Dropbox  Post-project, data copied to DVDs for long-term storage

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IT Services: Data Back-up on the HFS

 HFS is Oxford’s central back-up and archiving service  Free of charge to University staff and postgraduates  Automated back-ups of machines connected to University network  Copies kept in multiple places

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Think about your storage media… … and about file formats

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Slide adapted from the PrePARe Project Introduction to research data management Page 26

In small groups…

 What data management challenges have you encountered?

 What strategies have you personally found useful?

 Be ready to feed back to the group

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DOCUMENTATION AND METADATA

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Documentation and metadata

 Documentation is the contextual information required to make data intelligible and aid interpretation  A users’ guide to your data  Metadata is similar, but usually more structured  Conforms to set standards  Machine readable

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Make material understandable

What’s obvious now might not be in a few months, years, decades…

MAKE SURE YOU CAN UNDERSTAND IT LATER

A dapted from ‘Clay Tablets with Linear B Script’ by Dennis, via Flickr: http://www.flickr.com/photos/archer10/5692813531 /

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Slide adapted from the PrePARe Project Introduction to research data management Page 30

Make material verifiable

• Detailing your methods helps people understand what you did • And helps make your work reproducible • Conclusions can be verified Image by woodleywonderworks , via Flickr: http://www.flickr.com/photos/wwworks/4588700881/

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Slide adapted from the PrePARe Project Introduction to research data management Page 31

Make material reusable

 You may wish to re-use your own data later on  Or you may wish to make it available for others to use  Provide context to minimize the risk of misunderstanding or misuse  Good metadata makes it easier to locate relevant data

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Slide adapted from the PrePARe Project Introduction to research data management Page 32

Documentation – what to include

•

Who

created it,

when

and

why

• • • • •

Description

of the item

Methodology

and

methods Units

of measurement

Definitions

of jargon, acronyms and code

References

to related data www.texample.net

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Slide adapted from the PrePARe Project Introduction to research data management Page 33

Metadata – data about data

 A formal, structured description of a dataset  Used by archives to create catalogue records

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Missing metadata – or the riddle of the sixth toe

   This painting shows Georgiana, Duchess of Devonshire as Diana … or Cynthia She has six toes – but no one knows why Public domain image from Wikimedia Commons: http://commons.wikimedia.org/wiki/File:Georgiana_Cavendish,_Duchess_of_Devonshire_as_Diana.jpg

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WHAT HAPPENS AT THE END OF THE PROJECT?

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Data archiving

  Data generated during a research project is valuable Don’t leave it languishing on your hard drive  Consider depositing it in an archive or repository  A number of national disciplinary archives exist  DataBib provides a catalogue: http://databib.org/  Oxford will soon have its own data archive  If possible, make it available for others to re-use

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Why share data? Reputation

 Get credit for high quality research  Recognition for contribution to research community  Open data leads to increased citations  Of the data itself  Of associated papers

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Slide adapted from the PrePARe Project Introduction to research data management Page 38

Why share data? Reuse

 Reduces duplication of effort  Allows public research funding to be used more effectively  Contexts not currently envisaged  Extend research beyond your discipline

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Slide adapted from the PrePARe Project Introduction to research data management Page 39

Why share data? Be a trailblazer!

  A paradigm shift in how research outputs are viewed is occurring Data outputs are of increasing importance – and are likely to become even more so  Major journals are increasingly looking to publish datasets alongside articles  Be at the forefront of an important shift in the academic world

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Video by NYU Health Sciences Libraries: http://www.youtube.com/watch?v=N2zK3sAtr-4

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Data sharing – concerns

 Ethical concerns  Confidential or sensitive data  Legal concerns  Third party data  Professional concerns  Intended publication  Commercial issues (e.g. patent protection)

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Data sharing – concerns

• Redact or embargo if there is good reason • Planning ahead can reduce difficulties

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Slide adapted from the PrePARe Project Introduction to research data management Page 43

Data licensing

 A licence clarifies the conditions for accessing and making use of a dataset  User knows what’s allowed without asking further permission  Doesn’t exclude possibility of specific requests to go beyond the terms of the licence  For databases, structure and content may be covered by separate rights

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Data licences - examples

 Creative Common licences  Widely used and recognized  Six different flavours, plus CC0 public domain dedication  http://creativecommons.org/  Open Data Commons  Specifically designed for datasets  Recognizes the structure/content distinction  http://opendatacommons.org/

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Data licensing - guidance

 ‘How to License Research Data’  A guide from the Digital Curation Centre http://www.dcc.ac.uk/resources/how-guides/license-research-data

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DATA MANAGEMENT PLANNING

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Data management plans

 A document which may be created in the early stages of a project  While planning, applying for funding, or setting up  An initial plan may be expanded later  Details plans and expectations for data  Nature of data and its creation or acquisition  Storage and security  Preservation and sharing

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Exercise

  Using the resources available, have a go at drafting a data management plan for your own research If there are questions you can’t answer at this stage, make a note of  What you need to find out  Decisions you need to make

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Digital Curation Centre

 A national service providing advice and resources  Create a data management plan using the DMP

online

tool http://www.dcc.ac.uk/

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https://dmponline.dcc.ac.uk/ Introduction to research data management Page 50

‘In preparing for battle, I have always found that plans are useless but planning is indispensable .’ Dwight D. Eisenhower

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UNIVERSITY SERVICES

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DataBank and DataFinder

  Two new University of Oxford services Scheduled to launch later in 2013

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DataBank

  University of Oxford’s institutional data archive Long term preservation for datasets without another natural home    In some cases, may a suitable home for DPhil data Datasets will be assigned DOIs Will work alongside ORA, the University archive for research publications   Possible to link publications in ORA to datasets in DataBank Depositors can opt to make datasets publicly available, embargoed for a fixed period, or hidden

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DataFinder

 A catalogue of datasets   Information on the nature, location, and availability of the data Will harvest metadata from DataBank and other compatible data stores    So anything in DataBank will have a record in DataFinder Researchers depositing data elsewhere strongly encouraged to add a record to DataFinder Should provide a substantial resource for researchers seeking datasets for reuse

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FURTHER INFORMATION AND RESOURCES

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Research data management website

   Oxford’s central advisory website Covers data management planning, back-up and security, data sharing and archiving, funder requirements, etc.

University policy is available http://www.admin.ox.ac.uk/rdm/

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IT Services: Support for Research

 Can assist with technical aspects of research projects at all stages of the project lifecycle  But the earlier you seek advice, the better  Includes the DaMaRO (Data Management Rollout at Oxford) Project  For more information, email [email protected]

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Research Data MANTRA

 Free online interactive training modules  Aimed at postgraduates and early career researchers

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http://datalib.edina.ac.uk/mantra/ Introduction to research data management Page 59

Any questions?

Ask now, or email us on [email protected]

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Rights and re-use

    This slideshow is part of a series of research data management training resources prepared by the DaMaRO Project at the University of Oxford With the exception of clip art used with permission from Microsoft , the slideshow is made available under a Creative Commons Attribution Non-Commercial Share-Alike License Parts of this slideshow draw on teaching materials produced by the PrePARe Project , DATUM for Health , and DataTrain Archaeology Within the terms of this licence, we actively encourage sharing, adaptation, and re-use of this material

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