OU Library Research Support webinar: Working with research dataIzzyChad
Slides from a webinar delivered on 31st January 2018 for OU research staff and students. Covers practical strategies for managing research data, including policies, file naming, information security, metadata and working with sensitive data.
This slideshow was used in a research data management planning course taught at IT Services, University of Oxford, on 2017-02-01. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
Webinar delivered by the OU Library Research Support team on 21st March 2020. Covers essential tips for working with research data, including file storage, information security, file naming, metadata and working with participants.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
Working with Research Data 17th October 2019IzzyChad
Slides from a webinar delivered by the Open University Library on 17th October. This webinar covered practical details of how to manage data during research projects, including data security, file naming strategies and working with participants.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2016-11-16. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
This slideshow was used in a data management planning course taught at IT Services, University of Oxford, on 2016-11-09. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2016-02-08. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2016-02-03. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-02-18 and 2015-05-13. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-11-04. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
OU Library Research Support webinar: Working with research dataIzzyChad
Slides from a webinar delivered on 31st January 2018 for OU research staff and students. Covers practical strategies for managing research data, including policies, file naming, information security, metadata and working with sensitive data.
This slideshow was used in a research data management planning course taught at IT Services, University of Oxford, on 2017-02-01. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2017-02-15. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
Webinar delivered by the OU Library Research Support team on 21st March 2020. Covers essential tips for working with research data, including file storage, information security, file naming, metadata and working with participants.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
Working with Research Data 17th October 2019IzzyChad
Slides from a webinar delivered by the Open University Library on 17th October. This webinar covered practical details of how to manage data during research projects, including data security, file naming strategies and working with participants.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2016-11-16. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
This slideshow was used in a data management planning course taught at IT Services, University of Oxford, on 2016-11-09. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2016-02-08. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one. (The presentation has been very slightly edited: references to resources provided to course participants have been replaced with web links.)
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2016-02-03. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-02-18 and 2015-05-13. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-11-04. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
Presentation from a University of York Library workshop on research data management. The workshop provides an introduction to research data management, covering best practice for the successful organisation, storage, documentation, archiving, and sharing of research data.
An introduction to Research Data Management and Data Management Planning presented at the University of the West of England on Wednesday 9th July 2014.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
PIDs, Data and Software: How Libraries Can Support Researchers in an Evolving...Sarah Anna Stewart
Presentation given at the M25 Consortium of Academic Libraries, CPD25 Event on 'The Role of the Library in Supporting Research'. Provides an introduction to data, software and PIDs and a brief look at how libraries can enable researchers to gain impact and credit for their research data and software.
Presentation given at the VADS4R training event in Glasgow on 16th June. VADS4R is a project training PhD students and early career researchers in the visual and performing arts about research data management.
Are you interesting in offering data management services at your library but aren’t sure where to start? Then this class is for you! During this session, we will
• Outline the data management topics that are commonly offered in libraries
• Present strategies for how to determine what services might be most useful on your campus and create synergistic partnerships with other university entities
• Dive into how to offer support with data management plans
• Present a case study for using an institutional repository to archive and share research data
• Identify additional training opportunities and open educational resources you can use to develop robust DM services
The class will consist of a mix of presentations, hands on activities, and discussion. So come ready to participate!
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2014-10-27. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
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Getting to Grips with Research Data Management
1. Getting to grips with
Research Data Management
18th
May 2017
Isabel Chadwick,
Research Support Librarian
library-research-support@open.ac.uk
2. Overview of the workshop
• What is Research Data Management?
• Sharing data
• Working with data
• Planning for data
• Useful resources
• Questions?
3. What is Research Data Management?
“Research data management concerns the
organisation of data, from its entry to the research
cycle through to the dissemination and archiving of
valuable results. It aims to ensure reliable
verification of results, and permits new and
innovative research built on existing information."
Digital Curation Centre (2011)
Making the Case for Research Data Management
http://www.dcc.ac.uk/sites/default/files/documents/publications/Making%20the%20case.pdf
4. What is Research Data Management?
Discussion
• Describe your research
• What type of data do you create/use?
• What data management challenges do you face?
5. What is Research Data Management?
UK Data Archive Data Lifecycle model
http://www.data-archive.ac.uk/create-manage/life-cycle
Design research
Plan data
management
Plan consent for
sharing
Locate existing data
Collect data
Capture and create
metadata
Creating data
6. What is Research Data Management?
UK Data Archive Data Lifecycle model
http://www.data-archive.ac.uk/create-manage/life-cycle
Enter data, digitise,
transcribe, translate
Check, validate,
clean data
Anonymise data
Describe data
Manage and store
data
Processing data
7. What is Research Data Management?
UK Data Archive Data Lifecycle model
http://www.data-archive.ac.uk/create-manage/life-cycle
Interpret data
Derive data
Produce research
outputs
Author publications
Prepare data for
publications
Analysing data
8. What is Research Data Management?
UK Data Archive Data Lifecycle model
http://www.data-archive.ac.uk/create-manage/life-cycle
Migrate data to best
format
Migrate data to
suitable medium
Back-up and store
data
Create metadata
and documentation
Archive data
Preserving data
9. What is Research Data Management?
UK Data Archive Data Lifecycle model
http://www.data-archive.ac.uk/create-manage/life-cycle
Distribute data
Share data
Control access
Establish copyright
Assign licences
Promote data
Giving access to data
10. What is Research Data Management?
UK Data Archive Data Lifecycle model
http://www.data-archive.ac.uk/create-manage/life-cycle
Follow-up research
New research
Undertake research
reviews
Scrutinise findings
Teach and learn
Re-using data
11. What is Research Data Management?
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
12. What is Research Data Management?
What does the OU expect?
“In keeping with OU principles of openness, it is expected
that research data will be open and accessible to other
researchers, as soon as appropriate and verifiable,
subject to the application of appropriate safeguards
relating to the sensitivity of the data and legal and
commercial requirements.”
“Research data must be managed to the highest
standards throughout their lifecycle in order to support
excellence in research practice.”
OU Research Data Management Policy, November 2016
http://www.open.ac.uk/library-research-support/sites/www.open.ac.uk.library-research-s
13. What is Research Data Management?
What do funders expect?
“Open access to research data is an
enabler of high quality research, a
facilitator of innovation and
safeguards good research practice.”
“Good data management is
fundamental to all stages of the
research process and should be
established at the outset.”
Concordat on Open Research Data
http://www.rcuk.ac.uk/documents/documents/concordatonopenresearchdata-pdf/
14. What is Research Data Management?
What do funders expect?
http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies
18. Sharing data
What do you need to share?
• Raw data
• Derived data
• Data underpinning
publications
• Code
• Methods
What are research data in your context?
What would others need to understand your research?
19. Sharing data
Barriers to sharing data: discussion
Discuss barriers to sharing
your research data
These could be:
•Ethical
•Legal
•Professional
Can these barriers be
overcome?
20. Sharing data
How can I share my data?
Open Research Data Online
(ORDO)
Online data sharing services
•Figshare
•Zenodo
•CKAN DataHub
•Mendeley Data
Directories
•re3data
Funders’ repository services
•UK Data Service ReShare
•NERC data centres
21.
22. • ORDO: https://ou.figshare.com
• Online drop-in: Tuesday 23rd
May 14.30-15.30 (ask
me for details at end)
• Support: library-research-support@open.ac.uk
23. Working with data
“Start as you mean to go on”
The end point of all projects should
involve making the data publicly
available. Many data will be
deposited in national archives which
have regulations for files and
metadata.
Thinking about the requirements at
the beginning of the project will limit
the transformations needed at the
end of the project.
Data Sharing
24. • Shared areas or SharePoint
• Zendto
• Be wary of Dropbox & similar
• OU collaboration tool in pipeline
• Office 365 has OneDrive
• ORDO
Data storage for research projects comparison table:
http://www.open.ac.uk/library-research-support/sites/www.open.ac.uk.library-res
Working with data
External collaborators: IT Options
25. Working with data
Filing systems
Filing is more than saving files, it’s making
sure you can find them later in your project
•Naming
•Directory Structure
•File Types
•Versioning
All these help to keep your data safe and
accessible.
26. Decide on a file naming convention at the start of your project. Useful file
names are:
•consistent.
•meaningful to you and your colleagues.
•allow you to find the file easily.
Agree on the following elements of a file name:
•Vocabulary
•Punctuation
•Dates (YYYY-MM-DD)
•Order
•Numbers
•Version information
Ideally you should be able to tell what’s in a file before opening it.
Tip: create a readme file detailing the naming scheme.
Working with data
Naming conventions
27. Working with data
File formats
• Unencrypted
• Uncompressed
• Non-proprietary/patent-encumbered
• Open, documented standard
• Standard representation (ASCII, Unicode)
Type Recommended Avoid for data sharing
Tabular data CSV, TSV, SPSS portable Excel
Text Plain text, HTML, RTF
PDF/A only if layout matters
Word
Media Container: MP4, Ogg
Codec: Theora, Dirac, FLAC
Quicktime
H264
Images TIFF, JPEG2000, PNG GIF, JPG
Structured data XML, RDF RDBMS
Further examples: http://www.data-archive.ac.uk/create-manage/format/formats-table
28. Working with data
Metadata & documentation
• Metadata is additional information that is required to
make sense of your files – it’s data about data.
Guidance on disciplinary metadata standards:
http://www.dcc.ac.uk/resources/metadata-standards
29. Working with data
Metadata & documentation (2)
Think FAIR!
Findable
Accessible
Interoperable
Re-usable
Data FAIRport initiative: http://datafairport.org/
30. Working with data
Sensitive data
When working with research participants....
•Ensure you have obtained valid consent
•Consider who needs access to the data
•Inform your participants what will happen with the data after
the project has finished
•Pre-planning and agreeing with participants during the
consent process, on what may and may not be recorded or
transcribed, can be more effective than anonymisation
•Consider controlling access if anonymisation or consent for
sharing are impossible
31. Working with data
Sensitive data (2)
Managing sensitive data
•If possible, collect the necessary data without using
personally identifying information
•De-identify your data upon collection or as soon as
possible thereafter
•Avoid transmitting unencrypted personal data
electronically
•Consider whether you need to keep original collection
instruments (recordings, surveys etc.) once they have
been transcribed and quality assured
32. Planning for data
• Make informed decisions to anticipate
and avoid problems
• Avoid duplication, data loss and
security breaches
• Develop procedures early on for
consistency
• Ensure data are accurate, complete,
reliable and secure
• Save time and effort – make your life
easier!
Data Management Plans are useful
whenever you are creating data to:
33. Planning for data
Which funders require a DMP?
www.dcc.ac.uk/resources/policy-and-legal/ overview-funders-data-policies
Note: Data Management Plans are now a requirement of
all Horizon 2020 projects
34. Planning for data
Activity
Think about your own
research.
What actions would you
need to perform on your
data at each stage of the
UKDA’s Lifecycle model?
How would you do this?
Would you need any
additional funding/staff?
36. Planning for data
Tips
• Keep it simple, short and specific
• Seek advice - consult and
collaborate
• Base plans on available skills and
support
• Make sure implementation is
feasible
• Justify any resources or
restrictions needed
37. Library Services
How we can help
• Data Management Plan checking
• Support with setting up new projects
• Advice on preparation of data for sharing
• Online guidance
• Enquiries
• New service Open Research Data Online (ORDO)
Email: library-research-
support@open.ac.uk
38. Useful links
• The OU Library Research Support website: http://www.open.ac.uk/library-
research-support/research-data-management
• Open Research Data Online (ORDO): https://ou.figshare.com
• Digital Curation Centre: http://www.dcc.ac.uk/
• DMP Online: https://dmponline.dcc.ac.uk/
• UK Data Archive: http://www.data-archive.ac.uk/
• MANTRA: http://datalib.edina.ac.uk/mantra/
• The Orb: http://open.ac.uk/blogs/the_orb
(2 minutes)
Overview of the workshop
When I first planned this workshop, I intended to start with planning and end with sharing as that is the order that you would do things in your project. However the whole purpose of RDM is to make research data openly available, so I think it’s worth thinking about what it is and why it’s important, before working backwards through the research lifecycle.
There is quite a lot of content to get through, we will stop for discussion at various points throughout and there will be time for questions at the end but if you have a burning question please feel free to interrupt me!
1 min (5)
Read the quotation.
This quotation from the Digital Curation Centre sums up what Research Data Management is all about. It covers the management of data throughout your research lifecycle (more on that later) and beyond, when you will be sharing your data with other researchers. This is relevant to all research which produces data, although you may find that the methods you use differ depending on your type of research or academic discipline.
A quick word on the Digital Curation Centre (DCC). They are the leading experts in the UK on Research Data Management, and gave us a lot of help when we set up the RDM project. Their website is a great source of information and guidance.
5 minutes (10)
Slide 4 Discussion
Introduce yourself to the person sitting next to you & talk about the type of data which you produce, and any data management challenges you’ve come across.
7 minutes (17)
Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.
Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.
7 minutes (17)
Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.
Data cleaning means tidying up misspellings, typos, validating or correcting data against a known list of variables.
7 minutes (17)
Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.
Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.
7 minutes (17)
Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.
Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.
7 minutes (17)
Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.
Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.
7 minutes (17)
Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.
Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.
3 mins (20)
Good data management does require an investment of effort – but ultimately it’s something that can actually save you time, by helping you work more efficiently. Many of us are all too well acquainted with the frustration of trying to track down a fact or a document we know we have somewhere. Good research data management – setting up an organizational system that works for you, and ensuring everything is properly filed or labelled to enable re-identification and retrieval – can make life a lot easier.
And it’s not just a matter of saving time and reducing unnecessary effort (though clearly that’s a major benefit): having everything well ordered can also help you get a better feel of the shape and scope of your research material, which in turn can enable you to spot patterns or connections that might otherwise get missed.
It’s also well worth doing, because the data you’re producing or working with is valuable
As well as this being true for your own research, the data might ultimately be of use to other researchers. Having everything well organized and properly labelled also has the potential to save you a lot of time at the end of a research project, when it comes to deciding what to do with your data – but more of that later.
Finally, there may be requirements imposed by your funding body and/or the university which you need to meet
2 mins (22)
The OU’s RDM policy was approved by Research Committee in November 2016.
Make your data open wherever possible (including physical data) – no later than the first date of online publication of research.
Published research papers must include statements on how and on what terms supporting data may be accessed, or if there is no data the paper should make that clear.
Manage it responsibly throughout your project
The university will provide services and facilities, training support and guidance
Note: All those engaged in research at the OU, including those involved in collaborating with other institutions, must take personal responsibility for managing their research data in accordance with University and funder requirements
1 min (23)
28th July 2016.
The Concordat on Open Research Data has been developed by a UK multi-stakeholder group.
Individual funders (including all the separate UK Research Councils) have their own policies. On the next slide we’ll see an overview.
Here’s an overview of what the research councils expect.
If you haven’t done so already, find your funder’s research data policy and check that you are compliant.
It’s not only RCUK funders which have requirements, e.g. Horizon 2020, government funding, Royal Society,. Make sure you check out your funder policy as early as possible even if last time you checked they didn’t have one, as more and more policies are being released.
1 mins (24)
Sharing data can have huge impacts on collaboration between researchers world wide as this example shows.
1 min (25)
You might remember this news story about George Osborne basing the austerity plan on research data which had been incorrectly analysed. By making data public these kinds of anomalies are more likely to be spotted and incidents like this less likely to happen!
1 min (26)
And of course there is a personal benefit to you as a researcher. Studies have found that there is between a 9% and a 30% increase in citations for papers which make the underlying data available.
5 mins
Think about what research data are in your he data which underpins your publications, but you need to think about whether this will be understandable to others, would they be able to replicate your results? So you might also want to share your code or your methods to enable better understandingcontext.
Depending on your academic discipline and the data type, what you share may vary.
You might want to share raw data, but in some disciplines this might be totally inappropriate, as they will be too vast and meaningless to other people.
You might just want to share your derived, analysed data
Or you might only want to share t.
Describe the data your project will create to your neighbour, think about how much of that data you want , or think is practical , to share, then think between you how much context your neighbour would need in order to be able to work with the data.
5 mins discussion
3 mins feedback
(35)
In some cases, there may be concerns about sharing data, or reasons why all or part of a dataset needs to be kept private. These may be ethical (the data is confidential), legal (the dataset includes third party material with restrictions on usage), or professional (you intend to publish the results, and don’t want someone to get there first).
It’s worth noting that many difficulties or concerns about sharing data can be alleviated by advance planning. For example, ensuring you get proper permissions when data is collected can reduce problems with sharing personal data. If your dataset is a combination of third party data and new material, you may need to have a version of the data where these are kept separate. Proper documentation is also important here: this will help keep track of what you’re allowed to do with data, and what’s happened to it in the course of the project.
Now discuss with your neighbour some of the specific problems caused by your research, and how you might overcome them.
2 mins (37)
There are a number of ways that you can share your data.
The OU recently implemented ORDO – the OU’s institutional research data repository. You can use ORDO to store both live and archive research data. It is based upon the Figshare platform and, crucially, allows you to create a permanent link, a DOI, to your uploaded published research data. This makes it easier to share with others, and provides a means for others to cite and link to your data, thereby giving you proper credit for your work. In a second phase of development, we will be looking to integrate all research datasets added to ORDO, with ORO, so that they show in your staff profile pages. ORO is also our institutional workhorse for the REF, so we will bringing all your research outputs together in one place.
Currently you are required to crate a metadata record of your research datasets in ORO, with a link to their storage location, or details about how enquirers might gain access to them.
Externally, there are a number of repositories. Your funder may well have a repository in which you are required to deposit your data, like the ESRC Also, the NERC data centres. So funders often require you to use their data centres for funded research, and you may want to store your res data in discipline specific services, before you consider using ORDO. That’s fine, it’s supposed to be there as a backup service.
In addition to this there are several free, online services like Figshare, which was devised by someone from Imperial College and is used now by various journals to publish data underpinning research publications. It can also be used as a datastore throughout your project, as it allows online analysis of data, and collaboration with other partners. You may upload unlimited public data and you also get a 20GB allowance for private data.
Zenodo is a similar tool, but can only be used for publication, this was developed by CERN as part of the EU OpenAIRE project and is aimed at the long-tail of science. There is a maximum threshold for upload of 20GB per file, and you are able to include multiple files in one dataset or collection.
CKAN datahub is another similar, free-to-use tool.
There are now a number of journals which specialise in research data, such as Gigascience or BMC Research Notes. They publish peer reviewed articles describing datasets for future reuse. Other journals may allow you to link to your data stored in Figshare or Dryad.
And finally you can find possible repositories to publish your data in re3data, which lists repositories according to academic discipline. All the services here are linked from the RDM intranet pages, and soon to be released library research support website pages.
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Who can use it?
All University research staff and doctoral students, except those working at Affiliated research centres.
External collaborating researchers will also be allowed to use the service, as invited collaborators onto OU workspaces run by OU staff.
What can they use it for?
To store and publish OU research data that supports original research activity. This data store is intended to be used primarily as a research data store once a project has been completed, although it is possible to store live research data from “in-flight” projects in here too. Many funded projects will already have a recognised data store that researchers will be expected to use to make their research data available [use relevant examples here.. like the UK Data Archive as used by the ESRC, or the NERC (Natural Environment) Polar Data Centre and British Oceanographic Data Centre.] Others might use data stores required by specific publishers. What this facility provides is an easy to use alternative to those data stores, so that researchers can store their supporting research data which they deem has long term value.
[It was established particularly to answer a requirement by the EPSRC that all institutions working on research funded by them should provide a means within the institution of supporting research data publication.]
Time for a very quick demo of the system
You can access ORDO through the intranet A-Z list, through the LRS website or simply google ORDO OU.
The landing page shows you the most recently uploaded items. Some files will have a preview in this thumbnail version.
Show a couple of examples of things uploaded – video of clarinet thing
also this thing from public figshare on language acquisition in baby: https://figshare.com/articles/A_baby_s_first_250_words_time_stamped_at_Twitter_/991275 [change for demos in other disciplines)
Show different features on page - altmetrics, views, discussion, citations, share, cite etc.
Show the collection. Collections are useful as a way of curating your research material to give it particular value or show it in a particular context. Collections can be public (like this one) or private. Public collections are assigned a DOI.
To log in click on the red box in the corner, use your institution log in.
Uploading items is really easy – demo this on test site!
Confidential files – upload your data to keep a record and get a DOI but keep the data hidden
Embargoed file –make data available after a certain amount of time to meet ethical or commercial requirements
Metadata records – don’t store the data on figshare, but use it to keep a record and get a DOI (don’t do this if your data has a DOI from another repository)
Private links – share a private link with a colleague or reviewer without having to make the data public
What next?
We plan to harvest metadata from ORDO and populate ORO records with it. This is because ORO is our workhorse for integrating with other systems; ORO has established methods for populating OU staff profile pages with RSS feeds, it is indexed by Google Scholar, is our key tool for managing REF publications compliance.
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I hope that quick demo was useful – if you’re interested in using ORDO but unsure where to start please get in touch and we’d be happy to help. And please spread the word amongst your colleagues!
We’re planning to run a drop in session in the faculty soon. Watch this space!
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Start as you mean to go on
Consider all the preparation necessary for making your data shareable and how you can reduce the workload at the end of the project by doing the work during the project
Metadata and documentation (logs, instructions, records)
File formats
File naming
Data security and storage
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One Drive allows you to share materials rather like Dropbox, but is more secure, and stores are within EU locations, so are subject to EU safe harbour laws concerning the storage of personal data. IT are planning a press release about OneDrive soon, after which time I will update the IT FAQs to reflect their press release. We also now have ORDO, which you can use to store live research data. You may create a project workspace and then invite external collaborators to share this workspace; you can allow view only permissions, or data upload permissions. The file management in ORDO is a little clunky; you have to download, work on files, and then re-upload, so depending on the extent of your collaboration and other options, you may want to use another of the services provided centrally. If you have any specific queries about which service might be right for your live research data, please get in touch.
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Think about names and formats before clicking save
Where do you need this file; is it used by another program?
Do the name and location make sense?
Consideration at the beginning makes it easier to find files and related documents later.
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Vocabulary – choose a standard vocabulary for file names, so that everyone uses a common language.
Punctuation – decide on conventions for if and when to use punctuation symbols, capitals, hyphens and spaces.
Dates – agree on a logical use of dates so that they display chronologically i.e. YYYY-MM-DD.
Order - confirm which element should go first, so that files on the same theme are listed together and can therefore be found easily.
Numbers – specify the amount of digits that will be used in numbering so that files are listed numerically e.g. 01, 002, etc.
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When thinking about file formats, certain formats are more appropriate for long-term preservation and sharing.
Avoid using proprietary formats, these are formats which can only be opened by a specific type of software, like Work and Quicktime, as the software may become obsolete in the future and the files will more difficult to open.
You can of course migrate your files into different formats at the end of your project prior to deposit in a repository or archive, but by thinking about this from the beginning and ensuring the right formats have been used throughout will save you a lot of time when you come to thinking about sharing your data later.
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Slide 19- metadata (1) (2 mins)
It’s not a new idea
Most people do it to a certain extent without thinking
You might organize your collection by artist, title, even colour! This is made much easier in a digital environment
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1. To be Findable any Data Object should be uniquely and persistently identifiable [4]1.1. The same Data Object should be re-findable at any point in time, thus Data Objects should be persistent, with emphasis on their metadata, [4 and JDDCP 4 and JDDCP 6]1.2. A Data Object should minimally contain basic machine readable metadata that allows it to be distinguished from other Data Objects [seeJDDCP 5]1.3. Identifiers for any concept used in Data Objects should therefore be Unique and Persistent [5 and JDDCP 4 and JDDCP 6].
2. Data is Accessible in that it can be always obtained by machines and humans2.1 Upon appropriate authorization [6]2.2 Through a well-defined protocol [7 and JDDCP 5]2.3 Thus, machines and humans alike will be able to judge the actual accessibilty of each Data Object.
3. Data Objects can be Interoperable only if:3.1. (Meta) data is machine-readable [8]3.2. (Meta) data formats utilize shared vocabularies and/or ontologies [9]3.3 (Meta) data within the Data Object should thus be both syntactically parseable and semantically machine-accessible [10]
4. For Data Objects to be Re-usable additional criteria are:4.1 Data Objects should be compliant with principles 1-34.2 (Meta) data should be sufficiently well-described and rich that it can be automatically (or with minimal human effort) linked or integrated, like-with-like, with other data sources [11 and JDDCP 7 and JDDCP 8]4.3 Published Data Objects should refer to their sources with rich enough metadata and provenance to enable proper citation (ref to JDDCP 1-3).
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In the past researchers gained consent from participants primarily so that they could collect data.
However, many funders are now increasingly requesting researchers to share and preserve their data as part of their requirements.
It is therefore important that participants fully understand:
how you will store, publish and share their data
how you will ensure that their data remains confidential and anonymous (where applicable) throughout the duration of the project and after
Failure to obtain consent could result in non-compliance with your funder's requirements and limit the opportunities you have to share, publish and preserve your data.
If things change, you may be able to go back to your participants and change the details of the agreement.
Anonymisation can be time-consuming, so agreeing what can and can’t be recorded or transcribed may well save you time and effort. For example, if they don’t want you to use names, then conduct the interview without using names.
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As mentioned before, if possible in the collection process, not using personally identifying information can save time and effort as you will have less to anonymise.
Make sure you are storing your sensitive data sensibly. If possible, de-identify your data upon collection, this will reduce the damage is a security breach happens.
Make sure you are encrypting your data if you have to send it electronically (eg by email)
Do you need to keep the original recording? If it’s been transcribed, what value does it hold? By destroying it as early as possible you are reducing the risk.
Planning for data 2 minutes (64)
– Which funders require a DMP? (2 mins) 66
•Quick overview – point out EPSRC does not require one, and Horizon 2020 pilot has now ended and they want a plan for all projects.
•However, the OU recommends that all researchers write a DMP regardless of whether their funder requires them to do so or not, as it is a useful exercise for ensuring that data will be managed responsibly throughout the lifecycle.
– Data Management Planning Activity (5 minutes) 71 mins
Think about the research you are working on at the moment, or a recent project. Consider the actions you will need to take and the barriers you might face at all the different stages of the UKDA data curation lifecycle. How could they be overcome? This is a useful exercise to start thinking about the information you would need to put in your plan.
USE THE TEMPLATE FOR THIS
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DMPOnline is a tool developed by the DCC which helps you to write your data management plan.
There are templates for dmps for all the research councils, Horizon 2020, Wellcome Trust and CRUK.
It takes you through the sections of the templates and gives guidance as you work. We’ve now incorporated some OU guidance into this as well. There is also an OU template for researchers who are not funded by any of the bodies for which there is a template, but feel it would be helpful to write a data management plan anyway.
If you do try out this tool, please give me any feedback you might have.
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Keep it simple – not all the reviewers are going to be data management experts
Be specific – instead of saying “we will follow standards” explain WHICH standards, instead of “we will create a large amount of data” HOW MUCH data?
Short – some funders have requirements for how long the plan should be (eg. ESRC 3 pages)
Seek advice – from other researchers at the university who have written plans, or done similar projects. Example of the listening experience database taking advice from colleagues who had worked on the reading experience database.
Be realistic!
RDM is an allowable cost for all RCUK funders, but any costs have to be fully accounted for. All expenditure on direct costs must take place before the actual end date of the project and must be fully auditable.
No expenditure can be ‘double funded’ (a service that is centrally supported by the indirect costs paid on all research grants cannot then also be included as a direct cost on a grant)
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Send DMPs in advance of bid submission! Preferably a week ahead, if possible. But later is better than never!
I am happy to meet with Pis and project teams at the beginning of projects to discuss strategies for managing data and clarify funder requirements. Also able to set up bespoke training sessions for departments/research groups
At the end of your project, hopefully your data will have been managed in a way that facilitates sharing, but if in doubt get in touch for help
Guidance is on the intranet site, and will soon be on the public facing library research support website. URL on next slide.
Send enquiries to email at bottom of screen, this way anyone from the team can pick it up if I’m away.
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Links to additional resources are available on the RDM intranet site.
I’ll put this presentation on The Orb after the workshop.
Please do fill in the feedback questionnaire that you get sent after this - we’re going to revamp this session for next year so we will be keen to take any suggestions on board.
