1. Introduction to Research Data Management
Sarah Jones
Digital Curation Centre, Glasgow
Carpentry workshop - Open Research Data, 5 December 2016, Oslo

2. What will we cover? What is research data management?
Consideration and pointers when:
Creating data
Managing data
Sharing data
Useful tools and resources

3. What is RDM?
Image CC-BY-SA by Janneke Staaks

4. What is Research Data Management?
“the active management and appraisal of data over the lifecycle of scholarly and scientific interest”
Create
Document
Use
Store
Share
Preserve
Data management is part of
good research practice

5. What is involved in RDM? Data Management Planning Data creation
Annotating / documenting data
Analysis, use, versioning
Storage and backup
Publishing papers and data
Preparing for deposit
Archiving and sharing
Licensing
Citing…
Create
Document
Use
Store
Share
Preserve

6. Why manage and share data?
Direct benefits for you
To make your research easier!
Stop yourself drowning in irrelevant stuff
Make sure you can understand and reuse your data again later
Advance your career – data is growing in significance
Research integrity
To avoid accusations of fraud or bad science
Evidence findings and enable validation of research methods
Meet codes of practice on research conduct
Many research funders worldwide now require Data Management and Sharing Plans
Potential to share data
So others can reuse and build on your data
To gain credit – several studies have shown higher citation rates when data are shared
For greater visibility, impact and new research collaborations
Promote innovation and allow research in your field to advance faster
Learn good data habits now! You’ll need them later.

7. What if this was your laptop?
Why YOU need a Data Management Plan

8. Good data management is about making informed decisions

10. Creating data
Image CC-SA-ND by Bill Dickinson

11. Data creation tips Choose appropriate formats
Adopt a file naming convention
Create metadata and documentation as you go
Ensure consent forms, licences and agreements don’t restrict opportunities to share data

12. Choose appropriate file formats
Different formats are good for different things
open, lossless formats are more sustainable e.g. rtf, xml, tif, wav
proprietary and/or compressed formats are less preservable but are often in widespread use e.g. doc, jpg, mp3
One format for analysis then
convert to a standard format
Data centres may suggest preferred formats for deposit
BioformatsConverter batch converts a variety of proprietary microscopy image formats to the Open Microscopy Environment format - OME-TIFF

13. How will you name your files?
Keep file and folder names short, but meaningful
Agree a method for versioning
Include dates in a set format e.g. YYYYMMDD
Avoid using non-alphanumeric characters in file names
Use hyphens or underscores not spaces e.g. day-sheet, day_sheet
Order the elements in the most appropriate way to retrieve the record
Example from ARM Climate Research Facility

14. Data about data What is metadata?
Documentation and metadata are essentially descriptive information about the information contained in a dataset. There should be good documentation at the study level, for example a description of the research methodology that created the data – [the best metadata the data can have is the publication it supports] or a data paper. There should also be documentation at the file, item and variable level suitable so that someone reusing the data can understand it – this could be ensuring that excel spreadsheets have sensible row and column descriptions or that a document is included with the dataset which properly explains any abbreviations used.

15. Documentation and metadata
Standardised
Structured
Machine and human readable
Metadata helps to cite & disambiguate data
Documentation aids reuse
Documentation
Metadata is a subset of documentation
Documentation is a catch-all term which can include some very high-level, human-readable, loosely structured information about the dataset – for example a description of the laboratory method used to generate a set of results or a sketch book accompanying a work of sculpture. The term metadata has come to define a subset of documentation information that uses standardised terms and is presented in a structured way. This metadata will be in a form enabling it to be read by machines and/or may be presented in human readable form as well.
Metadata

16. Metadata standards These can be general – such as Dublin Core
Or discipline specific
Data Documentation Initiative (DDI) – social science
Ecological Metadata Language (EML) - ecology
Flexible Image Transport System (FITS) – astronomy
Provided in catalogues to aid discoverability
Structured so search engines can uncover it
Exposed in machine-readable form e.g. XML

17. Dublin Core metadata example
Creator: Donald Cooper
Role=Photographer
Subject: Shakespeare, William, , Antony and Cleopatra [LC]
Description: Vanessa Redgrave as Cleopatra
Date:
Type: Image
Format: JPEG
Identifier:4150 [catalogue no]
Source: negative no 235
Relation: Antony and Cleopatra: Thompson/73-8
IsPartOf
Coverage: Bankside Globe
Role=Spatial
Rights: Donald Cooper
Typically, not every one of the DC elements is required to document this resource
Although not apparent in this example, repetition of the Creator element is commonly found in metadata describing performing arts materials, which very often have multiple creators. The Creator element is further refined within this record with the use of a Qualifier.

18. http://rd-alliance.github.io/ metadata-directory
Use metadata standard
Biosharing
A portal of data standards, databases, and policies
Focused on life, environmental and biomedical sciences
Metadata Standards Directory
Broad, disciplinary listing of standards and tools. Maintained by RDA group
metadata-directory

19. Documentation Can others understand the data?
Think about what is needed in order to find, evaluate, understand, and reuse the data.
Have you documented what you did and how?
Did you develop code to run analyses? If so, this should be kept and shared too.
Is it clear what each bit of your dataset means?
Make sure the columns/rows are labelled, variable ranges defined, abbreviations explained in data dictionaries…

20. ReadMe files
We recommend that a ReadMe be a plain text file containing the following:
for each filename, a short description of what data it includes, optionally describing the relationship to the tables, figures, or sections within the accompanying publication
for tabular data: definitions of column headings and row labels; data codes (including missing data); and measurement units
any data processing steps, especially if not described in the publication, that may affect interpretation of results
a description of what associated datasets are stored elsewhere, if applicable
whom to contact with questions
These recommendations for developing a readme file were put together by the Dryad team and are published on the repository’s web-site.

21. Managing data
Image ‘tools’ CC-BY by zzpza

22. Legal and ethical issues
Be aware of legislation that applies to you:
Offentlighetsloven (FoI)
EIR (Environmental Information Regulations)
Data Protection
Health Research Act
Understand what this means in terms of how data are stored, transferred and shared
Your organisation must know what data it possesses
It must know whether exceptions to access may apply
It must know if some of the data belongs to others
It must know what data once existed, but has now been deleted – and why
These are difficult questions for most of us!

23. Use appropriate services
TSD provides a platform for researchers working at UiO and in other public research institutions to collect, store and analyze sensitive research data. TSD complies  with the directive of privacy and electronic communication in Norway.

24. Ask for consent for data sharing
If not, data centres won’t be able to accept the data
– regardless of any conditions on the original grant.

25. Where will you store the data?
Your own device (laptop, flash drive, server etc.)
And if you lose it? Or it breaks?
Departmental drives or university servers
“Cloud” storage
Do they care as much about your data as you do?
The decision will be based on how sensitive your data are, how robust you need the storage to be, and
who needs access to the data and when

26. One copy = risk of data loss
CC image by momboleum on Flickr
CC image by Sharyn Morrow on Flickr

27. Who will do the backup?
Use managed services where possible (e.g. University filestores rather than local or external hard drives), so backup is done automatically
3… 2… 1… backup!
at least 3 copies of a file
on at least 2 different media
with at least 1 offsite
Ask central IT team for advice

28. Backup and preservation – not the same thing!
Backups
Used to take periodic snapshots of data in case the current version is destroyed or lost
Backups are copies of files stored for short or near-long-term
Often performed on a somewhat frequent schedule
Archiving
Used to preserve data for historical reference or potentially during disasters
Archives are usually the final version, stored for long-term, and generally not copied over
Often performed at the end of a project or during major milestones
Remember that backup and preservation are not the same thing (though the terms are often used interchangeably).
Backups are performed regularly to take periodic snapshots of the data for the short to medium term, whereas archiving is preserving the final version of the data for the long-term.
You should make sure your data are backed-up during the active phase of research and that any data needed for the long-term are archived.

29. How to keep you data secure?
Develop a practical solution that fits your circumstances
Store your data on managed servers
Restrict access
Keep anti-virus software up-to-date
Encrypt mobile devices carrying sensitive information

30. Data sharing
Image CC-BY-NC-ND by talkingplant

31. The data deluge is upon us
Sensor’s ability to produce data outstrips IT’s ability to process it

32. Why not keep it all?
Globally, data volumes are doubling every two years
John Gantz and David Reinsel 2011 Extracting Value from Chaos

33. Storage mgmt costs rise long-term
Hardware costs decline, but power and staff costs keep rising
David Rosenthal blog.dshr.org/2012/05/lets-just-keep-everything-forever-in.html

34. The storage is cheap fallacy
Decreasing hardware costs offset by exponential growth in data volume
Backup and mirroring multiplies cost of preserved data
Discovery becomes harder as the chaff outweighs the wheat
Curation of unused data is a waste of resources

35. Select what to keep (and share…)
What must be kept to manage compliance risk?
What data could be re-used?
What data has value and should be kept?
Given costs what will or won’t be kept?
How will it be kept and shared, on what terms?

36. Kevin Ashley, DCC - Warsaw data workshop
Should all data be open?
NO Many reasons – most to do with human subjects But data existence should always be open Allows discovery & negotiation on use Avoids pointless replication
Medicine does, however, provide some clear reasons why we can’t just stick all research data on the internet for anyone to trawl through. When human subjects are involved there are real concerns about confidentiality. Yet what alltrials.net and other initiatives make clear is that the *existence* of the data should never be hidden. That allows it to be discovered and for negotiations to take place about its use. It avoids costly replication, which can delay scientific discovery and involve human suffering when the replication takes the form of a clinical trial.
CODATA Trieste - Kevin Ashley, DCC - CC-BY 2.0

37. How to make data open? Choose your dataset(s) Apply an open license
What can you may open? You may need to revisit this step if you encounter problems later.
Apply an open license 
Determine what IP exists. Apply a suitable licence e.g. CC-BY
Make the data available 
Provide the data in a suitable format. Use repositories.
Make it discoverable 
Post on the web, register in catalogues…

38. License research data openly
This DCC guide outlines the pros and cons of each approach and gives practical advice on how to implement your licence
CREATIVE COMMONS LIMITATIONS
NC Non-Commercial
What counts as commercial?
ND No Derivatives
Severely restricts use
These clauses are not open licenses
Horizon 2020 Open Access guidelines point to: or
Guidance from the DCC can also help researchers to understand data licensing. This guide outlines the pros and cons of each approach e.g. the limitations of some CC options
The OA guidelines under Horizon 2020 point to CC-0 or CC-BY as a straightforward and effective way to make it possible for others to mine, exploit and reproduce the data. See p11 at:
Also in existence:
Open Data Commons
Open Government Licence 38

39. EUDAT licensing tool
Answer questions to determine which licence(s) are appropriate to use

40. Deposit in a data repository
The EC guidelines point to Re3data as one of the registries that can be searched to find a home for data
/content/re3data-demo 40

41. How to select a repository?
Look for provision from your community, university, publisher, funder etc
Check they match your particular data needs: e.g. formats accepted; mixture of Open and Restricted Access.
See if they provide guidance on how to cite the deposited data.
Do they assign a persistent & globally unique identifier for sustainable citations and to links back to particular researchers and grants?
Look for certification as a ‘Trustworthy Digital Repository’ with an explicit ambition to keep the data available in long term.

42. Norwegian repository landscape

43. Zenodo
Zenodo is a multi-disciplinary repository that can be used for the long-tail of research data
An OpenAIRE-CERN joint effort
Multidisciplinary repository accepting
Multiple data types
Publications
Software
Assigns a Digital Object Identifier (DOI)
Links funding, publications, data & software 43

44. What are persistent identifiers?
They are an alphanumeric code identifying a resource, organisation or individual
They must be
Unique
Persistent
Ideally they should be actionable too

45. How do persistent identifiers work
The likely home of an electronic research data resource attached to a persistent identifier is a data repository (exceptions being resources not available over the web which have a PI attached to a metadata record – physical resource, cumbersomely large dataset etc.)

46. Citing research data: why?

47. How to cite data Key citation elements Author Publication date Title
Location (= identifier)
Funder (if applicable)

48. Resources
Image ‘Energy Resources | Energie Quelle’ CC-BY-NC by K. H. Reichert

49. Managing and sharing data: a best practice guide

50. Guidance and training resources
ESIP Data Management Training clearing house for environmental sciences
DataONE best practices
DCC resources
FOSTER open science portal

51. Acquire research data skills

52. Tools for managing data
managing-active-research-data

53. Also look for national & local support!
support/research/research-data

54. Finally…
Well-managed data makes your research easier, now and in future
Well-managed data is easier to share, more likely to be re-used
Sharing data is good for you
It’s good for all of us
It isn’t as hard as you think – we’re here to show you how!

55. How do you share data effectively?
Use appropriate repositories, this catalogue is a good place to start
Document and describe it enough for others to understand, use and cite
guides/cite-datasets
Licence it so others can reuse

56. Thanks for listening 
For DCC resources see: Follow DCC us on and #ukdcc