20170530_Open Research Data in Horizon 2020
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This document discusses open research data in Horizon 2020 projects. It provides an overview of the OpenAIRE network, the European Commission's open access mandate, and requirements for open research data under Horizon 2020. Projects starting in 2017 are included in the open data policy by default and must make their data openly available. Reasons for opting out of open data requirements are also presented.
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20170530_Open Research Data in Horizon 2020
- 1. Open Research Data in Horizon 2020 Marjan Grootveld, DANS Tony Ross-Hellauer, University of Göttingen OpenAIRE webinar 30th May, 2017 @MarjanGrootveld @tonyR_H @openaire_eu
- 2. Contents • What is OpenAIRE? • Data management: why? • The EC Guidelines for FAIR Data Management: • OpenAIRE services • Summary and recommendations • Links to EC and OpenAIRE information 2
- 3. Related webinars Introductory RDM webinar, Tony Ross-Hellauer & Sarah Jones, 26 May 2016: • Reasons to manage data • How to manage and share data (+ how to respond to concerns about sharing) • EUDAT & OpenAIRE services • Q&A document: https://www.openaire.eu/public-documents?id=835&task=document.viewdoc How to write a DMP, Sarah Jones & Marjan Grootveld, 7/14 July 2016: • What is a Data Management Plan and why to write it? • Example DMPs in different domains, with lots of links! • Guidance, e.g. storing =/= archiving; how to find a repository; file-naming conventions Open Research Data in H2020 and Zenodo, Marjan Grootveld & Krzysztof Nowak, 26 October 2016: • Sustainable file formats differ across domains and repositories • Funders embrace the FAIR data principles – implications for Data Management Planning? • Slides: www.slideshare.net/OpenAIRE_eu/openaire-webinar-on-open-research-data-in-h2020-oaw2016 • Q&A document: https://www.openaire.eu/public-documents?id=843&task=document.viewdoc FAIR data in Trustworthy Data Repositories, Peter Doorn & Ingrid Dillo, 12/13 December 2016: • Proposal for scoring datasets on Findability, Accessibility and Interoperability = Reusability levels • Inspired by the Data Seal of Approval criteria for Trustworthy Data Repositories • Slides: http://www.slideshare.net/EUDAT/fair-data-in-trustworthy-data-repositories-webinar-1213-december-2016 https://www.eudat.eu Research Data Services, Expertise & Technology 3 3
- 5. Human Network A “dual core” eInfrastructure for Open Scholarship Digital Network Fosters the social and technical links that enable Open Science in Europe and beyond 5
- 6. Linked Open Science Policies and practices hand in hand for sustainable OA Putting research in its proper context: Intelligent discovery Transparency and trust Reproducibility Monitoring & analysis 6
- 7. Who we are • 50 Partners from every EU country, and beyond • In 24/7 operation since 2010 • 4 project phases to date • Legal entity in 2017 • Institutional, national and international perspectives on OA policies & e-Infrastructures Open Access experts • Building efficient e-Infra technologies • State of the art technologies (big data, linked data) Information & Computer Science experts • Legal & policy recommendations Legal experts • Best practices for data • Linking to data infrastructures Data communities 7
- 8. People make the difference Local support for Europe’s diverse research landscape Human support network • 33 expert nodes all over Europe to help with: • OA training and support • OA policy development • Technical assistance • World-wide synergies 8
- 9. Researchers & research communities Data providers Funders & research administrators 3rd party service providers Smart services for all Dashboards for data providers, funders and researcher communities Open Science services for the whole research life-cycle 9
- 10. OPEN DATA IN H2020 10
- 11. EC Open Access Mandate Progression FP7 (2008) • 20% programme areas • Deposit in Repositories • APC payments during project • ERC OA Guidelines Horizon 2020 (2014) • 100% programme areas • Deposit in Repositories • APCs during and after project • Open Data Pilot (100% from 2017) 11
- 12. Which H2020 projects are affected? Projects starting from January 2017 are by default part of the Open Data policy. If your project started earlier and stems from one of these Horizon 2020 areas, you will automatically be part of the pilot as well: • Future and Emerging Technologies • Research infrastructures (including e-Infrastructures) • Leadership in enabling and industrial technologies – Information and Communication Technologies • Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology: ‘nanosafety’ and ‘modelling’ topics • Societal Challenge: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy - selected topics in the calls H2020-SFS-2016/2017, H2020-BG-2016/2017, H2020-RUR-2016/2017 and H2020-BB-2016/2017, as specified in the work programme • 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 • Cross-cutting activities - focus areas – part Smart and Sustainable Cities. 12
- 13. Open Research Data policy requirements • Deposit the data underlying your scientific publications, including the metadata, documentation and tools needed to validate the results, in a research data repository. • Sharing more data is encouraged. • Write, and keep up-to-date, a Data Management Plan. • Make data “as open as possible, as closed as necessary”: opting out – fully or in part – is possible, but needs justification. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf 13 13
- 14. 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.
- 15. EC Open Research Data Pilot Opt-out Reasons https://open-data.europa.eu/data/dataset/open-research-data-the-uptake-of-the-pilot-in-the-first-calls-of-horizon-2020 15
- 16. The EC Open Research Data policy Key sources of information • Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot- guide_en.pdf • Guidelines on Data Management in Horizon 2020 http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data- mgt_en.pdf • Annotated model grant agreement, clause 29.3 http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/amga/h2020-amga_en.pdf • Infographic summarising key policy points http://ec.europa.eu/research/press/2016/pdf/opendata-infographic_072016.pdf • Open Access and Data Management http://ec.europa.eu/research/participants/docs/h2020-funding- guide/cross-cutting-issues/open-access-dissemination_en.htm 16
- 18. Why manage data? Image CC-BY-NC-SA by Leo Reynolds www.flickr.com/photos/lwr/13442910354 18
- 19. Data explosion • More and more data is being created • Issue is not creating data, but being able to navigate and use it • Data management is critical to make sure data are well- organised, understandable and reusable 19
- 21. Data loss Digital data are fragile and susceptible to loss for a wide variety of reasons • Natural disaster • Facilities infrastructure failure • Storage failure • Server hardware/software failure • Application software failure • Format obsolescence • Human error • Malicious attack • Loss of staffing competencies • Loss of institutional commitment • Loss of financial stability • Changes in user expectations Image CC BY-NC-SA 2.0 by Dave Hill https://www.flickr.com/photos/dmh650/4031607067 21
- 22. And there is you! • Make your research easier • Stop yourself drowning in irrelevant stuff • Save data for later • Avoid accusations of fraud or sloppy science • Write a data paper, connect your nano publications • Share your data for re-use & get them validated in real life • Get credit for it 22
- 23. Managing & sharing data 23
- 24. Research data lifecycle CREATING DATA PROCESSING DATA ANALYSING DATA PRESERVING DATA GIVING ACCESS TO DATA RE-USING DATA 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 24
- 25. From the re-use perspective What is needed in order to find, evaluate, understand, and reuse someone’s data – and to give them credit? CREATING DATA PROCESSING DATA PRESERVING DATA GIVING ACCESS TO DATA RE-USING DATA 25
- 27. Making data FAIR • 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 don’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 and http://www.nature.com/articles/sdata201618 27
- 28. EC FAIR data EC in the Guidelines: “This template is not intended as a strict technical implementation of the FAIR principles, it is rather inspired by FAIR as a general concept (…) without suggesting any specific technology, standard, or implementation solution” 28
- 29. Example: raw data in Zenodo 29
- 30. Some other funders that require DMPs 30
- 31. Data Management Plans A DMP is a brief plan to define: • how the data will be created • how it will be documented • who can access it • where it will be stored • whether it will be shared • where it will be preserved DMPs are sometimes submitted as part of grant applications, sometimes afterwards, but they are useful whenever researchers are creating data. CREATING DATA PROCESSING DATA ANALYSING DATA PRESERVING DATA GIVING ACCESS TO DATA RE-USING DATA 31
- 32. DMPonline A web-based tool to help researchers write DMPs https://dmponline.dcc.ac.uk Choose your funder to get their specific template Choose any additional optional guidance 32
- 33. Some “F” questions §2.1 Making data findable, including provisions for metadata • 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. • Use search keywords • Persistent and unique identifiers such as DOI • File and folder naming conventions: see OpenAIRE-EUDAT July 2016 webinar • Versioning of the datasets and clear version numbers 33
- 34. Metadata and documentation • Metadata and documentation is needed to locate and understand research data. • Use relevant standards to enable interoperability. • Check what the long-term repository supports or expects. • Get others to check the metadata to improve quality. http://rd-alliance.github.io/metadata-directory 34
- 35. Documentation? • Code book explaining the variables • Study design • Lab journal • iPython or Jupyter notebook • Statistical queries • Software or instruments to understand or reproduce the data • Machine configurations • Consent information • Data usage licence • … In short: document and preserve everything that is needed to reproduce the study – ideally following the standard in your discipline 35
- 36. Some “A” questions § 2.2 Making data openly 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. Data Seal of Approval ICSU World Data System nestor seal ISO 16363 36
- 37. Where to find a repository? More information: https://www.openaire.eu/opendatapilot-repository Zenodo: http://www.zenodo.org Re3data.org: http://www.re3data.org 37
- 38. Keep everything? Forever? Select what data you’ll need and want to retain. Some selection criteria: • Data underlying publications • What can’t be recreated, like interviews or environmental recordings • What is potentially useful to others • What has scientific, cultural or historical value 10 years is often stated in data policies and academic codes, but data can be valuable for ages, in climatology, sociology, health sciences, astronomy, linguistics, … Look beyond minimal retention periods where relevant. RDNL Selection criteria: http://www.researchdata.nl/en/services/data-management/selecting-research-data/ DCC How-to guide: http://www.dcc.ac.uk/resources/how-guides/appraise-select-data 38
- 39. Interoperability 39 Before clocks were invented, people kept time using different instruments to observe the Sun’s zenith at noon. Towns and cities set clocks based on sunsets and sunrises. Time calculation became a serious problem for people travelling by train, sometimes hundreds of miles in a day. UTC is the World's Time Standard.
- 40. Some “R” questions § 2.4 Increase data re-use (through clarifying licences) • License the data to permit the widest reuse possible • Specify a data embargo, if this is needed • How long will the data remain reusable? • Describe data quality assurance processes Re-use over time 40
- 41. Licensing research data and software EUDAT licensing wizard helps you pick licences for data & software You should also license Open Access data, or waive rights. Horizon 2020 Open Access guidelines point to: or http://ufal.github.io/public-license-selector/ 41
- 42. DMPlanning recommendations • Plan for the desired end result: open and reuseable data. • Be specific and justify your decisions in the DMP. • Involve all work packages and partners to get a coherent plan. Also consult your Research Support staff and the repository. • Approach the DMP in whatever way best fits your project: • EC template is intended as a service, not an obligation. Read the background information and the guidance, and use it as a checklist. • More than one dataset? Describe generically what is possible and dataset-specific what is necessary. • Focus effort on datasets you’ll create rather than reuse. 42
- 43. Concerns about data sharing Concern Solution inappropriate use due to misunderstanding of research purpose or parameters security and confidentiality of sensitive data lack of acknowledgement / credit loss of advantage when competing for research funding 43
- 44. Concerns about data sharing Concern Solution inappropriate use due to misunderstanding of research purpose or parameters security and confidentiality of sensitive data lack of acknowledgement / credit loss of advantage when competing for research funding metadata metadata metadata metadata 44
- 45. Concerns about data sharing Concern Solution inappropriate use due to misunderstanding of research purpose or parameters provide rich Abstract, Purpose, Use Constraints and Supplemental Information where needed security and confidentiality of sensitive data • the metadata does NOT contain the data • Use Constraints specify who may access the data and how lack of acknowledgement / credit specify a required data citation within the Use Constraints and the license loss of data insight and competitive advantage when vying for research funding create second, public version with generalised Data Processing Description 45
- 47. Zenodo.org For all content types! With GitHub integration! Upload Describe Publish Create communities! 47
- 48. Zenodo now supports versioning! Alert to show you are not on latest version. “Newer version” is linked to latest version. In “owner view“, you can now easily edit metadata of previous versions and create new versions of a record. Each version gets its own DOI. List shows last 8 versions of a record, with link to “view all versions”. We also create one DOI that represents all the differing versions.
- 49. Link data to publications https://www.openaire.eu/search 49
- 50. OpenAIRE support materials https://www.openaire.eu/opendatapil ot https://www.openaire.eu/support • Briefing papers, factsheets, webinars, workshops, FAQs • Information on: • Open Research Data Pilot • Creating a data management plan • Selecting a data repository • Personal data 50
- 51. OpenAIRE Open Science Helpdesk If you cannot find an answer to your question, please contact us through our helpdesk: https://www.openaire.eu/support/ helpdesk If your question relates directly to your own country, your enquiry will be routed to your local OpenAIRE National Open Access Desk: 51
- 52. NOADs (National Open Access Desks) • 33 local experts on Open Access and Open Science • There to help you! • https://www.openaire.eu/contact-noads 52
- 53. New horizons for Open Data • Literature-Data Integration service: https://dliservice.research-infrastructures.eu • Co-chairing (with Elsevier, DataCite, etc.) to enable exchange of scholarly links across domains and platforms • Developing forthcoming AMNESIA tool for effective anonymization of sensitive data • OpenAIRE-Connect – new project started 1st Jan 2017 • Open Science as a Service • Customize OpenAIRE administration tools for research communities needs 53
- 54. Advising on Open Data • Aligning/harmonizing Open Data policies across Europe via our National Open Access Desks • Providing feedback to EC on Open Data policies • E.g., 2016 OpenAIRE-EUDAT advice on revising H2020 DMP template • Until 21 June 2017, OpenAIRE is collecting feedback on the Horizon 2020 template for Data Management Plans: https://www.surveymonkey.com/r/OpenAIRE_DMP_survey • Conducting legal research into RDM and Open Data • 2013 OpenAIRE study “Safe to be Open” on research data protection https://goo.gl/opMyNd • Follow-up study by same authors coming soon! • 2017 Legal Issues in Open Data Workshop slides and recordings: https://www.openaire.eu/workshop-legal-issues-ord 54
- 55. EUDAT B2 service suite Covering both access and deposit, from informal data sharing to long-term archiving, and addressing identification, discoverability and computability of both long-tail and big data, EUDAT’s services address the full lifecycle of research data https://www.eudat.eu/ 55
- 56. 56
- 57. Closing remarks Image “Fishbone” CC BY-NC-ND 2.0 by https://www.flickr.com/photos/mrjnl/ 57
- 58. Summary of RDM in H2020 • Research data should be as open as possible, as closed as necessary. • In H2020 the 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. • “Sharing” means “outside the consortium”. If (part of your) data cannot be shared with everyone, exemptions apply. • Manage and document all data FAIRly, whether they will be open or not. 58
Editor's Notes
- Data management and the planning of data management are very broad topics. Relevant for all disciplines and all research funders. Clearly we ‘re not going to tell the full story today, so when this is the first time you join us, make sure that you also check out recordings or slides of earlier webinars. Some of the webinars were organised together with EUDAT. This is also a project funded by H2020 and it offers a whole suite of data services for finding, storing, sharing, archiving data as well as for HPC. You see here very very briefly what the topics of those earlier webinars were.
- More formally, these are the requirements of the Open Research Data Pilot: At least the data on which your publications are based must be deposited at a repository, along with the context documentation. When you are not allowed to archive a tool or specific software, you can deposit a description of it. Of course you can also make other data, not underpinning a paper, available to others. Should be described in the DMP. Complementary to the project plan you should plan how to manage the data during and after the project. The DMP is s regular project deliverable, due by M6. The EC, like many other funders, regard it as a living document, which you should keep up to date. For instance, when at some point during the project you realise that part of the data cannot be shared openly, you must submit an updated DMP in which you explain this. Not really different from other official plans, is it? And opt out does not affect the evaluation of the project: neither at the proposal stage, nor during the project, will you be punished for opting out. And the EC also provides Guidelines on FAIR Data Management. These guidelines also contain a template for writing your DMP, and your find this template also in …
- Introduced at the start of 2015, covering just seven work programme areas, the Horizon 2020 Open Research Data Pilot has been a big success. In the first six months of the pilot, about a third of projects (65.4%, 431 signed grant agreements) that were part of the pilot chose to opt out. The most common reasons for opting out were: (1) concerns over intellectual property (37%), (2) the project did not expect to generate any data (18%), and privacy/data protection concerns (18%). Of those projects that were not originally part of the pilot, 11.9% (3268 projects) nonetheless have voluntarily opted in.
- So let’s begin by looking at the changing data landscape.
- There’s been a data explosion, and this graphic with the numbers is already “very old”, in terms of data… Whatever the exact volume of data, the rate of increase, or the variety of data types, it is vital that are ways to make sense of them. As the amount of data being created now is growing exponentially, the biggest challenge is being able to navigate and use it. This is why data management is critical.
- Many experimentally established "facts" don't seem to hold up to repeated investigation. Several studies have shown alarming numbers of published papers that don’t stand up to scrutiny. Over half of psychology studies fail reproducibility test (61/100) – Nosek et al, Science, 2015 Causes of reproducibility not well understood – but can say that it is obvious that where the original data is available, accountability is increased – able to review where questions arise.
- Digital data are fragile. There are lots of ways in which data can be lost. Hardware and software can fail, formats can become obsolete, you can lose the knowledge and skills needed to understand the data, and you can lose the investment needed to keep the data accessible. Despite significant investment, data is not being managed effectively The current estimated total global spend on research and development is $1.5 trillion, which could be at risk. Much of the data generated is lost – in one study, the odds of sourcing datasets declined by 17% each year. The same study found 80% of datasets over 20 years old not available.
- There are lots of reasons to manage research data, and they range between the carrot and the stick. In some cases, funder or institutional requirements are a trigger to to do it. 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.
- Let’s move on to the considerations to make when managing and sharing data
- This research data lifecycle is taken from the UK Data Archive. It shows you the different processes and activities you’ll go through. 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.
- Let’s take a somewhat different perspective on the life cycle: the perspective of a re-user. This could be you, now, when you want to use existing data in your project. OR it can be someone who, in future, might benefit from YOUR data. Either way, the data should be FAIR:…
- The EC in the H2020 programm has adopted the FAIR principles. It is important to realise that these are PRINCIPLES: it is up to you to plan and describe what in means in practice for your project, within your research discipline. Also, your data should be open by default: you should share them with others beyond your project consortium, unless you justify why this is not possible. This is why we talk about ”data managing and sharing”.
- This is an example of a data set in the Zenodo repository: the title already tells what it is. Other metadata is about the authors, publication date (NB: of the DATA), the persistent identifier assigned by Zenodo, keywords, a grant identifier, the reference to the associated publication, and the access rights in the form of a Creative Commons license. Clearly, rich discipline-specific metadata and documentation is very valuable, and this can be deposited along with the data.
- The EC is certainly not unique in making such demands; here you see some other research funders that require DMPs, and also many research institutes like universites require them.
- A Data Management Plan is typically written early on in the research process to determine what data will be created and how it will be managed. You will recognize that it is linked to steps in the life cycle. Sometime you are asked for a DMP as part of a grant application, and sometimes you need to deliver the plan after the proposal was awarded. But remember that they are useful to write regardless, as it helps to develop consistent procedures from the outset.
- The EC’s Guidelines contain a template for the DMP. This is also available from the DMPonline tool. You can work on the plan collaboratively and export it in different formats. In the Open Access week last year we gave a webinar on the full template. Today I will only pick a few elements from the FAIR sections, so you may want to check out the earlier recordings – the link is in the slide that Tony presented.
- Crucial for F is metadata: the information about the data. You should comply with disciplinary standards or explain why this is not possible.
- 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. 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 There are lots of standards that can be used. The DCC started a catalogue of disciplinary metadata standards which is now being taken forward as an international initiative via an RDA working group
- “Why are you talking about documentation? It’s all in my publication!” Sometimes researchers reason that all information is in there publications. Is that really true? Well, take a look at this list. Don’t bother with what exactly is metadata and what is documentation, because different research communities may have different customs. Together they provide the context of the data, and together should suffice to tell others everything that they need to Find, Access and Use the data, and relate them to other data sources.
- .. Sometimes we see in DMPs that after the project the data will remain on the project’s website or on the server of one of the partners. However, these are solutions for the active phase, and usually not sustainable: When data are stored on ‘active data storage’ like a networked filestore or a hard drive, 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. However, an archive is different as the data and associated metadata is packaged up together and protected. If you want your data to be accessible in the future, you should deposit in a trustworthy digital repository which commits to preserving it.
- It might not be possible to preserve and share all your data, so you may need to make a selection. Some factor to consider could be what has to be kept, for example for legal reasons or to evidence findings, what is potentially useful to others or can’t be recreated. You may also be under obligation to destroy certain data due to consent agreements or commercial non-disclosure restrictions. Explain your selection criteria in the DMP.
- ”I” may look like something new, but it isn’t, as you can see from the examples: Time, Distance, Musical Pitch and more recently medical classification are all based on standards, consensus, common reference points. In the same way that different fields have their own metadata, they can have their own standards and procedures to achieve interoperabilty of data sources, and these should be mentioned in the DMP.
- …
- Be specific: e.g. Regarding expected data volume, file formats, metadata standard, licences, long-term repository. Also, a generic word like “suitable” in “we will use suitable formats to ensure that our data can be preserved and sustained over the long term” is not convincing.
- Naturally, researchers may worry that the data will be taken out of context, misinterpreted or used inappropriately. They may also be concerned about maintaining the confidentiality and security of sensitive data. Business concerns may arise as well - will data users give proper credit and acknowledgement to the scientist? Will the scientist lose a competitive advantage by sharing this valuable resource? There are lots of reasons why researchers may be reluctant to share data, so what is the solution?
- Each of these issues can, in great part, be addressed by providing rich data documentation known as ‘metadata’.
- By providing metadata, the research scientist establishes the purpose, methods, sources and parameters of the data. As such, data users are given the information necessary to appropriately apply, protect and cite the data. If the metadata contains information about proprietary data processing or analysis techniques, the competitive advantage can be maintained by creating a second, more generalized, metadata record for public distribution.
- Catch-all repository Multiple data types Publications Long tail of research data Citable data (DOI) Links to funding, pubs, data, software
- Should happen automatically thanks to our data-literature interlinking services But where it doesn’t, you
- The services assist researchers to store, manage and process the data through-out the active phase of research, and also help to archive data and make it discoverable to others.