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EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016| www.eudat.eu |

EUDAT
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

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How to write a
Data Management Plan
Sarah Jones (DCC)
Marjan Grootveld (DANS)
both involved in EUDAT and OpenAIRE
This work is licensed under the Creative
Commons CC-BY 4.0 licence
Open Access Infrastructure
for Research in Europe
www.openaire.eu
Who we are
Research Data Services, Expertise &
Technology https://www.eudat.eu
Joint webinar held on 26 May 2016 covering:
• Reasons to manage data
• Horizon 2020 Open Research Data Pilot
• How to manage and share data
• EUDAT & OpenAIRE services
Slides, webinar recording and Q&A document online
www.openaire.eu/research-data-management-an-
introductory-webinar-from-openaire-and-eudat
Introduction to RDM
• What is a DMP and why write one?
• Requirements under Horizon 2020
• Example plans
• Lessons and guidance
Overview
WHAT IS A DMP & WHY WRITE ONE?
Image CC-BY-NC-SA by Leo Reynolds www.flickr.com/photos/lwr/13442910354
A DMP is a brief plan to define:
• how the data will be created
• how it will be documented
• who will be able to access it
• where it will be stored
• who will back it up
• whether (and how) it will be shared & preserved
DMPs are often submitted as part of grant applications, but
are useful whenever researchers are creating data.
Data Management Plans

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EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016| www.eudat.eu |

  • 1. How to write a Data Management Plan Sarah Jones (DCC) Marjan Grootveld (DANS) both involved in EUDAT and OpenAIRE This work is licensed under the Creative Commons CC-BY 4.0 licence
  • 2. Open Access Infrastructure for Research in Europe www.openaire.eu Who we are Research Data Services, Expertise & Technology https://www.eudat.eu
  • 3. Joint webinar held on 26 May 2016 covering: • Reasons to manage data • Horizon 2020 Open Research Data Pilot • How to manage and share data • EUDAT & OpenAIRE services Slides, webinar recording and Q&A document online www.openaire.eu/research-data-management-an- introductory-webinar-from-openaire-and-eudat Introduction to RDM
  • 4. • What is a DMP and why write one? • Requirements under Horizon 2020 • Example plans • Lessons and guidance Overview
  • 5. WHAT IS A DMP & WHY WRITE ONE? Image CC-BY-NC-SA by Leo Reynolds www.flickr.com/photos/lwr/13442910354
  • 6. A DMP is a brief plan to define: • how the data will be created • how it will be documented • who will be able to access it • where it will be stored • who will back it up • whether (and how) it will be shared & preserved DMPs are often submitted as part of grant applications, but are useful whenever researchers are creating data. Data Management Plans
  • 7. Why manage 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
  • 8. CREATING DATA PROCESSING DATA ANALYSING DATA PRESERVING DATA GIVING ACCESS TO DATA RE-USING DATA Research data lifecycle CREATING DATA: designing research, DMPs, planning consent, locate existing data, data collection and management, capturing and creating metadata RE-USING DATA: follow- up research, new research, undertake research reviews, scrutinising findings, teaching & learning ACCESS TO DATA: distributing data, sharing data, controlling access, establishing copyright, promoting data PRESERVING DATA: data storage, back- up & archiving, migrating to best format & medium, creating metadata and documentation ANALYSING DATA: interpreting, & deriving data, producing outputs, authoring publications, preparing for sharing PROCESSING DATA: entering, transcribing, checking, validating and cleaning data, anonymising data, describing data, manage and store data Ref: UK Data Archive: http://www.data-archive.ac.uk/create-manage/life-cycle
  • 9. What data organisation would a re-user like? Planning trick 1: think backwards CREATING DATA PROCESSING DATA PRESERVING DATA GIVING ACCESS TO DATA RE-USING DATA
  • 10. DMP and data organisation exercises Design a data organisation for the project (folder structure, file naming convention, …) Research Data Netherlands data support training: http://datasupport.researchdata.nl/en/start-de-cursus/iii-onderzoeksfase/organising-data/
  • 12. Planning trick 2: include RDM stakeholders Institution RDM policy Facilities €$£ Research funders Publishers Data Availability policy Commercial partners https://www.openaire.eu/briefpaper-rdm-infonoads
  • 14. A DMP is about ‘keeping’ data • Storing data < > archiving data • Archived data < > findable data • Findable < > accessible • Accessible < > understandable • Understandable < > usable • A USB stick is not safe • A persistent ID is essential but no guarantee for usability • Data in a proprietary format is not sustainable
  • 15. • Findable – Assign persistent IDs, provide rich metadata, register in a searchable resource,... • Accessible – Retrievable by their ID using a standard protocol, metadata remain accessible even if data aren’t... • Interoperable – Use formal, broadly applicable languages, use standard vocabularies, qualified references... • Reusable – Rich, accurate metadata, clear licences, provenance, use of community standards... www.force11.org/group/fairgroup/fairprinciples Making data FAIR
  • 16. How to deal with data and context? • Versioning, back-up, storage and archiving – During the project and in the long term • Ethics, consent forms, legal access • Security and technical access • Usage licences
  • 17. What should be preserved and shared? • The data needed to validate results in scientific publications (minimally!). • The associated metadata: the dataset’s creator, title, year of publication, repository, identifier etc. – Follow a metadata standard in your line of work, or a generic standard, e.g. Dublin Core or DataCite, and be FAIR. – The repository will assign a persistent ID to the dataset: important for discovering and citing the data. • Documentation: code books, lab journals, informed consent forms – domain- dependent, and important for understanding the data and combining them with other data sources. • Software, hardware, tools, syntax queries, machine configurations – domain- dependent, and important for using the data. (Alternative: information about the software etc.) Basically, everything that is needed to replicate a study should be available. Plus everything that is potentially useful for others. Research Data Alliance (RDA) http://rd-alliance.github.io/metadata-directory/standards/ FAIR Guiding Principles for scientific data management & stewardship http://www.nature.com/articles/sdata201618 How to select and appraise research data:www.dcc.ac.uk/resources/how-guides/appraise-select-research-data
  • 18. DMPS IN HORIZON 2020 Image “Open Data” CC BY 2.0 by http://www.descrier.co.uk
  • 19. Some funders that require DMPs
  • 20. Common themes in DMPs 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 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 Start planning and communicating early
  • 21. Horizon 2020: Open Research Data Pilot http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/o a_pilot/h2020-hi-oa-data-mgt_en.pdf • Open access to research data refers to the right to access and re-use digital research data. Openly accessible research data can typically be accessed, mined, exploited, reproduced and disseminated free of charge for the user. • The use of a Data Management Plan (DMP) is required for projects participating in the Open Research Data Pilot, detailing what data the project will generate, whether and how they will be exploited or made accessible for verification and re-use, and how they will be curated and preserved.
  • 22. H2020 - Open Data by Default from 2017
  • 23. The RDM basics, tuned to Horizon 2020 • The EC’s goal is Open Access to research data: as open as possible, as closed as necessary. • In H2020 the Data Management Plan (DMP) is a regular project deliverable, due by month 6. • A DMP is a living document: to be used, updated and shared. • You can use the H2020 template in DMPonline. • Deposit the data in a research data repository. Look early for a research data repository for sharing and preserving the data long term. • If (part of your) data cannot be shared with everyone, you may (partially) opt out of the pilot.
  • 24. Timing the DMP • Note that the Commission does NOT require applicants to submit a DMP at the proposal stage. • A DMP is therefore NOT part of the evaluation. • DMPs are a deliverable for those in the pilot. • Note that the Commission requires updates. A DMP is a living or “active” document.
  • 25. Initial DMP (at 6 months) The DMP should address the points below on a dataset by dataset basis: • Dataset reference and name • Data set description • Standards and metadata • Data sharing • Archiving and preservation (including storage and backup)
  • 26. More elaborate DMP Scientific research data should be easily: 1. Discoverable Are the data discoverable and identifiable by a standard mechanism e.g. DOIs? 2. Accessible Are the data accessible and under what conditions e.g. licenses, embargoes? 3. Assessable and intelligible Are the data and software assessable and intelligible to third parties for peer-review? E.g. can judgements be made about their reliability and the competence of those who created them? 4. Useable beyond the original purpose for which it was collected Are the data properly curated and stored together with the minimum software and documentation to be useful by third parties in the long-term? 5. Interoperable to specific quality standards Are the data and software interoperable, allowing data exchange? E.g. were common formats and standards for metadata used?
  • 27. DMPonline A web-based tool to help researchers write DMPs Includes a template for Horizon 2020 Guidance from EUDAT and OpenAIRE being added https://dmponline.dcc.ac.uk
  • 28. How the tool works Click to write a generic DMP Or choose your funder to get their specific template Pick your uni to add local guidance and to get their template if no funder applies Choose any additional optional guidance
  • 30. OpenAIRE support • Summary on the Open Research Data pilot https://www.openaire.eu/opendatapilot • Brief guide on developing a DMP https://www.openaire.eu/opendatapilot-dmp • Selecting a data repository https://www.openaire.eu/opendatapilot-repository • Developing guidance to add to DMPonline • Will be adding an ‘export to Zenodo’ feature in early 2017 to allow DMPs to be published and assigned a DOI
  • 31. Deliver the DMP and keep it up to date • EC: “Since DMPs are expected to mature during the project, more developed versions of the plan can be included as additional deliverables at later stages. (…) New versions of the DMP should be created whenever important changes to the project occur due to inclusion of new data sets, changes in consortium policies or external factors.” Focus on how you will ensure your data are “FAIR”
  • 32. Active DMPs • Interested in ways to support this active quality, where “active” is understood as “able to evolve and be monitored”? • Join the RDA’s Active Data Management Plans interest group https://rd-alliance.org/groups/active-data- management-plans.html • And see recordings, slides and notes of the international and interdisciplinary ADMP Workshop 28-30 June 2016 https://indico.cern.ch/event/520120
  • 33. Option: add SSI template for software projects Two templates available for Software Management Plans in DMPonline courtesy of SSI www.software.ac.uk/resources/guides/software-management-plans
  • 35. Example plans • 108 DMPs from the National Endowment for the Humanities www.neh.gov/divisions/odh/grant-news/data-management-plans-successful- grant-applications-2011-2014-now-available • 20+ scientific DMPs submitted to the NSF (USA) provided by UCSD – http://libraries.ucsd.edu/services/data-curation/data-management/ dmp- samples.html • Example DMP collection from Leeds University • https://library.leeds.ac.uk/research-data-tools • Further examples: • www.dcc.ac.uk/resources/data-management-plans/guidance-examples
  • 36. Example: OpenMinTed OpenMinTed aims to create an infrastructure for Text and Data Mining (TDM) of scientific and scholarly content Have adopted their own structure to create a ‘Data and Software Management Plan’ http://openminted.eu
  • 37. Example: OpenMinTed – Data chapter Six high-level datasets identified: 1. Scholarly publications 2. Language and knowledge resources 3. Services and workflows 4. Automatically and manually generated annotations 5. Consortium publications 6. Metadata Described in a table per dataset (see illustration)
  • 39. Example: CAPSELLA CAPSELLA aims to develop ICT solutions for farmers and other actors engaged in agrobiodiversity Devised a questionnaire to collate datset information from project partners Identified 13 datasets, 6 of which are imported as is, 3 aggregated, 3 transformed and 1 generated www.capsella.eu
  • 40. 4 types of data • Core Datasets - datasets related to the main project activities. The majority pre-exist CAPSELLA and are publicly available • Produced Datasets - datasets resulting from CAPSELLA’s pilot applications. These include sensor data, field data and user related datasets. • Project Related Data - datasets resulting from the operation of the project. They are collections of standard material e.g. deliverables, dissemination material, training material, scientific publications • Software - datasets resulting from the software developed in the frame of CAPSELLA. These datasets are mainly either software artefacts and source code and can be used for various purposes including research tasks or the development of new software components.
  • 43. 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
  • 44. 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
  • 45. 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
  • 46. 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
  • 47. Archiving examples 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 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
  • 48. Share your example DMPs! Send us links to your DMPs We will add them to the DCC list Aim to cover wide range of disciplines and funders www.dcc.ac.uk/ share-DMPs
  • 49. LESSONS AND RESOURCES Image ‘Energy Resources | Energie Quelle’ CC-BY-NC by K. H. Reichert www.flickr.com/photos/reupa/19502634575
  • 50. Tips for writing DMPs • Seek advice - consult and collaborate • Consider good practice for your field • Base plans on available skills & support • Make sure implementation is feasible • Think about things early…
  • 51. Plan to share data from the outset • 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 Decisions made early on affect what you can do later
  • 52. Sharing data: what is meant? With collaborators while research is active Data are mutable (Open) data sharing Data are stable, searchable, citable, clearly licensed
  • 53. Storing data: what is meant? Storing and backing up files while research is active Likely to be on a networked filestore or hard drive Easy to change or delete Archiving or preserving data in the long-term Likely to be deposited in a digital repository Safeguarded and preserved
  • 54. Archiving, repositories, ehm? • Horizon 2020 ORD pilot participants are asked to “deposit your data in a research data repository”: a digital archive collecting and displaying datasets and their metadata. • Select a data repository that will preserve your data, metadata and possibly tools in the long term. • It is advisable to contact the repository of your choice when writing the first version of your DMP. • Repositories may offer guidelines for sustainable data formats and metadata standards, as well as support for dealing with sensitive data and licensing.
  • 55. Where to find a repository? • More information: https://www.openaire.eu/opendatapilot-repository • Zenodo: http://www.zenodo.org • Re3data.org: http://www.re3data.org
  • 57. How to select a repository? 1/2 • Main criteria for choosing a data repository: Certification as a ‘Trustworthy Digital Repository’, with an explicit ambition to keep the data available in the long term. • Three common certification standards for TDRs: Data Seal of Approval: http://datasealofapproval.org/en nestor seal: http://www.langzeitarchivierung.de/Subsites/nestor/EN/nestor- Siegel/siegel_node.html ISO 16363: http://www.iso16363.org
  • 58. How to select a repository? 2/2 • Main criteria for choosing a data repository: Certification as a ‘Trustworthy Digital Repository’, with an explicit ambition to keep the data available in long term. • Matches your particular data needs: e.g. formats accepted; mixture of Open and Restricted Access. • Provides guidance on how to cite the data that has been deposited. • Gives your submitted dataset a persistent and globally unique identifier: for sustainable citations – both for data and publications – and to link back to particular researchers and grants. www.openaire.eu/opendatapilot-repository
  • 59. Licensing research data • Horizon 2020 guidelines point to CC-BY or CC-0 • EUDAT licensing wizard help you pick licence for data & software http://ufal.github.io/public-license-selector • DCC How-to guide helps you to license data www.dcc.ac.uk/resources/how-guides/license-research-data
  • 60. • How to develop a DMP www.dcc.ac.uk/resources/how-guides/develop-data-plan • RDM brochure and template https://dans.knaw.nl/en/about/organisation-and-policy/information- material?set_language=en • OpenAIRE guidelines • www.openaire.eu/opendatapilot-dmp • ICPSR framework for a DMP www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/fram ework.html Guidelines on DMPs
  • 61. • Guidelines on Data Management in Horizon 2020 • Provides summary of requirements • Includes templates for DMPs http://ec.europa.eu/research/participants/data/r ef/h2020/grants_manual/hi/oa_pilot/h2020-hi- oa-data-mgt_en.pdf EC guidance
  • 62. KEY MESSAGES Image “Fishbone” CC BY-NC-ND 2.0 by ttps://www.flickr.com/photos/mrjnl/
  • 63. Key messages • The principles of good research conduct hold for all of us, across disciplinary boundaries. • Data management is all in a day’s work. • Planning and reflection are more important than the plan – but write the DMP and keep it up to date. • Planning data management is team work. • Think about the desired end result and plan for this. • Decisions made early affect what you can do later.
  • 64. www.eudat.eu www.openaire.eu Thanks – any questions? Contact us: Marjan Grootveld: marjan.grootveld@dans.knaw.nl Sarah Jones: sarah.jones@glasgow.ac.uk Acknowledgements: Thanks to DANS and DCC for reuse of slides, and to the OpenMinTeD and CAPSELLA projects for sharing their Data Management Plans

Editor's Notes

  1. OpenAIRE: H2020 project in our third project phase. Started with the aim of supporting & monitoring the EC’s OA mandate, enabling OA publications, but as the OA movement has evolved, we are now an infrastructure for Oscience generally: Publications, Data and Processes within science. We run a network of national OA helpdesks and align OA policies across Europe. Also we have technologies to capture and interling research outputs of Europe. EUDAT: also H2020, also in a follow-up phase. Goal is to enable researchers and practitioners from any discipline to find, access, process, share and preserve data in the trustworthy environment of the Collaborative Data Infrastructure. There is a whole suite of data services, for different stages of the reseach life cycle. Like OpenAIRE it is a paneuropean network, with cooperating compute and data centers.
  2. There is an overlap and synergy in RDM between both projects, also for Sarah and me personally as we both work in both projects, and this is why we’ve come together again today. When you are interested in our projects’ services, check out the websites and the previous webinar, which was presented by Sarah and Tony Ross-Hellauer from OpenAIRE.
  3. 1. I will start by talking about the Why and What of DMPs, and also about the Who. 2. Then I’ll zoom in on Horizon 2020, the requirements that the EC imposes and the DMP templates that are available. Now we all know that there are huge differences between disciplines, e.g. in the typical duration of projects, team size, the sensitivity of the data that you collect, the cultures of collaboration and data sharing, the use of software, tools and other machinery. A webinar like this is not the ideal place to really go into those differences. 3. But when Sarah takes over, with the example plans, she will give extracts from DMPs from different fields, which will make it more concrete. 4. She will also present some lessons learned in the past and point to useful resources for Planning your RDM. This should leave us with about 15 minutes for questions and discussion. Don’t hesitate to put your questions into the chatbox.
  4. So let’s begin by looking at the changing data landscape.
  5. A Data Management Plan is often written early on in the research process to determine what data will be created and how it will be managed. Sometime you are asked for a DMP as part of a grant application, but they are useful to write regardless as it helps to develop consistent procedures from the outset.
  6. You may know the old saying “We do not learn for school, but for life”. For planning and carrying out data management we’d like to encourage a similar attitude in researchers and other stakeholders. There are lots of reasons to manage research data. You may be required to explain how you will manage your data by your funder or university. Ultimately though, it’s to make your research easier. If data are properly documented and organised, you can stop yourself drowning in irrelevant stuff and find the data when you need it – for example to validate findings. By managing your data you can also more easily share it with others to get more credit and impact. Well-managed data opens up opportunities for re-use, integration and new science. And RDM is just part of a researcher’s life…
  7. This research data lifecycle is taken from the UK Data Archive. It shows you the different processes and activities you’ll go through. As I’m sure you all know, data has a life beyond the project end. Depending on your line of work, you may enter the cycle at ‘half past ten’, by re-using existing data, or at 12 o’clock: Creating data: This is when you’ll design the research, write Data Management Plans, negotiate consent agreements, find any existing data you want to reuse, collect/capture your data and create any associated metadata Processing data: When processing your data, you’ll be entering, transcribing, checking, validating and cleaning it, you may also need to anonymise your data, you should describe it and make sure it’s properly managed and stored. Analysing data: when you analyse your data you’ll be interpreting it and creating derived data and outputs, you’ll probably also author publications and prepare the data for deposit and sharing. Preserving data: data repositories play a key role in preserving data: they will make sure it’s properly stored and archived, they will migrate the formats and storage medium and create associated metadata and documentation to explain any changes made Access to data: it may be that you share your data via a repository or handle access requests yourself. Either way, you need to establish copyright, decide who can have access and promote the data. Re-using data: data can be re-used in follow-up studies, new research, research reviews, to evidence findings or for teaching and learning. Try to keep an open mind about the different ways in which your data could be re-used and make it as open as possible.
  8. Let’s adopt the perspective of a future data user – maybe yourself: what should your data organisation – folders with data, metadata and documentation – look like at the moment that you start sharing - outside your team - and archiving? When you are part of a large project which has been going on for some years already, this may be obvious, but for many researchers it isn’t clear from the start. To answer that broad question, you want to come up, at an early stage, with answers regarding: Types and formats of data; New and/or existing; Expected size; Metadata; Documentation; Software.
  9. It can be a very useful exercise to sit together with colleagues and discuss for 15 minutes which data organisation would be good during the project and also for handing over data to an archive later on. I’ve been part of such “thinking aloud” exercises and that was a great success: file formats, access rights, versioning, sensitivity… So we strongly recommend you to start with making a plausible overview of the expected project output. Note that “output” is not “outcome”: for organising the data in their context and answering the first questions in the DMP the intellectual results of the project are irrelevant. And you may find the following reference helpful …
  10. It’s no fun to do the exercise by yourself, so use this as a communication opportunity.
  11. With so many parties who have a stake in RDM, it’s clear that a DMP is an instrument for communication. AND: for those of you who are not researchers: make sure that you get involved during, or even better, before the writing phase.
  12. DMPs are about ‘keeping’ data. There are some misconceptions about “keeping” data; that’s why this slide looks so gloomy and heavy.
  13. Working in a FAIR way can help you to deal with the first part of the previous slide. It’s becoming an international ambition to make data FAIR. We’ve put sugggestions back to the EC and they are reworking the guideline, and FAIR concepts will play a role. As always, namedropping is easy, so you do have to think at an early stage about what complying with the FAIR principles means in your situation. There are some pointer here to what it means that data are FAIR.
  14. With whom: Immediate collegeagues, researchers within your organisation, all researchers, the public at large? When: now, after an embargo periode? For publications H2020 allows 6 (STEM –science, technical, engineering and medical) to 12 months (SSH). For data no-one talks about embargos, but it is an option that LT repositories may offer. Under what terms: OA should be the default, but even then you are wise to make use of a license (and check this with your LT repository)
  15. Re Software etc: you might also think of virtual machines with the corresponding setup information. In many cases copyright will prevent the archiving of software and tools. The alternative is a sensible description of configuration settings etc.
  16. Let’s move on to what H2020 requires from DMPs
  17. Much of what was in the earlier slides relates to any funder or research organisation that requires a DMP, and…
  18. …and there are clear common themes in the templates and checklists. But let’s now focus on H2020…
  19. As you will know, the EC runs a pilot study with Open Research Data and in the pilot the EC requires that data will be preserved for later use; a DMP should describe the What and How.
  20. Starting next year, this with hold for all project call areas. As far as we know, opting out and partially opting out will remain possible als long as it is motivated.
  21. The EC’s goal is Open Access to research data. Participating in the Open Research Data Pilot does not necessarily mean opening up all research data. Rather, the focus of the Pilot is on encouraging good data management as an essential element of research best practice: awareness raising. In H2020 the Data Management Plan (DMP) is a regular project deliverable, due by month 6. The project officer and reviewers will ask for it, will evaluate it and give it a mark like any other deliverable (excellent, good, needs revision, rejected). This usually happens at the first review, unless the Project Officer is quite meticulous. In subsequent reviews (or any time they feel like) the PO and reviewers may check to see if the DMP is followed (e.g., data files deposited, access status, metadata format, ...).  A DMP is a living – or “active” - document: Use it to periodically review how well reality meets the stated goals for RDM, and adapt and communicate the plan accordingly. The EC requires the initial DMP and updated DMPs during mid-term and final review. You can use the H2020 template in DMPonline. In the briefing paper you find references to existing DMPs for different funders, for inspiration. Later on, I’ll show some screens from DMPonline. Look early for a research data repository for sharing and preserving the data long term. If (part of your) data cannot be shared with everyone, exemptions apply. In the DMP you can state reasons for not sharing (part of) your data. In planning and carrying out data management you’re not alone: RDM is a collaborative effort, as we saw.
  22. Contrary to some other funders, the EC does not require a DMP at the proposal stage.
  23. For the DMP you can use a word document in your project layout, but you can also use the template within DMPonline. Here is where you can log in.
  24. From the start, the DCC has offered guidance, independent of funder or discipline. EUDAT and OpenAIRE and others are developing extra guidance as well.
  25. For instance, should to want to archive your dataset later on in EUDAT’s B2SHARE facility, you see here that this will assign a persistent identifier to the dataset.
  26. [final bullet] Acting on requests from the community, DMPonline will add an ‘export to Zenodo’ feature alongside the other export options. You might want to use this to increase your project’s transparancy, share good practices, or maybe because you write your DMP as a (kind of ) data paper, which is interesting in its own right. At the moment there are a few H2020 DMPs in Zenodo and figshare.
  27. Make sure that you know what will be asked of you for the mid-term and the final review: the focus here is on enabling reuse of your data – by your future self and others. In subsequent reviews (or any time they feel like) the PO and reviewers may check to see if the DMP is followed (e.g., data files deposited, access status, metadata format, ...). 
  28. As an aside…
  29. When in your project SW is not only a tool for capturing or analysing data, but a planned project deliverable, you might consider to also plan your SW Management. Sample questions from the Minimal plan: 1. What will your software do? 2. Will your software have a name? Do you have one in mind? Is this name unique and meaningful and not in violation of any existing trademarks? What, if any, software installation and configuration skills, knowledge and expertise will your users need? Will they need to be familiar with building and installing software via the command-line? Will they need to develop their own code to be able to use your software? 3. There are many ways in which you can release your software, e.g.: a binary executable that can be run directly or a .zip archive, or as Python or R packages 4. Will it help to produce results more rapidly? Will it help to produce results to a higher degree of accuracy or a finer level of detail? Will it help to conduct analyses cannot be conducted at present? 5. Asking users to cite your software, directly or via a related paper, and providing a recommended citation, means you can search for these citations. Consider adding a citation requirement to your software’s licence, so it becomes a condition of its use. In addition the full plan asks for instance about how you make good SW (including tests and adhering to disability accessibility guidelines), dependencies like third-party tools, engaging with the users, etc.
  30. Example: the OpenMinTed project combines software with the H2020 DMP issues. OpenMinted is an EINFRA project, which means that it is building an e-Infrastructure and data is passing through
  31. Basically, the project partners have selected from the long list of the SSI template what is relevant for them.
  32. Capsella is an ICT project (RIA).
  33. These are not the same thing! When the EC asks about your approach to sharing data they’re interested in the latter.
  34. When data are stored on ‘active data storage’ they’re subject to change. Anyone with permission could edit or delete files. They may still be there in 10 years time, but this is not guaranteed. An archive is different as the data and associated metadata is packaged up together and protected. Backup is not the same as preservation. If you want your data to be accessible in the future, you should deposit in a trustworthy digital repository which commits to preserving it.
  35. Remember to give also your open data and software a proper licence. 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: http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf
  36. Let’s move on to the considerations to make when managing and sharing data
  37. Thanks