Tag Archives: open data

In conversation with Wellcome Trust and CRUK

On Friday 22 January Cambridge University invited our two main charity funders to discuss their views on data management and sharing with Cambridge researchers. David Carr from the Wellcome Trust and Jamie Enoch from Cancer Research UK came to the University to talk to our researchers.

The related blog ‘Charities’ perspective on research data management and sharing‘ summarises the presentations Jamie and David gave. After this event, a group of researchers from the School of Biological Sciences and from the School of Clinical Medicine at the University of Cambridge were invited to ask questions about the Wellcome Trust data management and sharing policy and CRUK data sharing and preservation policy directly of David and Jamie.

This blog is a summary of the discussion, with questions thematically grouped. These questions will be added to the list of Frequently Asked Questions on the University’s Research Data Management Website.

In summary:

  • It is not recommended that researchers simply share a link and release the data when requested. Research data should be available, accessible and discoverable.
  • The first responsibility is to protect the study participants. The funders provide guidance documents on sharing of patient data. Ethics committees also provide advice and guidance on what data can be shared. In principle, patient data should be safeguarded, but this should not preclude sharing. There are models for managed access to data that allow personal/sensitive data to be shared for legitimate purposes in a safe and secure manner.
  • The funders do not want to prevent new collaborations. When sharing data they recommend data generators provide a statement in the description of the data that they are willing to collaborate
  • It is recognised that it is often appropriate for researchers to have a defined period of exclusive access to the data they generate, but this should be determined by disciplinary norms. Any exemptions or delays have to be justified on a case by case basis, ideally at the outset of the project.
  • The funders expect research data that supports publications to be made accessible and publications should have a clear statement explaining how to access the underlying research data.
  • However researchers need to decide what is useful to be shared considering the effort of preparing the data for deposit and of sharing the data. If nobody is going to use the data, sharing is not a good use of researcher’s time.
  • Discipline-specific data repositories, where these exist, are recommended preferentially over general purpose or institutional repositories
  • Biosharing is an excellent resource with references to discipline-specific metadata schemas.
  • Staff members whose role is to manage data is an eligible cost on a grant
  • There are no funds for sharing data from old projects, although there are exceptions on a case by case basis
  • The funders are considering monitoring data management plans but their current primary goal is to encourage people to think about data management and sharing from the very start of the project

Access to research data

Q: Are funders benefiting from the expertise of organisations such as UK Data Service when providing advice on data access? UK Data Service has been managing controlled access to research data for a long time and it would be advantageous to benefit from their expertise.

A: Yes, we are in discussion with the UK Data Service. We are also working with the UK Data Service to consider whether it might be appropriate for hosting data from other disciplines beyond social science. We also believe there is significant scope to share lessons and best practices for data sharing between the social and biomedical sciences.

Q: Could we just share research data only when asked for it?

A: This is not a recommended solution: research data should be available, accessible and discoverable. Data access controls and criteria for what needs to happen for the access to be granted have to be made clear in metadata description.

Q: I have patient data which has to be stored in a secure space. I always say in my data management plan that I cannot share my data. I would like to get ethical guidance which will explain to me how to share these data. It is very easy to say that data cannot be shared. I would like to share my data, but I would like to do it properly. With patient data it is extremely difficult, especially with genomics data, where there is a risk that patients can be identified.

A: Sharing of clinical data is not easy. Both Wellcome Trust and Cancer Research UK are helping to drive a great deal of work which is considering access and governance models through which sensitive patient data can be made available for research in a safe, secure and trusted manner. They provide guidance documents on sharing of patient data. Safety of patients and patients’ data is important. Ethics committees also provide advice and guidance on what data can be shared.

Q: What about sharing of physical materials? I have received a request to share a culture derived from a patient material, but the Ethics Committee did not approve sharing of this material. What shall I do?

A (Peter Hedges, Head of Research Office): If your ethical approval says that you cannot share that material, you cannot share it. Your first responsibility is to protect your study participants.

Q: If I share my data via a repository and people can simply download my data, I can no longer collaborate with them to work on the data and I have lost the possibility of getting credit for my data.

A: Nobody wants to prevent new collaborations from happening. A solution might be to add a statement that you are willing to collaborate in the description of your data. Your data requestor might be interested in collaborating, simply because you know your data the best. Funders also expect that the data re-used by others is appropriately acknowledged/cited, and they want to ensure that due credit results from the secondary use of data.

Quality control of research data

Q: If researchers start sharing unpublished research data via data repositories there is a risk that these data will not be of good quality as they will not be peer-reviewed.

A: Authors of unpublished data can simply state in the data description that the item was not peer-reviewed. If applicable, funders also encourage reciprocal links between publications and supporting research data.

What data needs to be shared and when?

Q: If researchers start to share everything there will be a lot of useless data available in data repositories. How to prevent a flood of useless data on the internet?

A: We would like researchers to decide what data is useful to be shared. If nobody is likely to use the data, sharing is not a good use of researcher’s time. Repositories also need to make decisions over what is worth keeping over time.

Comment (Peter Hedges, Head of Research Office): The Research Council UK focuses on research data supporting publications and this is what we recommend to researchers: share research data which underpins publications.

Q: Are we expected to share large datasets resulting from bigger projects (databases, long-term datasets) or data supporting individual publications?

A: We expect research data that supports individual publications to be made available with a hyperlink to the data. We also want researchers to consider and plan more broadly how they can make data assets of value resulting from our funded research available to others in a timely and appropriate manner.

Q: What about images? Is it useful to share them? It involves a lot of time to organise images. Besides, a single confocal picture with multiple layers is 1GB. In theory it is possible to share all raw data and all raw images, but who would want to look at them? 10 figures of 10 images is already 100 GB of data. Where would I store all these images, who is going to use these data and how am I going to pay for this?

A: The effort of preparing the data for deposit and of sharing the data should be proportionate to the potential benefits of data sharing. Researchers need to decide what is useful to be shared, following disciplinary best practices and norms (recognising that disciplines are in very different places in terms of defining these).

Q: Is there a set amount of time for exclusive use of research data?

A: Researchers should adhere to disciplinary norms. For example, in genomics research data is frequently shared before publication (sometimes under a publication moratorium which protects the data generator’s right to first publication). Any exemptions or delays have to be justified on a case by case basis.

Comment (Peter Hedges, Head of Research Office): Research is competitive. Sometimes it might be useful for researchers to know who wants to get the access to data and what do they need them for.

Cost of data sharing

Q: Can I ask in my grant for a staff member to help me with data management?

A: Yes, this is an eligible cost on grant applications: you can request a salary to support a research data manager for your research project, as long as it is justified.

Q: According to CRUK policy, costs for data sharing can be budgeted in grant applications only from August 2015. What about research data from older projects, when these costs were not eligible in grant applications? Is there any transition fund available to pay for this?

A: Unfortunately, there are no additional funds to pay for these costs. Researchers who have older datasets that might be of significant value to the community should contact CRUK – all requests for support will be considered on a case by case basis.

Q: Wellcome Trust encourages data sharing and data re-use, but does not allow for costs of long-term data preservation to be budgeted in grant applications. This does not make sense to me.

A: We are still reviewing our policy on costs of data management and sharing and we might be revisiting this issue – however, it is problematic for us to consider estimated costs for preservation that extend before the life-time of the grant. Our understanding is that costs of long-term data preservation are often less significant than costs of initial data ingestion by the repository (and we will cover ingestion costs).

Q: Who is then going to pay for the long-term data storage?

A: Wellcome Trust funds some discipline-specific repositories, but this is done jointly with other funders. We support bigger undertakings and we are also working with partners to develop platforms for data sharing and discoverability in some priority areas (notably clinical trials). Cancer Research UK pays for some long-term storage options, if these are justified for particular needs of the project. These decisions are made on a case by case basis, depending on how the costs are justified and whether these are directly related to the scientific value of the project.

Metadata standards

Q: At the moment there are many general purpose and institutional repositories, which are not well structured. To support efficient re-use of data it is important to use structured data repositories and adhere to metadata standards. What are funders’ opinions about this?

A: Wherever possible, discipline-specific data repositories should be used preferentially over general purpose or institutional repositories. Adherence to discipline-specific metadata standards is also encouraged. It has to be acknowledged that development of well-structured data repositories is very resource-intensive and not all disciplines have good quality repositories to support them. For example, it took over 30 years to adapt unified metadata standards at Cambridge Crystallographic Data Centre. The time need to properly solve problems should never be underestimated.

Q: Are funders planning to provide researchers with a list of recommended schemas for metadata?

A: Biosharing is an excellent resource with references to discipline-specific metadata schemas. It is a useful suggestion to include a reference to Biosharing on our website.

Policy implementation

Q: Are you planning to monitor researchers’ adherence to data management plans? For example, the BBSRC does not have the manpower to check all data management plans manually, but they are planning to create a system to check if data has been uploaded automatically.

A: We are considering this. At the moment we require data management plans with the primary goal to encourage people to think about data management and sharing from the very start of the project.

Published 5 February 2016
Written by Dr Marta Teperek, verified by David Carr and Jamie Enoch
Creative Commons License

Charities’ perspective on research data management and sharing

In 2015 the Cambridge Research Data Team organised several discussions between funders and researchers. In May 2015 we hosted Ben Ryan from EPSRC, which was followed by a discussion with Michael Ball from BBSRC in August. Now we have invited our two main charity funders to discuss their views on data management and sharing with Cambridge researchers.

David Carr from the Wellcome Trust and Jamie Enoch from Cancer Research UK (CRUK) met with our academics on Friday 22 January at the Gurdon Institute. The Gurdon Institute was founded jointly by the Wellcome Trust and CRUK to promote research in the areas of developmental biology and cancer biology, and to foster a collaborative environment for independent research groups with diverse but complementary interests.

This blog summarises the presentations and discusses the data sharing expectations from Wellcome Trust and CRUK. A second related blog ‘In conversation with Wellcome Trust and CRUK‘ summarises the question and answer session that was held with a group of researchers on the same day.

Wellcome Trust’s requirements for data management and sharing

Sharing research data is key for Wellcome’s goal of improving health

David Carr started his presentation explaining that the Wellcome Trust’s mission is to support research with the goal of improving health. Therefore, the Trust is committed to ensuring research outputs (including research data) can be accessed and used in ways that will maximise health and societal benefits. David reminded the audience of benefits of data sharing. Data which is shared has the potential to:

  • Enable validity and reproducibility of research findings to be assessed
  • Increase the visibility and use of research findings
  • Enable research outputs to be used to answer new questions
  • Reduce duplication and waste
  • Enable access to data to other key communities – public, policymakers, healthcare professionals etc.

Data sharing goes mainstream

David gave on overview of data sharing expectations from various angles. He started by referring to the Royal Society’s report from 2012: Science as an open enterprise, which sets sharing as the standard for doing science. He then also mentioned other initiatives like the G8 Science Ministers’ statement, the joint report from the Academy of Medical Sciences, BBSRC, MRC and Wellcome Trust on reproducibility and reliability of biomedical research and the UK Concordat on Open Research Data with a take-home message that sharing data and other research outputs is increasingly becoming a global expectation, and a core element of good research practice.

Wellcome Trust’s policy for open data

The next aspect of David’s presentation was Wellcome Trust’s policy on data management and sharing. The policy was first published almost a decade ago (2007) with subsequent modifications in 2010. The principle of the policy is simple: research data should be shared and preserved in a manner which maximises its value to advance research and improve health. Wellcome Trust also requires data management plans as a compulsory part of grant applications, where the proposed research is likely to generate a dataset that will have significant value to researchers and other users. This is to ensure that researchers understand the importance of data management and sharing and to plan for it from the start their projects.

Cost of data sharing

Planning for data management and sharing involves costing for these activities in the grant proposal. The Wellcome Trust’s FAQ guidance on data sharing policy says that: “The Trust considers that timely and appropriate data management and sharing should represent an integral component of the research process. Applicants may therefore include any costs associated with their proposed approach as part of their proposal.” David then outlined the types of costs that can be included in grant applications (including for dedicated staff, hardware and software, and data access costs). He noted that in the current draft guidance on costing for data management estimated costs for long-term preservation that extend beyond the lifetime of the grant are not eligible, although costs associated with the deposition of data in recognised data repositories can be requested.

Key priorities and emerging areas in data management and sharing


The Wellcome Trust also identified key priorities and emerging areas where work needs to be done to better support of data management and sharing. The first one was to provide resources and platforms for data sharing and access. David pointed out that wherever available, discipline-specific data repositories are the best home for research data, as they provide rich metadata standards, community curation and better discoverability of datasets.

However, the sustainability of discipline-specific repositories is sometimes uncertain. Discipline-specific resources are often perceived as ‘free’. However, research data submitted to ‘free’ data repositories has to be stored somewhere and the amount of data produced and shared is growing exponentially – someone has to pay for the cost of storage and long-term curation in discipline-specific data repositories. An additional point for consideration is that many disciplines do not have their own repositories and therefore need to heavily rely on institutional support.


Wellcome Trust funds a large number of projects in clinical areas. Dealing with patient data requires careful ethical considerations and planning from the very start of the project to ensure that data can be successfully shared at the end of the project. To support researchers in dealing with patient data The Expert Advisory Group on Data Access (a cross-funder advisory body established by MRC, ESRC, Cancer Research UK and the Wellcome Trust) has developed guidance documents and practice papers about handling of sensitive data: how to ask for informed consent, how to anonymise data and the procedures that need to be in place when granting access to data. David stressed that balance needs to be struck between maximising the use of data and the need to safeguard research participants.

Incentives for sharing

Finally, if sharing is to become the normal thing to do, researchers need incentives to do so. Wellcome Trust is keen to work with others to ensure that researchers who generate and share datasets of value receive appropriate recognition for their efforts. A recent report from the Expert Advisory Group on Data Access proposed several recommendations to incentivise data sharing, with specific roles for funders, research leaders, institutions and publishers. Additionally, in order to promote data re-use, the Wellcome Trust joined forces with the National Institutes of Health and the Howard Hughes Medical Institute and launched the Open Science Prize competition to encourage prototyping and development of services, tools or platforms that enable open content.

Cancer Research UK’s views on data sharing

The next talk was by Jamie Enoch from Cancer Research UK. Jamie started by saying that because Cancer Research UK (CRUK) is a charity funded by the public, it needs to ensure it makes the most of its funded research: sharing research data is elemental to this. Making the most of the data generated through CRUK grants could help accelerate progress towards the charity’s aim in its research strategy, to see three quarters of people surviving cancer by 2034. Jamie explained that his post – Research Funding Manager (Data) – has been created as a reflection of data sharing being increasingly important for CRUK.

The policy

Jamie started talking about the key principles of CRUK data sharing policy by presenting the main issues around research data sharing and explaining the CRUK’s position in relation to them:

  • What needs to be shared? All research data, including unpublished data, source code, databases etc, if it is feasible and safe to do so. CRUK is especially keen to ensure that data underpinning publications is made available for sharing.
  • Metadata: Researchers should adhere to community standards/minimum information guidelines where these exist.
  • Discoverability: Groups should be proactive in communicating the contents of their datasets and showcasing the data available for sharing

Jamie explained that CRUK really wants to increase the discoverability of data. For example, clinical trials units should ideally provide information on their websites about the data they generate and clear information about how it can be accessed.

  • Modes of sharing: Via community or generalist repositories, under the auspices of the PI or a combination of methods

Jamie explained that not all data can be/should be made openly available. Due to ethical considerations sometimes access to data will have to be restricted. Jamie explained that as long as restrictions are justified, it is entirely appropriate to use them. However, if access to data is restricted, the conditions on which access will be granted should be considered at the project outset, and these conditions will have to be clearly outlined in metadata descriptions to ensure fair governance of access.

  • Timeframes: Limited period of exclusive use permitted where justified

Jamie suggested adhering to community standards when thinking about any periods of exclusive use of generated research data. In some communities research data is made accessible at the time of publication. Other communities will expect data release at the time of generation (especially in collaborative genomics projects). Jamie further explained that particularly in cases where new data can affect policy development, it is key that research data is released as soon as possible.

  • Preservation: Data to be retained for at least 5 years after grant end
  • Acknowledgement: Secondary users of data should credit original researcher and CRUK
  • Costs: Appropriately justified costs can be included in grant proposals

As of late 2015, financial support for data management and sharing can be requested as a running cost in grant applications. Jamie explained that there are no particular guidelines in place explaining eligible and non-eligible costs and that the most important aspect is whether the costs are well justified or not, and reasonable in the context of the research envisaged.

Jamie stressed that the key point of the CRUK policy is to facilitate data sharing and to engage with the research community, recognising the challenges of data sharing for different projects and the need to work through these collaboratively, rather than enforce the policy in a top-down fashion.

Policy implementation

Subsequently, the presentation discussed ways in which CRUK policy is implemented. Jamie explained that the main tool for the policy implementation is the new requirement for data management plans as compulsory part of grant applications.

Two of the three main response mode committees: Science Committee and Clinical Research Committee have a two-step process of writing a data management plan. During the grant application stage researchers need to write a short, free-form description about how they plan to adhere to CRUK’s policy on data sharing. Only if the grant is accepted, the beneficiary will be asked to write a more detailed data management plan, in consultation with CRUK representatives.

This approach serves two purposes as it:

  • ensures that all applicants are aware of CRUK’s expectations on data sharing (they all need to write a short paragraph about data sharing)
  • saves researchers’ time: only those applicants who were successful will have to provide a detailed data management plan, and it allows the CRUK office to engage with successful applicants on data sharing challenges and opportunities

In contrast, applicants for the other main CRUK response mode committee, the Population Research Committee, all fill out a detailed data management and sharing plan at application stage because of the critical importance of sharing data from cohort and epidemiological studies.

Outlooks for the future

Similarly to the Wellcome Trust, CRUK realised that cultural change is needed for sharing to become the normality. CRUK have initiated many national and international partnerships to help the reward of data sharing.

One of them is a collaboration with the YODA (Yale Open Data Access) project aiming to develop metrics to monitor and evaluate data sharing. Other areas of collaborative work include collaboration with other funders on development of guidelines on ethics of data management and sharing, platforms for data preservation and discoverability, procedures for working with population and clinical data. Jamie stressed that the key thing for CRUK is to work closely with researchers and research managers – to understand the challenges and work through these collaboratively, and consider exciting new initiatives to move the data sharing field forwards.


Published 5 February 2016
Written by Dr Marta Teperek, verified by David Carr and Jamie Enoch
Creative Commons License

Open Data – moving science forward or a waste of money & time?

On the 4 November the Research Data Facility at Cambridge University invited some inspirational leaders in the area of research data management and asked them to address the question: “is open data moving science forward or a waste of money & time?”. Below are Dr Marta Teperek’s impressions from the event.

Great discussion

Want to initiate a thought-provoking discussion on a controversial subject? The recipe is simple: invite inspirational leaders, bright people with curious minds and have an excellent chair. The outcome is guaranteed.

We asked some truly inspirational leaders in data management and sharing to come to Cambridge to talk to the community about the pros and cons of data sharing. We were honoured to have with us:

  • PRE_IntroSlide_V3_20151123Rafael Carazo-Salas, Group Leader, Department of Genetics, University of Cambridge
  • Sarah Jones, Senior Institutional Support Officer from the Digital Curation Centre; @sjDCC
  • Frances Rawle, Head of Corporate Governance and Policy, Medical Research Council; @The_MRC
  • Tim Smith, Group Leader, Collaboration and Information Services, CERN/Zenodo; @TimSmithCH
  • Peter Murray-Rust, Molecular Informatics, Dept. of Chemistry, University of Cambridge, ContentMine; @petermurrayrust

The discussion was chaired by Dr Danny Kingsley, the Head of Scholarly Communication at the University of Cambridge (@dannykay68).

What is the definition of Open Data?

IMG_PMRWithText_V1_20151126The discussion started off with a request for a definition of what “open” meant. Both Peter and Sarah explained that ‘open’ in science was not simply a piece of paper saying ‘this is open’. Peter said that ‘open’ meant free to use, free to re-use, and free to re-distribute without permission. Open data needs to be usable, it needs to be described, and to be interpretable. Finally, if data is not discoverable, it is of no use to anyone. Sarah added that sharing is about making data useful. Making it useful also involves the use of open formats, and implies describing the data. Context is necessary for the data to be of any value to others.

What are the benefits of Open Data?

IMG_RCSWithText_V1_20151126Next came a quick question from Danny: “What are the benefits of Open Data”? followed by an immediate riposte from Rafael: “What aren’t the benefits of Open Data?”. Rafael explained that open data led to transparency in research, re-usability of data, benchmarking, integration, new discoveries and, most importantly, sharing data kept it alive. If data was not shared and instead simply kept on the computer’s hard drive, no one would remember it months after the initial publication. Sharing is the only way in which data can be used, cited, and built upon years after the publication. Frances added that research data originating from publicly funded research was funded by tax payers. Therefore, the value of research data should be maximised. Data sharing is important for research integrity and reproducibility and for ensuring better quality of science. Sarah said that the biggest benefit of sharing data was the wealth of re-uses of research data, which often could not be imagined at the time of creation.

Finally, Tim concluded that sharing of research is what made the wheels of science turn. He inspired further discussions by strong statements: “Sharing is not an if, it is a must – science is about sharing, science is about collectively coming to truths that you can then build on. If you don’t share enough information so that people can validate and build up on your findings, then it basically isn’t science – it’s just beliefs and opinions.”

IMG_TSWithText_V1_20151126Tim also stressed that if open science became institutionalised, and mandated through policies and rules, it would take a very long time before individual researchers would fully embrace it and start sharing their research as the default position.

I personally strongly agree with Tim’s statement. Mandating sharing without providing the support for it will lead to a perception that sharing is yet another administrative burden, and researchers will adopt the ‘minimal compliance’ approach towards sharing. We often observe this attitude amongst EPSRC-funded researchers (EPSRC is one of the UK funders with the strictest policy for sharing of research data). Instead, institutions should provide infrastructure, services, support and encouragement for sharing.

Big data

Data sharing is not without problems. One of the biggest issues nowadays it the problem of sharing of big data. Rafael stressed that with big data, it was extremely expensive not only to share, but even to store the data long-term. He stated that the biggest bottleneck in progress was to bridge the gap between the capacity to generate the data, and the capacity to make it useful. Tim admitted that sharing of big data was indeed difficult at the moment, but that the need would certainly drive innovation. He recalled that in the past people did not think that one day it would be possible just to stream videos instead of buying DVDs. Nowadays technologies exist which allow millions of people to watch the webcast of a live match at the same time – the need developed the tools. More and more people are looking at new ways of chunking and parallelisation of data downloads. Additionally, there is a change in the way in which the analysis is done – more and more of it is done remotely on central servers, and this eliminates the technical barriers of access to data.

Personal/sensitive data

IMG_FRWithText_V1_20151126Frances mentioned that in the case of personal and sensitive data, sharing was not as simple as in basic sciences disciplines. Especially in medical research, it often required provision of controlled access to data. It was not only important who would get the data, but also what they would do with it. Frances agreed with Tim that perhaps what was needed is a paradigm shift – that questions should be sent to the data, and not the data sent to the questions.

Shades of grey: in-between “open” and “closed”

Both the audience and the panellists agreed that almost no data was completely “open” and almost no data was completely “shut”. Tim explained that anything that gets research data off the laptop to a shared environment, even if it was shared only with a certain group, was already a massive step forward. Tim said: “Open Data does not mean immediately open to the entire world – anything that makes it off from where it is now is an important step forward and people should not be discouraged from doing so, just because it does not tick all the other checkboxes.” And this is yet another point where I personally agreed with Tim that institutionalising data sharing and policing the process is not the way forward. To the contrary, researchers should be encouraged to make small steps at a time, with the hope that the collective move forward will help achieving a cultural change embraced by the community.

Open Data and the future of publishing

Another interesting topic of the discussion was the future of publishing. Rafael started explaining that the way traditional publishing works had to change, as data was not two-dimensional anymore and in the digital era it could no longer be shared on a piece of paper. Ideally, researchers should be allowed to continue re-analysing data underpinning figures in publications. Research data underpinning figures should be clickable, re-formattable and interoperable – alive.

IMG_DKWithText_V1_20151126Danny mentioned that the traditional way of rewarding researchers was based on publishing and on journal impact factors. She asked whether publishing data could help to start rewarding the process of generating data and making it available. Sarah suggested that rather than having the formal peer review of data, it would be better to have an evaluation structure based on the re-use of data – for example, valuing data which was downloadable, well-labelled, re-usable.

Incentives for sharing research data

IMG_SJWithText_V1_20151126The final discussion was around incentives for data sharing. Sarah was the first one to suggest that the most persuasive incentive for data sharing is seeing the data being re-used and getting credit for it. She also stated that there was also an important role for funders and institutions to incentivise data sharing. If funders/institutions wished to mandate sharing, they also needed to reward it. Funders could do so when assessing grant proposals; institutions could do it when looking at academic promotions.

Conclusions and outlooks on the future

This was an extremely thought-provoking and well-coordinated discussion. And maybe due to the fact that many of the questions asked remained unanswered, both the panellists and the attendees enjoyed a long networking session with wine and nibbles after the discussion.

From my personal perspective, as an ex-researcher in life sciences, the greatest benefit of open data is the potential to drive a cultural change in academia. The current academic career progression is almost solely based on the impact factor of publications. The ‘prestige’ of your publications determines whether you will get funding, whether you will get a position, whether you will be able to continue your career as a researcher. This, connected with a frequently broken peer-review process, leads to a lot of frustration among researchers. What if you are not from the world’s top university or from a famous research group? Will you be able to still publish your work in a high impact factor journal? What if somebody scooped you when you were about to publish results of your five years’ long study? Will you be able to find a new position? As Danny suggested during the discussion, if researchers start publishing their data in the ‘open”’ there is a chance that the whole process of doing valuable research, making it useful and available to others will be rewarded and recognised. This fits well with Sarah’s ideas about evaluation structure based on the re-use of research data. In fact, more and more researchers go to the ‘open’ and use blog posts and social media to talk about their research and to discuss the work of their peers. With the use of persistent links research data can be now easily cited, and impact can be built directly on data citation and re-use, but one could also imagine some sort of badges for sharing good research data, awarded directly by the users. Perhaps in 10 or 20 years’ time the whole evaluation process will be done online, directly by peers, and researchers will be valued for their true contributions to science.

And perhaps the most important message for me, this time as a person who supports research data management services at the University of Cambridge, is to help researchers to really embrace the open data agenda. At the moment, open data is too frequently perceived as a burden, which, as Tim suggested, is most likely due to imposed policies and institutionalisation of the agenda. Instead of a stick, which results in the minimal compliance attitude, researchers need to see the opportunities and benefits of open data to sign up for the agenda. Therefore, the Institution needs to provide support services to make data sharing easy, but it is the community itself that needs to drive the change to “open”. And the community needs to be willing and convinced to do so.

Further resources

  • Click here to see the full recording of the Open Data Panel Discussion.
  • And here you can find a storified version of the event prepared by Kennedy Ikpe from the Open Data Team.

Thank you

We also wanted to express a special ‘thank you’ note to Dan Crane from the Library at the Department of Engineering, who helped us with all the logistics for the event and who made it happen.

Published 27 November 2015
Written by Dr Marta Teperek
Creative Commons License