How to elaborate a data management plan
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Presentación de Joy Davidson, Digital Curation Centre (UK) en FOSTER event: Data Management Plan and Social Impact of Research. Universitat Jaume I, 27 mayo 2016
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How to elaborate a data management plan
- 1. Facilitating Open Science Training in European Research What is the Open Data Pilot? What is required from Horizon 2020 signatories? Joy Davidson, Digital Curation Centre Acknowledgements: content contributed by Sarah Jones, Jonathan Rans
- 2. Digital Curation Centre (DCC)
- 3. Definition of research data ‘Research data’ refers to information, in particular facts or numbers, collected to be examined and considered as a basis for reasoning, discussion or calculation. In a research context, examples of data include statistics, results of experiments, measurements, observations resulting from fieldwork, survey results, interview recordings and images. The focus is on research data that is available in digital form. Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 v.1.0, 11 December 2013, Footnote 5, p3
- 4. How does research data fit in with the theme of open science? “science carried out and communicated in a manner which allows others to contribute, collaborate and add to the research effort, with all kinds of data, results and protocols made freely available at different stages of the research process.” Research Information Network, Open Science case studies www.rin.ac.uk/our-work/data-management-and-curation/ open-science-case-studies
- 5. Levels of open data make your stuff available on the Web (whatever format) under an open licence make it available as structured data (e.g. Excel instead of a scan of a table) use non-proprietary formats (e.g. CSV instead of Excel) use URIs to denote things, so that people can point at your stuff link your data to other data to provide context Tim Berners-Lee’s proposal for five star open data - http://5stardata.info “Open data and content can be freely used, modified and shared by anyone for any purpose” http://opendefinition.org
- 6. How does RDM fit into the picture? Create Document Use Store Share Preserve • Data Management Planning • Creating data • Documenting data • Accessing / using data • Storage and backup • Selecting what to keep • Sharing data • Data licensing and citation • Preserving data
- 7. Funders have expectations about data sharing… “The European Commission’s vision is that information already paid for by the public purse should not be paid for again each time it is accessed or used, and that it should benefit European companies and citizens to the full.” http://ec.europa.eu/research/participants/data/ ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf Data management plans requested for those participating in Open Data pilot.
- 8. “Data sets are becoming the new instruments of science” Dan Atkins, University of Michigan …but RDM is part of good research practice!
- 9. DMPs can help Projects participating in the pilot will be required to develop a Data Management plan (DMP), in which they will specify what data will be open. 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 participating in the pilot.
- 10. 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!
- 12. What is the difference? • Metadata • Standardised • Structured • Machine and human readable Metadata Documentation
- 13. How should you describe your data? http://www.dcc.ac.uk/resources/metadata-standards
- 14. What is the minimum required? • DataCite metadata used by OpenAIRE • Citation/disambiguation • Identifier e.g. DOI • Creator • Title • Publisher • Publication Year • Licencing/access conditions https://www.datacite.org/
- 15. Where will you store the data during your research? • Your own laptop? • University systems? • Cloud storage? • Combination? Your decision will be based on how sensitive your data are, how robust you need the storage to be, who needs access to the data, and when they need access to the data!
- 16. Which data must be kept? • Data, including associated metadata, needed to validate the results in scientific publications • Other curated and/or raw data, including associated metadata, as specified in the DMP Doesn’t apply to all data (researchers to define as appropriate) Don’t have to share data if inappropriate – exemptions apply
- 17. Responsible researchers: know about exemptions • If results are expected to be commercially or industrially exploited • If participation is incompatible with the need for confidentiality in connection with security issues • Incompatible with existing rules on the protection of personal data • Would jeopardise the achievement of the main aim of the action • If the project will not generate / collect any research data • • If there are other legitimate reasons to not take part in the Pilot Can opt out at proposal stage OR during lifetime of project Should describe issues in the project Data Management Plan
- 18. Which additional data might be kept after the project ends? - Could this data be re-used? - Must it be kept as evidence or for legal reasons? - Should it be kept for its value to you or others? - Consider costs – do benefits outweigh cost? 5 steps to decide what data to keep www.dcc.ac.uk/resources/how-guides/five-steps-decide-what-data-keep
- 19. Assign persistent identifiers • They are an alphanumeric code identifying a resource, organisation or individual • They must be • Unique • Persistent • Ideally they should be actionable too https://www.datacite.org/ http://ezid.cdlib.org/ http://orcid.org/ http://isni.org/
- 20. https://ssi-dev.epcc.ed.ac.uk/ Remember to consider physical data, software and models http://spatialinformationdesignlab.org/project_sites/library/catalog.html http://www.ukcrcexpmed.org.uk/Coventry_Warwick_CRF/PublishingImages/Tissue%20Bank%201.jpg
- 21. Can your data be shared with others? • PI/researcher • Data repository and support staff • Research participants • Commercial partners • Secondary data user
- 22. How will it be shared? http://service.re3data.org/search Zenodo • Joint effort by OpenAIRE-CERN • Multidisciplinary repository • Multiple data types • Citable data (DOI) • Links funding, publications, data & software www.zenodo.org • Does your publisher or funder suggest a repository? • Are there data centres or community databases for your discipline? • Does your university offer support for long-term preservation?
- 23. www.dcc.ac.uk/resources/how-guides/license-research-data Licensing research data This 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
- 25. Options for open data • Domain repository • General repository – Figshare, Zenodo, Dryad • Institutional repository • Journal supplementary material • Departmental web page
- 26. General directories Re3data.org Domain specific directories e.g. life sciences – Biosharing.org Data journal recommendations Edinburgh research data blog: Sources of dataset peer review Funding body recommendations E.g. Wellcome Trust Data repositories and database sources Finding external repositories
- 27. Considerations • There may be an accepted repository used by peers or required by funders • Multidisciplinary studies may not have an obvious home • Data types and volumes will impact on decision
- 28. How will you make your data discoverable? http://ckan.data.alpha.jisc.ac.uk/dataset https://www.researchfish.com/ http://researchdata.gla.ac.uk/ Institutional catalogues National catalogues Funders’ catalogues https://www.openaire.eu/intro-data-providers European wide
- 29. Options for closed data • Institutional data archive/vault • Safe havens – (e.g. secure patient data) • 3rd party data archiving • Cloud storage • Institutional servers – the ‘do nothing’ option
- 30. As open as possible but as closed as necessary. Image: ‘Balancing rocks’ by Viewminder CC-BY-SA- ND www.flickr.com/photos/light_seeker/7780857224
- 31. Refer to free guides andbriefing papers www.dcc.ac.uk/resources/
- 32. Guidelines from the Commission • Factsheet on Open Access – https://ec.europa.eu/programmes/horizon2020/sites/horizon2020/files/FactS heet_Open_Access.pdf • Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020 – http://ec.europa.eu/research/participants/data/ref/h2020/grants_manu al/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_manu al/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
- 33. https://dmponline.dcc.ac.uk Make use of free tools
- 34. • More visible research outputs and increased impact - even for negative results • Easier outputs reporting • Better and more reproducible research! Mayseemlike a lot, butjusttake it stepby step!
- 35. Thanks for listening! joy.davidson@glasgow.ac.uk www.fosteropenscience.eu www.dcc.ac.uk Follow us on twitter: @jd162a @fosterscience / #fosteropenscience
Hinweis der Redaktion
- H2020 open data pilot focuses on research data specifically While focus is on digital research data, worth considering all data that you will produce and how you will maintain links between various outputs.
- As seen in Tim Berners Lee’s model, if you want to embrace the open data approach, it involves more than just making data available. You need to consider making your approaches and methodologies more transparent; you need to provide context; you need to make that data intelligible! Worth noting here that even if you don’t plan to be open, RDM is good practice so that you are able to stand up to scrutiny if questions arise about your findings and practices.
- Funding body mandates have been a key driver for improving RDM infrastructure and practice. RCUK issued its common principles on research data several years ago to address this need for transparency and value. Funders have an obligation to show that public funds are being allocated appropriately. Important to note that funders need to demonstrate impact too. Otherwise research budgets shrink! Not just UK, H2020 open data pilot and enthusiasm for Open science. Important to stress that funders are NOT saying that ALL data must be kept or shared. Funders expects responsible research practice. The background to this is about making the most of the data that has been created through publicly funded research. The guidelines speak of: Improved quality of results Greater efficiency Faster to market = faster growth Improved transparency of the scientific process
- Data driven research is becoming the norm for all disciplines even arts and humanities. To be effective in this era, researchers need access to high quality data. Currently, there are very worrying stats around the percentage of published findings that are reproducible.
- 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.
- Documentation and metadata are essentially descriptive information about the information contained in a dataset. There should be good documentation at the study level, for example a description of the research methodology that created the data – [the best metadata the data can have is the publication it supports] or a data paper. There should also be documentation at the file, item and variable level suitable so that someone reusing the data can understand it – this could be ensuring that excel spreadsheets have sensible row and column descriptions or that a document is included with the dataset which properly explains any abbreviations used.
- Metadata is a subset of documentation Documentation is a catch-all term which can include some very high-level, human-readable, loosely structured information about the dataset – for example a description of the laboratory method used to generate a set of results or a sketch book accompanying a work of sculpture. The term metadata has come to define a subset of documentation information that uses standardised terms and is presented in a structured way. This metadata will be in a form enabling it to be read by machines and/or may be presented in human readable form as well.
- Use an existing standard whenever possible (almost always!) DCC Metadata catalogue, explaining what they are, who's using them and how to use them. Ideally, use of standard unique IDs (grant, ORCIDs, funders, institution, DOIs). Metadata should capture provenance info. WC3 PROV-O standard and Australian National Data Services (ANDS) work might be relevant. Think about what is needed in order to find, evaluate, understand, and reuse the data. Have you documented what you did and how? Did you develop code to run analyses? If so, this should be kept and shared too. Is it clear what each bit of your dataset means? Make sure the units are labelled and abbreviations explained. Record metadata so others can find your work e.g. title, date, creator(s), subject, format, rights…,
- Potentially, the bare minimum of metadata required will be defined by the repository that accepts the dataset. This could correspond to the DataCite mandatory minimum metadata set although it is very likely that other elements will be required, whether by the repository or by the research funder. This metadata set is designed for citation and disambiguation, in other words ensuring that the dataset a researcher is reusing is identical to one cited in a journal article or online. This set of metadata does little to make the resource visible to researchers speculatively searching for relevant data to reuse.
- Your own device (laptop, flash drive, server etc.). And if you lose it? Or it breaks? Departmental drives or university servers “Cloud” storage. Do they care as much about your data as you do? Read the fine print! Most likely a mixture so be clear on which data will be stored in which place. Remember to consider backup! Use managed services where possible (e.g. University filestores rather than local or external hard drives), so backup is done automatically Good practise is to have at least 3 copies of a file on at least 2 different media with at least 1 offsite Ask central IT team for advice
- For those that do take part in the pilot, the starting point is to make all data that underpin publications open. After that, it’s for researchers to define what else should be shared and can be made open. This should be outlined in the DMP. Sometimes sharing is not appropriate (e.g. due to ethical rules of personal data, intellectual property protection, commercial restrictions etc). It’s fine to apply restrictions in such cases. This could be an embargo period prior to publication or while a patent is sought, or controlling access and re-use to protect participants’ identities (e.g. via the use of secure data services / data enclaves or data sharing agreements). Restrictions should be outlined up-front in the DMP.
- Projects in these seven areas aren’t forced to participate. There are various reasons to opt out e.g. if results can’t be shared due to confidentiality, legal restrictions or commercial opportunities. Opt out is available at any point. It can also apply to certain parts of the data e.g. they could share some aspects but not others.
- Most funders expect enough data to be kept to enable validation of published findings at a minimum. But other data may also have value. Not always the data – sometimes the code or algorithm more valuable and data can be easily recaptured.
- International Standard Name Identifier (ISNI) is an identifier for uniquely identifying the public identities of contributors to media content such as books, TV programmes, and newspaper articles. Such an identifier consists of 16 digits. It can optionally be displayed as divided into four blocks. http://isni.org/
- Remember that not all research data is digital. Consider how you will maintain links between research outputs and analogue data like tissue samples. How will access to these resources be provided if needed? In some cases, algorithms and models are more valuable than the data being run through them so be clear on what it is you need to keep (e.g., genomic data and models).
- While the researchers themselves will have a key role to play in sharing data, it is important to note that they are not the only stakeholders involved in the process. Repository – how will data be made visible? Especially if physical data? Support staff – e.g., Library staff to help describe data to make it easier to find and understand. Research participants – informed consent is required. Anonymisation can be very costly. Commercial – identify what can and can’t be shared and make this explicit. Develop consortium agreements that make each partners rights explicit. Secondary user – when sharing data, researchers can ask reusers to tick that they accept terms and conditions regarding fair use
- Ideally, we’d like to see researchers embrace Open Science and make as much of their data available with as few restrictions as possible. However, where that isn’t possible, it is important to be clear on why data can’t be shared. Provide a recommended data citation for data set. UK Data Service provides information on tools that are helpful for citing specific sections of interviews and other types of social science data that might be collected.
- 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
- Answer questions to determine which licence(s) are appropriate to use
- Domain best General – need to ensure it’s suitable for your use Institutional – will keep data of low value or data with no other home but may not ensure targeted visibility Journal – fulfils journal requirements but does not offer repo functionality or longevity required by funders Web page – may target domain academics and allow manipulation of data and support for living datasets. Does not have citability, longevity or engender user trust that the resource is the same as the one described in publications.
- Data journals require that the data being described in articles be freely available and usually mandate where it should be deposited, this can help to identify community-accepted repositories. Some journals may also offer recommendations for appropriate places to deposit research data
- Depositing into a repository is good. But how can you make sure that your data can be most easily found and reused? A myriad of discovery mechanisms. Institutional data catalogues, national discovery services and funder registries. It would be ideal if data was entered once and shared between these systems more effectively.
- DCC provides free access to a number of short, practical guides on various topics: -How to discover RDM requirements -How to develop RDM services -How to develop a data management and sharing plan -How to appraise and select research data -How to write a lay summary -How to licence research data -How to cite datasets and link to publications
- 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.