This document provides an overview of research data management and outlines the steps for creating a data management plan. It discusses why research data management is important, including enabling data reuse and sharing and meeting funder requirements. The document then walks through creating a data management plan, covering topics like the types and formats of data that will be generated, ethical and intellectual property issues, how data will be stored and backed up, and long-term preservation and deposition of data. It emphasizes that planning early helps ensure accurate, complete and secure data, and avoids problems down the line.
A basic course on Research data management: part 1 - part 4Leon Osinski
Slides belonging to a basic course on research data management. The course consists of 4 parts:
Part 1: what and why
1.1 data management plans
Part 2: protecting and organizing your data
2.1 data safety and data security
2.2 file naming, organizing data (TIER documentation protocol)
Part 3: sharing your data
3.1 via collaboration platforms (during research)
3.2 via data archives (after your research)
Part 4: caring for your data, or making data usable
4.1 tidy data
4.2 documentation/metadata
4.3 licenses
4.4 open data formats
This presentation was delivered at the Elsevier Library Connect Seminar on 6 October 2014 in Johannesburg, 7 October 2014 in Durban and 9 October 2014 in Cape Town and gives an overview of the potential role that librarians can play in research data management
This slideshow was used in an Introduction to Research Data Management course for the Social Sciences Division, University of Oxford, on 2015-05-27. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
University of Bath Research Data Management training for researchersJez Cope
Slides from a workshop on Research Data Management for research staff and students at the University of Bath.
Part of the Research360 project (http://blogs.bath.ac.uk/research360).
Authors: Cathy Pink and Jez Cope, University of Bath
A basic course on Research data management: part 1 - part 4Leon Osinski
Slides belonging to a basic course on research data management. The course consists of 4 parts:
Part 1: what and why
1.1 data management plans
Part 2: protecting and organizing your data
2.1 data safety and data security
2.2 file naming, organizing data (TIER documentation protocol)
Part 3: sharing your data
3.1 via collaboration platforms (during research)
3.2 via data archives (after your research)
Part 4: caring for your data, or making data usable
4.1 tidy data
4.2 documentation/metadata
4.3 licenses
4.4 open data formats
This presentation was delivered at the Elsevier Library Connect Seminar on 6 October 2014 in Johannesburg, 7 October 2014 in Durban and 9 October 2014 in Cape Town and gives an overview of the potential role that librarians can play in research data management
This slideshow was used in an Introduction to Research Data Management course for the Social Sciences Division, University of Oxford, on 2015-05-27. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
University of Bath Research Data Management training for researchersJez Cope
Slides from a workshop on Research Data Management for research staff and students at the University of Bath.
Part of the Research360 project (http://blogs.bath.ac.uk/research360).
Authors: Cathy Pink and Jez Cope, University of Bath
Good (enough) research data management practicesLeon Osinski
Slides of a lecture on research data management (RDM), given for 3rd year students (Eindhoven University of Technology, major Psychology & Technology), as part of the course 0HV90 Quantitative Research. At the end of the slides a handy summary 'Research data management basics in a nutshell' is added.
Introduction to research data management; Lecture 01 for GRAD521Amanda Whitmire
Lesson 1: Introduction to research data management. From a series of lectures from a 10-week, 2-credit graduate-level course in research data management (GRAD521, offered at Oregon State University).
The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines."
Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation.
See also, "Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835. Retrieved 23:25, Jan 07, 2015 (GMT)"
This slideshow was used in an Introduction to Research Data Management course taught in the Social Sciences Division, University of Oxford, on 2014-01-27. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
This is a presentation for the Erwin Hahn Instiutute in Essen, explaining the background, functional design and technical architecture of the Donders Repository. Furthermore, it explains how it aligns with the DCCN project management and with the researchers workflow
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
Meeting the NSF DMP Requirement June 13, 2012IUPUI
June 13 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
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Presentation by Susan Reilly at Bibsys2013 on the opportunties for libraries and their role in the collaborative data infrastructure. Looks at data sharing, authentication, preservation and advocacy.
Donders Repository - removing barriers for management and sharing of research...Robert Oostenveld
This is the presentation I gave at the monthly meeting of the Donders Institute PhD council. It shortly explains the Donders Repository, but mainly addresses how to deal with direct and indirectly identifying personal data, with anonymization, pseudomimization and de-identification, and with blurring of research data prior to sharing.
Introduction to research data managementMichael Day
Slides from a presentation given at the JIBS User Group / RLUK joint event "Demystifying research data: don't be scared, be prepared" held at the SOAS Brunei Gallery, London, 17 July 2012.
The Brain Imaging Data Structure and its use for fNIRSRobert Oostenveld
These slides were prepared for the NIRS toolkit course at the Donders, which due to the Corona crisis has been postponed. The slides present BIDS, explain how fNIRS often involves multiple signals, and relates the two to synchronization and data management
Meeting the NSF DMP Requirement: March 7, 2012IUPUI
March 7 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
Data Equivalence
Mark Parsons, Lead Project Manager, Senior Associate Scientist, National Snow and Ice Data Center
Data citation, especially using persistent identifiers like Digital Object Identifiers (DOIs), is an increasingly accepted scientific practice. Recently, several, respected organizations have developed guidelines for data citation. The different guidelines are largely congruent in that they agree on the basic practice and elements of data citation, especially for relatively static, whole data collections. There is less agreement on the more subtle nuances of data citation that are sometimes necessary to ensure precise reference and scientific reproducibility--the core purpose of data citation. We need to be sure that if you follow a data reference you get to the precise data that were used or at least their scientific equivalent. Identifiers such as DOIs are necessary but not sufficient for the precise, detailed, references necessary. This talk discusses issues around data set versioning, micro-citation, and scientific equivalence. I propose some interim solutions and suggest research strategies for the future.
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William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico; DataONE Principal Investigator
The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
Good (enough) research data management practicesLeon Osinski
Slides of a lecture on research data management (RDM), given for 3rd year students (Eindhoven University of Technology, major Psychology & Technology), as part of the course 0HV90 Quantitative Research. At the end of the slides a handy summary 'Research data management basics in a nutshell' is added.
Introduction to research data management; Lecture 01 for GRAD521Amanda Whitmire
Lesson 1: Introduction to research data management. From a series of lectures from a 10-week, 2-credit graduate-level course in research data management (GRAD521, offered at Oregon State University).
The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines."
Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation.
See also, "Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835. Retrieved 23:25, Jan 07, 2015 (GMT)"
This slideshow was used in an Introduction to Research Data Management course taught in the Social Sciences Division, University of Oxford, on 2014-01-27. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
This is a presentation for the Erwin Hahn Instiutute in Essen, explaining the background, functional design and technical architecture of the Donders Repository. Furthermore, it explains how it aligns with the DCCN project management and with the researchers workflow
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
Meeting the NSF DMP Requirement June 13, 2012IUPUI
June 13 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
Where is the opportunity for libraries in the collaborative data infrastructure?LIBER Europe
Presentation by Susan Reilly at Bibsys2013 on the opportunties for libraries and their role in the collaborative data infrastructure. Looks at data sharing, authentication, preservation and advocacy.
Donders Repository - removing barriers for management and sharing of research...Robert Oostenveld
This is the presentation I gave at the monthly meeting of the Donders Institute PhD council. It shortly explains the Donders Repository, but mainly addresses how to deal with direct and indirectly identifying personal data, with anonymization, pseudomimization and de-identification, and with blurring of research data prior to sharing.
Introduction to research data managementMichael Day
Slides from a presentation given at the JIBS User Group / RLUK joint event "Demystifying research data: don't be scared, be prepared" held at the SOAS Brunei Gallery, London, 17 July 2012.
The Brain Imaging Data Structure and its use for fNIRSRobert Oostenveld
These slides were prepared for the NIRS toolkit course at the Donders, which due to the Corona crisis has been postponed. The slides present BIDS, explain how fNIRS often involves multiple signals, and relates the two to synchronization and data management
Meeting the NSF DMP Requirement: March 7, 2012IUPUI
March 7 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
Data Equivalence
Mark Parsons, Lead Project Manager, Senior Associate Scientist, National Snow and Ice Data Center
Data citation, especially using persistent identifiers like Digital Object Identifiers (DOIs), is an increasingly accepted scientific practice. Recently, several, respected organizations have developed guidelines for data citation. The different guidelines are largely congruent in that they agree on the basic practice and elements of data citation, especially for relatively static, whole data collections. There is less agreement on the more subtle nuances of data citation that are sometimes necessary to ensure precise reference and scientific reproducibility--the core purpose of data citation. We need to be sure that if you follow a data reference you get to the precise data that were used or at least their scientific equivalent. Identifiers such as DOIs are necessary but not sufficient for the precise, detailed, references necessary. This talk discusses issues around data set versioning, micro-citation, and scientific equivalence. I propose some interim solutions and suggest research strategies for the future.
Scientific discovery and innovation in an era of data-intensive science
William (Bill) Michener, Professor and Director of e-Science Initiatives for University Libraries, University of New Mexico; DataONE Principal Investigator
The scope and nature of biological, environmental and earth sciences research are evolving rapidly in response to environmental challenges such as global climate change, invasive species and emergent diseases. Scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new tools that promote scientific data preservation, discovery, and innovation. This talk describes the challenges facing scientists as they transition into this new era of data intensive science, presents current solutions, and lays out a roadmap to the future where new information technologies significantly increase the pace of scientific discovery and innovation.
GBIF and reuse of research data, Bergen (2016-12-14)Dag Endresen
Biodiversity informatics seminar at the Department of Biology, University of Bergen on data publication and reuse of GBIF-mediated biodiversity data on 14th December 2016. Organized by the Norwegian GBIF Node and the Norwegian Biodiversity Information Center (NBIC, Artsdatabanken).
See also: http://www.gbif.no/events/2016/data-publishing-seminar-in-bergen.html
See also: http://doi.org/10.13140/RG.2.2.24290.32969
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Want to Learn More About This Topic or Any Other?
Go to labs.psfk.com to learn more about accessing in-depth trend reports on industries, markets, and topics, database access, workshops, presentations and events.
http://kulibrarians.g.hatena.ne.jp/kulibrarians/20170222
Presentation by Cuna Ekmekcioglu （The University of Edinburgh）
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CC BY-NC-SA 4.0
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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.
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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.
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1. Planning for
Research Data Management
7th February 2017
Wendy Mears,
Research Support Librarian
library-research-support@open.ac.uk
2. Overview of session
• What is Research Data Management?
• Why bother?
• Data Management Planning: step-by-step
• Questions
with a little help from my friends...
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
http://www.data-archive.ac.uk/create-
manage/life-cycle
6. Good data management...
• Helps you work more
efficiently and effectively
– Save time and reduce
frustration
– Highlight patterns or
connections that might
otherwise be missed
• Enable data re-use and
sharing
• Allow you to meet funders’
and institutional requirements
8. OU Principles of
Research Data Management
“Research data must be managed to the highest
standards throughout their life-cycle in order to
support excellence in research practice.
In keeping with OU principles of open-ness, 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 requirements.”
OU Principles of Research Data Management, April 2013
http://intranet.open.ac.uk/research-school/strategy-info-
governance/docs/CoPamendedJuly2013mergedwithappendix-forintranet.pdf
9. Data Management Planning
• 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:
14. “Describe the data aspects of your
research, how you will capture/generate
them, the file formats you are using and
why. Mention how metadata will be created
to describe the data, and your reasons for
choosing particular data standards and
approaches.”
2. Data types, formats,
standards and capture methods
16. 2. Data types, formats,
standards and capture methods
Metadata tips:
• Use disciplinary standards
• Create a data file
• Use file properties
• Use functions in data
analysis software, e.g.
NVIVO, R, SPSS, Electronic
Lab Notebooks
18. “Detail any ethical and privacy issues,
including the consent of participants.
Explain the copyright/IPR and whether
there are any data licensing issues – either
for data you are reusing, or your data which
you will make available to others.”
3. Ethics and Intellectual Property
24. “Note who would be interested in your data,
and describe how you will make them
available (with any restrictions). Detail any
reasons not to share, as well as embargo
periods or if you want time to exploit your
data for publishing.”
4. Access, Data Sharing
and Re-use
28. ORDO
Online data sharing services
• Figshare
• Zenodo
• CKAN DataHub
• Mendeley Data
Directories
• re3data
Funders’ repository services
• UK Data Service ReShare
• NERC data centres
4. Access, Data Sharing and
Re-use
30. “Give a rough idea of data volume. Say
where and on what media you will store
data, and how they will be backed-up.
Mention security measures to protect data
which are sensitive or valuable.”
5. Short-term storage and data
management
31. 5. Short-term Storage and
Data Management
• Follow the 3-2-1 rule:
• 3 copies
• At least 2 formats
• 1 offsite
32. • Shared areas or SharePoint
• Zendto
• Be wary of Dropbox & similar
• ORDO
IT support for research:
http://intranet6.open.ac.uk/library/main/supporting-ou-
research/research-data-management/creating-your-data
5. Short-term Storage and
Data Management
34. • Thinking ahead will help when you need to share/archive
your data
• Define processes at project start
• Think about:
–File naming and versioning
–File directory structure
–Metadata
–File formats
–Quality assurance
–Data security
5. Short-term Storage and
Data Management
36. “Consider what data are worth selecting for
long-term access and preservation and how
you will need to prepare those data for
archiving. Say where you intend to deposit
the data.”
6. Deposit and long-term
preservation
37. 6. Deposit and long-term
preservation
Deciding what to keep:
• Raw data
• Derived data
• Data underpinning publications
• Code
• Methods
What are research data in your context?
What would others need to understand your research?
38. 6. Deposit and long-term
preservation
To allow long-term access to data:
• Don't use obscure formats
• Don't use obscure media
• Don't rely on technology being
available
• Provide sufficient documentation
39. For preservation, file formats should be…
• 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
6. Deposit and long-term
preservation
40. • 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
6. Deposit and long-term
preservation
42. Library Services
How we can help
• Data Management Plan checking
• Support with setting up new projects
• Advice on preparation of data for sharing
• ORDO
• Online guidance
• Enquiries
Email: library-research-support@open.ac.uk
43. Useful links
• The OU Research Data Management intranet site:
http://intranet6.open.ac.uk/library/main/supporting-ou-research/research-
data-management
• VRE: http://www.open.ac.uk/students/research/activities/lists/organising-
your-research
• Digital Curation Centre: http://www.dcc.ac.uk/
• DMPOnline: 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
45. Image credits
Other cartoons from the Research Data Alliance 4th Plenary, Amsterdam 2014:
https://rd-alliance.org/plenary-meetings/fourth-plenary/plenary-cartoons.html (CC-BY)
BASF (2007) Crop Design – the fine art of gene
discovery,
https://www.flickr.com/photos/basf/48372670
13 (CC BY-NC-ND 2.0)
Jay Oliver (2005) UGA research in Tifton, GA.
June 2005,
https://www.flickr.com/photos/ugacommunicati
ons/6254516052 (CC BY-NC 2.0)
Teddy-rised (2008) Making every litter count,
https://www.flickr.com/photos/teddy-
rised/2947952302 (CC BY-NC-ND 2.0)
Stan Leary (2009) University of Georgia
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https://www.flickr.com/photos/ugacommu
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Morten Oddvik (2011) Papers,
https://www.flickr.com/photos/mortsan/543041854
5 (CC BY 2.0)
Lars Rosengreen (2012) Using a GoPro camera to
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https://www.flickr.com/photos/46369606@N04
/7543827396/ (CC BY-NC-ND 2.0)
Casldlyrose (2009) Be Prepared
https://www.flickr.com/photos/calsidyrose/35524
73207 (CC-BY 2.0)
Caleb Roenigk (2012) Writing? Yeah.
https://www.flickr.com/photos/crdot/685553826
8/ (CC-BY 2.0)
Jamie Henderson (2010) Day 22
https://www.flickr.com/photos/xelcise/42967348
26 (CC-BY-NC-ND 2.0)
PHDComics.com (2007)
http://www.phdcomics.com/comics/archiv
e.php?comicid=814 (CC-BY 2.0)
Sybren Stuvel (2008) Frustration
https://www.flickr.com/photos/sybrenstuvel (CC-
BY-NC-ND 2.0)
Brian Yap (2012) Blowing Questions
https://www.flickr.com/photos/sybrenstuvel (CC-
BY-NC 2.0)
