Guest post by Andrew Gray, Bibliometrics Support Officer

This panel discussion at the 2025 Open Science and Scholarship Festival was made up of three professionals with expertise in different aspects of publishing and scholarly writing, across different sectors – Ayanna Prevatt-Goldstein, from the UCL Academic Communication Centre focusing on student writing; Rachel Safer, the executive publisher for ethics and integrity at Oxford University Press, and also an officer of the Committee on Publication Ethics, with a background in journal publishing; and Dhara Snowden, from UCL Press, with a background in monograph and textbook publishing.

We are very grateful to everyone who attended and brought questions or comments to the session.

This is a summary of the discussion from all three panel members, and use of any content from this summary should be attributed to the panel members. If you wish to cite this, please do so as A. Prevatt-Goldstein, R. Safer & D. Snowden (2025). Authorship in the Era of AI. [https://blogs.ucl.ac.uk/open-access/2025/07/09/authorship-in-the-era-of-ai/]

Where audience members contributed, this has been indicated. We have reorganised some sections of the discussion for better flow.

The term ‘artificial intelligence’ can mean many things, and often a wide range of different tools are grouped under the same general heading. This discussion focused on ‘generative AI’ (large language models), and on their role in publishing and authorship rather than their potential uses elsewhere in the academic process.

Due to the length of this write-up, you can directly access each question using the following links:
1. There is a growing awareness of the level of use of generative AI in producing scholarly writing – in your experience, how are people currently using these tools, and how widespread do you think that is? Is it different in different fields? And if so, why?

2. Why do you think people are choosing to use these tools? Do you think that some researchers – or publishers – are feeling that they now have to use them to keep pace with others?

3. On one end of the spectrum, some people are producing entire papers or literature reviews with generative AI. Others are using it for translation, or to generate abstracts. At the other end, some might use it for copyediting or for tweaking the style. Where do you think we should draw the line as to what constitutes ‘authorship’?

4. Do you think readers of scholarly writing would draw the line on ‘authorship’ differently to authors and publishers? Should authors be expected to disclose the use of these tools to their readers? And if we did – is that something that can be enforced?

5. Do you think ethical use of AI will be integrated into university curriculums in the future? What happens when different institutions have different ideas of what is ‘ethical’ and ‘responsible’?

6. Many students and researchers are concerned about the potential for being falsely accused of using AI tools in their writing – how can we help people deal with this situation? How can people assert their authorship in a world where there is a constant suspicion of AI use?

7. Are there journals which have developed AI policies that are noticeably more stringent than the general publisher policies, particularly in the humanities? How do we handle it if these policies differ, or if publisher and institutional policies on acceptable AI use disagree?

8. The big AI companies often have a lack of respect for authorship, as seen in things like the mass theft of books. Are there ways that we can protect authorship and copyrights from AI tools?

9. We are now two and a half years into the ‘ChatGPT era’ of widespread AI text generation. Where do you see it going for scholarly publishing by 2030?

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1. There is a growing awareness of the level of use of generative AI in producing scholarly writing – in your experience, how are people currently using these tools, and how widespread do you think that is? Is it different in different fields? And if so, why?

Among researchers, a number of surveys by publishers have suggested that 70-80% of researchers are using some form of AI, broadly defined, and a recent Nature survey suggested this is fairly consistent across different locations and fields. However, there was a difference by career stage, with younger researchers feeling it was more acceptable to use it to edit papers, and by first language, where non-English speakers were more likely to use it for this as well.

There is a sense that publishers in STEM fields are more likely to have guidance and policy for the use of AI tools; in the humanities and social sciences, this is less well developed, and publishers are still in the process of fact-finding and gathering community responses. There may still be more of a stigma around the use of AI in the humanities.

In student writing, a recent survey from HEPI found that from 2024 to 2025, the share of UK undergraduates who used generative AI for generating text had gone from a third of students to two thirds, and only around 8% said they did not use generative AI at all. Heavier users included men, students from more advantaged backgrounds, and students with English as a second or additional language.

There are some signs of variation by discipline in other research. Students in fields where writing is seen as an integral part are more concerned with developing their voice and a sense of authorship, and are less likely to use it for generating text – or at least are less likely to acknowledge it – and where they do, they are more likely to personalise the output. By comparison, students in STEM subjects are more likely to feel that they were being assessed on the content – the language they use to communicate it might be seen as less important.

[For more on this, see A. Prevatt-Goldstein & J. Chandler (forthcoming). In my own words? Rethinking academic integrity in the context of linguistic diversity and generative AI. In D. Angelov and C.E. Déri (Eds.), Academic Writing and Integrity in the Age of Diversity: Perspectives from European and North American Higher Education. Palgrave.)]

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2. Why do you think people are choosing to use these tools? Do you think that some researchers – or publishers – are feeling that they now have to use them to keep pace with others?

Students in particular may be more willing to use it as they often prioritise the ideas being expressed over the mode of expressing them, and the idea of authorship can be less prominent in this context. But at a higher level, for example among doctoral students, we find that students are concerned about their contribution and whether perceptions of their authorship may be lessened by using these tools.

A study among publishers found that the main way AI tools were being used was not to replace people at specific tasks, but to make small efficiency savings in the way people were doing them. This ties into the long-standing use of software to assist copyediting and typesetting.

Students and academics are also likely to see it from an efficiency perspective, especially among those who are becoming used to working with generative AI tools in their daily lives, and so are more likely to feel comfortable using it in academic and professional contexts. Academics may feel pressure to use tools like this to keep up a high rate of publication. But the less involvement of time in a particular piece of work might be a trade-off of time spent against quality; we might also see trade-offs in terms of the individuality and nuance of the language, of fewer novel and outlier ideas being developed, as generative AI involvement becomes more common.

Ultimately, though, publishers struggle to monitor researchers’ use of generative AI in their original research – they are dependent on institutions training students and researchers, and on the research community developing clearer norms, and perhaps there is also a role for funders to support educating authors about best practices.

Among all users, a significant – and potentially less controversial – role for generative AI is to help non-native English speakers with language and grammar, and to a more limited degree translation – though quality here varies and publishers would generally recommend that any AI translation should be checked by a human specialist. However, this has its own costs.

With English as a de facto academic lingua franca, students (and academics) who did not have it as a first language were inevitably always at a disadvantage. Support for this could be found – perhaps paying for help, perhaps friends or family or colleagues who could support language learning – but this was very much support that was available more to some students than others, due to costs or connections, and generative AI tools have the potential to democratise this support to some degree. However, this causes a corresponding worry among many students that the bar has been raised – they feel they are now expected to use these tools or else they are disadvantaged compared to their peers.

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3. On one end of the spectrum, some people are producing entire papers or literature reviews with generative AI. Others are using it for translation, or to generate abstracts. At the other end, some might use it for copyediting or for tweaking the style. Where do you think we should draw the line as to what constitutes ‘authorship’?

In some ways, this is not a new debate. As we develop new technologies which change the way we write – the printing press, the word processor, the spell checker, the automatic translator – people have discussed how it changes ‘authorship’. But all these tools have been ways to change or develop the words that someone has already written; generative AI can go far beyond that, producing vastly more material without direct involvement beyond a short prompt.

A lot of people might treat a dialogue with generative AI, and the way they work with those outputs, in the same way as a discussion with a colleague, as a way to thrash out ideas and pull them together. We have found that students are seeing themselves shifting from ‘author’ to ‘editor’, claiming ownership of their work through developing prompts and personalising the output, rather than through having written the text themselves. There is still a concept of ownership, a way of taking responsibility for the outcome, and for the ideas being expressed, but that concept is changing, and it might not be what we currently think of as ‘authorship’.

Sarah Eaton’s work has discussed the concept of ‘Post-plagiarism’ as a way to think about writing in a generative AI world, identifying six tenets of post-plagiarism. One of those is that humans can concede control, but not responsibility; another is that attribution will remain important. This may give us a useful way to consider authorship.

In publishing, ‘authorship’ can be quite firmly defined by the criteria set by a specific journal or publisher. There are different standards in different fields, but one of the most common is the ICMJE definition which sets out four requirements to be considered an author – substantial contribution to the research; drafting or editing the text; having final approval; and agreeing to be accountable for it. In the early discussions around generative AI tools in 2022, there was a general agreement that these could never meet the fourth criteria, and so could never become ‘authors’; they could be used, and their use could be declared, but it did not conceptually rise to the level of authorship as it could not take ownership of the work.

The policy that UCL Press adopted, drawing on those from other institutions, looked at ways to identify potential responsible uses, rather than a blanket ban – which it was felt would lead to people simply not being transparent when they had used it. It prohibited ‘authorship’ by generative AI tools, as is now generally agreed; it required that authors be accountable, and take responsibility for the integrity and validity of their work; and it asked for disclosure of generative AI.

Monitoring and enforcing that is hard – there are a lot of systems claiming to test for generative AI use, but they may not work for all disciplines, or all kinds of content – so it does rely heavily on authors being transparent about how they have used these tools. They are also reliant on peer reviewers flagging things that might indicate a problem. (This also raises the potential of peer reviewers using generative AI to support their assessments – which in turn indicates the need for guidance about how they could use it responsibly, and clear indications on where it is or is not felt to be appropriate.)

Generative AI potentially has an interesting role to play in publishing textbooks, which tend to be more of a survey of a field than original thinking, but do still involve a dialogue with different kinds of resources and different aspects of scholarship. A lot of the major textbook platforms are now considering ways in which they can use generative AI to create additional resources on top of existing textbooks – test quizzes or flash-cards or self-study resources.

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4. Do you think readers of scholarly writing would draw the line on ‘authorship’ differently to authors and publishers? Should authors be expected to disclose the use of these tools to their readers? And if we did – is that something that can be enforced?

There is a general consensus emerging among publishers that authors should be disclosing use of AI tools at the point of submission, or revisions, though where the line is drawn there varies. For example, Sage requires authors to disclose the use of generative AI, but not ‘assistive’ AI such as spell-checkers or grammar checkers. The STM Association recently published a draft set of recommendations for using AI, with nine classifications of use. (A commenter in the discussion also noted a recent proposed AI Disclosure Framework, identifying fourteen classes.)

However, we know that some people, especially undergraduates, spend a lot of time interacting with generative AI tools in a whole range of capacities, around different aspects of the study and writing process, which can be very difficult to define and describe – there may not be any lack of desire to be transparent, but it simply might not fit into the ways we ask them to disclose the use of generative AI.

There is an issue about how readers will interpret a disclosure. Some authors may worry that there is a stigma attached to using generative AI tools, and be reluctant to disclose if they worry their work will be penalised, or taken less seriously, as a result. This is particularly an issue in a student writing context, where it might not be clear what will be done with that disclosure – will the work be rejected? Will it be penalised, for example a student essay losing some marks for generative AI use? Will it be judged more sceptically than if there had been no disclosure? Will different markers, or editors, or peer-reviewers make different subjective judgements, or have different thresholds?

These concerns can cause people to hesitate before disclosing, or to avoid disclosing fully. But academics and publishers are dependent on honest disclosure to identify inappropriate use of generative AI, so may need to be careful in how they frame this need to avoid triggering these worries about more minor use of generative AI. Without honest disclosure, we also have no clear idea of what writers are using AI for – which makes it all the harder to develop clear and appropriate policies.

For student writing, the key ‘reader’ is the marker, who will also be the person to whom generative AI use is disclosed. But for published writing, once a publisher has a disclosure of AI use, they may need to decide what to pass along to the reader. Should readers be sent the full disclosure, or is that overkill? It may include things like idea generation, assistance with structure, or checking for more up-to-date references – these might be useful for the publisher to know, but might not need to be disclosed anywhere in the text itself. Conversely, something like images produced by generative AI might need to be explicitly and clearly disclosed in context.

The recent Nature survey mentioned earlier showed that there is no clear agreement among academics as to what is and isn’t acceptable use, and it would be difficult for publishers to draw a clear line in that situation. They need to be guided by the research community – or communities, as it will differ in different disciplines and contexts.

We can also go back to the pre-GenAI assumptions about what used to be expected in scholarly writing, and consider what has changed. In 2003, Diane Pecorari identified the three assumptions for transparency in authorship:

1. that language which is not signaled as quotation is original to the writer;
2. that if no citation is present, both the content and the form are original to the writer;
3. that the writer consulted the source which is cited.

There is a – perhaps implicit – assumption among readers that all three of these are true unless otherwise disclosed. But do those assumptions still hold among a community of people – current students – who are used to the ubiquitous use of generative AI? On the face of it, generative AI would clearly break all three.

If we are setting requirements for transparency, there should also be consequences for breach of transparency – from a publisher’s perspective, if an author has put out a generative AI produced paper with hallucinated details or references, the journal editor or publisher should be able to investigate and correct or retract it, exactly as would be the case with plagiarism or other significant issues.

But there is a murky grey area here – if a paper is otherwise acceptable and of sufficient quality, but does not have appropriate disclosure of generative AI use, would that in and of itself be a reason for retraction? At the moment, this is not on the COPE list of reasons for retraction – it might potentially justify a correction or an editorial note, but not outright retraction.

Conversely, in the student context, things are simpler – if it is determined that work does not belong to the student, whether that be through use of generative AI or straightforward plagiarism, then there are academic misconduct processes and potentially very clear consequences which follow from that. These do not necessarily reflect on the quality of the output – what is seen as critical is the authorship.

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5. Do you think ethical use of AI will be integrated into university curriculums in the future? What happens when different institutions have different ideas of what is ‘ethical’ and ‘responsible’?

A working group at UCL put together a first set of guidance on using generative AI in early 2023, and focused on ethics in the context of learning outcomes – what is it that students are aiming to achieve in their degree, and will generative AI help or not in that process? But ethical questions also emerged in terms of whose labour had contributed to these tools, what the environmental impacts where, and importantly whether students were able to opt out of using generative AI. There are no easy answers to any of these, but they very much are ongoing questions.

Recent work from MLA looking at AI literacies for students is also informative here in terms of what it expects students using AI to be aware of.

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6. Many students and researchers are concerned about the potential for being falsely accused of using AI tools in their writing – how can we help people deal with this situation? How can people assert their authorship in a world where there is a constant suspicion of AI use?

There was no easy answer here and a general agreement that this is challenging for everyone – it can be very difficult to prove a negative. Increasing the level of transparency around disclosing AI use – and how much AI has been used – will help overall, but maybe not in individual cases.

Style-based detection tools are unreliable and can be triggered by normal academic or second-language writing styles. A lot of individuals have their own assumptions as to what is a ‘clear marker’ of AI use, and these are often misleading, leading to false positives and potentially false accusations. Many of the plagiarism detection services have scaled back or turned off their AI checking tools.

In publishing, a lot of processes have historically been run on a basis of trust – publishers, editors, and reviewers have not fact-checked every detail. If you are asked to disclose AI use and you do not, the system has to trust you did not use it, in the same way that it trusts you obtained the right ethical approvals or that you actually produced the results you claim. Many publishers are struggling with this, and feeling that they are still running to catch up with recent developments.

In academia, we can encourage and support students to develop their own voice in their writing. This is a hard skill to develop, and it takes time and effort, but it can be developed, and it is a valuable thing to have – it makes their writing more clearly their own. The growth of generative AI tools can be a very tempting shortcut for many people to try and get around this work, but there are really no shortcuts here to the investment of time that is needed.

There was a discussion of the possibility of authors being more transparent with their writing process to help demonstrate research integrity – for example, documenting how they select their references, in the way that systematic review does, or using open notebooks? This could potentially be declared in the manuscript, as a section alongside acknowledgements and funding. Students could be encouraged to keep logs of any generative AI prompts they have used and how they are handling them, to be able to disclose this in case of concerns.

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7. Are there journals which have developed AI policies that are noticeably more stringent than the general publisher policies, particularly in the humanities? How do we handle it if these policies differ, or if publisher and institutional policies on acceptable AI use disagree?

There are definitely some journals that have adopted more restrictive policies than the general guidance from their publisher, mostly in the STEM fields. We know that many authors may not read the specific author guidelines for a journal before submitting. Potentially we could see journals highlighting these restrictions in the submission process, and requiring the authors to acknowledge they are aware of the specific policies for that journal.

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8. The big AI companies often have a lack of respect for authorship, as seen in things like the mass theft of books. Are there ways that we can protect authorship and copyrights from AI tools?

A substantial issue for many publishers, particularly smaller non-commercial ones, is that so much scholarly material is now released under an open-access license that makes it easily available for training generative AI; even if the licenses forbid this, it can be difficult in practice to stop it, as seen in trade publishing. It is making authors very concerned, as they do not know how or where their material will be used, and feel powerless to prevent it.

One potential way forward is by reaching agreements between publishers and AI companies, making agreements on licensing material and ensuring that there is some kind of renumeration. This is more practical for larger commercial publishers with more resources. There is also the possibility of sector-wide collective bargaining agreements, as has been seen with the Writers Guild of America, where writers were able to implement broader guardrails on how their work would be used.

It is clear that the current system is not weighted in favour of the original creators, and some form of compensation would be ideal, but we also need to be careful that any new arrangement doesn’t continue to only benefit a small group.

The issue of Creative Commons licensing regulating the use of material for AI training purposes was discussed – Creative Commons take the position that this work may potentially be allowed under existing copyright law, but they are investigating the possibility of adding a way to signal the author’s position. AI training would be allowed by most of the Creative Commons licenses, but might require specific conditions on the final model (eg displaying attribution or non-commercial restrictions).

A commenter in the discussion also mentioned a more direct approach, where some sites are using tools to obfuscate artwork or building “tarpits” to combat scraping – but these can shade into being malware, so not a solution for many publishers!

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9. We are now two and a half years into the ‘ChatGPT era’ of widespread AI text generation. Where do you see it going for scholarly publishing by 2030?

Generative AI use is going to become even more prevalent and ubiquitous, and will be very much more integrated into daily life for most people. As part of that integration, ideally we would see better awareness and understanding of what it can do, and better education on appropriate use in the way that we now teach about plagiarism and citation. That education will hopefully begin at an early stage, and develop alongside new uses of the technology.

Some of our ideas around what to be concerned about will change, as well. Wikipedia was suggested as an analogy – twenty years ago we collectively panicked about the use of it by students, feeling it might overthrow accepted forms of scholarship, but then – it didn’t. Some aspects of GenAI use may simply become a part of what we do, rather than an issue to be concerned with.

There will be positive aspects of this, but also negative ones; we will have to consider how we keep a space for people who want to minimise their use of these tools, and choose not to engage with them, for practical reasons or for ethical ones, particularly in educational contexts.

There are also discussions around the standardisation of language with generative AI – as we lose a diversity of language and of expression, will we also lose the corresponding diversity of thought? Standardised, averaged language can itself be a kind of loss.

The panel concluded by noting that this is very much an evolving space, and encouraged greater feedback and collaboration between publishers and the academic community, funders, and institutions, to try and navigate where to draw the line. The only way forward will be by having these discussions and trying to agree common ground – not just on questions of generative AI, but on all sorts of issues surrounding research integrity and publication ethics.

 

Here in the Office for Open Science & Scholarship we like to start every new year by taking a look back over the last and sharing our highlights with you.

In 2024 the Open Access Team facilitated the Gold open access publication of 3,963 papers. UCL Discovery continued to go from strength to strength, with over 53 million downloads. The publications repository now boasts over 185,000 open access items, including 24,900 theses, with over 15,500 uploads in the preceding twelve months.

The Research Data Management Team has had an equally productive year, publishing over 230 items in the data repository which has now exceeded 230,000 views and a similar number of downloads. They have reviewed over 30 data management plans and held classes for over 70 people, both online and in person. There are still seats available for term 2 that can be booked online.

Updates and publications

Across all of the teams that make up the Office we have published a whole host of documents and updates such as:

• New version of research data policy
• New-look RDR website
• Newly launched principles for Citizen Science
• Co-created UCL Principles of Authorship
• Open Science Case Studies

Our blog highlights

The blog has been super busy throughout the year with one of our personal highlights being the brilliant series of posts by Christine Daoutis, UCL’s Copyright Officer that looked at a range of issues around copyright in open science including a deep dive across three posts into Copyright and AI, how copyright exceptions can support your research and how copyright applies to Text and Data mining.

We also had some great events throughout the year that you can catch up on, from our annual Open Science Conference, the second annual Open Science & Scholarship Awards, and our first ever Citizen Science Community Event.

We also had a great time working with the UCL Digital Accessibility team throughout the year, they have been a great support in improving the accessibility of everything we do. We were able to highlight their work in one of our Newsletters and we also interviewed Ben Watson, Head of Digital Accessibility, who was a great sport and is an overall inspirational guy!

Upcoming in 2025

We have a great year ahead in 2025, we have the imminent publication of our next Operational plan, designed to push the team to bigger and better things for the office. We are hoping to get that out in the first quarter of the year, its just going through various stages of internal feedback before we can get it out there! We will be continuing to grow our newest social media channels LinkedIn and BlueSky, and if you don’t subscribe to our newsletter, now’s your chance!

We will also be challenging ourself to bigger and better things when it comes to our conference. You all know that we like to change it up and this year we are reaching out to friends and colleagues to change our conference into our first festival!

We will also be continuing the brilliant series with Ilan Kelman on the Risks and Opportunities of Open Science. We have already shared the first two parts but keep an eye out for the last three parts coming soon!

This is the last article of our Copyright and Open Science series by Christine Daoutis, UCL Copyright Support Officer, which explored important aspects of copyright and its implications for open research and scholarship.

In this post, we conclude our Copyright and Open Science series by focusing on open education. Broadly defined, open education is “a philosophy about how people should produce, share, and build on knowledge” (source: What is open education? Opensource.com). It refers to values, practices and resources that aim to make scholarship more accessible, equitable, sustainable, transparent and collaborative.

The UNESCO definition of OERs highlights the importance of freely accessible educational materials in advancing open education practices globally. This includes the creation and reuse of OERs—materials that are either out of copyright or licensed to allow reuse. However, open education extends beyond resources to include practices such as integrating open science into teaching, sharing educational practices, and co-creating resources with learners.

OERs include a wide range of materials, such as open textbooks, open access articles, lecture handouts, images, film, slides, lecture recordings, assessment resources, software and whole courses such as Massive Open Online Courses (MOOCS). By default, all these resources are protected by copyright. If you’re planning to create open educational resources, here’s some copyright advice.

Addressing copyright in OERs

1. Know who owns what. If you are creating or collaborating on a teaching resource, it is essential to clarify who holds the copyright. This could be you, the author; your employer, if the work was created in the course of employment; or the resource could be co-owned with others, including students or sponsors. To license a resource for reuse (for example, to make it available under a Creative Commons licence), you must own the copyright to the resource and/or agree such licensing with co-owners. ♦ Copyright ownership at UCL is addressed in the UCL IP Policy.

2. Make the resources openly available and reusable. Once you are certain that the resource is yours to license, consider making it openly available, under a licence that allows reuse. Open access repositories support the discovery and access of different types of materials, including OERs. UCL has a dedicated OER repository, which accepts materials created by its staff and students.

As for licensing: we have explained in a previous post how Creative Commons licences work; and you can read more on how CC licences support OERs on the Creative Commons wiki. Licensing under the most permissive of the licences, the Creative Commons Attribution licence (CC BY), supports the ‘five Rs’ of OERs: enabling others to “retain, revise, remix, reuse and redistribute the materials”. (David Wiley, Defining the “Open” in Open Content and Open Educational Resources, Improving Learning blog).

3. Address any third-party materials. If the resource contains materials you don’t own the copyright to (such as third-party content), you have a few options:

• Reuse works that are out of copyright (public domain) or openly licensed. These might include Creative Commons images and videos, open access articles, and OERs created by others. ♦ See UCL’s guidance on finding OERs and a reading list with links to many openly licensed resources.
• Get permission from the copyright owner. If the material is not openly licensed, you might consider seeking permission to reuse it. Be clear about how the resource containing the material will be shared (i.e., as an OER). Third-party materials included in an OER should be shared under their own copyright terms (e.g., their reuse may be more restricted than the rest of the resource) and this should be communicated when sharing.
• Rely on a copyright exception. In some cases, instead of getting permission you may decide to rely on a copyright exception, notably the quotation exception in UK copyright law. Using exceptions requires judgement. You’ll need to determine whether the use of the material is ‘fair dealing’: does the purpose justify the use? Does it affect the copyright owner’s market? Overall, is it “fair” to all parties involved? Be aware that copyright exceptions vary by country, which is important when making a resource globally available. The Code of Best Practices in Fair Use for Open Educational Resources explores these approaches further, putting forward a framework that could be applied internationally.

Putting the copyright advice to practice: examples from UCL’s copyright online tutorials.

While creating UCL’s Copyright Essentials and Copyright and your Teaching, two online tutorials introducing copyright, the UCL Copyright support team drew on its own advice. Specifically:

• Copyright ownership and attribution were addressed. Copyright Essentials is an adaptation of an original resource, which was also openly licensed. Attribution to all original authors was included.
• Both tutorials are publicly available online, allowing anyone to access and complete them. They are also licensed for reuse under the Creative Commons Attribution licence, permitting others to adapt and redistribute the materials with appropriate attribution.
• Third-party materials mostly included openly licensed images and links to lawfully shared videos and documents. However, for some materials, we opted to rely on copyright exceptions, which involved a degree of interpretation and risk. This was highlighted in the tutorials, inviting learners to reflect on the use of exceptions.

It should be noted that using proprietary e-learning tools (like Articulate Rise) to develop the tutorials restricts reuse. While the shared resources can be accessed, they cannot be downloaded or edited. Authors wishing to adapt the resources have the option to recreate the materials under the licence terms or contact us for an editable copy. Ideally, these resources should be created with open-source tools, but there’s a trade-off between the advantages of user-friendly, accessible proprietary tools and these limitations.

For more advice on copyright and OERs please contact copyright@ucl.ac.uk.

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Read more from the Copyright and Open Science Series:

• Developing a Copyright Literacy Community at UCL (UCL Copyright blog, 29 April 2024): An introduction to building a community around copyright literacy at UCL.
• Copyright Literacy Matters: Developing a Copyright Literacy Strategy at UCL (UCL Copyright blog, 14 October 2024): A deep dive into UCL’s strategy for copyright literacy across the institution.
• Keeping it Fair and Honest: How Copyright Exceptions Can Support Your Thesis, Publications, and Teaching (UCL Copyright blog, 22 February 2024): This post explores copyright exceptions, with practical examples for researchers and educators.
• Copyright and Open Science in the Age of AI: What Can We All Do to Ensure Free and Open Access to Knowledge for All? (Open@UCL blog, 24 October 2024): A discussion on the intersection of copyright, open science, and AI, focusing on access and equity.
• Copyright and AI Part 1: How Does Copyright Apply to AI-Generated Works? (Open@UCL blog, 21 June 2024): An exploration of the implications of AI in copyright law, particularly concerning AI-generated works.
• Copyright and AI Part 2: Perceived Challenges, Suggested Approaches, and the Role of Copyright Literacy (Open@UCL blog, 15 August 2024): A follow-up on the challenges AI presents to copyright, along with potential solutions and the role of copyright literacy in addressing these challenges.
• Text and Data Mining (TDM) and Your Research: Copyright Implications and New Website Guidance (Open@UCL blog, 13 May 2024): Insights into how copyright affects text and data mining (TDM) in research, including new guidance.
• How Understanding Copyright Can Help You as a Researcher (Open@UCL blog, 4 April 2024): A beginner’s guide to understanding copyright and its importance for academic research.

We are approaching the end of International Open Access Week, and we have been enjoying a series of interesting insights and discussions across UCL!  Earlier this week, we explored the balance between collaboration and commercialisation, highlighted the important work of Citizen Science initiatives and the growing significance of open access textbooks.

Today, Christine Daoutis, UCL Copyright Support Officer, will build on our ongoing series about copyright and open science, focusing on how we can ensure free and open access to knowledge in the age of AI, by addressing copyright challenges, advocating for rights retention policies, and discussing secondary publication rights that benefit both researchers and the public.

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Open Access Week 2024 builds on last year’s theme, Community over Commercialisation, aiming not only to continue discussions but to take meaningful action that prioritises the interests of the scholarly community and the public. This post focuses on copyright-related issues that, when addressed by both individual researchers and through institutional, funder, and legal reforms, can help create more sustainable and equitable access to knowledge.

Retaining author rights

Broadly speaking, rights retention means that authors of scholarly publications avoid the traditional practice of signing away their rights to publishers, typically done through a copyright transfer agreement or exclusive licence. Instead, as an author, you retain at least some rights that allow you to share and reuse your own research as openly as possible. For example, you could post your work in an open access repository, share it on academic networks, reuse it in your teaching, and incorporate it into other works like your thesis.

Many funders and institutions have specific rights retention policies that address related legal issues. If such a policy applies, and publishers are informed in advance, authors typically need to retain rights and apply an open licence (usually CC BY) to the accepted manuscript at the point of submission.

Rights retention ensures that your research can be made open access without relying on unsustainable pay-to-publish models, and without facing delays or restrictions from publishers’ web posting policies. Importantly, rights retention is not limited to published research—it can be applied to preprints, data, protocols, and other outputs throughout the research process.

Secondary Publication Rights (SPRs)

Secondary publication rights (SPRs) refer to legislation that allows publicly funded research to be published in an open access repository or elsewhere, at the same time as its primary publication in academic journals. Some European countries already have SPRs, as highlighted by the Knowledge Rights 21 study conducted by LIBER, and LIBER advocates for #ZeroEmbargo on publicly funded scientific publications. There are ongoing calls to harmonise and optimise these rights across countries, ensuring that the version of record becomes immediately available upon publication, overriding contractual restrictions imposed by publishers.

SPRs can apply to different types of research output and are meant to complement rights retention policies. However, introducing SPRs depends on copyright reform, which is not an action individual researchers can take themselves, though it’s still useful to be aware of developments in this area.

Artificial Intelligence and your rights

The rise of Generative AI (GenAI) has introduced broader issues affecting researchers, both as users and as authors of copyrighted works. These include:

• Clauses in subscription agreements that seek to prevent researchers from using resources their institution has subscribed to for AI-related purposes.
• Publishers forming agreements with AI companies to share content from journal articles and books for AI training purposes, often without clear communication to authors. A recent deal between Taylor & Francis and Microsoft for $10 million has raised concerns among scholars about how their research will be used by AI tools. In some cases, authors are given the option to opt in, as seen with Cambridge Press.
• For works already licensed for reuse, such as articles under a CC BY licence or those used under copyright exceptions, questions arise about how the work will be reused, for what purposes, and how it will be attributed.

While including published research in AI training should help improve the accuracy of models and reduce bias, researchers should have enough information to understand and decide how their work is reused. Creative Commons is exploring ‘preference signals’ for authors of CC-licensed works to address this issue.

The key issue is that transferring your copyright or exclusive rights to a publisher restricts what you can do with your own work and allows the publisher to reuse your work in ways beyond your control, including training AI models.

Using Copyright exceptions in research

UK copyright law includes exceptions (known as ‘permitted acts’) for non-commercial research, private study, criticism, review, quotation, and illustration for instruction. As a researcher, you can rely on these exceptions as long as your use qualifies as ‘fair dealing’, as previously discussed in a blog post during Fair Dealing Week. Text and data mining for non-commercial research is also covered by an exception, allowing researchers to download and analyse large amounts of data to which they have lawful access.

Relying on copyright exceptions involves evaluating your purpose and, for some exceptions, making a decision around what is ‘fair’. This also involves some assessment of risk. Understanding copyright exceptions helps you exercise your rights as users of knowledge and make confident assessments as to whether and when a copyright exception is likely to apply, and when permission is necessary. [see links for UK legislation at the end of this article]

Engage with copyright at UCL

The conversations sparked during Open Access Week continue throughout the year at UCL as part of ongoing copyright support and education. To engage further with these issues, you can:

• Add your voice to how copyright literacy is shaped at UCL. Provide feedback on the draft UCL copyright literacy strategy and consider joining the new UCL copyright literacy community.
• Attend a copyright training session or email copyright@ucl.ac.uk to arrange a bespoke session. The newly introduced ‘Copyright, Open Science and Creativity’ game is a fun way of engaging with current debates and learning about recent developments. More dates for this will be released soon.
• Follow copyright news on the Open@UCL blog and the Copyright blog.

Useful Legislation

• Copyright, Designs and Patents Act 1988, Section 29: Research and Private Study
• Copyright, Designs and Patents Act 1988, Section 29A: Text and Data Mining for Non-Commercial Research
• Copyright, Designs and Patents Act 1988, Section 30: Text and Data Mining
• Copyright, Designs and Patents Act 1988, Section 32: Copying for non-commercial research

We’re excited to announce a packed programme of events for this year’s #OAWeek at UCL! Throughout the week, we’ll be sharing daily blog posts and updates on social media that highlight the latest activities from UCL Press and the UCL Copyright team, alongside exciting news on our growing Citizen Science Community on MS Teams. This year’s theme, ‘Community over Commercialisation’, will be at the heart of our discussions, exploring how we can prioritise openness and collaboration in research to benefit the public and academic communities rather than profit-driven initiatives.

Read more about Open Access week and this year’s theme.

Tuesday 22 October (11:00am-2:00 pm) – Open Science and ARC Roadshow

As part of this year’s Open Access Week activities, we’re launching the first in a series of pilot roadshows, jointly organised by the UCL Office for Open Science & Scholarship and the Centre for Advanced Research Computing.

Come and find us outside the Academic Staff Common Room in the North Cloisters between 11:00 am and 2:00 pm, where our team will be on hand to answer all your questions about Open Access publishing, Research Data Management, Research IT, Data Stewardship, Citizen Science, and any other Open Science-related topics you’re curious about! Stop by to find out more—we might even have some goodies waiting for you!

No registration needed – find the event location on the webpage.

Tuesday 22 October (2:30-4:00 pm) – Copyright, Open Science & Creativity

One event we’re particularly excited about is happening on Tuesday, 22 October (2:30–4:00 pm). We’ll be hosting a brand-new card game designed by Christine Daouti, titled ‘Copyright, Open Science, and Creativity’. This engaging game provides a fun and interactive way to explore key topics like equity in open science, authors’ rights, and open access publishing. You’ll have the opportunity to debate various aspects of copyright with fellow participants and explore issues such as open licences, AI in research, rights retention, and the challenges of equity in open science.

Spaces are limited, so be sure to sign up early! For more details and registration information, visit the event page.

Wednesday 23 October (2:00–3:30 pm) – Annual Open Science & Scholarship Awards

We’re also really glad to invite you to our second Annual Open Science & Scholarship Awards! Join us in celebrating the incredible contributions of colleagues and students to the future of open research and scholarship. The event will feature short talks from the winners in each category, followed by the award presentations. Afterwards, stay for drinks, nibbles, and a chance to network with peers.

Register today via our Eventbrite page!

Thursday 24 October, 2.30 pm – 4 pm, drop-in session on Copyright, Licences and Open Science

Join the UCL Copyright team for an online drop-in session where they’ll be available to answer your questions about copyright, licensing, and how to share your research openly. This is a great opportunity to clarify any issues related to your research, thesis, publications, or data. Feel free to drop in on Teams between 2:30 and 3:50 pm or send your questions in advance to copyright@ucl.ac.uk.

To get you started, here are a few questions you might want to consider:

• Why do research funders prefer CC BY licences for journal articles and monographs?
• What copyright considerations should you keep in mind when making your data open and FAIR?
• Can you use someone else’s copyrighted materials in your own thesis or publication that you plan to make open access?

This session offers a chance to resolve these and other copyright and licensing concerns so you can better understand the open research landscape.

Visit the event page for more information and sign up now!

Stay connected!

While we prepare for the events coming up, make sure you stay informed about new articles, events, and projects by signing up for your mailing list to receive the next issue of our Open@UCL newsletter. Also, join in the conversation during #OAWeek by checking this blog page for daily updates, and following us on LinkedIn or our newly created BlueSky account.

See you there!

 

Guest post by Christine Daoutis (UCL), Alex Fenlon (University of Birmingham) and Erica Levi (Coventry University).

This blog post is part of a collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team exploring important aspects of copyright and its implications for open research and scholarship. 

A previous post outlined copyright-related questions when creating GenAI materials—questions related to ownership, protection/originality, and infringement when using GenAI. The post discussed how answers to these questions are not straightforward, largely depend on what is at stake and for whom, and are constantly shaped by court cases as they develop.

What does this uncertainty mean for students, academics, and researchers who use GenAI and, crucially, for those in roles that support them? To what extent does GenAI create new challenges, and to what extent are these uncertainties inherent in working with copyright? How can we draw on existing expertise to support and educate on using GenAI, and what new skills do we need to develop?

In this post, we summarise a discussion we led as part of our workshop for library and research support professionals at the Research Libraries UK (RLUK) annual conference in March 2024. This year’s conference title was New Frontiers: The Expanding Scope of the Modern Research Library. Unsurprisingly, when considering the expanding scope of libraries in supporting research, GenAI is one of the first things that comes to mind.

Our 16 workshop participants came from various roles, research institutions, and backgrounds. What they had in common was an appetite to understand and support copyright in the new context of AI, and a collective body of expertise that, as we will see, is very useful when tackling copyright questions in a novel context. The workshop consisted of presentations and small group discussions built around the key themes outlined below.

Perceived Challenges and Opportunities
Does the research library community overall welcome GenAI? It is undoubtedly viewed as a way to make scholarship easier and faster, offering practical solutions—for example, supporting literature reviews or facilitating draft writing by non-English speakers. Beyond that, several participants see an opportunity to experiment, perhaps becoming less risk-averse, and welcome new tools that can make research more efficient in new and unpredictable ways.

However, concerns outweigh the perceived benefits. It was repeatedly mentioned that there is a need for more transparent, reliable, sustainable, and equitable tools before adopting them in research. Crucially, users need to ask themselves what exactly they are doing when using GenAI, their intention, what sources are being used, and how reliable the outputs are.

GenAI’s concerns over copyright were seen as an opportunity to place copyright literacy at the forefront. The need for new guidance is evident, particularly around the use of different tools with varying terms and conditions, and it is also perceived as an opportunity to revive and communicate existing copyright principles in a new light.

Suggested Solutions
One of the main aims of the workshop was to address challenges imposed by GenAI. Participants were very active in putting forward ideas but expressed concerns and frustration. For example, they questioned the feasibility of shaping policy and processes when the tools themselves constantly evolve, when there is very little transparency around the sources used, and when it is challenging to reach agreement even on essential concepts. Debates on whether ‘copying’ is taking place, whether an output is a derivative of a copyrighted work, and even whether an output is protected are bound to limit the guidance we develop.

Drawing from Existing Skills and Expertise
At the same time, it was acknowledged that copyright practitioners already have expertise, guidance, and educational resources relevant to questions about GenAI and copyright. While new guidance and training are necessary, the community can draw from a wealth of resources to tackle questions that arise while using GenAI. Information literacy principles should still apply to GenAI. Perhaps the copyright knowledge and support are already available; what is missing is a thorough understanding of the new technologies, their strengths, and limitations to apply existing knowledge to new scenarios. This is where the need for collaboration arises.

Working Together
To ensure that GenAI is used ethically and creatively, the community needs to work collaboratively—with providers, creators, and users of those tools. By sharing everyday practices, decisions, guidance, and processes will be informed and shaped. It is also important to acknowledge that the onus is not just on the copyright practitioners to understand the tools but also on the developers to make them transparent and reliable. Once the models become more transparent, it should be possible to support researchers better. This is even more crucial in supporting text and data mining (TDM) practices—critical in many research areas—to limit further restrictions following the implementation of AI models.

Magic Changes
With so much excitement around AI, we felt we should ask the group to identify the one magic change that would help remove most of the concerns. Interestingly, the consensus was that clarity around the sources and processes used by GenAI models is essential. How do the models come up with their answers and outputs? Is it possible to have clearer information about the sources’ provenance and the way the models are trained, and can this inform how authorship is established? And what criteria should be put in place to ensure the models are controlled and reliable?

This brings the matter back to the need for GenAI models to be regulated—a challenging but necessary magic change that would help us develop our processes and guidance with much more confidence.

Concluding Remarks
While the community of practitioners waits for decisions and regulations that will frame their approach, it is within their power to continue to support copyright literacy, referring to new and exciting GenAI cases. Not only do those add interest, but they also highlight an old truth about copyright, namely, that copyright-related decisions always come with a degree of uncertainty, risk, and awareness of conflicting interests.

About the authors 

Christine Daoutis is the UCL Copyright Support Officer at UCL. Christine provides support, advice and training on copyright as it applies to learning, teaching and research activities, with a focus on open science practices. Resources created by Christine include the UCL Copyright Essentials tutorial and the UCL Copyright and Your Teaching online tutorial.

Alex Fenlon is the Head of Copyright and Licensing within Libraries and Learning Resources at the University of Birmingham. Alex and his team provide advice and guidance on copyright matters, including text, data mining, and AI, to ensure that all law and practice are understood by all.

Erica Levi is the Digital repository and Copyright Lead at Coventry University. Erica has created various resources to increase awareness of copyright law and open access through gamification. Her resources are available on her website.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Join our mailing list, and follow us on X, formerly Twitter and LinkedIn, to be part of the conversation and stay connected for updates, events, and opportunities.

 

 

 

Guest post by Christine Daoutis, UCL Copyright Support Officer. 

This the third blog post of the collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team. Here, we continue our exploration of important aspects of copyright and its implications for open research and scholarship.

In a previous post we introduced questions that arise when using and creating materials protected by copyright. What options are available to you if you want to reuse others’ work (e.g. articles, theses, images, film, code) in your research? And what do you need to consider before you share your own research with others? Issues around copyright protection, permissions, exceptions, licences, and ownership need to be examined when creating new works and including others’ materials. These questions are also relevant when we think about works that are created with the use of GenAI tools, such as ChatGPT. However, with the use of these technologies still being relatively new and the legal aspects being shaped as we speak, answers are not always straightforward.

GenAI Training Data: GenAI models are trained on a large number of materials, usually protected by copyright (unless copyright has expired or been waived). Does this mean AI companies are infringing copyright by using these materials? How would copyright exceptions and fair dealing/fair use apply in different countries? How would licence terms – including the terms of open licences – be respected? Answers will come both from legislation and codes of practice introduced by governments and regulatory bodies (such as the EU AI Act) and from the outcomes of court cases (see, for example, Getty Images vs Stability AI, the Authors’ Guild against OpenAI and Microsoft.

User Prompts: The prompts a user provides to the model (instructions, text, images) may also be protected. You should also consider whether the prompts you enter include any confidential/commercially sensitive information that should not be shared. Please see UCL’s IP policy for guidance on this.

AI-Generated Work: Is the AI-generated work an original work protected by copyright? Is it a derivative of other original works, and therefore, possibly infringing? If it is protected, who owns the copyright? The answer to this will vary by case and jurisdiction. In the US, a court ruled that AI-generated images in a comic book were not protected, although the whole comic book and story were. In China, it was ruled that images generated with the use of GenAI tools would be protected, with the owner being the person who provided the prompts. The UK’s CDPA (9.3) states that ‘in the case of a literary, dramatic, musical or artistic work which is computer-generated, the author shall be taken to be the person by whom the arrangements necessary for the creation of the work are undertaken’.

In short, GenAI raises questions about what constitutes an original work, what constitutes infringement, how copyright exceptions and fair dealing/fair use are applied, and how authorship is established. While these questions are still being shaped, here are three things you can do:

1. Consider any limitations in using GenAI besides copyright (e.g., confidentiality, biases, publishers’ policies). See UCL’s Generative AI hub for guidance.
2. Be transparent about how you use GenAI. See UCL Library guidance on acknowledging the use of AI and referencing AI.
3. If you have any copyright-related questions on the use of GenAI, contact the copyright support service.

 While GenAI has opened up more questions than answers around copyright, it also offers an opportunity to think about copyright critically. Stay connected with us for Part 2 of this blog post, which will discuss how new technologies, including GenAI, are changing our understanding of copyright. We look forward to continuing this important conversation with you.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, LinkedIn, and join our mailing list to be part of the conversation!

 

 

This the second blog post of our collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team. Here, we continue our exploration of important aspects of copyright and its implications for open research and scholarship. In this instalment, we examine Text and Data Mining (TDM) and its impact on research along with the associated copyright considerations.

Image by storyset on Freepik.

The development of advanced computational tools and techniques for analysing large amounts of data has opened up new possibilities for researchers. Text and Data Mining (TDM) is a broad term referring to a range of ‘automated analytical techniques to analyse text and data for patterns, trends, and useful information’ (Intellectual Property Office definition). TDM has many applications in academic research across disciplines (Intellectual Property Office definition). TDM has many applications in academic research across disciplines.

In an academic context, the most common sources of data for TDM include journal articles, books, datasets, images, and websites. TDM involves accessing, analysing, and often reusing (parts of) these materials. As these materials are, by default, protected by copyright, there are limitations around what you can do as part of TDM. In the UK, you may rely on section 29A of the Copyright, Designs and Patents Act, a copyright exception for making copies for text and data analysis for non-commercial research. You must have lawful access to the materials (for example via a UCL subscription or via an open license). However, there are often technological barriers imposed by publishers preventing you from copying large amounts of materials for TDM purposes – measures that you must not try to circumvent. Understanding what you can do with copyright materials, what may be more problematic and where to get support if in doubt, should help you manage these barriers when you use TDM in your research.

The copyright support team works with e-resources, the Library Skills librarians, and the Office for Open Science and Scholarship to support the TDM activities of UCL staff and students. New guidance is available on the copyright website. TDM libguide and addresses questions that often arise during TDM, including:

• Can you copy journal articles, books, images, and other materials? What conditions apply?
• What do you need to consider when sharing the outcomes of a TDM analysis?
• What do publishers and other suppliers of the TDM sources expect you to do?

To learn more about copyright (including how it applies to TDM):

• Visit the new TDM guidance.
• Register for one of our copyright sessions.
• Contact the copyright team if you have specific questions or would like to organise a session for your department.
• The new UCL Copyright Literacy Community aims to bring together staff and students from across UCL to identify and collaborate on areas where an understanding of copyright should be strengthened. If you are interested in joining, please contact the copyright team.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, LinkedIn, and join our mailing list to be part of the conversation!

 

 

Guest post by Christine Daoutis, Copyright Support Officer

Welcome to the inaugural blog post of a collaborative series between the UCL Office for Open Science and Scholarship and the UCL Copyright team. In this series, we will explore important aspects of copyright and its implications for open research and scholarship.

Research ideas, projects, and their outcomes often involve using and producing materials that may be protected by copyright. Copyright protects a range of creative works, whether we are talking about a couple of notes in a notebook, a draft thesis chapter, the rough write-up of a data, a full monograph and the content of this very blog. While a basic knowledge of copyright is essential, particularly to stay within the law, there is much more to copyright than compliance. Understanding certain aspects of copyright can help you use copyright materials with more confidence, make use of your own rights and overall, enhance the openness of your research.

This first post in our series is dedicated to exploring common questions that arise during research projects. In future posts, we will explore some of these questions further, providing guidance, linking to new resources, and signposting relevant workshops. Copyright-related enquiries often arise in the following areas:

Reusing other people’s materials: How do you GET permission to reuse someone else’s images, figures, software, questionnaires, or research data? Do you always need permission? Is use for ‘non-commercial, research’ purposes always permitted, or are there other factors to consider? How do licenses work, and what can you do when a license does not cover your use? It’s easy to be overconfident when using others’ materials, for example, by assuming that images found on the internet can be reused without permission. It’s equally easy to be too cautious, ending up not making use of valuable resources for fear of infringing someone’s rights. Understanding permissions, licenses, and copyright exceptions – what may be within your rights to do as a user – can help you.

Disseminating your research throughout the research cycle: There are open access options for your publications and theses, supporting access to and often, reuse of your work. How do you license your work for reuse? What do the different licenses mean, and which one is most suitable? What about materials produced early on in your research: study preregistrations, research data, preprints? How can you make data FAIR through licensing? What do you need to consider when making software and other materials open source?

Is your work protected in the first place? Documents, images, video and other materials are usually protected by copyright. Facts are not. For a work to be protected it needs to be ‘original’. What does ‘original’ mean in this context? Are data protected by copyright? What other rights may apply to a work?

Who owns your research? We are raising questions about licensing and disseminating your research, but is it yours to license? What does the law say, and what is the default position for staff and students at UCL? How do contracts, including publisher copyright transfer agreements and data sharing agreements, affect how you can share your research?

‘Text and data mining’. Many research projects involve computational analysis of large amounts of data. This involves copying and processing materials protected by copyright, and often publishing the outcomes of this analysis. In which cases is this lawful? How do licences permit you to do, exactly, and what can you do under exceptions to copyright? How are your text and data mining activities limited if you are collaborating with others, across institutions and countries?

The use of AI. Speaking of accessing large amounts of data, what is the current situation on intellectual property and generative AI? What do you need to know about legal implications where use of AI is involved?

These questions are not here to overwhelm you but to highlight areas where we can offer you support, training, and opportunities for discussion. To know more:

• Follow future posts on this blog.
• Register for an upcoming copyright session. If you cannot make those dates, please contact copyright@ucl.ac.uk to arrange a bespoke session.
• Visit our training and resources page for more ideas and links to online tutorials.
• Have a head start: visit our guidance on copyright and publications, copyright and research data and look out for upcoming guidance on text and data mining.

Get involved!

The UCL Office for Open Science and Scholarship invites you to contribute to the open science and scholarship movement. Stay connected for updates, events, and opportunities. Follow us on X, formerly Twitter, LinkedIn, and join our mailing list to be part of the conversation!