This document provides information about creating a data management plan (DMP) for researchers. It begins with defining what a DMP is - a short plan that outlines what data will be created, how it will be managed and stored, and plans for sharing and preservation. It then discusses the common components of a DMP, including describing the data, standards and methodologies, ethics and intellectual property, data sharing plans, and preservation strategies. The document provides examples of DMP requirements and recommendations from funders. It offers tips for creating a good DMP, including thinking about the needs of future data re-users, consulting stakeholders, grounding plans in reality, and planning for sharing from the outset. Finally, it discusses tools and resources
The first workshop of the series "Services to support FAIR data" took place in Prague during the EOSC-hub week (on April 12, 2019).
Speaker: Kostas Repanas (EC DG RTD)
FAIR data: what it means, how we achieve it, and the role of RDASarah Jones
Presentation on FAIR data, the FAIR Data Action Plan developed by the European Commission Expert Group and the role of the Research Data Alliance on implementing FAIR. The presentation was given at the RDAFinland workshop held on 6th June - https://www.csc.fi/web/training/-/rda_and_fair_supporting_finnish_researchers
The first workshop of the series "Services to support FAIR data" took place in Prague during the EOSC-hub week (on April 12, 2019).
Speaker: Kostas Repanas (EC DG RTD)
FAIR data: what it means, how we achieve it, and the role of RDASarah Jones
Presentation on FAIR data, the FAIR Data Action Plan developed by the European Commission Expert Group and the role of the Research Data Alliance on implementing FAIR. The presentation was given at the RDAFinland workshop held on 6th June - https://www.csc.fi/web/training/-/rda_and_fair_supporting_finnish_researchers
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.
Access to biomedical data is increasingly important to enable data driven science in the research community.
The Linked Open Data (LOD) principles (by Tim Berner-Lee) have been suggested to judge the quality of data by its accessibility (open data access), by its format and structures, and by its interoperability with other data sources.
The objective is to use interoperable data sources across the Web with ease.
The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability.
In this presentation we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles.
This assessment helps to clarify the relationship between both schemes and gives a better understanding, what extension FAIR represents in comparison to LOD.
We conclude, that LOD gives a clear mandate to the openness of data, whereas FAIR asks for a stated license for access and thus includes the concept of reusability under consideration of the license agreement.
Furthermore, FAIR makes strong reference to the contextual information required to improve reuse of the data, e.g., provenance information.
According to the LOD principles, such meta-data would be considered interoperable data as well, however, the requirement of extending of data with meta-data does indicate that FAIR is an extension of the LOD (in contrast to the inverse).
A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.
LIBER Webinar: 23 Things About Research Data ManagementLIBER Europe
These are the slides for the LIBER Webinar "23 Things About Research Data Management", held on 23 February 2017. A recording of the webinar is available here: https://www.youtube.com/watch?v=HGH6fVHrnKQ
Presentation given at the European Research Council workshop on research data management and sharing in Brussels on 18th-19th September 2014. The presentation covers the benefits and drivers for RDM, points to relevant tools and resources and closes with some open questions for discussion.
Overview of the UKRDDS pilot project at Univwersity of Edinburgh employing PhD interns to validate metadata about research data created by University of Edinburgh researchers and held in local RDM services solutions. This was presented at IASSIST in June 2016, Bergen, Norway.
In June 2013, the Alfred P. Sloan Foundation awarded NISO a grant to undertake a two-phase initiative to explore, identify, and advance standards and/or best practices related to a new suite of potential metrics in the community.The NISO Altmetrics Project has successfully moved to Phase Two, the formation of three working groups, A, B, & C. Working Group B, led by Kristi Holmes, PhD, Director, Galter Health Sciences Library at Northwestern University, and Mike Taylor, Senior Product Manager, Informetrics at Elsevier, is focused on the Output Types & Identifiers within the alternative metrics landscape.
FAIR Data Experiences - Kees van Bochove - The HyveKees van Bochove
Talk at Bio IT World 2018 FAIR Data for Genomic Applications track.
Implementation of the FAIR Data Principles is a crucial step for all organizations pursuing a (biomedical) data-driven strategy, both to improve the effectiveness of scientists and doctors as well as computerized aides and autonomous programs. This talk will provide a number of concrete examples of how various customers of The Hyve, including large pharma companies, biobanks and registries and national health data sharing initiatives, have employed data FAIRification strategies to improve the (re)usability of their healthcare and biology data, and of the open source software tools and standards that are used and being further developed for that purpose.
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
This presentation was provided by Dr. Christine Borgman of UCLA during the NISO Symposium, Privacy Implications of Research Data, held on September 11, 2016, as part of the International Data Week event in Denver, Colorado.
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 for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Access to biomedical data is increasingly important to enable data driven science in the research community.
The Linked Open Data (LOD) principles (by Tim Berner-Lee) have been suggested to judge the quality of data by its accessibility (open data access), by its format and structures, and by its interoperability with other data sources.
The objective is to use interoperable data sources across the Web with ease.
The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability.
In this presentation we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles.
This assessment helps to clarify the relationship between both schemes and gives a better understanding, what extension FAIR represents in comparison to LOD.
We conclude, that LOD gives a clear mandate to the openness of data, whereas FAIR asks for a stated license for access and thus includes the concept of reusability under consideration of the license agreement.
Furthermore, FAIR makes strong reference to the contextual information required to improve reuse of the data, e.g., provenance information.
According to the LOD principles, such meta-data would be considered interoperable data as well, however, the requirement of extending of data with meta-data does indicate that FAIR is an extension of the LOD (in contrast to the inverse).
A presentation offering an introduction to managing and sharing research data given at the Czech Open Science days as part of the EC-funded FOSTER project.
LIBER Webinar: 23 Things About Research Data ManagementLIBER Europe
These are the slides for the LIBER Webinar "23 Things About Research Data Management", held on 23 February 2017. A recording of the webinar is available here: https://www.youtube.com/watch?v=HGH6fVHrnKQ
Presentation given at the European Research Council workshop on research data management and sharing in Brussels on 18th-19th September 2014. The presentation covers the benefits and drivers for RDM, points to relevant tools and resources and closes with some open questions for discussion.
Overview of the UKRDDS pilot project at Univwersity of Edinburgh employing PhD interns to validate metadata about research data created by University of Edinburgh researchers and held in local RDM services solutions. This was presented at IASSIST in June 2016, Bergen, Norway.
In June 2013, the Alfred P. Sloan Foundation awarded NISO a grant to undertake a two-phase initiative to explore, identify, and advance standards and/or best practices related to a new suite of potential metrics in the community.The NISO Altmetrics Project has successfully moved to Phase Two, the formation of three working groups, A, B, & C. Working Group B, led by Kristi Holmes, PhD, Director, Galter Health Sciences Library at Northwestern University, and Mike Taylor, Senior Product Manager, Informetrics at Elsevier, is focused on the Output Types & Identifiers within the alternative metrics landscape.
FAIR Data Experiences - Kees van Bochove - The HyveKees van Bochove
Talk at Bio IT World 2018 FAIR Data for Genomic Applications track.
Implementation of the FAIR Data Principles is a crucial step for all organizations pursuing a (biomedical) data-driven strategy, both to improve the effectiveness of scientists and doctors as well as computerized aides and autonomous programs. This talk will provide a number of concrete examples of how various customers of The Hyve, including large pharma companies, biobanks and registries and national health data sharing initiatives, have employed data FAIRification strategies to improve the (re)usability of their healthcare and biology data, and of the open source software tools and standards that are used and being further developed for that purpose.
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
This presentation was provided by Dr. Christine Borgman of UCLA during the NISO Symposium, Privacy Implications of Research Data, held on September 11, 2016, as part of the International Data Week event in Denver, Colorado.
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.
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATTony Ross-Hellauer
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATOpenAIRE
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
An introduction to Research Data Management and Data Management Planning presented at the University of the West of England on Wednesday 9th July 2014.
EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016|...EUDAT
| www.eudat.eu | 1st Session: July 7, 2016.
In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition
Presentation given at the Consorcio Madrono conference on Data Management Plans in Horizon 2020 http://www.consorciomadrono.es/info/web/blogs/formacion/217.php
A talk outlining the virtues and processes of Research Data Management for PhD students in the geosciences. Given by Stuart Macdonald at the Introduction to RDM Workshop, School of Geosciences, University of Edinburgh, on 2 November 2015
Keynote presentation given at the Data Fellows 2023 workshop in Berlin on 22-23 June. Presentation gives examples of good communication to explain data management concepts and how to use games and other forms of interactivity in training events
Presentation given at the DMPonline 10 year anniversary week, reflecting on lessons learned developing the business model. See https://www.dcc.ac.uk/events/dmponline-10th-year-anniversary-celebration-week and #10yearsDMPonline
Keynote presentation given at the 10th anniversary of the 4TU.researchdata repository https://data.4tu.nl/info/en/news-events/training-events/news-item/4turesearchdatas-role-in-fostering-open-science-10th-anniversary-celebration-29-sep-2020-1530-1730-c/
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A Strategic Approach: GenAI in EducationPeter Windle
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
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Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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1. www.geant.org
1 |
Click to edit Master title style
• Click to edit Master text styles
• Second level
• Third level
• Fourth level
• Fifth level
01/07/2021 1
Data Management Planning
for researchers
www.geant.org
Sarah Jones
EOSC Engagement Manager
sarah.jones@geant.org
Twitter: @sarahroams
Indonesian RDM webinar series
Friday 2nd July 2021
2. What is a DMP?
Image CC-BY-NC-SA by Leo Reynolds www.flickr.com/photos/lwr/13442910354
3. All manner of things that you produce in
the course of your research
What is research data?
4. “the active management and
appraisal of data over the lifecycle
of scholarly and scientific interest”
Data management is part of
good research practice
What is research data management?
Create
Document
Use
Store
Share
Preserve
5. A short plan that outlines:
• what data will be created and how
• how it will be managed (storage, back-up, access…)
• plans for data sharing and preservation
DMPs are often submitted as part of grant applications,
but are useful whenever researchers are creating data
What is a DMP?
6. 1. Description of data to be collected / created
(i.e. content, type, format, volume...)
2. Standards / methodologies for data collection & management
3. Ethics and Intellectual Property
(highlight any restrictions on data sharing e.g. embargoes, confidentiality)
4. Plans for data sharing and access
(i.e. how, when, to whom)
5. Strategy for long-term preservation
Five common themes / questions in DMPs
7. Why create a DMP?
Image CC-BY by Ian Dooley https://unsplash.com/photos/DuBNA1QMpPA
10. www.geant.org
What do research funders want?
• A brief plan usually submitted in grant applications
• Some funders may want multiple stages of plans e.g. pre-
award, in-project, final report…
• 1-4 sides of A4 as attachment or a section in application
• Typically a prose statement covering suggested themes
• An outline of data management and sharing plans, justifying
decisions and any limitations
11. www.geant.org
Trend for DMPs to cover more than data
• Wellcome Trust issued new guidelines in 2017 that ask for an
Outputs Management Plan covering:
– datasets generated by your research
– original software created in the course of your research
– new materials you create – like antibodies, cell lines and reagents
– IP such as patents, copyright, design rights and confidential know-how
• The EPSRC has a requirement for Software Management Plans
12. www.geant.org
Why write a DMP / manage your data?
NON PECUNIAE INVESTIGATIONIS CURATORE
SED VITAE FACIMUS PROGRAMMAS DATORUM PROCURATIONIS
(Not for the research funder, but for life we make data management plans)
• Make your research easier
• Stop yourself drowning in irrelevant stuff
• Save data for later
• Avoid accusations of fraud or bad science
• Write a data paper
• Share your data for re-use
• Get credit for it
14. How can we make a good DMP?
14 |
Image CC-BY by Kelly Sikkema https://unsplash.com/photos/v9FQR4tbIq8
15. www.geant.org
Planning trick 1: think backwards
What data organisation would a re-user like?
CREATING
DATA
PROCESSING
DATA
PRESERVING
DATA
GIVING
ACCESS TO
DATA
RE-USING
DATA
17. www.geant.org
Planning trick 2: include RDM stakeholders
Institution
RDM policy
Facilities
€$£
Research funders
Publishers
Data Availability
policy
Commercial partners
www.openaire.eu/briefpaper-rdm-infonoads
18. www.geant.org
Use the DMP as a talking point
Consulting, supporting and networking with
researchers & all other interest groups
Slide content courtesy of Mari Elisa
Kuusniemi (MEK), University of
Helsinki Library
19. www.geant.org
Planning trick 3: ground your plan in reality
Base plans on available skills, support and good practice
for the field – show it’s feasible to implement
20. www.geant.org
Planning trick 4: plan to share from the outset
Decisions made early on affect what you can do later
• Negotiation on licenses and consent agreement may preclude
later sharing if not careful
• Costings can’t be included retrospectively
• Useful to consider data issues at the consortium negotiation
stage to make sure potential issues are identified and sorted asap
21. Key tools and support
21 |
Image CC-BY by Barn Images https://unsplash.com/photos/t5YUoHW6zRo
22. www.geant.org
DCC support on DMPs
• Webinars and training materials
• How-to guides and other advisory documents
• Checklist on what to cover in DMPs
• Example DMPs
• DMPonline
https://www.dcc.ac.uk/dmps
24. www.geant.org
How does DMPonline work?
Select options to get tailored guidance and support
Guidance and examples from
funders, unis, research
disciplines and others
DMP
Requirements from
funders, institutions
and others
Create Share Review Export Update …..
25. www.geant.org
Many DMP tools available…
Platform Organisation(s) Resource link(s)
DMPRoadmap CDL| DCC | Portage Network | INIST CNRS https://github.com/DMPRoadmap/roadmap
University of Queensland
Research Data Manager
University of Queensland https://research.uq.edu.au/project/research-data-
manager-uqrdm
ReDBox DLC QCIF https://www.redboxresearchdata.com.au/rbdlc.html
RDMOrganiser (RDMO) AIP | FHP | KIT http://rdmorganiser.github.io/en
Data Stewardship Wizard ELIXIR | DTL https://github.com/DataStewardshipPortal
ezDMP IEDA https://www.iedadata.org
Data planning tool UNINETT Sigma2 https://www.sigma2.no/content/data-planning-tool
And more….
Please update at: https://activedmps.org
26. www.geant.org
Managing and sharing data:
a best practice guide
• How to write a DMP
• Formatting your data
• Documentation
• Ethics and consent
• Copyright
• Data sharing
• …
http://data-archive.ac.uk/media/2894/managingsharing.pdf
27. Questions and worked examples
Image Israel Palacio https://unsplash.com/photos/P6FgiDNe6W4
28. www.geant.org
1. Describing data to be collected
• What type of data will you produce?
• What file format(s) will your data be in?
• How much data will be produced?
• How will you create your data?
29. www.geant.org
Data description examples
The final dataset will include self-reported demographic and behavioural data from
interviews with the subjects and laboratory data from urine specimens provided.
From NIH data sharing statements
Every two days, we will subsample E. affinis populations growing under our
treatment conditions. We will use a microscope to identify the life stage and sex of
the subsampled individuals. We will document the information first in a laboratory
notebook and then copy the data into an Excel spreadsheet. The Excel spreadsheet
will be saved as a comma separated value (.csv) file.
From DataOne – E. affinis DMP example
30. www.geant.org
Some formats are better for long-term
It’s preferable to opt for formats that are:
• Uncompressed
• Non-proprietary
• Open, documented
• Standard representation (ASCII, Unicode)
Data centres may have preferred formats for deposit e.g.
Type Recommended Non-preferred
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: https://www.ukdataservice.ac.uk/manage-data/format/recommended-formats.aspx
31. www.geant.org
2. Standards and methodologies
• What metadata and documentation will you record?
• What standards are used in your field?
• How will your data be organised?
• Where will it be stored and backed-up?
32. www.geant.org
Metadata examples
Metadata will be tagged in XML using the Data Documentation Initiative (DDI) format.
The codebook will contain information on study design, sampling methodology,
fieldwork, variable-level detail, and all information necessary for a secondary analyst
to use the data accurately and effectively.
From ICPSR Framework for Creating a DMP
We will first document our metadata by taking careful notes in the laboratory notebook that
refer to specific data files and describe all columns, units, abbreviations, and missing value
identifiers. These notes will be transcribed into a .txt document that will be stored with the
data file. After all of the data are collected, we will then use EML (Ecological Metadata
Language) to digitize our metadata. EML is one of the accepted formats used in ecology, and
works well for the types of data we will be producing. We will create these metadata using
Morpho software, available through KNB. The metadata will fully describe the data files and the
context of the measurements.
From DataOne – E. affinis DMP example
33. www.geant.org
Where to find relevant standards?
Metadata Standards Directory
Broad, disciplinary listing of standards
and tools. Maintained by RDA group
https://rd-alliance.github.io/metadata-
directory
FAIRsharing
A portal of data standards,
databases, and policies
Focused on life, environmental and
biomedical sciences, but expanding
to other disciplines
https://fairsharing.org
34. www.geant.org
3. Ethical and IPR implications
• Are you seeking consent from participants?
• Are you re-using other people’s data?
• Who owns your data or has rights in it?
• Are restrictions on sharing needed?
35. www.geant.org
Examples restrictions
Because the STDs being studied are reportable diseases, we will be collecting identifying
information. Even though the final dataset will be stripped of identifiers prior to release
for sharing, we believe that there remains the possibility of deductive disclosure of
subjects with unusual characteristics. Thus, we will make the data and associated
documentation available to users only under a data-sharing agreement.
From NIH data sharing statements
1. Share data privately within 1 year.
Data will be held in Private Repository, but metadata will be public
2. Release data to public within 2 years.
Encouraged after one year to release data for public access.
3. Request, in writing, data privacy up to 4 years.
Extensions beyond 3 years will only be granted for compelling cases.
4. Consult with creators of private CZO datasets prior to use.
Pis required to seek consent before using private data they can access
From Boulder Creek Critical Zone Observatory DMP
36. www.geant.org
Seek consent for data sharing & preservation
•If you don’t ask, data centres won’t be able to accept
your data – regardless of any conditions on the original
grant or your desire for the data to be shared.
37. www.geant.org
4. Data sharing and reuse
• Are you allowed to share your data?
• Who will you share with and how?
• When and where will you make the data available?
• Do you need to impose conditions on reuse?
• How will you license the data for clarity?
38. www.geant.org
Data sharing examples
We will make the data and associated documentation available to users under a data-sharing
agreement that provides for: (1) a commitment to using the data only for research purposes and not
to identify any individual participant; (2) a commitment to securing the data using appropriate
computer technology; and (3) a commitment to destroying or returning the data after analyses are
completed.
From NIH data sharing statements
The videos will be made available via the bristol.ac.uk website (both as streaming media and downloads) HD and
SD versions will be provided to accommodate those with lower bandwidth. Videos will also be made available via
Vimeo, a platform that is already well used by research students at Bristol. Appropriate metadata will also be
provided to the existing Vimeo standard.
All video will also be available for download and re-editing by third parties. To facilitate this Creative Commons
licenses will be assigned to each item. In order to ensure this usage is possible, the required permissions will be
gathered from participants (using a suitable release form) before recording commences.
From University of Bristol Kitchen Cosmology DMP
40. www.geant.org
5. Preservation
• Which data do you need to keep?
• Will you deposit your data in a repository?
• Do you need to prepare it for deposit?
41. www.geant.org
Archiving examples
Data will be provided in file formats considered appropriate for long-term access, as
recommended by the UK Data Service. For example, SPSS Portal format and tab-
delimited text for qualitative tabular data and RTF and PDF/A for interview
transcripts. Appropriate documentation necessary to understand the data will
also be provided. Anonymised data will be held for a minimum of 10 years
following project completion, in compliance with LSHTM’s Records Retention and
Disposal Schedule. Biological samples (output 3) will be deposited with the UK
BioBank for future use.
From Writing a Wellcome Trust Data Management and Sharing Plan
The investigators will work with staff at the UKDA to determine what to archive and
how long the deposited data should be retained. Future long-term use of the data
will be ensured by placing a copy of the data into the repository.
From ICPSR Framework for Creating a DMP
42. www.geant.org
Lists of repositories to choose from
http://databib.org
http://service.re3data.org/search
Zenodo
• OpenAIRE-CERN joint effort
• Multidisciplinary repository
• Multiple data types
– Publications
– Long tail of research data
• Citable data (DOI)
• Links to funder, publications, data
& software
www.zenodo.org
44. www.geant.org
Example DMPs
• Public plans on DMPonline
https://dmponline.dcc.ac.uk/public_plans
• Plans from several funders and disciplines via DCC
www.dcc.ac.uk/resources/data-management-plans/guidance-examples
• 108 DMPs from the National Endowment for the Humanities
https://www.neh.gov/sites/default/files/inline-files/dmp_from_successful_grants.zip
• LIBER DMP catalogue in Zenodo
• https://libereurope.eu/working-group/research-data-management/plans
• DMPs published in RIO journal
• http://riojournal.com/browse_user_collection_documents.php?collection_id=3&journal_id=17
45. www.geant.org
Key messages
• Data management is part of good practice whether you
plan to make the data open or not
– it benefits you!
• Seek advice when developing your DMP - consider good
practice for your field
• Base plans on available skills & support so
implementation is feasible
• Justify decisions – particularly restrictions or costs