Tag Archives: reproducibility

Open Research at Cambridge Conference – Opening session

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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. 

The opening session, chaired by Dr Jessica Gardner (University Librarian and Director of Library Services) included talks by Professor Anne Ferguson-Smith (Pro-Vice-Chancellor for Research), Professor Steve Russell (Acting Head of Department of Genetics and Chair of Open Research Steering Committee), Mandy Hill (Managing Director of Academic Publishing at Cambridge University Press) and Dr Neal Spencer (Deputy Director for Collections and Research at the Fitzwilliam Museum). All four speakers foresee an increasingly open future, with benefits for both institutions and researchers. They also considered some of the challenges that still need to be worked through to avoid potential problems.

What is working well?

In recent years, we have made great progress in the proportion of publications that are open access. Over three quarters of publications with Cambridge authors last year were openly available in some form.

The trend is continuing and it is not unique to our institution. CUP have set an ambitious goal for the vast majority of research articles they publish to be open access by 2025.

Other forms of publication are becoming common, meeting different dissemination needs. Preprints have been the star of the show during the pandemic, allowing rapid dissemination while formal peer review follows down the line.

Diagram from Mandy Hill’s slide: ‘Increasingly open platforms and formal publishing will meet different dissemination needs’

In the scholarly communication arena, open access articles benefit from more downloads and citations. Museum-based projects involving artisans, schools and artists all found enthusiastic responses.

What can we look forward to?

Research culture is coming under the spotlight across the sector, and Cambridge has committed to an ambitious action plan to create a thriving environment to do research. Key principles include openness, collaboration, inclusivity, and fair recognition of all contributions.

Diagram from Prof Steve Russell: ‘Going Forward’

Implementing the San Francisco Declaration on Research Assessment (DORA) is part of this progress. We want to assess research on its own merits rather than on the basis of journal or publisher metrics. This also means recognising all research outputs and a broad range of impacts.

Reproducibility is increasingly recognised as critical in a number of disciplines. A developing UKRN group within the University aims to ‘take nobody’s word for it’ – but rather support reproducible workflows that underpin confidence in the conclusions of research. By sharing and rewarding best practice we can become world leaders in this area, and in open research more widely.

In the past, museum collections have tended to be documented in limited ways, with poor accessibility and interoperability, which made it hard to discover and use materials. Several exciting projects at the Fitzwilliam Museum and more broadly have started to change that. There are opportunities for a single discovery portal, tying together different collections. The Fitzwilliam Museum is also making its collection discovery process richer, by providing opportunities for deeper dives, and more connected, by linking with other collections and resources.

Deep zoom access to an image in the Fitzwilliam collection. Adapted from Dr Neal Spencer’s slide ‘Fitzwilliam Museum Collections Search’.

What problems should we be mindful of?

There are still barriers that hinder some open research aspirations. Historical constraints on the ways we find materials, conduct research, and publish results remain. Some systems may need to be reimagined, while not scrapping structures that are still serving us well.

Cambridge is a large and complex institution, where change takes time. Nevertheless, there is an established governance structures and an evolving set of policies that support open research.

Most importantly, researchers should be at the centre of the move towards open research. It is important that they benefit from open practices, rather than finding themselves torn between competing priorities. Conversations continued throughout the week to explore possible approaches in different disciplines, drawing from the rich diversity of experiences to shape the future of open research at Cambridge.

Practical steps toward more reproducible research

Open Research at Cambridge Conference,

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/

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

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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

Introduction

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.

Conclusion

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.

Resources

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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Beyond compliance – dialogue on barriers to data sharing

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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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Could Open Research benefit Cambridge University researchers?

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This blog is part of the recent series about Open Research and reports on a discussion with Cambridge researchers  held on 8 June 2016 in the Department of Engineering. Extended notes from the meeting and slides are available at the Cambridge University Research Repository. This report is written by  Lauren Cadwallader, Joanna Jasiewicz and Marta Teperek (listed alphabetically by surname).

At the Office of Scholarly Communication we have been thinking for a while about Open Research ideas and about moving beyond mere compliance with funders’ policies on Open Access and research data sharing. We thought that the time has come to ask our researchers what they thought about opening up the research process and sharing more: not only publications and research data, but also protocols, methods, source code, theses and all the other elements of research. Would they consider this beneficial?

Working together with researchers – democratic approach to problem-solving

To get an initial idea of the expectations of the research community in Cambridge, we organised an open discussion hosted at the Department of Engineering. Anyone registering was asked three questions:

  • What frustrates you about the research process as it is?
  • Could you propose a solution that could solve that problem?
  • Would you be willing to speak about your ideas publicly?

20160608_163000Interestingly, around fifty people registered to take part in the discussion and almost all of them contributed very thought-provoking problems and appealing solutions. To our surprise, half of the people expressed their will to speak publicly about their ideas. This shaped our discussion on the day.

So what do researchers think about Open Research? Not surprisingly, we started from an animated discussion about unfair reward systems in academia.

Flawed metrics

A well-worn complaint: the only thing that counts in academia is publication in a high impact journal. As a result, early career researchers have no motivation to share their data and to publish their work in open access journals, which can sometimes have lower impact factors. Additionally, metrics based on the whole journal do not reflect the importance of the research described: what is needed is article-level impact measurements. But it is difficult to solve this systemic problem because any new journal which wishes to introduce a new metrics system has no journal-level impact factor to start with, and therefore researchers do not want to publish in it.

Reproducibility crisis: where quantity, not quality, matters

Researchers also complained that the volume of produced research is higher and higher in terms of quantity and science seems to have entered an ‘era of quantity’. They raised the concern that the quantity matters more than the quality of research. Only the fast and loud research gets rewarded (because it is published in high impact factor journals), and the slow and careful seems to be valued less. Additionally, researchers are under pressure to publish and they often report what they want to see, and not what the data really shows. This approach has led to the reproducibility crisis and lack of trust among researchers.

Funders should promote and reward reproducible research

The participants had some good ideas for how to solve these problems. One of the most compelling suggestions was that perhaps funding should go not only to novel research (as it seems to be at the moment), but also to people who want to reproduce existing research. Additionally, reproducible research itself should be rewarded. Funders could offer grant renewal schemes for researchers whose research is reproducible.

Institutions should hire academics committed to open research

Another suggestion was to incentivise reward systems other than journal impact factor metrics. Someone proposed that institutions should not only teach the next generation of researchers how to do reproducible research, but also embed reproducibility of research as an employment criteria. Commitment to Open Research could be an essential requirement in job description. Applicants could be asked at the recruitment stage how they achieve the goals of Open Research. LMU University in Munich had recently included such a statement in a job description for a professor of social psychology (see the original job description here and a commentary here).

Academia feeding money to exploitative publishers

Researchers were also frustrated by exploitative publishers. The big four publishers (Elsevier, Wiley, Springer and Informa) have a typical annual profit margin of 37%. Articles are donated to the publishers for free by the academics, and reviewed by other academics, also free of charge. Additionally, noted one of the participants, academics also act as journal editors, which they also do for free.

[*A comment about this statement was made on 15 August 2017 noting that some editors do get paid. While the participant’s comment stands as a record of what was said, we acknowledge that this is not an entirely accurate statement.]

In addition to this, publishers take away the copyright from the authors. As a possible solution to the latter, someone suggested that universities should adopt institutional licences on scholarly publishing (similar to the Harvard licence) which could protect the rights of their authors

Pre-print services – the future of publishing?

Could Open Research aid the publishing crisis? Novel and more open ways of publishing can certainly add value to the process. The researchers discussed the benefits of sharing pre-print papers on platforms like arXiv and bioRxiv. These services allow people to share manuscripts before publication (or acceptance by a journal). In physics, maths and computational sciences it is common to upload manuscripts even before submitting the manuscript to a journal in order to get feedback from the community and have the chance to improve the manuscript.

bioRxiv, the life sciences equivalent of arXiv, started relatively recently. One of our researchers mentioned that he was initially worried that uploading manuscripts into bioRxiv might jeopardise his career as a young researcher. However, he then saw a pre-print manuscript describing research similar to his published on bioRxiv. He was shocked when he saw how the community helped to change that manuscript and to improve it. He has since shared a lot of his manuscripts on bioRxiv and as his colleague pointed out, this has ‘never hurt him’. To the contrary, he suggested that using pre-print services promotes one’s research: it allows the author to get the work into the community very early and to get feedback. And peers will always value good quality research and the value and recognition among colleagues will come back to the author and pay back eventually.

Additionally, someone from the audience suggested that publishing work in pre-print services provides a time-stamp for researchers and helps to ensure that ideas will not be scooped by anyone – researchers are free to share their research whenever they wish and as fast they wish.

Publishers should invest money in improving science – wishful thinking?

It was also proposed that instead of exploiting academics, publishers could play an important role in improving the research process. One participant proposed a couple of simple mechanisms that could be implemented by publishers to improve the quality of research data shared:

  • Employment of in-house data experts: bioinfomaticians or data scientists, who could judge whether supporting data is of a good enough quality
  • Ensure that there is at least one bioinfomatician/data scientist on the reviewing panel for a paper
  • Ask for the data to be deposited in a public, discipline-specific repository, which would ensure quality control of the data and adherence to data standards.
  • Ask for the source code and detailed methods to be made available as well.

Quick win: minimum requirements for making shared data useful

A requirement that, as a minimum, three key elements should be made available with publications – the raw data, the source code and the methods – seems to be a quick win solution to make research data more re-usable. Raw data is necessary as it allows users to check if the data is of a good quality overall, while publishing code is important to re-run the analysis and methods need to be detailed enough to allow other researchers to understand all the processes involved in data processing. An excellent case study example comes from Daniel MacArthur who has described how to reproduce all the figures in his paper and has shared the supporting code as well.

It was also suggested that the Office of Scholarly Communication could implement some simple quality control measures to ensure that research data supporting publications is shared. As a minimum the Office could check the following:

  • Is there a data statement in the publication?
  • If there is a statement – is there a link to the data?
  • Does the link work?

This is definitely a very useful suggestion from our research community and in fact we have already taken this feedback aboard and started checking for data citations in Cambridge publications.

Shortage of skills: effective data sharing is not easy

The discussion about the importance of data sharing led to reflections that effective data sharing is not always easy. A bioinformatician complained that datasets that she had tried to re-use did not satisfy the criteria of reproducibility, nor re-usability. Most of the time there was not enough metadata available to successfully use the data. There is some data shared, there is the publication, but the description is insufficient to understand the whole research process: the miracle, or the big discovery, happens somewhere in the middle.

Open Research in practice: training required

Attendees agreed that it requires effort and skills to make research open, re-usable and discoverable by others. More training is needed to ensure that researchers are equipped with skills to allow them to properly use the internet to disseminate their research, as well as with skills allowing them to effectively manage their research data. It is clear that discipline-specific training and guidance around how to manage research data effectively and how to practise open research is desired by Cambridge researchers.

Nudging researchers towards better data management practice

Many researchers have heard or experienced first-hand horror stories of having to follow up on somebody else’s project, where it was not possible to make any sense of the research data due to lack of documentation and processes. This leads to a lot of time wasted in every research group. Research data need to be properly documented and maintained to ensure research integrity and research continuity. One easy solution is to nudge researchers towards better research data management practice could be formalised data management requirements. Perhaps as a minimum, every researchers should have a lab book to document research procedures.

The time is now: stop hypocrisy

Finally, there was a suggestion that everyone should take the lead in encouraging Open Research. The simplest way to start is to stop being what has been described as ‘a hypocrite’ and submit articles to journals which are fully Open Access. This should be accompanied by making one’s reviews openly available whenever possible. All publications should be accompanied by supporting research data and researchers should ensure that they evaluate individual research papers and that their judgement is not biased by the impact factor of the journal.

Need for greater awareness and interest in publishing

One of the Open Access advocates present at the meeting stated that most researchers are completely unaware of who are the exploitative and ethical publishers and the differences between them. Researchers typically do not directly pay the exploitative publishers and are therefore not interested in looking at the bigger picture of sustainability of scholarly publishing. This is clearly an area when more training and advocacy can help and the Office of Scholarly Communication is actively involved in raising awareness in Open Access. However, while it is nice to preach in a room of converts, how do we get other researchers involved in Open Access? How should we reach out to those who can’t be bothered to come to a discussion like the one we had? This is the area where anyone who understands the benefits Open Access has a job to do.

Next steps

We are extremely grateful to everyone who came to the event and shared their frustrations and ideas on how to solve some problems. We noted all the ideas on post it notes – the number of notes at the end of the discussion was impressive, an indication of how creative the participants were in just 90 minutes. It was a very productive meeting and we wish to thank all the participants for their time and effort.

20160608_160721

We think that by acting collaboratively and supporting good ideas we can achieve a lot. As an inspiration, McGill University’s Montreal Neurological Institute and Hospital (the Neuro) in Canada have recently adopted a policy on Open Research: over the next five years all results, publications and data will be free to access by everyone.

Follow up

If you would like to host similar discussions directly in your departments/institutes, please get in touch with us at info@osc.cam.ac.uk – we would be delighted to come over and hear from researchers in your discipline.

In the meantime, if you have any additional ideas that you wish to contribute, please send them to us. Everyone who is interested in being informed about the progress here is encouraged to sign up for a mailing distribution list here.

Extended notes from the meeting and slides are available at the Cambridge University Research Repository. We are particularly grateful to Avazeh Ghanbarian, Corina Logan, Ralitsa Madsen, Jenny Molloy, Ross Mounce and Alasdair Russell (listed alphabetically by surname) for agreeing to publicly speak at the event.

Published 3 August 2016
Written by Lauren Cadwallader, Joanna Jasiewicz and Marta Teperek
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The case for Open Research: reproducibility, retractions & retrospective hypotheses

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This is the third instalment of ‘The case for Open Research’ series of blogs exploring the problems with Scholarly Communication caused by having a single value point in research – publication in a high impact journal. The first post explored the mis-measurement of researchers and the second looked at issues with authorship.

This blog will explore the accuracy of the research record, including the ability (or otherwise) to reproduce research that has been published, what happens if research is retracted, and a concerning trend towards altering hypotheses in light of the data that is produced.

Science is thought to progress  through the building of knowledge through questioning, testing and checking work. The idea of ‘standing on the shoulders of giants’ summarises this – we discover truth by building on previous discoveries. But scientists are very rarely rewarded for being right, they are rewarded for publishing in certain journals and for getting grants. This can result in distortion of the science.

How does this manifest? The Nine Circles of Scientific Hell describes questionable research practices that occur, ranging from Overselling, Post-Hoc storytelling, p-value Fishing, Creative use of Outliers to Non or Partial Publication of Data. We will explore some of these below. (Note this article appears in a special issue of Perspectives on Psychological Science on the Replicability in Psychological Science – which contains many other interesting articles).

Much as we like to think of science as an objective activity it is not. Scientists are supposed to be impartial observers, but in reality they need to get grants, and publish papers to get promoted to more ‘glamorous institutions’. This was the observation of Professor Marcus Munafo in his presentation ‘Scientific Ecosystems and Research Reproducibility’ at the Research Libraries UK conference held earlier this year (the link will take you to videos of the presentations). Monafo observed that scientists are rarely rewarded for being right, so the scientific record is being distorted by the scientific ecosystem.

Monafo, a Biological Psychologist at Bristol University, noted that research, particularly in the biomedical sciences, ‘might not be as robust as we might have hoped‘.

The reproducibility crisis

A recent survey of over 1500 scientists by Nature tried to answer the question “Is there a reproducibility crisis?” The answer is yes, but whether that matters appears to be debatable: “Although 52% of those surveyed agree that there is a significant ‘crisis’ of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature.”

There are certainly plenty of examples of the inability to reproduce findings. Pharmaceutical research can be fraught. Some research into potential drug targets found that in almost two-thirds of the projects looked at, there were inconsistencies between published data and the data resulting from attempts to reproduce the findings. 

There are implications for medical research as well. A study published last month looked at functional MRI (fMRI), noting that when analysing data using different experimental designs they should in theory find a significance threshold of 5% (a p-value of less than 0.05  which is conventionally described as statistically significant). However they found “the most common software packages for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of up to 70%. These results question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results.”

A 2013 survey of cancer researchers found that approximately half of respondents had experienced at least one episode of the inability to reproduce published data. Of those people who followed this up with the original authors, most were unable to determine why the work was not reproducible. Some of those original authors were (politely) described as ‘less than “collegial”’.

So what factors are at play here? Partly it is due to the personal investment in a particular field. A 2012 study of authors of significant medical studies concluded that: “Researchers are influenced by their own investment in the field, when interpreting a meta-analysis that includes their own study. Authors who published significant results are more likely to believe that a strong association exists compared with methodologists.”

This was also a factor in a study Why Most Published Research Findings Are False that considered the way research studies are constructed. This work found that “for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias.”

Psychology is a discipline where there is a strong emphasis on novelty, discovery and finding something that has a p-value of less than 0.05. There is such an issue with reproducibility in psychology that there are large efforts to try and reproduce psychological studies to estimate the reproducibility of the research. The Association for Psychological Science has launched a new article type of Registered Replication Reports which consists of “multi-lab, high-quality replications of important experiments in psychological science along with comments by the authors of the original studies”.

This is a good initiative, although there might be some resistance to this type of scrutiny. Something that was interesting from the Nature survey on reproducibility was the question of what happened when researchers attempted to publish a replication study. Note that only a few of respondents had done this, possibly because incentives to publish positive replications are low and journals can be reluctant to publish negative findings. The study found that “several respondents who had published a failed replication said that editors and reviewers demanded that they play down comparisons with the original study”.

What is causing this distortion of the research? It is the emphasis on publication of novel results in high impact journals. There is no reward for publishing null results or negative findings.

HARKing problem

The p-value came up again in a discussion about HARKing at this year’s FORCE2016 conference (HARK stands for Hypothesising After the Results are Known – a term coined in 1998).

In his presentation at FORCE2016 Eric Turner, Associate Professor OHSU, spoke about HARKing (see this video 37 minutes onward).  The process is that the researcher conceives the study, writes the protocol up for their eyes only, with a hypothesis and then collects lots of other data – ‘the more the merrier’ according to Turner. Then the researcher runs the study and analyses the data. If there is enough data, the researcher can try alternative methods and can play with statistics. ‘You can torture the data and it will confess to anything’ noted Turner. At some point the p-value will come out below 0.05. Only then does the research get written up.

Turner noted that he was talking about the kind of research where the work is trying to confirm a hypothesis (like clinical trials). This is different to hypothesis-generating research.

In the US clinical trials with human participants must be registered with the Federal Drug Agency (FDA) so it is possible to see the results of all trials. Turner talked about his 2008 study looking at antidepressant trials, where the journal version of the results supported the general view that antidepressants always beat placebo.  However when they looked at the FDA version of all of the studies of the same drugs it happened that half of the studies were positive and half and half were not positive. The published record does not reflect the reality.

The majority of the negative studies were simply not published, but 11 of the papers had been ‘spun’ from negative to positive. These papers had a median impact factor of 5 and median citations of 68 – these were highly influential articles. As Turner noted ‘HARKing is deceptively easy’.

This perspective is supported by the finding that a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. Indeed Munafo noted that over 90% of the psychological literature finds what it purports to set out to do. Either the research being undertaken is extraordinarily mundane, or something is wrong.

Increase in retractions

So what happens when it is discovered that something that is published is incorrect? Journals do have a system which allows for the retraction of papers, and this is a practice which has been increasing over the past few years.  Research looking at why the number of retractions have increased found that it was partly due to lower barriers to publication of flawed articles. In addition papers are now being retracted for issues like plagiarism and retractions are now happening more quickly.

Retraction Watch is a service which tracks retractions ‘as a window into the scientific process’. It is enlightening reading with several stories published every day.

An analysis of correction rates in the chemical literature found that the correction rate averaged about 1.4 percent for the journals examined. While there were numerous types of corrections, chemical structures, omission of relevant references, and data errors were some of the most frequent types of published corrections. Corrections are not the same as retractions, but they are significant.

There is some evidence to show that the higher the impact factor of the journal a work is published in, the higher the chance it will be retracted. A 2011 study showed a direct correlation between impact factor and the number of retractions, with New England Journal of Medicine topping the list. This situation has led to claims that the top ranking journals publish the least reliable science.

A study conducted earlier this year demonstrated that there are no commonly agreed definitions of academic integrity and malpractice. (I should note that amongst other findings the study found 17.9% (± 6.1%) of respondents reported having fabricated research data. This is almost 1 in 5 researchers. However there have been some strong criticisms of the methodology.)

There are questions about how retractions should be managed. In the print era it was not unheard of for library staff to put stickers into printed journals notifying a retraction. But in the ‘electronic age’ asked one author in 2002 when the record can be erased, is this the right thing to do because erasing the article entirely is amending history.  The Committee on Publication Ethics (COPE) do have some guidelines for managing retractions which suggest the retraction be linked to the retracted article wherever possible.

However, from a reader’s perspective, even if an article is retracted this might not be obvious. In 2003* a survey of 43 online journals found 17 had no links between the original articles and later corrections. When present, hyperlinks between articles and errata showed patterns in presentation style, but lacked consistency. There are some good examples – such as Science Citation Index but there was a lack of indexing in INSPEC, and a lack of retrieval with SciFinder Scholar.

[*Note this originally said 2013, amended 2 September 2016]

Conclusion

All of this paints a pretty bleak picture. In some disciplines the pressure to publish novel results in high impact journals results in the academic record being ‘selectively curated’ at best. At worst it results in deliberate manipulation of results. And if mistakes are picked up there is no guarantee that this will be made obvious to the reader.

This all stems from the need to publish novel results in high impact journals for career progression. And when those high impact journals can be shown to be publishing a significant amount of subsequently debunked work, then the value of them as a goal for publication comes into serious question.

The next instalment in this series will look at gatekeeping in research – peer review.

Published 14 July 2016
Written by Dr Danny Kingsley
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Consider yourself disrupted – notes from RLUK2016

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The 2016 Research Libraries UK conference was held at the British Library from 9-11 March on the theme of disruptive innovation. This blog pulls out some of the highlights personally gained from the conference:

  • If librarians are to be considered important – we as a community need to be strong in our grasp of understanding scholarly communication issues
  • We need to know the facts about our subscriptions to, usage of and contributions to scholarly publishing
  • We need high level support in institutions to back libraries in advocacy and negotiation with publishers
  • Scientists are rarely rewarded for being right, so the scientific record is being distorted by the scientific ecosystem
  • Society needs more open research to ensure reproducibility and robust research
  • The library of the future will have to be exponentially more customisable than the current offering
  • The information seeking behaviour of researchers is iterative and messy and does not match library search services
  • Libraries need to ‘create change to triumph’ – to be inventors rather than imitators
  • Management of open access issues need to be shared across institutions with positive outcomes when research offices and libraries collaborate.

I should note this is not a comprehensive overview of the conference, and I have blogged separately about my own contribution ‘The value of embracing unknown unknowns’. Some talks were looking at the broader picture, others specifically at library practice.

Stand your ground – tips for successful publisher negotiations

The opening keynote presentation was by Professor Gerard Meijer, President of Radboud University who conducted the recent Dutch negotiations with Elsevier.

The Dutch position has been articulated by Sander Dekker, the State Secretary  of Education who said while the way forward was gold Open Access, the government would not provide any extra money. Meijer noted this was sensible because every extra cent going into the system goes into the pocket of publishers – something that has been amply demonstrated in the UK.

All universities in the Netherlands are in top 200 universities in the world. This means all research is good quality – so even if it is only 2% of the world output, the Netherlands has some clout.

Meijer gave some salient advice about these types of negotiations. He said this work needs to be undertaken at the highest level at the universities. There are several reasons for this. He noted that 1.5 to 2 percent of university budget goes to subscriptions – and this is growing as budgets are being cut – so senior leadership in institutions should take an active position.

In addition if you are not willing to completely opt out of licencing their material then you can’t negotiate, and if you are going to opt out you will need the support of the researchers. To that end communication is crucial – during their negotiations, they would send a regular newsletter to researchers letting them know how things were going.

Meijer also stressed the importance of knowing the facts, and the need to communicate and inform the researchers about these facts and the numbers. He noted that most researchers don’t know how much subscriptions cost. They do know however about article processing charges – creating a misconception that Open Access is more expensive.

Institutions in the Netherlands spent €9.2 billion million on Elsevier publications in 2009, which rose to €11billion million* in 2014. Meijer noted that he was ‘not allowed’ to tell us this information due to confidentiality clauses. He drolly observed “It will be an interesting court case to be sued for telling the taxpayers how their money is being spent”. He also noted that because Elsevier is a public company their finances are available, and while their revenue goes up, their costs stay the same.

Apparently Wiley and Springer are willing to go into agreements. However Elsevier are saying that a global business model doesn’t match with a local business requirement. The Netherlands  has not yet signed the contract with Elsevier as they are working out the detail.

Broadly the deal is for three years, from 2016 to 2018. The plan is to grow the Open Access output from nothing to 10% in 2016, 20% in 2017, 30% in 2018 and want to do that without having to pay APCs. To achieve this they have to identify journals that we make Open Access , by defining domains where all journals in these domains we make open access.

Meijer concluded this was a big struggle – he would have liked to have seen more – but what we have is good for science. Dutch research will be open in fields where most Open Access is happening and researchers are paying APCs. Researchers can look at the long list of journals that are OA and then publish there.

*CORRECTION: Apologies for my mistyping.  Thanks to    @WvSchaik for pointing out this error on Twitter. The slide is captured in this tweet.

The future of the research library

Nancy Fried Foster from Ithaka S+R and Kornelia Tancheva from Cornell University Library spoke about research practices and the disruption of the research library. They started by noting that researchers work differently now, using different tools. The objective of their ‘A day in the life of a serious researcher’ work was exploring research practices to inform the vision of library of the future and identify improvements we could make now.

They developed a very fine-grained method of seeing what people do which focuses on what people really do in the workplace. This used a participatory design approach. Participants (who were mainly post graduates) were asked to map or log their movements in one single day where at least some of their time was engaged in research. The team then sat with the person the following day to ask them to narrate their day – and talk about seeking, finding and using information. There was no distinction between academic and non-academic activity.

The team looked at the things that people were doing and the things that the library could and will be. The analysis took a lot of time, organising into several big categories:

  • Seeking information
  • Academic activities
  • Library resources
  • Space, self management and
  • Circum-academic activities – activities allied to the researchers academic line but not central.

They also coded for ‘obstacles’ and ‘brainwork’.

The participants described their information seeking as fluid and constant – ‘you can just assume I am kind of checking my email all the time’. They also distinguished between search and research. One quote was ‘I know the library science is very systematic and organised and human behaviour is not like that’.

Information seeking is an iterative process, it is constant and not systematic. The search process is highly idiosyncratic – our subjects have developed ways of searching for information that worked for them. It doesn’t matter if it is efficient or not. They are self conscious that it is messy. ‘I feel like the librarians must be like “this is the worst thing I have ever heard”’.

Information evaluation is multi-tiered – eg: ‘If an article is talking about people I have heard of it is worth reading’. Researchers often use a mash up of systems that will work for that project. For example email is used as an information management tool.

Connectivity is important to researchers, it means you can work anywhere and switch rapidly between tasks. It has a big impact on collaboration – working with others was continuously mentioned in the context of writing. However sometimes researchers need to eliminate technology to focus.

Libraries have traditionally focused too much on search and not enough on brain work – this is a potential role for libraries. References to the library occurred throughout the process. Libraries are often thought of as a place for refuge – especially for the much needed brain work. The need for self management – enable them to manage their time prioritise the demands on their attention. Strategies depended on a complicated relationship with technology.

One of the major themes emerging from the work is search is idiosyncratic and not important, research has no closure, experts rule and research is collaboration. The implications for the future library are that the future library is a hub, not just focusing on a discovery system but connecting people with knowledge and technologies.

If we were building a library from scratch today what would it look like? There will need to be a huge amount of customisation to adjust tools to suit researchers personal preferences. The library of the future will have to be exponentially more customisable than the current offering. Libraries will have to make available their resources on customisable platforms. We need to shift from non-interoperable tools to customisation.

So if the future were here today we would think of future library – an academic hub (improving current library services) and an application store. We should take on even more of a social media aspect. Think of a virtual ‘app store’ – on an open source platform that provides the option for people to suggest short cuts – employ developers to develop these modules quickly. Take a leadership role in ensuring vendor platforms can be integrated. All library resources will speak easily to the systems our users are using. We need to provide individualised services rather than one size fits all.

Scientific Ecosystems and Research Reproducibility

The scientific reward structure determines the behaviour of researchers and that this has spawned the reproducibility crisis according to Marcus Munafo from the University of Bristol.

Marcus started by talking about the P value where the statistically significant value is 95% – that is, the chance of the hypothesis being wrong is less than five in 100. Generally, studies need to cross this threshold to get published, so there is evidence to show that original studies often suggest a large effect – however when attempted, these effects are not able to be replicated.

Scientists are supposed to be impartial observers, but in reality they need to get grants, and publish papers to get promoted to more ‘glamorous institutions’ (Marcus’ words). Scientists are rarely rewarded for being right, so the scientific record is being distorted by the scientific ecosystem.

Marcus noted it is common to overstate your data or error check your data if your first analysis doesn’t tell you what you are looking for. This ‘flexible analysis’ is quite commonplace, if we look at literature as a whole. Often there is not enough detail in the paper to allow the reproducibility of the work. There are nearly as many unique analysis pipelines as there were studies in the sample – so this flexibility in the joint analysis tool gets leveraged to get the result you want.

There is also evidence that journal impact factors are a very poor indicator of quality, indeed it is a stronger indicator of retraction than quality. The idea is that the whole science will self correct. But science won’t sort itself out in a reasonable timeframe. If you look at the literature you see that replication is the exception rather than the norm.

One study showed among 83 articles recommending effective interventions, 40 had not been replicated, and of those that had been replicated many showed the works had stronger findings in the first paper than in the replication, and some were contradicted in the replication.

Your personal investment in the field shapes your position – unconscious biases that affects all of us. If you come in as an early career scientist you get an impression that the field is more robust than it is in reality. There is hidden literature that is not citable – only by looking at this you have a balanced sense of how robust the literature is. There are many studies that make a claim in the abstract that is not supported by more impartial reading. Others are ‘optimistic’ in the abstract. The articles that describe bad news receive far fewer citations than would be expected. People don’t want to cite bad news. So is science self correcting?

We can introduce measures to help science self correct. In 2000 the requirement to register the outcome of clinical trials began. Once they had to pre-specify what the outcome would be then most of the findings were null. That is why it is a scientific ecosystem – the way we are incentivised has become distorted over the years.

Researchers are incentivised to produce a small number of papers that are eye catching.  It is understandable why you would want to focus on quality over quantity. We can give more weight to confirmatory studies and try to move away from the focus on publishing in certain types of studies. We shouldn’t be putting all our effort into high risk, high return.

What do we do about this? There can be top down measures, but individual groups can work in ways to improve the ways we work, such as adopting the open science way of working. This is not trivial – for example we can’t make data available without the consent of participants. Possible solutions include pre-registering all the plans, set up studies so the data can be made open, ensure publications are gold OA. These measures serve as a quality control method because everything gets checked because people know it is going to be made available. We come down hard on academics who make conscious mistakes – but we should be encouraging people to identify their own errors.

We need to introduce quality control methods implicitly into our daily practice. Open data is a very good step in that direction. There is evidence that researchers who know their data is going to be made open are more thorough in their checking of it. Maybe it is time for an update in the way we do science – we have statistical software that can run hundreds of analysis, and we can do text and data mining of lots of papers. We need to build in new processes and systems that refine science and think about new ways of rewarding science.

Marcus noted that these are not new problems, quoting from Reflections on the Decline of Science in England written by Babbage in 1830.

Marcus referred to many different studies and articles in his talk, some of which I have linked out to here:

Creating change to triumph: A view from Australia

The idea of creating change to triumph was the message of Jill Benn, the Librarian at the University of Western Australia. She discussed Cambietics, the science of managing change. This was a theory developed in 1985 by Barrett, with three stages:

  • Coping with change to survive
  • Capitalising on change
  • Creating change to triumph.

This last is the true challenge – to be an inventor rather than an imitator. Jill gave the Australian context. The country is 32 times bigger than UK, but has a third of the population, with 40 universities around the country. She noted that one of the reasons libraries in Australia have collaborated is the isolation.

Research from Australia counts for 4% of the world’s research output, it is the third largest export after energy, and out-performs tourism. The political landscape really affects higher education. There has been a series of five prime ministers in five years.

Australia has invested heavily in research infrastructure – mostly telescopes and boats. The Australian National Data Service was created and this has built the Research Data Australia interface – an amazing system full of data. The libraries have worked with researchers to populate the repository. There has been a large amount of capacity building. ANDS worked with libraries to build the capacities – the ’23 things’ training programme. You self register – on 1 March, 840 people had signed up for the programme.

The most recent element of the government’s agenda has been innovation. Prime Minister Turnbull has said he wanted to end the ‘publish or perish’ culture of research to increase the impact on community. There is a national innovation and science agenda and the government would not longer take into account publications for research. It is likely the next ERA (Australia’s equivalent of the REF) will involve impact in the community. The latest call is “innovation is the new black”.

There is financial pressure on the University sector – which pays in US dollars which is a problem. The emphasis on efficiency means the libraries have to show value and impact to the research sector.

Many well-developed services exist in university libraries to support research. Australian institutional repositories now have over 650K full text items, which are downloaded over 1 million times annually, there are data librarians and scholarly communication librarians. Some of the ways in which libraries have been asked to deliver capacity is CAUL and its Research Advisory Committee – to engage in the government’s agenda. There are three pillars – capacity building, engagement and advocacy, to promote the work of our libraries to bodies like Universities Australia.

Jill also mentioned the Australasian Open Access Strategy Group which has had a green rather than a gold approach. Australians are interested in open access. It is not yet clear what our role will be of institutional repositories into the future. In an environment where the government wants us to share our research.

How can we benchmark the Australian context? It is difficult. Look at our associations and about what data we might be able to share. Quote from Ross Wilkinson – yes there are individuals but the collective way Australia has managed data we are better able to engage internationally. Despite the investment into repositories in Australia – the UK outperforms Australia.

Australian libraries see themselves as genuine partners for research and we have a healthy self confidence (!). Libraries must demonstrate value and impact and provide leadership. Australian libraries have created change to triumph.

Open access mega-journals and the future of scholarly communication

This talk was given by Professor Stephen Pinfield from Sheffield University. He talked about the Open Access Mega Journal project he is working on with potentially disruptive open access journals (the Twitter handle is @oamj_project).

He began where it all began – with PLOS ONE, which is now the biggest journal in the world. Stephen noted that mega journals are full of controversy, listing comments ranging from them being the future of academic publishing, a disruptive innovation to the best possible future system.

However critics see them variously as a dumping ground, career suicide for early career researchers publishing in them and a cynical money making venture. However, Pinfield noted that despite considerable searching acknowledging what ‘people say’ is different from being able to provide attributed negative statements about mega-journals.

The open access and wide scope nature of mega-journals reverses the trend over past few years where journals have been further specialising, They are identifiable by their approach to quality control, with an emphasis on scientific soundness only rather than subjective assessments of novelty and also by their post publication metrics.

Pinfield noted that there are economies of scale for mega journals – this means that we have single set of processes and technologies. This enables a tiered scholarly publishing system. Mega-journals potentially allow highly selective journals to go open access (who often argue that they reject so much they couldn’t afford to go OA). Pinfield hypothesised that a business model could be where a layer of highly selective titles sits above a layer of moderately selective mega journals. The moderately selective journals provide the financial subsidy but the highly selective ones provide the reputational subsidy. PLOS is a good example of this symbiotic relationship.

The emphasis on ‘soundness’ in the quality control process reduces the subjectivity of judgements of novelty and importance and potentially shifts the role and the power of the gatekeepers. Traditionally the editors and editorial board members have been the arbiters of what is novel.

However this opens up some questions. If it is only a ‘soundness’ judgement then the question is whether power is shifted for good or ill? Also does the idea of ‘soundness’ translate to the Humanities? There is also the problem of an overreliance on metrics. Are the citation values of journals driven by the credibility or the visibility of the journals?

Pinfield emphasised the need for librarians to be informed and credible about their understanding of these topics. If librarians are to be considered important – we as a community need to be strong in our grasp of understanding these issues. There is an ongoing need to keep up to date and remain credible.

Working together to encourage researcher engagement and support

There were several talks about how institutions have been engaging researchers, and many of them emphasised the need to federate the workload across the institution. Chris Aware from the University of Hull discussed some work he was doing with Valerie McCutcheon on the current interaction between library and other parts of the institution in supporting OA, understand how OA is and could be embedded.

The survey revealed a desire for the management of Open Access to be more spread across the institution into the future. Libraries should be more involved in the management of the research information system and managing the REF. However Library involvement in getting Open Access into grant applications is lower – this is a research role, but it is worth asking how much this underpins subsequent activity.

As an aside Chris noted a way of demonstrating the value of something is to call it an ‘office’ – this is something the Americans do. (Indeed it is something Cambridge has done with the Office of Scholarly Communication).

Chris noted that if researchers don’t think about open access as part of the scholarly communications workflow then they won’t do it. Libraries play a key role in advocating and managing OA – so how can they work with other institutional stakeholders in supporting research?

Valerie later spoke about blurring and blending the borders between the Library and the Research Office. She noted that when she was working for Research and Enterprise (RSEO) she thought library people were nice, but she was not sure what the people do there. When she transferred to working in the Library, the perception back the other way was the same.

But the Research Office and the Library need to cooperate on shared strategic priorities. They are both looking out for changes in policy landscape they need to share information and collaborate on policy development and dissemination. They need better data quality in the research process to find solutions to create agile systems to support researchers.

At Glasgow the Library & RSEO were a good match because they had similar end uses and the same data. So this began a close collaboration between the two offices which worked together on the REF, used Enlighten. They also linked their systems (Enlighten and Research Systems) in 2010 where users can browse in the repository by the funder name. Glasgow has had a publications policy rather than an open access policy since 2008.

Valerie also noted that it was crucial to have high-level support and showed a video of Glasgow’s PVC-R singing the praises of the work the Library was doing.

The Glasgow Open Access model has been ‘Act on acceptance’ since 2013 – a simple message with minimal bureaucracy. A centralised service with ‘no fancy meetings’. Valerie also noted that when they put events on they don’t say it is a Library event, the sessions subject based not department based.

Torsten Reimer and Ruth Harrison discussed the support offered at Imperial College, where Torsten said he was originally employed for developing the College’s OA mandate but then the RCUK and the HEFCE policy came into place and changed everything. At Imperial, scholarly communications is seen as an overall concern for the College rather than specifically a Library issue.

Torsten noted the Library already had a good relationship with the departments. The Research Office is seen by researchers as a distraction from their research, but the Library is seen as helping research. However because the two areas have been able to approach everything with one single aim, this has allowed open access and scholarly support to happen across the institution and allowed the library to expand.

Imperial have one workflow and one system for open access which is all managed through Symplectic (there had been separate systems before). They have a simple workflow and form to fill in, then have a ticketing type customer workflow system plugged into Symplectic to pull information out at the back end. This system has replaced four workflows, lots of spreadsheets and much cut and pasting.

Sally Rumsey talked about how Oxford have successfully managed to engage their research community with their recently launched ‘Act on Acceptance’ communication programme.

Summary

This is a rundown of a few of the presentations that spoke to me. There were also excellent speed presentations, Lord David Willetts, the former Minister for Universities and Science spoke, we split up into workshops and there was a panel of library organisations around the world who discussed working together.

The personal outcomes from the conference include:

  • An invitation to give a talk at Cornell University
  • An invitation to collaborate with some people at CILIP about ensuring scholarly communication is included in some of the training offered
  • Discussion about forming some kind of learned society for Scholarly Communication
  • Discussion about setting up a couple of webinars – ‘how to start up an office of scholarly communication’ and ‘successful library training programmes’
  • Also lots of ideas about what to do next – the issue of language and the challenges we are facing in scholarly communication because of language deserves some investigation.

I look forward to next year.

Published 14 March 2016
Written by Dr Danny Kingsley
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Software Licensing and Open Access

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As part of the Office of Scholarly Communication Open Access Week celebrations, we are uploading a blog a day written by members of the team. Wednesday is a piece by Dr Marta Teperek reporting on the Software Licensing Workshop held on 14 September 2015 at Cambridge.

Uncertainties about sharing and licensing of software

If the questions that the Research Data Service Team have been asked during data sharing information sessions with over 1000 researchers at the University of Cambridge are any indicator, then there is a great deal of confusion about sharing source code.

There have been a wide range of questions during the discussions in these sessions, and the Research Data Service Team has recorded these. We are systematically ensuring that the information we are providing to our research community is valid and accurate. To address the questions about source code we decided to call in expert help. Shoaib Sufi and Neil Chue Hong* from the Software Sustainability Institute agreed to lead a workshop on Software Licensing in September, at the Computer Lab in Cambridge. Shoaib’s slides are here, and Neil’s slides on Open Access policies and software sharing are here.

Malcolm Grimshaw and Chris Arnot from Cambridge Enterprise also came to the workshop to answer questions about Cambridge-specific guidance on software commercialisation.

We had over 50 researchers and several research data managers from other UK universities attending the Software Licensing workshop. The main questions we were trying to resolve was: Are researchers expected to share source code they used in their projects? And if so, under what conditions?

Is software considered as ‘research data’ and does it need to be shared?

The starting question in the discussion was whether software needed to be shared. Most public funders now require that research data underpinning publications is made available. What is the definition of research data? According to the EPSRC research data “is defined as recorded factual material commonly retained by and accepted in the scientific community as necessary to validate research findings”. Therefore, if software is needed to validate findings described in a publication, researchers are expected to make it available as widely as possible. There are some exceptions to this rule. For example, if there is an intention to commercialise the software there might not be a need to share it, but the default assumption is that the software should be shared.

The importance of putting a licence on software

It is important that before any software is shared, the creator considers what they would like others to be able to do with it. The way to indicate the intended reuse of the software is to place a licence on it. This governs the permission being granted to others with regards to source code by the copyright holder(s). A licence determines whether the person who wants to get hold of software is allowed to use, copy, resell, change, or distribute it. Additionally, a licence should also determine who is liable if something goes wrong with the software.

Therefore, a licence not only protects the intellectual property, but also helps others to use the software effectively. If people who are potentially interested in a given piece of software do not know what they are allowed to do with it, it is possible they will search for alternative solutions. As a consequence, researchers could lose important collaborators, buyers, or simply decrease the citation rate that could have been gained from people using and citing software in their publications.

Who owns the copyright?

The most difficult question when it comes to software licensing is determining who owns the copyright – who is allowed to license the software used in research? If this is software created by a particular researcher then it is likely that s/he will be the copyright owner. At the University of Cambridge researchers are the primary owners of intellectual property. This is however a very generous right – typically employers do not allow their employees to retain copyright ownership. Therefore, the issue of copyright ownership might get very complicated for researchers involved in multi-institutional collaborations. Additionally, sometimes funders of research will retain copyright ownership of research outputs.

Consequences of licensing

An additional complication with licensing software is that most licences cannot be revoked. Once something has been licensed to someone under a certain licence, it is not possible to take it back and change the licence. Moreover, if there is one licence for a set of software, it might not be possible to license a patch to the software under a different licence. The issue of licence compatibility sparked a lot of questions during the workshop, with no easy answers available. The overall conclusion was that whenever possible, mixing of licences should be avoided. If use of various licences is necessary, researchers are recommended to get advice from the Legal Services Office.

Good practice for software management

So what are the key recommendations for good practice for software management? Before the start of a research project, researchers should think about who the collaborators and funders are, and what the employer’s expectations are with regards to intellectual property. This will help to determine who will own the copyright over the software. Funders’ and institutional policies for research data sharing should be consulted for expectations about software sharing With this information it is possible to prepare a data management plan for the grant application.

During the project researchers need to ensure that their software is hosted in an appropriate code repository – for example, GitHub or Bitbucket. It is important to create (and keep updating!) metadata describing any generated data and software.

Finally, when writing a paper, researchers need to deposit all releases of data/software relevant to the publication in a suitable repository. It is best to choose a repository which provides persistent links e.g. Zenodo (which has a GitHub integration), or the University of Cambridge data repository (Apollo). It is important to ensure that software is licensed under an appropriate licence – in line with what others should be allowed to do with the software, and in agreement with any obligations there might be with any other third parties (for example, funders of the research). If there is a need to restrict the access to the software, metadata description should give reasons for this restriction and conditions that need to be met for the access to be granted.

Valuable resources to help make right decisions

Both Neil and Shoaib agreed that proper management and licensing of software might be sometimes complicated. Therefore, they recommended various resources and tools to provide guidance for researchers:

The workshop was organised in collaboration with Stephen Eglen from the Department of Applied Mathematics and Theoretical Physics (University of Cambridge) who chaired the meeting, and with Andrea Kells from the Computer Lab (University of Cambridge) who hosted the workshop.

The Research Data Service is also providing various other opportunities for our research community to pose questions directly of the funding bodies. We invited Ben Ryan from the EPSRC to come to speak to a group of researchers in May and the resulting validated FAQs are now published on our research data management website. Similarly, researchers met with Michael Ball from the BBSRC in August.

These opportunities are being embraced by our research community.

*About the speakers

Shoaib Sufi – Community Lead at the Software Sustainability Institute

Shoaib leads the Institute’s community engagement activities and strategies. Graduating in Computer Science from the University of Manchester in 1997, he has worked in the commercial sector as a systems programmer and then as software developer, metadata architect and eventually a project manager at the Science and Facilities Technologies Council (STFC).

Neil Chue Hong – Director at the Software Sustainability Institute

Neil is the founding Director of the Software Sustainability Institute. Graduating with an MPhys in Computational Physics from the University of Edinburgh, he began his career at EPCC, becoming Project Manager there in 2003. During this time he led the Data Access and Integration projects (OGSA-DAI and DAIT), and collaborated in many e-Science projects, including the EU FP6 NextGRID project.

Published 21 October 2015
Written by Dr Marta Teperek
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