What is the Open Research Data Pilot?
What is the Open Research Data Pilot?
Open data is data that is free to access, reuse, repurpose, and redistribute. The Open Research Data Pilot 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 access.
If your Horizon 2020 project is part of the pilot, and your data meets certain conditions, you must deposit your data in a research data repository where they will be findable and accessible for others. Don’t panic - you are not expected to share sensitive data or breach any IPR agreements with industrial partners. You do not need to deposit all the data you generate during the project either – only that which underpins published research findings and/or has longer-term value. In addition to supporting your research’s integrity, openness has many other benefits. Improved visibility means your research will reach more people and have a greater impact – for science, society and your own career. Recent studies have shown that citations increase when data is made available alongside the publication; these papers also have a longer shelf-life.
Which H2020 strands are required to participate?
Projects starting from January 2017 are by default part of the Open Data Pilot. If your project started before earlier and stems from one of these Horizon 2020 areas, you are automatically 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.
What is a Data Management Plan (DMP)?
To help you optimise the potential for future sharing and reuse, a Data Management Plan (DMP) can help you to consider any problems or challenges that may be encountered and helps you to identify ways to overcome these. A DMP should be thought of as a “living” document outlining how the research data collected or generated will be handled during and after a research project. Remember, the plan should be realistic and based around the resources available to you and your project partners. There is no point in writing a gold plated plan if it cannot be implemented!
It should describe:
- The data set: What kind of data will the project collect or generate, and to whom might they be useful later on? The pilot applies to (1) the data and metadata needed to validate results in scientific publications and (2) other curated and/or raw data and metadata that may be required for validation purposes or with reuse value.
- Standards and metadata: What disciplinary norms will you adopt in the project? What is the data about? Who created it and why? In what forms it is available? Metadata answers such questions to enable data to be found and understood, ideally according to the particular standards of your scientific discipline. Metadata, documentation and standards help to make your data Findable, Accessible, Interoperable and Re-usable or FAIR for short.
- Data sharing: By default as much of the resulting data as possible should be archived as Open Access. Therefore legitimate reasons for not sharing resulting data should be explained in the DMP. Remember, no one expects you to compromise data protection or breach any IPR agreements. Data sharing should be done responsibly. The DMP Guidelines therefore ask you to describe any ethical or legal issues that can have an impact on data sharing. Furthermore,
- Archiving and preservation: Funding bodies are keen to ensure that publicly funded research outputs can have a positive impact on future research, for policy development, and for societal change. They recognise that impact can take quite a long time to be realised and, accordingly, expect the data to be available for a suitable period beyond the life of the project. Remember, it is not simply enough to ensure that the bits are stored in a research data repository, but also consider the usability of your data. In this respect, you should consider preserving software or any code produced to perform specific analyses or to render the data as well as being clear about any proprietary or open source tools that will be needed to validate and use the preserved data.
The DMP is not a fixed document. The first version of the DMP is expected to be delivered within the first 6 months of your project, but you don’t have to provide detailed answers to all the questions yet. The DMP needs to be updated over the course of the project whenever significant changes arise, such as new data or changes in the consortium policies or consortium composition. The DMP should be updated at least in time with the periodic evaluation or assessment of the project as well as in time for the final review. Consider reviewing your DMP at regular intervals in the project and consider how you might make use of scheduled WP and/or project staff meetings to facilitate this review.
What practical steps should you take?
1. When your project is part of the pilot, you should create a Data Management Plan. Your institution may offer Research Data Management support to help you planning.
2. Also, you should select a data repository that will preserve your data, metadata and possibly tools in the long term. It is advisable to contact the repository of your choice when writing the first version of your DMP. Repositories may offer guidelines for sustainable data formats and metadata standards, as well as support for dealing with sensitive data and licensing.
3. As noted earlier, you do not need to keep everything. Curating data requires time and effort so you want to make sure that you are putting your effort into the outputs that really matter. Select what data you’ll need to retain to support validation of your finding but nalso consider any data outputs that may have longer term value as well – for you and for others..
EC’s Guide on Open Access to Scientific Publications and Research Data in Horizon 2020 (updated August 25, 2016) http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf
EC’s Guidelines on Data Management in Horizon 2020 (updated July 26, 2016): http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
EC’s Agenda on Open Science: https://ec.europa.eu/digital-agenda/en/open-science
DMPonline tool: https://dmponline.dcc.ac.uk/
DCC How to Write a DMP guide:http://www.dcc.ac.uk/resources/how-guides/develop-data-plan
DCC How to Select What Data to Keep guide: http://www.dcc.ac.uk/resources/how-guides/five-steps-decide-what-data-keep
DCC How to Licence Research Data guide: http://www.dcc.ac.uk/resources/how-guides/license-research-data
RDNL video The what, why and how of data management planning: http://datasupport.researchdata.nl/en/start-de-cursus/ii-planfase/datamanagementplanning/
Software Sustainability Institute’s Software Management Plan: https://www.software.ac.uk/sites/default/files/images/content/SMP_Checklist_2016_v0.1.pdf