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OpenAIRE and Eudat services and tools to support FAIR DMP implementation

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OpenAIRE and Eudat services and tools to support FAIR DMP implementation

  1. 1. Credits: OpenAIRE team Sarah Jones, Data Curation Centre, DCC (UK) Marjon Grootweld, Dans (NL) Natalia Manola, ATR (GR) FAIR Data Management: best practices and open issues Paola Gargiulo, OpenAIRE NOAD/Cineca OpenAIRE and Eudat services and tools to support FAIR DMP implementation
  2. 2. Agenda • The Open Research Data Pilot • The data management plan • OPENAIRE tools and services for the Data Pilot • EUDAT data services 2
  3. 3. Open Research Data Pilot (2015- 2016): aims To make the research data generated by selected Horizon 2020 projects accessible with as few restrictions as possible, while at the same time protecting sensitive data from inappropriate EC: information already paid for by the public should not be paid for again. Open data is data that is free to access and reuse
  4. 4. To whom does the Data Pilot concern? Current situation 2015-2016: • Researchers funded by Horizon 2020 within 9 specified call areas. • Opt out and opt in are possible and are being used • Call areas: https://www.openaire.eu/opendatapilot As of 2017: • European Cloud Initiative to give Europe a global lead in the data- driven economy. • Open data will become the default option. The pilot will be extended to cover all call areas. Opting out remains possible. • Press release: http://europa.eu/rapid/press-release_IP-16- 1408_en.htm Daniel Spichtinger (EC) at OpenCon 14-11-15: 3,699 Horizon 2020 signed grant agreements – 149/431 projects in core areas opted out - 409/3268 projects in other areas opted in 4
  5. 5. Which research has to partipate in the pilot? (2015- 2016) • Future and Emerging Technologies • Research infrastructures – (new: coverage of the whole area) • Leadership in enabling and industrial technologies – Information and Communication Technologies • Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology: ‘nanosafety’ and ‘modelling’ topics (new) • Societal Challenge: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy - selected topics as specified in the work programme (new) • Societal Challenge: Climate Action, Environment, Resource Efficiency and Raw materials – except raw materials • Societal Challenge: Europe in a changing world – inclusive, innovative and reflective Societies • Science with and for Society 5
  6. 6. Horizon 2020 Open Data by Default from 2017 Just announced!
  7. 7. Two types of data: Data, including metadata, needed to validate the results in scientific publications Other data, including metadata, as specified in the Data Management Plan, like raw data
  8. 8. The following slides come from the EC’s open access team and provide an overview to the key points. Content from Jean-Francois Dechamp and colleagues. Mail: RTD-open-access@ec.europa.eu Web: http://ec.europa.eu/research/openscience/index.cfm Twitter: @OpenAccessEC RDA National Event in Italy, 14-15 November 2016 8
  9. 9. Publications Openly accessible and minable. Eligible costs for APCs. Research data Openly accessible research data can typically be accessed, mined, exploited, reproduced and disseminated free of charge for the user.
  10. 10. Three top reasons to opt out Whether a (proposed) project participates in the ORD or chooses to opt out does not affect the evaluation of that project. Proposals will not be penalised for opting out
  11. 11. Reasons for opting out: 14 • participation is incompatible with the Horizon 2020 obligation to protect results that can reasonably be expected to be commercially or industrially exploited; • participation is incompatible with the need for confidentiality in connection with security issues; • participation is incompatible with rules on protecting personal data; • the project will not generate / collect any research data; or • there are other legitimate reasons not to take part in the Pilot. • Note that partial opt out is possible – and preferable to full opt out!
  12. 12. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
  13. 13. FAIR data • 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...
  14. 14. Findable • Use metadata and specify standards for metadata creation (if any). If there are no standards in your discipline describe what type of metadata will be created and how • Search keywords • Persistent and unique identifiers such as DOIs or other handles • File and folder naming conventions • Versioning of the datasets and clear version numbers 18
  15. 15. Metadata and documentation • Metadata and documentation is needed to find and understand research data • Think about what others would need in order to find, evaluate, understand, and reuse your data • Get others to check the metadata to improve quality • Use standards to enable interoperability 19
  16. 16. Where to find metadata standards Metadata Standards Directory Broad, disciplinary listing of standards and tools Maintained by RDA group http://rd-alliance.github.io/metadata- directory Biosharing A portal of data standards, databases, and policies for life, environmental and biomedical sciences https://biosharing.org 20
  17. 17. Accessible • Explain which data can’t be shared openly, if any • Specify how access will be provided in case of restrictions, e.g., through a data committee, a license, or arranged with the repository • Will methods or software tools needed to access the data (if any) be included or documented? • Deposit the data and associated metadata, documentation and code preferably in certified repositories which support Open Access 21
  18. 18. Where to find a repository? More information: https://www.openaire.eu/opendatapilot-repository What to deposit? a. the data needed to validate the results presented in scientific publications, including the metadata; b. any other data, including the metadata, as specified in the DMP; c. plus for a-b the documentation and the tools that are needed to validate the results, e.g. specialised software or software code, algorithms and analysis protocols (when possible, these instruments themselves). 22
  19. 19. Shop around http://databib.org http://service.re3data.org Re3data is a registry of data repositories RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 Searching with Re3data.org www.fosteropenscience.eu/content/re3data-demo 23
  20. 20. Interoperable • Interoperability on data and metadata, on data exchange formats and protocols • Specify what data and metadata vocabularies, standards or methodologies you will follow to facilitate interoperability • Standard vocabulary to allow inter-disciplinary interoperability or a mapping from your vocabulary to more commonly used ontologies? Aim for compliance to globally accepted practices RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 24
  21. 21. • Clarify licences early on • License the data to permit the widest reuse possible • Specify a data embargo, if needed • If data re-use by third parties is restricted, explain why • How long will the data remain reusable? • Describe data quality assurance processes Reusable RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 25
  22. 22. www.dcc.ac.uk/resources/how-guides/license-research-data License research data openly DCC guide outlines the pros and cons of each approach and gives practical advice on how to implement your licence CREATIVE COMMONS LIMITATIONS NC Non-Commercial What counts as commercial? ND No Derivatives Severely restricts use These clauses are not open licenses Horizon 2020 Open Access guidelines point to: or RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 26
  24. 24. What is a data management plan? A plan written at the start of a project to define: • how the data will be created? • how it will be documented? • who will 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 The DMP is a living document. You are not required to provide detailed answers to all the questions in the first version of the DMP (due M6) 28 Explain any selection criteria in the DMP
  25. 25. When to submit 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 • Note that the Commission requires updates. A DMP is a living or “active” document.
  26. 26. What aspects of RDM should be in a DMP?  What data will be created (format, types, volume...)  Standards and methodologies to be used (incl. metadata)  How ethics and Intellectual Property will be addressed  Plans for data sharing and access  Strategy for long-term preservation Create Document Use Store Share Preserve A DMP is a plan to share!
  27. 27. What should be deposited? • 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 like 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 for others. Research Data Alliance (RDA) http://rd-alliance.github.io/metadata-directory/standards/ FAIR Guiding Principles for scientific data management http://www.nature.com/articles/sdata201618 31
  28. 28. Archive the data openly, unless… • Confidentiality and security issues can be good reasons not to publish or share – all – data. Note in the DMP the reasons for not giving access, and deposit that part of the data under a Restricted Access regime. • When regenerating data would be cheaper than archiving, don’t archive. Spend time on selecting what data you’ll need and want to retain. Motivate your criteria in the DMP. See http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf For selection criteria see https://www.openaire.eu/opendatapilot 32 Grant Agreement, Art. 29.3, Open Access to research data:
  29. 29. 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 • Figshare is not a Trustworthy Digital Repository • a persistent identifier is essential but no guarantee for usability • Data in a proprietary format are not sustainable
  30. 30. How much does it cost? Who pays? • What are the costs for making data FAIR in your project? • Resources needed for long term preservation • Check the UK Data Service Costing model • The High Level Expert Group on the European Open Science Cloud recommends that “well budgeted data stewardship plans should be made mandatory and we expect that on average about 5% of research expenditure should be spent on properly managing and stewarding data” • Who pays? How?UKDS model http://www.data-archive.ac.uk/create-manage/planning-for-sharing/costing HLEG report http://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud_2016.pdf#view=fit&pagemode=none 34
  31. 31. http://www.curationexchange.org/ Costs?
  33. 33. OPEN ACCESS OpenAIRE implements the EC requirements & SUPPORTS THE OPEN DATA PILOT
  34. 34. Human Network e-infrastructure  NOADS: National Open Access Desks  Monitor and foster the adoption of Open Access policies at the local level  Support researchers at the implementation of the Open Data Pilot  FP7 post grant APCs Pilot  e-infrastructure for monitoring impact of OA mandates and research projects  OpenAIRE guidelines for metadata exchange  Zenodo Repository for the deposition of research products THE POINT OF REFERENCE FOR OPEN ACCESS IN EUROPE 50 Partners: EU countries, data centers, universities, libraries, repositories Open Access infrastructure for research in Europe
  35. 35. Integrated Scientific Information System RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 17.3 mi unique publications 760+ validated data providers 370Κ publications linked to projects from 6 funders 28 K datasets linked to publications 3.5K links to software repositories 33K organizations Organization s Projects AuthorsDatasets Publications Data Providers Software Facilities Methods Research Communities OpenAIRE-Connect From January 2017 39
  36. 36. OpenAIRE support materials Briefing papers, factsheets, webinars, workshops, FAQs Information on • Open Research Data Pilot • Creating a data management plan • Selecting a data repository • Personal data Developing guidance to add to DMPonline https://www.openaire.eu/opendatapilot https://www.openaire.eu/support RDM Seminar @ ISERD, Tel Aviv - Oct 1, 2016 40
  37. 37. Information at the OpenAIRE website • Open Research Data Pilot https://www.openaire.eu/opendatapilot • What is the pilot? Which H2020 strands are required to participate? What practical steps should the researcher take? • Create a Data Management Plan https://www.openaire.eu/opendatapilot-dmp • Information about how to create a Data Management Plan. First steps; When to write and revise your Data Management Plan • Select a Data Repository https://www.openaire.eu/opendatapilot-repository • Information about how to select a repository • Frequently Asked Questions about the Open Research Data Pilot https://www.openaire.eu/support/faq 41
  38. 38. Webinars Webinar page https://www.openaire.eu/webinars/ • E.g. about Pilot, Zenodo and data management, but also about OA publications and how to make your repository OpenAIRE compatible. • Various presenters; slides included. 42
  39. 39. OpenAIRE factsheets 43
  40. 40. Briefing PaperRDM OpenAIRE Research Data Management Briefing Paper • https://www.openaire.eu/briefpaper-rdm-infonoads • This extensive briefing paper gives an overview of Research Data Management with practical sections about data management planning, and archiving the research data for reuse. 44
  41. 41. OpenAIRE services • Researchers • Zenodo for all types of publications, data and software • Claiming – linking research results • Amnesia, an anonymization tool for all • Data providers – Interoperability Guidelines, validation,… • Project coordinators – reporting • Funders and institutions – monitoring • Research communities – gathering, monitoring all research 45 DASHBOARDS
  42. 42. Zenodo Multi-disciplinary repository used for the long-tail of research data • An OpenAIRE-CERN joint effort • Multidisciplinary repository accepting – Multiple data types – Publications – Software – link to Github • Assigns a Digital Object Identifier (DOI), up t 50GB per dataset • Links funding, publications, data & software www.zenodo.org 46
  43. 43. What is DMPonline? • A web-based tool to help researchers write Data Management and Sharing Plans • Includes requirements and guidance from funders, universities and other groups • Developed by the Digital Curation Centre
  44. 44. How to write a DMP • Template available from https://dmponline.dcc.ac.uk/ • • And from a few national DMPonline sites, e.g. in Spain and Belgium See https://www.openaire.eu/opendatapilot-dmp - Spain: http://pgd.consorciomadrono.es/ - Belgium: pilot and therefore limited to authorised persons 48 1
  45. 45. https://dmponline.dcc.ac.uk DMPonline (free tool)
  46. 46. 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
  47. 47. New H2020 template 51
  48. 48. Deliver the DMP 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.” 52
  49. 49. Where to find a repository? More information: https://www.openaire.eu/opendatapilot-repository Zenodo: http://www.zenodo.org/ 54
  50. 50. 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. • Network of trustworthy digital repositories for long-term preservation of the data after the research is finished. • Three common certification standards for TDRs: Data Seal of Approval: http://datasealofapproval.org/en/ nestor seal for DIN 31644: http://www.langzeitarchivierung.de/Subsites/nestor/EN/nestor-Siegel/siegel_node.html ISO 16363: http://www.iso16363.org/ 55
  51. 51. 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. • Matches your particular data needs and is FAIR compliant: e.g. certain file formats; mixture of Open and Restricted Access. So contact the repository of your choice when writing the first version of your DMP, or earlier. • Provides guidance on metadata and 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. How to select a repository? 2/2 https://www.openaire.eu/opendatapilot-repository 56
  53. 53. EUDAT project https://eudat.eu/ 58 EUDAT offers common data services to both research communities and individuals through a network of 35 European organisations.
  54. 54. EUDAT offers data services EUDAT services are designed, built and implemented based on user community requirements. 59 PHYSICAL SCIENCES & ENGINEERING MATERIALS & ANALYTICAL FACILITIES MAPPER BIOMEDICAL & MEDICAL SCIENCES
  55. 55. EUDAT services 60 B2 SERVICE SUITE B2ACCE SS B2HANDLE https://eudat.eu/services
  56. 56. • Store and exchange data with colleagues and team members, including research data not finalized for publishing • share data with fine-grained access controls • synchronize multiple versions of data across different devices e.g. B2DROP – a solution for researchers and scientists to: Features: 20GB storage per user Living objects, so no PIDs Versioning and offline use Desktop synchronisation B2DROP is hosted at the Jülich Supercomputing Centre Daily backups of all files in B2DROP are taken and kept on tape b2drop.eudat.eu
  57. 57. • move large amounts of data between data stores and high-performance compute resources • re-ingest computational results back into EUDAT • deposit large data sets into EUDAT resources for long-term preservation Features: high-speed transfer reliable and light-weight manages permanent PIDs 62 e.g. B2STAGE - Facilitating communities to: eudat.eu/b2stage
  58. 58. EUDAT training 63 https://eudat.eu/training https://eudat.eu/events/webina
  59. 59. www.openaire.eu @openaire_eu facebook.com/groups/openaire linkedin.com/groups/OpenAIRE-3893548 Openaire-it@cineca.it 64 Questions?

Notes de l'éditeur

  • Basis : 3,699 Horizon 2020 signed grant agreements

    Calls in core-areas: opt out 35% (149/431 proposals)

    Other areas: voluntary opt in 13% (409/3,268 proposals)
  • In multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out
  • Please take time to read Background information and the guidance in the Annex, because the questions in the template are not all clear on their own.

    What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how.
    Are the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)?
    What naming conventions do you follow?
    Will search keywords be provided that optimize possibilities for re-use?
    Do you provide clear version numbers?
  • Metadata is needed to locate and understand the data. When you are deciding what information to capture, think about what others would need in order to find, evaluate, understand, and reuse your data; the EC template also mentions keywords. Also get others to check your metadata to improve the quality and make sure it’s understandable to others. Standards should be used where possible.

    To make sure their data can be understood by themselves, their community and others, researchers should create metadata and documentation.

    Metadata is basic descriptive information to help identify and understand the structure of the data e.g. title, author...
    Documentation provides the wider context. It’s useful to share the methodology / workflow, software and any information needed to understand the data e.g. explanation of abbreviations or acronyms

  • For Accessibility the Guidance contains more questions:

    Which data produced and/or used in the project will be made openly available as the default? If certain datasets cannot be shared (or need to be shared under restrictions), explain why, clearly separating legal and contractual reasons from voluntary restrictions.
    Note that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out.
    How will the data be made accessible (e.g. by deposition in a repository)? What methods or software tools are needed to access the data? Is documentation about the software needed to access the data included? Is it possible to include the relevant software (e.g. in open source code)?
    Where will the data and associated metadata, documentation and code be deposited? Preference should be given to certified repositories which support open access where possible.
    Have you explored appropriate arrangements with the identified repository? If there are restrictions on use, how will access be provided? Is there a need for a data access committee? Are there well described conditions for access (i.e. a machine readable license)? How will the identity of the person accessing the data be ascertained?
  • Are the data produced in the project interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and in particular facilitating re-combinations with different datasets from different origins)?
    What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?
    Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?
    In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies?
  • How will the data be licensed to permit the widest re-use possible?
    When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible.
    Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why.
    How long is it intended that the data remains re-usable? Are data quality assurance processes described?
  • 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
  • Contrary to some other funders, the EC does not require a DMP at the proposal stage.
  • First of all, what is a Data Management Plan (dmp)?

    Essentially, most funders just want evidence at the grant stage that data has been considered – how much will be generated? Usually just a 1-2 page summary covering the expected data to be produced through research along with an idea of what might be shared and how and when it will be shared.

    It is important to stress that funders aren’t expecting something carved in stone at this stage. Projects often change quite radically from what is submitted at the proposal stage and this is ok. Researchers just need to be able to provide evidence that they have thought about the data they might be generating and how it will be managed and shared.

    In terms of preservation, it is important to remember that not ALL data has to be retained. Selecting what data needs to be kept is something that only the researcher can do. Essentially, he/she will need to retain any data that underpins published findings to allow for validation of results. Additional data that is not required for validation purposes but is deemed to have longer-term value might also be worth keeping.

    DMPs are often submitted as part of grant applications, but are useful whenever you’re creating data. Some HEIs are introducing policies that require DMPs for all research undertaken by staff – whether externally funded or not.

  • http://www.data-archive.ac.uk/create-manage/planning-for-sharing/costing
  • [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.
  • Web-based tool to help researchers write Data Management and Sharing Plans according to different funder / institutional requirements

    There are various templates in DMP Online based on different funder requirements and institutional customisations.

    We’re currently enhancing it with practical examples, boilerplate text and tailored support. TEDDINET may wish to develop discipline specific guidance within the tool for future related projects.
  • You may get the feeling that there is so much to do and to know. It is important to realise that you don’t have to build or buy all data services. Instead, institutes and academic communities should support researchers to find & use what is there already. That holds for the repositories that I mentioned, but also for the services that our sister project EUDAT offers.
  • Note that these are all ”technical” services. The notion “RDM” has different meanings in EUDAT and OpenAIRE.
  • B2STAGE was conceived to deal with modern day research challenges. As hardware and research software improve, scope for research is broadening. Communities now pursue large-scale simulations, for example developing models for climate simulation encompassing the whole of the Earth, as opposed to isolated regions. Scientists simulate not only organs in the human body, but also their interactions. Similarly, earthquake data are now collected and processed for areas as large as entire continents. The common requirement of such research challenges is that they generate and process increasing volumes of data, with typical workflows requiring data to be processed in a distributed fashion, so as to cope with the pace of data generation. Efficient transfer of data to high performance computing (HPC) workspaces is essential especially in distributed. In order for this to be possible, data need to be transferred in an efficient way to the high-performance or high-throughput computing resources, and this is where B2STAGE comes in.
    The service also takes care of depositing the computation output from the HPC facilities to EUDAT. B2STAGE can also be used to deposit the community data into the EUDAT facilities. B2STAGE uses the established gridFTP protocol to ensure high-speed transfer between the sites. Data transfer is reliable and requires very little user interaction. B2STAGE also assigns PIDs to computational output that the user elects to inject back into the EUDAT datacentres.

  • When you are interested in learning more about EUDAT services you can contact CINECA.