OpenAIRE and Eudat services and tools to support FAIR DMP implementation

Editor's Notes

• #5: 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)
• #14: 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
• #19: 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?
• #20: 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
• #22: 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?
• #25: 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?
• #26: 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?
• #27: 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
• #30: Contrary to some other funders, the EC does not require a DMP at the proposal stage.
• #31: 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.
• #35: http://www.data-archive.ac.uk/create-manage/planning-for-sharing/costing http://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud_2016.pdf#view=fit&pagemode=none
• #41: [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.
• #50: 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.
• #59: 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.
• #61: Note that these are all ”technical” services. The notion “RDM” has different meanings in EUDAT and OpenAIRE.
• #63: 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.
• #64: When you are interested in learning more about EUDAT services you can contact CINECA.