Tag Archives: research data

Making the connection: research data network workshop

During International Data Week 2016, the Office of Scholarly Communication is celebrating with a series of blog posts about data. The first post was a summary of an event we held in July. This post looks at the challenges associated with financially supporting RDM training.

corpus-main-hallFollowing the success of hosting the Data Dialogue: Barriers to Sharing event  in July we were delighted to welcome the Research Data Management (RDM) community to Cambridge for the second Jisc research data network workshop. The event was held in Corpus Christi College with meals held in the historical dining room. (Image: Corpus Christi )

RDM services in the UK are maturing and efforts are increasingly focused on connecting disparate systems, standardising practices and making platforms more usable for researchers. This is also reflected in the recent Concordat on Research Data which links the existing statements from funders and government, providing a more unified message for researchers.

The practical work of connecting the different systems involved in RDM is being led by the Jisc Research Data Shared Services project which aims to share the cost of developing services across the UK Higher Education sector. As one of the pilot institutions we were keen to see what progress has been made and find out how the first test systems will work. On a personal note it was great to see that the pilot will attempt to address much of the functionality researchers request but that we are currently unable to fully provide, including detailed reporting on research data, links between the repository and other systems, and a more dynamic data display.

Context for these attempts to link, standardise and improve RDM systems was provided in the excellent keynote by Dr Danny Kingsley, head of the Office of Scholarly Communication at Cambridge, reminding us about the broader need to overhaul the reward systems in scholarly communications. Danny drew on the Open Research blogposts published over the summer to highlight some of the key problems in scholarly communications: hyperauthorship, peer review, flawed reward systems, and, most relevantly for data, replication and retraction. Sharing data will alleviate some of these issues but, as Danny pointed out, this will frequently not be possible unless data has been appropriately managed across the research lifecycle. So whilst trying to standardise metadata profiles may seem irrelevant to many researchers it is all part of this wider movement to reform scholarly communication.

Making metadata work

Metadata models will underpin any attempts to connect repositories, preservation systems, Current Research Information Systems (CRIS), and any other systems dealing with research data. Metadata presents a major challenge both in terms of capturing the wide variety of disciplinary models and needs, and in persuading researchers to provide enough metadata to make preservation possible without putting them off sharing their research data. Dom Fripp and Nicky Ferguson are working on developing a core metadata profile for the UK Research Data Discovery Service. They spoke about their work on developing a community-driven metadata standard to address these problems. For those interested (and Git-Hub literate) the project is available here.

They are drawing on national and international standards, such as the Portland Common Data Model, trying to build on existing work to create a standard which will work for the Shared Services model. The proposed standard will have gold, silver and bronze levels of metadata and will attempt to reward researchers for providing more metadata. This is particularly important as the evidence from Dom and Nicky’s discussion with researchers is that many researchers want others to provide lots of metadata but are reluctant to do the same themselves.

We have had some success with researchers filling in voluntary metadata fields for our repository, Apollo, but this seems to depend to a large extent on how aware researchers are of the role of metadata, something which chimes with Dom and Nicky’s findings. Those creating metadata are often unaware of the implications of how they fill in fields, so creating consistency across teams, let alone disciplines and institutions can be a struggle. Any Cambridge researchers who wish to contribute to this metadata standard can sign up to a workshop with Jisc in Cambridge on 3rd October.

Planning for the long-term

A shared metadata standard will assist with connecting systems and reducing researchers’ workload but if replicability, a key problem in scholarly communications, is going to be possible digital preservation of research data needs to be addressed. Jenny Mitcham from the University of York presented the work she has been undertaking alongside colleagues from the University of Hull on using Archivematica for preserving research data and linking it to pre-existing systems (more information can be found on their blog.)

Jenny highlighted the difficulties they encountered getting timely engagement from both internal stakeholders and external contractors, as well as linking multiple systems with different data models, again underlining the need for high quality and interoperable metadata. Despite these difficulties they have made progress on linking these systems and in the process have been able to look into the wide variety of file formats currently in use at York. This has lead to conversations with the National Archive about improving the coverage of research file formats in PRONOM (a registry of file formats for preservation purposes), work which will be extremely useful for the Shared Services pilot.

In many ways the project at York and Hull felt like a precursor to the Shared Services pilot; highlighting both the potential problems in working with a wide range of stakeholders and systems, as well as the massive benefits possible from pooling our collective knowledge and resources to tackle the technical challenges which remain in RDM.

Published 14 September 2016
Written by Rosie Higman
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Beyond compliance – dialogue on barriers to data sharing

Welcome to International Data Week. The Office of Scholarly Communication is celebrating with a series of blog posts about data, starting with a summary of an event we held in July.

JME_0629.jpgOn 29 July 2016 the Cambridge Research Data Team joined forces with the Science and Engineering South Consortium to organise a one day conference at the Murray Edwards College to gather researchers and practitioners for a discussion about the existing barriers to data sharing. The whole aim of the event was to move beyond compliance with funders’ policies. We hoped that the community was ready to change the focus of data sharing discussions from whether it is worth sharing or not towards more mature discussions about the benefits and limitations of data sharing.

What are the barriers?

So what are the barriers to effective sharing of research data? There were three main barriers identified, all somewhat related to each other: poorly described data, insufficient data discoverability and difficulties with sharing personal/sensitive data. All of these problems arise from the fact that research data does not always shared in accordance to FAIR principles: that data is Findable, Accessible, Interoperable and Re-usable.

Poorly described data

The event started with an inspiring keynote talk from Dr Nicole Janz from the Department of Sociology at the University of Cambridge: “Transparency in Social Science Research & Teaching”. Nicole regularly runs replication workshops at Cambridge, where students select published research papers and they work hard for several weeks to reproduce the published findings. The purpose of these workshop is to allow students to learn by experience on what is important in making their own work transparent and reproducible to others.

Very often students fail to reproduce the results. Frequently, the reasons for failures are insufficient methodology available, or simply the fact that key datasets were not made available. Students learn that in order to make research reproducible, one not only needs to make the raw data files available, but that the data needs to be shared with the source code used to transform it and with written down methodology of the process, ideally in a README file. While doing replication studies, students also learn about the five selfish benefits of good data management and sharing: data disasters are avoided, it is easier to write up papers from well-managed data, transparent approach to sharing makes the work more convincing to reviewers, the continuity of research is possible and researchers can build their reputation for being transparent. As a tip for researchers, Nicole suggested always asking a colleague to try to reproduce the findings before submitting a paper for peer-review.

The problem of insufficient data description/availability was also discussed during the first case study talk by Dr Kai Ruggeri from the Department of Psychology, University of Cambridge. Kai reflected on his work on the assessment of happiness and wellbeing across many European countries, which was part of the ESRC Secondary Data Analysis Initiative. Kai re-iterated that missing data make the analysis complicated and sometimes prevent one from being able to make effective policy recommendations. Kai also stressed that frequently the choice of baseline for data analysis can affect the final results. Therefore, proper description of methodology and approaches taken is key for making research reproducible.

Insufficient data discoverability

JME_0665We also heard several speakers describing problems with data discoverability. Fiona Nielsen founded Repositive – a platform for finding human genomic data. Fiona founded the platform out of frustration that genomic data was so difficult to find and access. Proliferation of data repositories made it very hard for researchers to actually find what they need.

IMG_SearchingForData_20160911Fiona started with doing a quick poll among the audience: how do researchers look for data? It turned out that most researchers find data by doing a literature research or by googling for it. This is not surprising – there is no search engine enabling looking for information simultaneously across the multiple repositories where the data is available. To make it even more complicated, Fiona reported that in 2015 80PB of human genomic data was generated. Unfortunately, only 0.5PB of human genomic data was made available in a data repository.

So how can researchers find the other datasets, which are not made available in public repositories? Repositive is a platform harvesting metadata from several repositories hosting human genomic data and providing a search engine allowing researchers to simultaneously look for datasets shared in all of them. Additionally, researchers who cannot share their research data via a public repository (for example, due to lack of participants’ consent for sharing), can at least create a metadata record about the data – to let others know that the data exist and to provide them with information on data access procedure.

The problem of data discoverability is however not only related to people’s awareness that datasets exists. Sometimes, especially in the case of complex biological data with a vast amount of variables, it can be difficult to find the right information inside the dataset. In an excellent lightening talk, Jullie Sullivan from the University of Cambridge described InterMine –platform to make biological data easily searchable (‘mineable’). Anyone can simply upload their data onto the platform to make it searchable and discoverable. One example of the platform’s use is FlyMine – database where researchers looking for results of experiments conducted on fruit fly can easily find and share information.

Difficulties with sharing personal/sensitive data

The last barrier to sharing that we discussed was related to sharing personal/sensitive research data. This barrier is perhaps the most difficult one to overcome, but here again the conference participants came up with some excellent solutions. First one came from the keynote speech by Louise Corti – with a talk with a very uplifting title: “Personal not painful: Practical and Motivating Experiences in Data Sharing”.

Louise based her talk on the long experience of the UK Data Service with providing managed access to data containing some forms of confidential/restricted information. Apart from being able to host datasets which can be made openly available, the UKDS can also provide two other types of access: safeguarded access, where data requestors need to register before downloading the data, and controlled data, where requests for data are considered on a case by case basis.

At the outset of the research project, researchers discuss their research proposals with the UKDS, including any potential limitations to data sharing. It is at this stage – at the outset of the research project, that the decision is made on the type of access that will be required for the data to be successfully shared. All processes of project management and data handling, such as data anonymisation and collection of informed consent forms from study participants, are then carried in adherence to that decision. The UKDS also offers protocols clarifying what is going to happen with research data once they are deposited with the repository. The use of standard licences for sharing make the governance of data access much more transparent and easy to understand, both from the perspective of data depositors and data re-users.

Louise stressed that transparency and willingness to discuss problems is key for mutual respect and understanding between data producers, data re-users and data curators. Sometimes unnecessary misunderstandings make data sharing difficult, when it does not need to be. Louise mentioned that researchers often confuse ‘sensitive topic’ with ‘sensitive data’ and referred to a success case study where, by working directly with researchers, UKDS managed to share a dataset about sedation at the end of life. The subject of study was sensitive, but because the data was collected and managed with the view of sharing at the end of the project, the dataset itself was not sensitive and was suitable for sharing.

As Louise said “data sharing relies on trust that data curators will treat it ethically and with respect” and open communication is key to build and maintain this trust.

So did it work?

JME_0698The purpose of this event was to engage the community in discussions about the existing limitation to data sharing. Did we succeed? Did we manage to engage the community? Judging by the fact that we have received twenty high quality abstract applications from researchers across various disciplines for only five available case study speaking slots (it was so difficult to shortlist the top five ones!) and also because the venue was full – with around eighty attendees from Cambridge and other institutions, I think that the objective was pretty well met.

Additionally, the panel discussion was led by researchers and involved fifty eight active users on the Sli.do platform for questions to panellists. There were also questions asked outside of Sli.do platform. So overall I feel that the event was a great success and it was truly fantastic to be part of it and to see the degree of participant involvement in data sharing.

Another observation is also the great progress of the research community in Cambridge in the area of sharing: we have successfully moved away from discussions whether research data is worth sharing to how to make data sharing more FAIR.

It seems that our intense advocacy, and the effort of speaking with over 1,800 academics from across the campus since January 2015 paid off and we have indeed managed to build an engaged research data management community.

Read (and see!) more:

Published 12 September 2016
Written by Dr Marta Teperek
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