Tag Archives: research data management

Open Research in the Humanities: CORE Data

Authors: Emma Gilby, Matthias Ammon, Rachel Leow and Sam Moore

This is the third of a series of blog posts, presenting the reflections of the Working Group on Open Research in the Humanities. Read the opening post at this link. The working group aimed to reframe open research in a way that was more meaningful to humanities disciplines, and their work will inform the University of Cambridge approach to open research. This post reflects on the concept of FAIR data and proposes an alternative way of thinking about data in the humanities.

As a rule, data in the arts and humanities is collected, organised, recontextualised and explained. We are therefore putting forward this acronym as an alternative to LERU’s FAIR data (findable, accessible, interoperable, reusable). Our data is collected rather than generated; organised and recontextualised in order to further a cultural conversation about discoveries, methods and debates; and explained as part of the analytical process. Any view of scholarly comms as uniquely about the distribution of and access to FAIR data (‘from my bench to yours’) will seem less relevant to A&H academics. Similarly, the goal of reproducibility of data – in the sense in which this often appears in the sciences and social sciences, where it refers to the results of a study being perfectly replicable when the study is repeated – is, if anything, contrary to the aim of CORE data: i.e. the aim that this data should be built upon and thereby modified through the process of further recontextualization. Our CORE data, then, understood as information used for reference and analysis, is made up of texts, music, pictures, fabrics, objects, installations, performances, etc. Sometimes, this information does not belong to us, but is owned by another person or institution or community, in which case it is not ours to make public.


The A&H tend to bring information together in new ways to further discussion about socio-cultural developments across the globe. Available digital data is only the tip of the iceberg when it comes to the material that is worked with.[1] Arts and humanities scholars, who spend their lives thinking about the arrangement and communication of information, are acutely aware that archives (digital and otherwise) are not neutral spaces, but man-made and the product of human choices. This means that information available online, to a broadband-enabled public, is asymmetrical and distorted.

One of the main benefits of open research is that it is thought to make data globally accessible, especially to ‘the global south’ and to institutions with fewer available funds to ‘buy data in’. As we explore below (‘research integrity’), this unidirectional view of open access is problematic. In general, digital material tends to reproduce English-speaking structures and epistemologies. As FAIR data is redefined as CORE data, an attention to context will hopefully promote the diverse positions occupied by all those who make up the world and who produce research about it.

Support required

In order usefully to employ CORE data in the A&H, we need to bring to the surface and examine underlying assumptions about knowledge creation as well as knowledge dissemination.

The work of the digital humanities – rooted explicitly in digital technologies and the forms of communication that they enable – is obviously a vital part of these discussions about opening up the CORE data of the humanities. Digital work, in the same way as any other successful A&H research, needs to consider its own materiality and conditions of production, evaluate its own history, draw attention to its own limits, and navigate its trans-temporal relationships with data in other forms (the manuscript, the printed text, the painting, the piece of music). This is a developing field and one that still has an uneasy relationship with the existing tenure/promotions system.[2] Colleagues noted that training needs are evolving constantly. It is often hard to know where to turn for specific guidance in e.g. how to manage one’s own ‘born digital’ archives, how to deconstruct a twitter archive, and so on.

This issue also overlaps with the need, as part of the ‘rewards and incentives’ process outlined below, to evaluate the success of colleagues as they undertake this training and negotiate with these processes. DH is one of the most exciting and rapidly developing areas of research and needs to be widely resourced. But it would also be harmful to collapse all A&H research into ‘the digital humanities’. The work of colleagues whose CORE data is resistant, for whatever reason, to wide online dissemination in English also needs to be allocated the value it deserves: some publics are simply smaller than others.

Postscript: the group subsequently became aware of the CARE Principles of Indigenous Data Governance. These principles will also be considered when developing our services in support of data management and ethical sharing.

[1] Erzsébet Tóth-Czifra, ‘The Risk of Losing the Thick Description: Data Management Challenges Faced by the Arts and Humanities in the Evolving FAIR Data Ecosystem’, in Digital Technologies and the Practices of Humanities Research, edited by Jennifer Edmond (Open Book Publishers, 2014), https://doi.org/10.11647/OBP.0192.10

[2]See the excellent article by Cait Coker and Kate Ozment ‘Building the Women in Book History Bibliography, or Digital Enumerative Bibliography as Preservation of Feminist Labor’, Digital Humanities Quarterly 13 (3), 2019, http://www.digitalhumanities.org/dhq/vol/13/3/000428/000428.html – where the authors of the ‘Women in Book History’ digital bibliography still see the tenure system as ‘monograph-driven’, and had to fund their research through selling merchandise.

Cambridge Data Week 2020 day 2: Who is reusing data? Successes and future trends?

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 rest of the blogs comprising this series are as follows:
Cambridge Data Week day 1 blog
Cambridge Data Week day 3 blog
Cambridge Data Week day 4 blog
Cambridge Data Week day 5 blog


Reuse of data is the final element of the FAIR principles and has long been argued as a central benefit of data sharing, allowing others access to a wealth of research and making research funding more efficient by removing the need to duplicate work. Yet we are still in the process of evaluating success in this area. This webinar brought together speakers to discuss what we know about the current state of play around data reuse, what researchers can do to increase the reuse potential of their data, and possible future developments in data reuse.

Our speakers – Louise Corti (UK Data Archive) and Tiberius Ignat (Scientific Knowledge Services) – looked at data reuse from two different perspectives. Louise focused on the reuse of UK Data Service collections, sharing some examples of their most widely used data sets, discussing what makes them popular and sharing some principles that can be used both to make data more reusable and to promote it for reuse. Tiberius discussed the prevalence of data reuse by machines and the possibility of granting machines data reuse rights.

Louise’s presentation gave an overview of the portfolio of data sets hosted by the UK Data Service, looked at their top 20 most downloaded datasets and discussed the underlying principles that have led to them being widely reused. As well as demonstrating some commonalities between these datasets, Louise also outlined the principles used by the UK Data Service to promote their collections for reuse.

Tiberius’ presentation looked at data reuse from a different perspective, serving as a call to action to share research data responsibly and protect it against the reuse of machines designed to persuade humans. One of Tiberius’ main arguments was that no research data from public projects should be made available to feed and develop persuasive algorithms.

The presentations motivated an interesting discussion covering a broad range of topics. These included the reuse of qualitative data, how we can implement ethical safeguards data reuse, the idea of data ethics as a continuum, whether we can accept positive cases of algorithmic persuasion such as to promote equality and diversity, and the possibility of creating specific licences prohibiting data reuse by persuasive algorithms. See below for a video and transcript of the session.

Audience composition

We had 341 registrations with just over 65% originating from the Higher Education sector. Researchers and PhD students accounted for nearly 37% of the registrations whilst research support staff accounted for an additional 33%. We also had registrations from at least 30 countries outside of the UK including significant attendance from Denmark, Holland, Germany and Canada. We were thrilled to see that on the actual day 187 people attended the webinar.

We held five online webinars during Cambridge Data Week and were pleased to see that nearly 25% of the participants attended more than one webinar. A total of 1364 people registered and more than 700 attended all together, with the rest possibly watching the recordings at a later date. Most of all we were pleased to welcome participants from all over the world and see how important research data management topics are globally.

Where data was available, we identified the following countries apart from the UK:  Australia, Austria, Bangladesh, Brazil, Canada, Colombia, Croatia, Czech Republic, Denmark, France, Germany, Greece, Holland, Hungary, Iran, Luxembourg, Moldova, Norway, Poland, Romania, Singapore, Spain, Sweden, Switzerland, Turkey, Ukraine and the USA.

Recording , transcript and presentations

The video recording of the webinar can be found below and the recording, transcript and presentations are present in Apollo, the University of Cambridge repository

Bonus material

After the session ended, we continued the discussion with Louise and Tiberius looking in particular at one question posed by an audience member:

AI can always be used either for good or bad. Instead of locking-in, how can we enhance technology through data and regulation? 

Tiberius Ignat I think at this point we need regulation. I’m not a big fan of using regulations, to be honest. I think it’s much better to motivate people but, in this case, it’s quite a bit of control that has been lost, so I think we should have a regulation on how research data can be reused by others. This is how the internet has been made profitable during the last decade — through non-human persuasion. All these companies that are giving so much away for free are making billions of dollars when you look at the stock market. We were not clear how they were making this profit until recently when we realised that they are doing it by changing our behaviour and I think the rest of society – including research organisations – are behind them, so we need some regulation.

A good example is with GDPR. It has been introduced to protect our data, our digital footprint. On ResearchGate or Eurosport, or any other website, we used to be asked to agree to cookies or not. Recently, a new option called “Legitimate interest” has been slipped in and our digital data is again collected – less noticeably – by invoking questionable legitimate rights. The organisations whose model is based on persuading need cookie data, so they have moved the discussion away from remaining GDPR compliant to defending their legitimate interests. They are fighting to take data away from us. We can tackle this with regulation faster but in the long term we need to educate people to be more aware. We do have licenses such as Creative Commons but I’m not sure we have the right ones to protect us.

Louise Corti There are a variety of licenses, but they are abused and it’s very hard to track along the way what has gone wrong. I quite like the UK Government’s approach with some of their statistical data that has to go through a legal gateway. Some data can be made available for research, but it has to be done for the public good. We also have the Ethics Self-Assessment Tool, which is a grid you go through provided by the Statistics Authority and it asks you to think along lots of different dimensions of ethics. This helps researchers get a better sense of what they are trying to do, but whether the people we are talking about would care about it is a very different matter. Having been in research ethics for a very long time, that is by far the best tool I’ve seen and I recommend everyone uses it. The UK Data Archive uses it to evaluate some of the projects we deal with because you find often university ethics approvals are not good enough for the Statistics Authority because often they don’t understand quantitative secondary analysis, so the ethics scrutiny is not good enough. Self-Assessment is a much more nuanced thinking about the different dimensions of ethics and it helps researchers to be a bit more reflective about what’s good and what’s not.


Overall, the session provided a compelling blend of both the practical and conceptual elements of data reuse, each raising questions which could have easily been entire sessions in themselves. Louise’s presentation gave an excellent overview of the UK Data Service’s approach to making their datasets more reusable and promoting them to maximise their chances of being reused. Tiberius’ session raised some interesting questions surrounding data reuse and the ethics of using algorithms to persuade humans, as well as looking at some practical options for protecting research data from reuse for nefarious ends. At the end of the session, the audience were asked to participate in a poll on “What future developments are needed to increase the prevalence of data reuse?”.

Audience responses to poll held at the end of the event

The results were unsurprising to either speaker, with each touching on the idea that a change in research culture is necessary to ensure data reuse projects are seen as equal to data-generating projects. The need for cultural change is a theme that ran throughout each of the sessions in Data Week and is perhaps one of the current major challenges in scholarly communication.


Data Access and Research Transparency (DA-RT): A Joint Statement by Political Science Journal Editors

Robots appear more persuasive when pretending to be human

Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation

The next-generation bots interfering with the US election

IBM’s AI Machine Makes A Convincing Case That It’s Mastering The Human Art Of Persuasion

AI Learns the Art of Debate


Published on 25 January 2021

Written by Dominic Dixon

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Cambridge Data Week 2020 day 3: Is data management just a footnote to reproducibility?

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 rest of the blogs comprising this series are as follows:
Cambridge Data Week day 1 blog
Cambridge Data Week day 2 blog
Cambridge Data Week day 4 blog
Cambridge Data Week day 5 blog


The third day of Cambridge Data Week consisted of a panel discussion about the relationship between reproducibility and Research Data Management (RDM), looking for ways to advocate effectively to reach positive outcomes in both areas. Alexia Cardona (University of Cambridge), Lennart Stoy (European University Association), Florian Markowetz (University of Cambridge & UK Reproducibility Network), and René Schneider (Geneva School of Business Administration) offered their perspectives on whether RDM really is just a ‘footnote’ to the more popular concept of reproducibility.

The speakers agreed that we are still in need of cultural change towards better data management and reproducibility. The word ‘reproducibility’ is more likely to excite researchers and it is important to craft messages that work for each group, hence the emphasis on this term. In contrast to the Cambridge Data Week event on data peer review, the discussion here focused on engaging senior researchers, from PIs to Heads of Institutions, motivating them to be not just good data managers, but great data leaders.

Among the key elements needed to drive best practice in this area, two stood out. The first is communities. Whether these are reproducibility circles of peers, or networks like the Cambridge Data Champions, communities are key to creating and implementing guidelines for data management. The second element is a solid technological infrastructure. For instance, block chains could be used to enable reproducibility in citations in the humanities, or Persistent Identifiers, used at a very granular level, could lead to better data reuse.

Recording , transcript and presentations

The video recording of the webinar can be found below and the recording, transcript and presentations are present in Apollo, the University of Cambridge repository.

Bonus material

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 good practices regarding data deletion?

Florian Markowetz It very much depends on what kind of data you have, it’s hard to give general directions. However, drives and other hardware are becoming cheaper and cheaper, so I would say ‘save everything’.

René Schneider I would agree. I have spoken to researchers who keep all their data, because it would create too much work to sort what to keep and what to delete.

Alexia Cardona We tend to talk more about data archiving than data deletion. I often hear about data deletion where it has created problems, for example an account has been deleted in bulk when a researcher left an institution, so unpublished data and scripts are lost due to lack of communication. There are also cases on the internet of PhD students losing all their thesis when the laptop crashed, so this issue goes hand in hand with data storage and backup. Let’s focus on good practices and archiving of data, deletion is the very last thing to worry about.

Lennart Stoy It’s worth mentioning that there is often a compulsory period that data should be kept for, perhaps 3 years or 5 years according to funders mandates, so data should be stored for some time. I suppose the expense could become an issue in the coming years, some Universities are already concerned about the cost of having to buy large amounts of cloud storage space. There are also discussions in the Open Science Could teams about what to preserve in the long term. We want to make sure we preserve the higher value datasets, but of course it’s hard to define which ones those are.

Couldn’t scholarly communities of practice or learned societies create guidelines for reproducibility and good data management?

Lennart Stoy Absolutely, they must be involved as they are the ones with the specific knowledge. This is the idea behind Research Data Alliance (RDA) and the National Research Data Infrastructure (NFDI) in Germany. In those cases, you have to prove a link to the community in that field to establish a consortium. It is great when communities structure their areas of infrastructure from the bottom up.

What roles could Early Career Researchers (ECRs) have? Could they act as code-checkers to assist reproducibility, or are we asking too much of them given their busy schedules? Would they receive credit for this?

Florian Markowetz Senior academics have no excuses for not getting more involved in this once they have stable positions. It’s easy for people in my position to point to students, or to funders, saying they are not doing enough, but we should not be pointing away from ourselves, we should do the work. It could be coupled to pay rises: if you hold any role above grade 12 it’s your job now to sort this all out.

René Schneider I have been thinking about the role of data custodians or similar. If we ask researchers to spend a lot of time just checking data, like ‘warehouse workers’, we could be undervaluing their role. I don’t think it’s necessarily the researchers who should do the work, especially not ECRs, there should be other roles dedicated to this.

Alexia Cardona I second that, researchers are supposed to focus on the research, not necessarily the data checking and curation. But the unfortunate truth is that with short contracts and lack of resources the work is left to them. Another problem is the lack of rewards. For instance in my area, training, there’s no reward for people who take the time to make their training FAIR. We should embrace more openness and fairness, including rewarding those who do the work.

Lennart Stoy This is something we’ve been working on but it’s a challenging system to change because there are so many elements to disentangle. It relates to intense competition for jobs, the culture in different disciplines, and the pressure to publish in certain journals. Some Universities are very serious about implementing DORA and I hope that in a few years these will be able to show high levels of satisfaction among PhD students and ECRs. A lot depends on the leadership at the institutional level to initiate change, for instance the rector at Ghent University in Belgium has been driving DORA-inspired reward mechanisms and the Netherlands is also moving ahead and moving away from journal-based factors. The University of Bath is an example in the UK that I’ve heard mentioned a lot. We’re following progress in all these examples and will write up DORA good practice case studies to inspire other organisations. But it is a hard problem, ECRs have a lot on the line, it’s important not to jeopardise their careers.


This compelling discussion left us feeling that it does not matter too much which words we emphasise: reproducibility, data management, data leadership, or something else entirely. What matters is that we spark interest and commitment in the right groups of researchers to drive progress. Creating a culture where great research practices are routine will take effective advocacy, but also rewards that align with our aims and the right technical infrastructure to underpin them.


UK data service is a data repository funded by the Economic and Social Research Council (ESRC), which also provides extensive resources on data practices.

The journal PLOS Computational Biology introduced a pilot in 2019 where all papers are checked for the reproducibility of models.

Is there a reproducibility crisis? Baker’s 2016 paper in Nature reporting the results of a survey that exposed the extent of the reproducibility crisis.

San Francisco Declaration on Research Assessment (DORA), a set of recommendations for institutions, funders, publishers, metrics companies and researchers, aiming for a fairer and more varied system of research quality assessment.

Published on 25 January 2021

Written by Beatrice Gini

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