The Open Research at Cambridge conference took place between 22–26 November 2021. In a series of talks, panel discussions and interactive Q&A sessions, researchers, publishers, and other stakeholders explored how Cambridge can make the most of the opportunities offered by open research. This blog is part of a series summarising each event.
On 26 November 2021 the University’s Reproducibility Working Group hosted a workshop for researchers from across Cambridge to explore approaches to supporting more reproducible research. Talks were provided by Professor Alexander Bird (Faculty of Philosophy), Dr Florian Markowetz (Cancer Research UK Cambridge Institute) and Dr Maria Tsapali (Faculty of Education) exploring approaches to reproducible research and reasons to work reproducibility across qualitative and quantitative research.
The recording of the session can be found below:
Talks were followed by interdisciplinary discussion sessions designed to identify the obstacles to reproducible research across Cambridge and how these might be tackled. The key findings from the discussions included:
Training on reproducibility, including statistical training, reproducible methods and use of key tools exist in departments across the University, but more needs to be done to share provision and create synergies and central provision where possible.
Training should begin at undergraduate or Masters level to build key skills early.
Awareness of training, and the importance of reproducibility training, needs to be enhanced.
The need for University guidance on how to make research reproducible, particularly to overcome key challenges to reproducibility such as balancing reproducibility with the need to protect sensitive or confidential data.
That the University can help by making the production of open and reproducible research as painless as possible, for example by facilitating peer review of codes and providing easy access to data storage and expertise in best practice.
That reproducibility looks very different across the disciplines and that in some areas transparency and methods reproducibility will be the focus, rather than reproducible outcomes.
The Reproducibility Working Group will draw on the ideas raised at this workshop to help shape proposals for future University approaches to supporting reproducible research. The group plans to host a number of further events to map, consolidate, and extend existing resources for reproducibility across Cambridge with the aim of boosting grassroots activities and magnifying their impact across all levels of the institution.
For more information and resources on reproducible research see: UK Reproducibility Network: https://www.ukrn.org/
Dr. Sacha Jones and Dr. Samuel Moore, Office of Scholarly Communication, Cambridge University Libraries
The Open Research at Cambridge conference took place between 22–26 November 2021. In a series of talks, panel discussions and interactive Q&A sessions, researchers, publishers, and other stakeholders explored how Cambridge can make the most of the opportunities offered by open research. This blog is part of a series summarising each event.
As part of the Cambridge Open Research conference, the Office of Scholarly Communication hosted a ‘101’ session on open research, covering the basics and answering queries for the audience on all aspects of open access publication and open data. With over 80 participants, we were thrilled with the response and wanted to recap some of the topics we covered in this post.
Firstly, as we discussed in the session, it is easy to assume that open research is simply an issue for the sciences rather than all academic disciplines. Practices such as open access and open data have been taken up widely in the sciences, although in different ways, and there is a common association with science and openness. This is compounded by the fact that in many European countries Open Science is inclusive of arts and humanities scholarship and so is functionally equivalent to open research. At the OSC, we are keen to support open practices across all disciplines while being sensitive to different ways of working. We are guided by the university’s Open Research Position Statement that requires work to be ‘as open as possible, as closed as necessary’.
After an introduction to open research, Sam then outlined the key issues in open access, including the different licences for making your research open access, the differences between green and gold open access, and the many and various reasons for making your work open access. Open access allows us to reach new audiences, improve the economics of research access, and reassess knowledge production and dissemination in a digital world. We also learned about open access monographs, the complex policy landscape and the various ways in which you can make your research open access through repositories and journals. The OSC’s Open Access webpages are an excellent set of resources for learning more.
We then moved onto open data – research data shared publicly – and how this fits into open research (see the University’s policy framework on research data). After highlighting that all research regardless of discipline generates or uses data of one kind or another (e.g. text, audio-visual, numerical, etc.), Sacha posed a series of questions with answers, anticipating what the audience might want to know more about. Do I have to share my data? What data do I share – is it meant to be everything from my research? My data contains sensitive information so I can’t share my data, or can I? How do I share my data? I don’t want to be criticised after making my data open, so how can I prevent this? How can I stop someone else from taking my data, using it, and getting all the credit? The OSC’s Research Data website contain information about data management and data sharing, and check out our list of Cambridge Data Champion experts to see if there’s anyone who’s volunteered to be a local source of data-related advice in your department or discipline.
We are always available as a source of support and guidance in all matters relating to open research and encourage you to contact us if you have any questions. The OSC has webpages on open research and sites dedicated to both open access and research data. For general open research enquires, we can be emailed at info@osc.cam.ac.uk, for open access at info@openaccess.cam.ac.uk and for data at info@data.cam.ac.uk. There are also a number of training sessions provided throughout the year and online that relate to the topics covered in this session. If you think that those in your department or institute at Cambridge would like to know more about the topics covered here then please do get in touch as we’d be happy to speak to these and answer any questions you may have.
Cambridge Data Week 2020 was an event run by the Office of Scholarly Communication at Cambridge University Libraries from 23–27 November 2020. In a series of talks, panel discussions and interactive Q&A sessions, researchers, funders, publishers and other stakeholders explored and debated different approaches to research data management. This blog is part of a series summarising each event.
The first day of Cambridge Data Week 2020 kicked off with a tantalisingly open question: who are the winners and losers of good data practices? This question was addressed via two different perspectives: those of a funder, provided by Dr Georgie Humphreys (Wellcome), and of a publisher, provided by Dr Catriona MacCallum (Hindawi). Discussion of this topic during presentations and the Q&A session looked through various (but not mutually exclusive) lenses, including those of data sharing, quality, ethics, and research culture. Funder mandates for data sharing and what these have achieved (e.g. saving research funds related to data reuse) were reflected upon, as were disciplinary differences between STEMM, social sciences, arts and humanities. There was also a discussion of evidence relating to shifts in research culture and if this is pointing to better data practices. As a whole, the webinar explored a broader view of good data practices, the consequences of these, and the progress being made in embedding good data management in research.
Topical for this year, both speakers discussed data sharing related to Covid-19 research. Catriona stated that Covid has exposed systemic flaws in the existing system (in relation to data sharing), and Georgie highlighted some surprising results regarding data availability statements in Covid-related articles. The CARE Principles for Indigenous Data Governance were also bought to the fore by Catriona, who argued for attention to be placed on potential power issues surrounding data sharing. These are a set of principles, complementary to the FAIR principles, but which encourage the open research movement to fully engage with Indigenous Peoples rights and interests. A pervasive undercurrent ran throughout the webinar – research culture and some problems therein. These were addressed explicitly by both speakers, with both stating that more needs to be done by institutions to implement DORA and reward researchers for their achievements and good research practices and not just according to where (i.e. in what journals) their research is published. Catriona highlighted results from a 2019 EUA report that shows that institutions have some way to go in this regard, that the value of data is not fully recognised, and that responsible research assessment is at the heart of cultural change in the right direction.
We had some great questions from the audience that were answered in the Q&A session, such as “In countries without the REF, is data sharing better?”, and “How do you get qualitative researchers on board with this?”, and “What is the role of universities in the so-called data-driven economy?”. Our audience also responded to the poll we held at the end of the webinar, where we asked participants to select one from seven given options that they regard as most likely to prevent good data practices among researchers. Resource indicators (knowledge, time, money for RDM) amounted to 46% of responses (blue in the chart below) and cultural indicators amounted to 53% (orange in the chart). Overall, the results were rather surprising but optimistic, revealing that a dominant perception among the participants is that a shift in cultural practices is one of the leading factors necessary to drive forward good data practices in research.
Figure 1. Results of the poll held during the webinar, where participants were asked to choose one of seven factors that they consider most likely to prevent or inhibit good data practices.
Audience composition
We had 274 registrations for this webinar, with just over 70% originating from the Higher Education sector. Researchers and PhD students accounted for 40% of registrations and research support staff for an additional 30%. On the day, we were thrilled to see that 164 people attended the webinar, participating from a wide range of countries.
There were a few questions we did not have time to address during the live session, so we put them to the speakers afterwards. Here are their answers:
What are the ethics of using secondary data, particularly in relation to primary versus secondary researchers’ objectives, meaning of data/methods, consent of participants, and in the case of qualitative data, the personal relationships built between researcher and participants?
Georgie Humphreys This question seems to allude to informed consent which is still a topic of active discussion in terms of what one tries to build into the original informed consent to allow subsequent secondary use down the line. There is this idea of broad consent now where a participant would consent to that particular project but they’re also consenting to their data being kept and maybe reused for other purposes related to different scientific questions, but maybe with clauses such as ‘not for commercial benefits’. There are potential concerns about re-identification but there are mechanisms for dealing with that – mechanisms which reduce risk whilst retaining value, such as anonymisation or synthetic data creation. But there are other datasets where that’s just not going to be possible, where you lose all value of the original dataset. The UKDS have a nice page on informed consent, providing information on what you put in your consent forms to enable secondary use. This needs to be thought about at the very start of the study prior to collection of the primary data.
Catriona MacCallum This question is really focusing on data privacy issues. The primary researcher collects the data, the secondary researcher reuses the data. There are ways that researchers can be given access to the data while maintaining privacy. The primary researcher is creating the relationships with participants in order to obtain data, so what does this mean ethically for those wishing to reuse the data? Safety nets do need to be put into place. Here, it’s important to raise the CARE principles again. These were the result of a working group that came about as a result of concerns about how data from indigenous people are being treated. The slogan is now ‘Be FAIR and CARE’. The CARE principles are emerging in the UN’s agenda, and UNESCO, and I’m sure it will come up with the Research Council’s too.
What are the best practices to ensure data quality?
Catriona MacCallum It depends what is meant by ‘quality’ as there are various ways of looking at this. The European Commission came up with the economic loss of not publishing failed experiments; in other words, the publication bias that results. We need to redefine what we mean by quality, integrity and again this speaks to the research culture as no one gets rewarded for publishing a failed result and in fact the researchers end up feeling embarrassed and tend not to do it. Publication bias is huge! It also applies to the humanities and social sciences as well but potentially in a different way, and there are huge biases in terms of what gets published and what is allowed to get published.
Georgie Humphreys This issue is probably a plug for the open peer review model where the filter is not at the beginning but later on. [In open peer review, authors and reviewers are aware of each other’s identity and encouraged to engage in open discussion. This makes the process more transparent, removing bias or conflicts of interest. Manuscripts are made publicly available pre-review, and reviews are published alongside the article].
Conclusion
So, who are the winners and losers of good data practices? Georgie believes that everyone, in the long term, will be a winner. If time is spent ensuring data is well-documented, well-organised, has dictionaries, is stored somewhere for the long term, then it will benefit the data creators just as much as anyone else. In the short term, she acknowledges that there may be people that find being a champion in this field a challenge for them individually, but it’s just about continuing along this journey to get to the point where everything is in place to truly reward and recognise those that have good open practices and good data management practices. Catriona says that there are so many winners: the economy, society, and science, the social sciences and humanities – all will benefit from data sharing. Taking society as an example, sharing data and sharing it well (through good research data management) will increase public trust in science, benefit public health and even help toward achieving multiple sustainable development goals.
Resources
A Covid-19 press release by Wellcome in January 2020 called on researchers, publishers and funders to share or facilitate the sharing of interim and final data as rapidly as possible. Wellcome have been exploring the impact of this statement on data sharing.
An April 2020 publication by Colavizza et al. on ‘The citation advantage of linking publications to research data’ showing that article citations are greater when they have data availability statements that include a link (e.g. DOI) to data archived in a repository.
