1. Managing your research data: an introduction for Computer Science PGRs
Jenny Mitcham (Digital Archivist)
Lindsey Myers (Research Support Librarian)
16 June 2015

2. Overview What is research data management? Why manage research data?
How to manage research data (best practice)
Preserving and sharing research data
Data management planning (this is key)
Help & advice

3. What is research data management?

4. What is data?
“A reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing.”
Digital Curation Centre
The first question to address is what the term ‘data’ actually refers to. Definitions vary, and to some extent, what counts as data will depend on the field of study.
For many people, their initial association with the word ‘data’ will be numerical information (statistics, spreadsheets, or experimental results, for example), or perhaps the contents of highly structured information sources such as relational databases.
However, data is far from being limited to these. Other examples include: Textual sources (literary or historical works that are being analysed, or interview transcripts); Websites (including all sorts of sites such as social media sites, as well as established academic sources); Works of art and other images; Audio files (e.g. oral history, recordings of interviews or focus groups); Videos; s; Computer source code; Books; Papers; Catalogues, concordances and indexes.
QUOTE: Research data is information that is involved directly in funded or unfunded research activities. Research data is often arranged or formatted in a such a way as to make it suitable for communication, interpretation and processing.

5. What is data?
2.1 Recorded material, irrespective of format or media, commonly retained and accepted in the academic community as being necessary to validate research findings. Created in the course of the research process, research data will be the recorded facts, observations, measurements, computations, statistics and results that underpin the research paper and grant or project outcomes.
University definition of research data.
Whatever your area of research is, you will be dealing with data in one form or another. Bear in mind that not all data is digital: print resources, handwritten notes, tape recordings, and hard copies of images may also be important sources.

6. 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

7. Why manage your research data?

8. Carrots and sticks
Work efficiently and with minimum hassle over the lifetime of the project
Save time and avoid problems in the future
Make it easy to share your data
University of York Research Data Management Policy
Funding body requirements
CARROTS – RDM = good research practice
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. You want to complete your research project to the best of your ability, but with minimum stress – and good research data management is one of the tools that can help you to do that. For example: 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.
Your 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.
STICKS - University and research funders place requirements on the management and sharing of research data – both consider your research data to be a valuable asset.
Image credit: Microsoft clip art.

9. University Policy “4. Responsibilities
4.1 All staff and research students involved in research under the University’s auspices have a responsibility to manage data they create and maintain it effectively and in line with University policy, regulations, codes of practice and associated training and guidance.”
University Research Data Management Policy:
Outlines broad principles of the University’s approach to research data.
Everyone involved in the research process is responsible for data management.
Also starts to specify the roles and responsibilities of individual researchers and the University more broadly.
It is expected that all members of the research team will be engaged in data management activities with support and infrastructure coordinated at the University level.
What roles will you have and who will you need to work with?

10. The Policy: what you need to know
Research data must be:
accurate, complete, authentic and reliable
identifiable, retrievable and available when needed
kept safe and secure, avoiding data loss
kept in a manner that is compliant with legal and ethical obligations, and (if applicable) funder requirements
disposed of securely.

11. The Policy: What you need to know
Research data that are considered to have long-term value and potential re-use (with no legal, ethical or commercial constraints) must be:
kept for 10 years from the date of last requested access
preserved and deposited, with appropriate documentation.

12. Funder requirements
Funding bodies are taking an increasing interest in what happens to research data
UK's seven Research Councils – e.g. Arts & Humanities Research Council (AHRC); Engineering & Physical Sciences Research Council (EPSRC); Economic & Social Research Council (ESRC) – have adopted a Data Policy, which mandates researchers to share data and outputs to avoid duplication of effort and reduce data collection costs.

13. Funder requirements
Publicly funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner that does not harm intellectual property.
Data with acknowledged long term value should be preserved and remain accessible and usable for future research.
You may be required to make your data publicly available at the end of a project
check the small print in your grant conditions

14. EPSRC expectations
From 1 May 2015 both the University and its EPSRC-funded researchers are required to adhere to the EPSRC set of expectations
Required to: Manage your research data in accordance with the University RDM Policy.
For example:
Research data must be kept safe and secure, avoiding data loss
EPSRC make specific requirements on the University and researchers they fund in relation to the management of research data (e.g. to store it safely)

15. EPSRC expectations
In addition, for papers (incl. theses) which acknowledge EPSRC funding, with a publication date after 1 May 2015, you will need to:

16. EPSRC expectations
State in your published papers how the underlying data can be accessed
Store your data in a format which would facilitate sharing and use by others
Ensure your data are securely preserved for at least ten years
Describe your data using appropriate metadata to enable others to find your data, understand it and how to access it (PURE)
Anticipate the costs and resources needed to manage your data
Key actions you need to undertake to meet/be compliant with the EPSRC:
1. To meet this expectation you will need to:
select data that underpins the published research
decide how and on what terms it might be made available to others
include a data access statement in your published papers describing how and on what terms the underlying research data may be accessed.
3. deposit your data with a suitable external data repository (e.g. subject specific or journal/publisher specific) or make your data (and information about retention periods and any access restrictions) available to the University for long-term storage.
4. PURE ( will be used to record this metadata.

17. Day-to-day data management a
Day-to-day data management a. organising your data (good file management)

18. Can you find what you need, when you need it?
At the start of a research project it is easy to believe that you'll remember what name you gave to a file and where you put it. However once your research gets underway there may be multiple files in various formats, multiple versions, websites, citations, blogs, articles, methodologies, notes, spreadsheets, etc. all relating to your research. Trying to find a data file that you need which has been stored or named incorrectly or inaccurately can be both frustrating and a waste of valuable time.
‘What a mess’ by .pst, via Flickr:

19. The Policy: what you need to know
Research data must be:
accurate, complete, authentic and reliable
identifiable, retrievable and available when needed
kept safe and secure, avoiding data loss
kept in a manner that is compliant with legal and ethical obligations, and (if applicable) funder requirements
disposed of securely.
Thus good file management practises are required to enable you to identify, locate and use your research data files efficiently and effectively.

20. In practice
Are you using helpful, consistent file naming conventions? Is your file structure clear?
Develop plans for:
file structures - where to put data so you won’t lose it
file (and folder) naming - what to call data so you know what it is
version control - keeping track of data
Develop a system/convention that works for your project (and document it) - be consistent
One question to ask yourself about your personal data management practice.
Develop a system that works for your project
Be consistent. It is only effective if you follow the rules consistently (especially important if you are working in a group).
Good file management practises such as following group file naming protocols are also required should you wish to share your files with others in a shared filespace.

21. File (and folder) naming
Decide on a file naming convention at the start of your project. Useful file names:
are consistent
are concise but informative
classify broad file types
are meaningful to you (and your colleagues)
allow you to find the file easily
do not contain special characters or spaces
should not conflict when moved from one location to another.
What to call data so you know what it is [IMPORTANT - Don’t rely on file names as your sole source of documentation]
Decide on a file naming convention at the start of your project. Useful file names:
are consistent (within a research group, it's recommended that you agree on file naming conventions early on in the project)
are concise but informative (ideally, you should be able to tell what’s in a file without opening it)
classify broad file types.
are meaningful to you and your colleagues.
allow you to find the file easily.
do not contain special characters or spaces.
should not conflict when moved from one location to another.

22. File naming strategies Think about the ordering of elements within a filename, e.g. YYYY-MM-DD dates allow chronological sorting
Order by date:
_interview-recording_THD.mp3
_interview-transcript_THD.docx
_interview-recording_MBD.mp3
_interview-transcript_MBD.docx
Order by subject:
MBD_interview-recording_ mp3
MBD_interview-transcript_ docx
THD_interview-recording_ mp3
THD_interview-transcript_ docx
Order by type: Interview-recording_MBD_ mp3 Interview-recording_THD_ mp3 Interview-transcript_MBD_ docx Interview-transcript_THD_ docx Forced order with numbering: 01_THD_interview-recording_ mp3 02_THD_interview-transcript_ docx 03_MBD_interview-recording_ mp3 04_MBD_interview-transcript_ docx
The order of elements in a filename will also usually make a difference to the order of files within a folder, so a bit of planning can help ensure similar items are grouped together.
Using the year-month-date format at the beginning of a filename makes it easy to sort files into chronological order. (The date that a file was created and last edited will often be recorded automatically, but you may sometimes want to associate a file with a date that is neither of these (e.g. when a particular meeting happened).)
You can also force a particular order by adding a number to the start of a filename, or by adding a leading underscore to a file you want to appear at the top of the list.

23. Version control
A common form for expressing data file versions is to use ordinal numbers (1,2,3 etc.) for major version changes and the decimal for minor changes (e.g. v1, v1.1, v2.6)
Beware of using confusing labels (e.g. revision, final, final2, definitive_copy) these can accumulate
Record every change irrespective of how minor that change may be
Discard or delete obsolete versions (whilst retaining the original 'raw' copy)
May create a version control table or file history w/in or alongside data file
It is important to identify and distinguish versions of research data files consistently. This ensures that a clear audit trail exists for tracking the development of a data file and identifying earlier versions when needed. Thus you will need to establish a method that makes sense to you that will indicate the version of your data files.

24. Day-to-day data management b
Day-to-day data management b. storing your data (keeping your data safe)

25. The Policy: what you need to know
Research data must be:
accurate, complete, authentic and reliable
identifiable, retrievable and available when needed
kept safe and secure, avoiding data loss
kept in a manner that is compliant with legal and ethical obligations, and (if applicable) funder requirements
disposed of securely.
Storage

26. DON’T LET THIS BE YOU!
Your data are the life blood of your research. If you lose your data, recovery could be slow, costly or worse, it could be impossible.
Losing crucial research material is the stuff of nightmares… but nightmares come true sometimes.
This is a genuine poster from a pub in Cambridge [the picture has only been altered to straighten it, change the contrast to make it easier to read, and remove some of the details, e.g. the address of the pub and the person’s contact information]
Slide adapted from PrePARe Project slideshow “What is data?”:

27. Storage – Do’s
My Project
The University offers a range of facilities to securely store your data, helping it live a long and useful life. The University recommends:
University filestore/s – individual or shared
required for guaranteed UK storage
required for “must be kept on site”
University (york.ac.uk) Google Drive
does not guarantee UK location, but does guarantee compliance with UK & EU data protection legislation
To keep your data safe and accessible to you and your collaborators within and without the University.
University filestore provides a convenient and secure storage option. It also has the advantage of being regularly backed up by IT Services. University filestores may be necessary to meet requirements for guaranteed UK storage or storage on site.
Your individual filestore is convenient for your own use. If you need more space to store your data you should contact IT Services through the Footprints Customer Portal.
IT Services also provide additional storage in the form of shared filestore (FlexFS). This additional filestore can range in size from 1GB to 2TB and can be restricted to a defined group of users. See the IT Services web pages for details. No charge is made for this service. All these help to keep your data safe and accessible to you and your collaborators within and without the University.

28. Storage: Don’ts never store personal data in services like Dropbox
My Project
Don’t keep your data just on your working machine (laptop or a desktop) – it’s the perfect way to lose your data easily and permanently.
Don’t upload personal/sensitive data to services the University does not have a contract with
never store personal data in services like Dropbox
Don’t use USB memory sticks
encrypt them if you have to use them
use strong passphrases with all encryption products or the encryption has little value
never keep the only copy of your data on one.
DON’T
Dropbox - As the University does not have a contract with them, using these services to hold or transfer any personal information (sensitive research data etc) is a breach of the Data Protection Act and opens the University up to a fine of up to £500,000.
USB sticks -very susceptible to loss or damage

29. Backing up
Mountbatten Building, So’ton Uni.
What is the risk of losing your data? (likelihood / consequence) Don’t have only 1 copy of your data or use only 1 type of data storage (LOCKSS) multiple copies keep in different automate places the process
Lindsey
You should consider all the risks of losing your data and the consequences of losing your data – and mitigate against these – back-up
…sometimes things are out of your control.
FIRE: In 2005, an electrical fault in the electronics and laser research building at the University of Southampton cost £50-100M including temporary building hire and transfer of work to Holland. - what would happen if there was a fire in your department – would you have a (back-up) copy of your data somewhere else?
Keeping backups is probably the most important data management task. There is a real risk of losing data through hard drive failure or accidental deletion. It is therefore recommended that you keep at least 3 copies of your data on at least 2 different media, keeping storage devices in separate locations with at least 1 off-site, and check that they work regularly. You should also have a policy for maintaining regular backups.
It’s also a good idea to keep these copies in different places, for example you might keep a copy of some material in a cloud-based service (WARNING: if your research deals with sensitive data you may not be able to do this), on an external hard-drive or on DVDs/CDs. Consider asking a friend/colleague or family member to look after one copy, or keep one copy at home and one in your office, so your material is physically in separate places. This minimises the risk of data loss in the case of flood, fire or theft.
Backing up should be an automatic part of your everyday research activities.

30. Day-to-day data management c
Day-to-day data management c. documentation and metadata (describing your data)
We’ve looked at how you organise your data and how you keep it safe, but that is not enough. You also need to ensure that you have enough info about it to enable yourself (and others) to make sense of it.

31. 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
Can conform to set standards
Sometimes machine readable
So, we’re going to talk about documentation and metadata
Documentation could be a data dictionary for a database you have created – it explains the purpose of all of the columns, the relationships between the tables and describes any abbreviations or codes used
Metadata could be an XML file that you extract from a scientific instrument you are using that tells you how that instrument was set up
I see these 2 categories as being not quite so fixed – there is a lot of overlap especially when you talk to people in different disciplines

32. Make material understandable
MAKE SURE YOU CAN UNDERSTAND IT LATER
What’s obvious now might not be in a few months, years, decades…
Documentation should be thorough it will contain a lot of information that might seem obvious. But as time passes the information will become less obvious so worth documenting it now.
I know it is hard when you are really in to a particular project to step back from it and really think about whether everything makes sense and is understandable. You are normally so involved in your research that it all appears obvious – but try and stand back and view it with fresh eyes.
Before this job I worked in a data archive in the archaeology dept for about 10 years. I have numerous examples of archaeologists giving me a database to preserve and when I looked at it I could immediately see things that needed documenting –
for example a column in a database full of abbreviations and with no key
a column that recorded measurements but no information anywhere saying what unit of measurement was used
It took me to ask the right questions to get the documentation I needed from them.
Slide adapted from PrePARe Project slideshow “Explain It”:
Adapted from ‘Clay Tablets with Linear B Script’ by Dennis, via Flickr:

33. Make material verifiable
Detailing your methods helps people understand what you did
And helps make your work reproducible
Conclusions can be verified
Important to record your methodology – this may be documented within an associated publication – or may need to be a separate document.
This is important as it means that people can reproduce your research, either to verify your conclusions or as a starting point to develop your work further.
Also useful for research where you want to show change over time – it can be hard to compare 2 datasets if you don’t know how they were collected. For example 3d laser scanning could capture the surface of a stone. You may want to be able to repeat this in 10 and 20 years time to show erosion. Useful to know exactly how first data was captured in order to understand the significance of your results
Image by woodleywonderworks , via Flickr:

34. 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
Context is key – you need to document the context within which the data was collected or produced. This will enable others both to trust it and to re-use it.
In archaeology when we try and reuse data produced by archaeologists from the 1950’s – for example a plan of a site that was excavated, we might find it really hard to use because it is lacking really crucial contexual information such as a north arrow or a scale.
Metadata is also important for discovery of resources – some metadata is designed for search engines to pick up and use for search and retrieval.
Slide adapted from PrePARe Project slideshow “Explain It”:

35. Make material reusable
What access issues are of concern to you?
A couple of years ago we carried out a research data management survey and encouraged researchers to complete it.
We asked people about allowing others to access their data and what would be the main issues of concern for them.
Some of the issues raised are insurmountable – clearly some information can not be shared for legal or data protection reasons.
However, other issues relate directly to documentation.
This is in your power – if you document your data well, people are far less likely to misinterpret it
York Research Data Management Questionnaire results –2013

36. Will someone else understand your data if it isn’t documented?
"The single most useful thing you can do to ensure the long-term preservation of your data is to plan for it to be re-used. Imagining it being reused by someone else who has never met you and who never will meet you, will cause you to approach the creation and design of your data in a new light In short, always plan for re-use" Prof. Julian D. Richards, Director, Archaeology Data Service, University of York
When thinking about what documentation you need for your research data one of the best things you can do is just what Julian Richards here suggests, which is to imagine it being reused by someone who has never met you....even better, imagine it being re-used several years from now. Will they be able to understand it?

37. 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 ?
M. Farinelli et al. (2012) PLoS ONE 7(3): e34047
These are the sorts of things you might want to include in your documentation.
Who/when/why – really important for someone to know this – people use this info to assess the trustworthiness of the data (among other things) – may also help explain why things were done in a certain way
Description of the item – this couple be a caption of an image or a larger piece of documentation for a database. Some items are self describing – a report
Methodology and methods – perhaps the most important
Units of measurement – I’ve seen many databases deposited for archiving that did not include any info about what unit of measurement was used – inches or cm or mm?
Definitions of jargon, acronyms and code - Especially if working on a database – include a key. At the ADS we were once given a database that was fully coded – every cell had codes and abbreviations in it rather than more descriptive data. This dated from the earlier days of computing where you wanted to keep file sizes really small so you fit it on to a floppy disk. There was no key to work out what the codes meant. The only way we managed to find out what the database was all about was because a single cell contained the word ‘kneecap’
Some of this might be in an associated publication but not all.
Slide adapted from PrePARe Project slideshow “Explain It”:

38. What happens at the end of the project?
So – what we’ve been looking at so far are really what things you can do WHILE YOU ARE CARRYING OUT YOUR RESEARCH to manage your data. RDM is an active and continuous process and it should be something you can integrate into your working methods.
You also need to think about what happens to your data at the end of your project ...and this isn’t a decision you should just make on the last day. Think about this as early as possible.

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
The first thing you might want to consider is whether you can share your data.
A major change is happening within academia at the moment. Data outputs are being viewed as increasingly important, and this trend is only likely to continue. Both research funders and publishers are viewing the data outputs as increasingly important
This provides an exciting opportunity for researchers: a chance to be at the forefront of a new movement.
Image credit: Microsoft clip art

40. Why share data?
This picture from Ubiquity Press sums up some of these benefits of data sharing for you as the researcher, for the wider research community and also for the public
You:
Recognition - Sharing data can be good for your academic reputation and profile.
Citations - There is also substantial evidence that making your data openly available leads to increased citations – of the datasets themselves, and of the papers or other publications based on the data.
Collaborations – may lead to other things – collaborations beyond your discipline perhaps
Wider research community:
Greater efficiency
Generally reduces duplication of effort
Allows future research to focus on things that haven’t been done before
Teaching
There are public benefits
..and of course the funder:
The main reason the funders are pushing for data preservation and sharing is that they get more for their money if others can reuse it – this is a more effective use of their resources

41. So, we’ve been telling you how to manage your research data – this video gives a good example of how not to manage your research data!
Link to video from A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci, of NYU Health Sciences Libraries. (If embedded version doesn’t work, use link to view via YouTube.)
Start to 1:35 – how to get hold of data
Then problems opening the files – didn’t have the right software
2:41 to end – documentation
Points to make:
Part 1: Sharing – be prepared for requests to share. You need to be able to locate the data if someone asks for it.
Part 2: Obsolescence of software – also documentation required (and backup!)
Part 3: Documentation – if you document your data well you shouldn’t have to answer these sorts of questions!
Video by NYU Health Sciences Libraries:

42. Data sharing – concerns
Ethical concerns
Confidential or sensitive data
Legal concerns
Third party data
Professional concerns
Intended publication
Commercial issues (e.g. patent protection)
Of course not all data can be shared
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).
Image credit: Microsoft clip art

43. EMBARGOED Data sharing – concerns
Redact or embargo if there is good reason
Planning ahead can reduce difficulties
EMBARGOED
However it is sometimes possible to work around some of these concerns
You can redact material, for example 3rd party copyrighted material in a PhD thesis, or place embargoes so that it cannot be accessed for a certain period
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.
Slide adapted from PrePARe Project slideshow “Share It”:

44. What data should you keep?
You need to put some thought into what data you will keep at the end of the research project. Don’t just keep everything.
We don’t just keep everything – preserving and storing data is costly in terms of storage space and time taken to manage it after the life of the project. Also there are risks in just keeping everything. It makes it harder to find the meaningful data if most of a retained dataset is of limited value.
Screenshot taken from a useful checklist from the PrePARe project at the University of Cambridge and funded by Jisc. I’ve put the url at the bottom there but this is also linked from our RDM website in the section that talks about data retention.
First think about retention periods that you are required to adhere to
Think about whether you are legally obliged to keep it
Do you have the master copy or is this in fact data that is already held elsewhere and you can just reference in your documentation?
How important is the data to your project?
Could your data be recreated if necessary – is the experiment repeatable? Sometimes it is cheaper/easier to regenerate a dataset than archive it. Other datasets record a moment in time and can not be repeated.
Is it useful evidence?
Does it have reuse potential? Could you imagine someone else being able to do something with this? ...and is it documented well enough to allow someone to reuse it. Remember, though that funding bodies will not be happy if you simply decide not to retain data because you haven’t documented it well enough!
Checklist can be found at:

45. What data should you bin?
...and from the same resource we also have a helpful checklist of what to bin:
If someone else is responsible for the master copy
If your data is a duplicate
If it is an early draft that is not needed
If it can never be reused then we need to think hard about keeping it (unless strictly specified)
If cheaper to recreate the data as needed? Perhaps then you just document exactly what you did to create the data so that someone else could recreate the dataset.
Again, this is something that you should be thinking about early on in your project – don’t leave these decisions to the last minute. Planning what you intend to do with your data at the end of the project is really important ....on to DMP
Checklist can be found at:

46. Data management planning
Recommend the creation of Data Management Plans (DMPs) for research projects, so that both time and resources are considered for data management activities (e.g. the preparation of datasets for deposit in an appropriate repository) at an early stage.

47. ‘In preparing for battle, I have always found that plans are useless but planning is indispensable.’
Dwight D. Eisenhower
Even though we shouldn’t compare carrying out a piece of research with going into battle, there are clearly some parallels...
I think the key point here is that it is the actual process of planning is the most useful thing. The plan itself may change, things do not always go to plan, but taking that time to think about how you will manage your data is invaluable and can save you some time in the long run.
Public domain image, from

48. Data Management Plans (DMPs)
A document which outlines how data will be managed over the course of a project.
Should include:
Description of the data to be collected / created
Standards / methodologies for data collection and management
Ethics and Intellectual Property concerns or restrictions
Plans for data sharing and access
Strategy for long-term preservation.
A data management plan is, as the name suggests, a document which outlines how data will be managed over the course of a project
These are the sorts of things that a DMP should include (taken from DCC website):
Description of the data to be collected / created
Standards / methodologies for data collection and management
Ethics and Intellectual Property concerns or restrictions
Plans for data sharing and access
Strategy for long-term preservation

49. Data Management Plans (DMPs)
Many funders require a data management plan as part of grant applications.
DMPs might be included in departmental policies – so that departments can oversee good RDM practice

50.
Create a data management plan using the DMPonline tool
To help with the process of producing a data management plan, the Digital Curation Centre have come up with an online tool. You can have a look at this and a play now.
DMPonline is an online tool, created by the Digital Curation Centre, which is designed to help you create personalised data management plans according to your context or funder. The tool also helps you by providing examples of guidance and best practice (e.g. from the DCC).

51. Further information and resources

52. RDM web pages www.york.ac.uk/rdm
Created by the Library’s Research Support Team, the pages provide guidance on good practice in managing research data and link to established tools and sources of advice.

53. Research Data MANTRA Free online interactive RDM training modules
Research Data MANTRA is a series of free interactive online training modules covering key research data management issues.
The modules are designed for postgraduates and early career researchers. The course describes itself as being particularly geared towards people working in geosciences, social and political sciences, and clinical psychology, but don’t be put off by this – in fact much of the course material is relevant to all research disciplines.

54. We are here to help
Research Support Team:
IT Support Office:
Research Support Team for queries re. RDM but also for guidance on Open Access, copyright, citation analysis and bibliometrics.
IT Support Office for all technical and/or security queries for your research.
Record Management web pages include information that applies to the management of data although not specifically written for this purpose. Includes information on Data Protection, Copyright and Freedom of Information all issues which you will need to consider when managing data – particularly when you manage personal/sensitive data.
IT web pages on security (e.g. file security and encryption) but also a web page for the Research High Performance Computing Service (RHPCS) – detailing the support and delivery of computing resource for researchers at York.

55. Rights and re-use This presentation is an adapted version of the slideshow prepared by the DaMaRO Project at the University of Oxford. The University of York is re-using the slideshow within the terms of the 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. All materials are shared under the same license. Within the terms of this license, we actively encourage sharing, adaptation, and re-use of this material.
This slideshow was prepared by the DaMaRO Project (a joint endeavour between IT Services, the Bodleian Libraries, and Research Services at the University of Oxford) and is made available under the Creative Commons Attribution Non-Commercial Share-Alike License: Subject to the terms of the license, you are welcome to reuse or adapt this material for your own purposes.
The original version of this slideshow was developed for presentation to Oxford researchers. This version of the presentation has been edited to remove references to services only available to members of the University of Oxford. However, it does still contain references to some resources provided by Oxford, but freely available online (e.g. the Oxford Research Data Management website). A copy of the original version is available from the DaMaRO Project website:

56. Any questions?