20170530_Open Research Data in Horizon 2020

Editor's Notes

• #4: 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.
• #14: 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 …
• #16: 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.
• #19: So let’s begin by looking at the changing data landscape.
• #20: 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.
• #21: 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.
• #22: 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.
• #23: 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.
• #24: Let’s move on to the considerations to make when managing and sharing data
• #25: 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.
• #26: 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:…
• #29: 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”.
• #30: 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.
• #31: 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.
• #32: 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.
• #33: 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.
• #34: Crucial for F is metadata: the information about the data. You should comply with disciplinary standards or explain why this is not possible.
• #35: 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
• #36: “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.
• #37: .. 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.
• #39: 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.
• #40: ”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.
• #42: …
• #43: 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.
• #44: 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?
• #45: Each of these issues can, in great part, be addressed by providing rich data documentation known as ‘metadata’.
• #46: 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.
• #48: Catch-all repository Multiple data types Publications Long tail of research data Citable data (DOI) Links to funding, pubs, data, software
• #50: Should happen automatically thanks to our data-literature interlinking services But where it doesn’t, you
• #56: 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.