Jon Treadway, Director at Great North Wood Consulting, delivered the opening keynote, outlining the challenges facing scholarly communication. He highlighted the shift towards isolationism in the geopolitical landscape, impacting scientific collaboration. Treadway emphasized that scholarly communication remains inefficient and fragmented, with AI advancements posing both opportunities and risks. He cautioned that AI’s rapid progress demands vigilance and noted that Open Access has yet to reach its full potential due to siloed discussions rather than collaborative efforts. 

The first panel focused on ensuring transparency and trustworthiness in research. Panelists included Leslie McIntosh, Vice President of Research Integrity at Digital Science; Laura Wilson, Head of Research Integrity & Ethics at Taylor & Francis; Jennifer Wright, Head of Publication Ethics & Research Integrity at Cambridge University Press; and Doug Melville, Technical Product Manager at Sage. Key concerns discussed were AI’s impact on research, the need for transparent licensing conditions, and the challenge of ensuring research security. The panel emphasized that publishers play a critical role in upholding standards, holding the “gold” in narratives and research culture. Collaboration across the industry, especially in disambiguating author identities and spotting bad actors, was stressed as vital to safeguarding integrity.

Ann Campbell, Technical Solutions Manager at Digital Science, and Katie Davison, Insights Analyst at Emerald Publishing, presented a case study on how they’ve worked together to leverage Dimensions on GBQ for data-driven insights. They demonstrated how combining Emerald Publishing’s strategic goals with Dimensions’ extensive dataset allowed them to uncover valuable insights about research trends, author collaborations, and institutional impact. By harnessing these insights, Emerald was able to identify key growth areas, refine their editorial strategies, and better align their publishing efforts with the needs of the academic community.

The second panel addressed how publishers are adapting their strategies in response to evolving social media platforms. The panel included Lou Peck, CEO & Founder of The International Bunch; Jitske de Vries, Head of Marketing at The Company of Biologists; Rowena Gordon, Senior Managing Editor at the British Ecological Society; Daisy Veysey, Social Media Manager at eLife; and Marion Schnelle, Social Media Manager at De Gruyter Brill. Discussions highlighted the emergence of platforms like Mastodon and Bluesky as an alternative to X (formerly Twitter). It was raised that in a poll of 6000 readers of Nature, 70% had moved over from X to Bluesky declaring the platform nicer, kinder and less antagonistic. The panel also raised a growing emphasis on community-building strategies over traditional marketing tactics among publishers. 

Next, Sarah Greaves, Director and Publishing Consultant at Sarah Greaves STEM Consulting, discussed the ongoing trend of consolidation in the publishing industry. She explored how mergers and acquisitions are reshaping the landscape, the potential benefits of improved efficiencies, and concerns about reduced diversity and potential monopolistic behaviors. 

Our lightning talks provided valuable insights into our latest innovations for publishers. Nicholas Bailey, our Senior Product Manager, introduced Dimensions Author Check, our new research integrity tool designed to help publishers verify potential authors, editors, and reviewers. Meanwhile, Mike Taylor, Head of Data Insights, showcased the Altmetric Journal Benchmark dashboard – a powerful resource for publishers seeking to measure success, benchmark their journals against competitors, and identify growth opportunities.

In the closing keynote, Jo Wixon, Director of External Analysis at Wiley, explored how publishers can actively contribute to advancing the United Nations’ Sustainable Development Goals (SDGs). She emphasized the growing importance of aligning publishing practices with global sustainability efforts, showcasing strategies for integrating SDG-related content and metrics into editorial workflows, peer review processes, and publication strategies. By adopting these practices, publishers can amplify research that addresses critical global challenges while enhancing their impact within the academic community.

Our 2025 Publisher Day highlighted both the opportunities and challenges publishers face in adapting to AI advancements, strengthening research integrity, and responding to shifts in social media landscapes. With increasing consolidation and technological disruption, the industry must work collaboratively to ensure the integrity, accessibility, and credibility of scholarly communication.

About the Author

Olivia King, Marketing Segment Lead, Publisher | Digital Science

Olivia King is Marketing Segment Lead for the Publisher segment at Digital Science. In this role, she manages the marketing activities and strategy across the Digital Science publisher solutions, including Altmetric and Dimensions. Before joining Digital Science, Olivia worked in journals marketing at Sage Publishing.

The post Publisher Day 2025: Key Insights on Integrity, Disruption, and Innovation in Scholarly Publishing appeared first on Digital Science.

Traditionally, September and October have always been one of the busiest – and most interesting – times to be in the publishing industry. Back in the day, September would be the deadline for the first of the following year’s issues to be collated by editors, while in more recent times big events like the ALPSP Conference, the Frankfurt Book Fair and Open Access Week have set the agenda for the remainder of the year and beyond.

In 2024, this period has perhaps more intrigue than most given a number of deadlines and political events occurring in the next 12 months or so, many of them revolving around open access (OA) and its further adoption. But will things pan out the way people anticipate, and are there solutions that can be used to help forge a path through so many uncertainties about the future?

Conference season

At the recent ALPSP Conference in Manchester in September, there was a good deal of discussion about how open access had developed this year, and its potential progress in 2025 and beyond. Perhaps unsurprisingly at a conference full of publishers, the mood was a little downbeat when it came to the theme of OA, but not for the reasons one might think. Reading between the lines, there was a frustration at the shifting sands many felt they had to constantly navigate, in the shape of changing or newly introduced policies, and a sense that innovation was being stymied as a result.

For example, the tone for OA seemed to have been set by the JISC report on transformative agreements (TAs) which was published in the UK earlier in 2024. This made for somber reading, with the headline prediction that while the UK’s transitioning to OA was faster than most countries, based on the journal flipping rates observed between 2018–2022 it would take at least 70 years for the big five publishers to flip their TA titles to OA. 

With this in mind, the fact that there were deadlines for Plan S set for 2025 around transition that seemed unlikely to be met, and with the OSTP memo in the US mired in committees and a potential change on the cards in the White House, the belief among many publishers was that the move to OA was not happening at the pace or in the direction that many thought it would.

Geopolitical calculations

In addition to what is happening in the UK, Europe and in the US, events further afield are also causing publishers to take stock of their medium-to-long-term strategies. The publication of authors based in Russia has declined sharply since the invasion of Ukraine in early 2022, and collaboration between US authors and those based in China have also decreased, possibly due to policy changes by the Chinese government favoring publication in China-based journals, but also potentially due to fears about research security issues in the US and in other countries. 

China’s move to OA is also happening at a much lower level than many countries, which is significant as it takes up such a high percentage of published articles, passing the US a few years ago as the world’s most prolific publisher of research articles. As a result, despite the increase in the number of TAs being agreed with universities, publishers are still seeing a high degree of uncertainty in the transition to OA.

Forward motion

This uncertainty will be in the back of publishers’ minds when celebrating OA Week this year, coming as it does every year on the back of major conferences such as ALPSP and Frankfurt, and in the midst of fine tuning budgets for the following year. At Digital Science, we understand this predicament given how closely we work with publishers as customers, and also because many of us have worked in the publishing industry ourselves. As such, we have been analyzing how Digital Science solutions can help publishers steer a path forward on OA and transformative agreements, and have created this use case for Dimensions in support of our community.

This resource has been designed to reflect the period of change that the publishing industry is undergoing, supporting the need for publishers to create, evaluate and negotiate TAs by delivering a strong range of historical and predictive data through Dimensions. Using the Dimensions database – which now holds data on almost 150m publications as well as details on funding, grants and patents – publishers can easily find and analyze data surrounding authorship across categories such as country, geography, institution and funder. Understanding a given discipline’s current or future state of play can complement publishers’ own data and inform their strategies accordingly.

Solid state

The theme of this year’s OA Week – ‘Community over Commercialization’ – is a deliberately provocative one, and should engender a good deal of debate during the week and beyond. It should also broaden the conversation to adjacent areas such as open research and open science, as here we have policy and geopolitics making waves for everyone involved in the research ecosystem. 

The origin of some of these ripples can be seen in two upcoming reports from Digital Science. At the end of October, a new report on Research Transformation includes substantial input from those involved in academia on how OA is impacting on their work, while November sees the ninth annual State of Open Data report, tracking how researchers see open data issues developing as part of their work. Without giving too much away, both of these reports call for greater awareness of – and support using – the myriad of fast-developing technologies that are starting to impact academics and their institutions. As such, the community of interest that supports OA Week every year needs to work together in the ecosystem they all inhabit if those OA deadlines are to be met.

About the Author

Simon Linacre, Head of Content, Brand & Press | Digital Science

Simon has 20 years’ experience in scholarly communications. He has lectured and published on the topics of bibliometrics, publication ethics and research impact, and has recently authored a book on predatory publishing. Simon is an ALPSP tutor and has also served as a COPE Trustee.

The post Will 2025 be a turning point for Open Access? appeared first on Digital Science.

At this year’s World Conference on Research Integrity, a team of researchers from Digital Science led by Pritha Sarkar presented a poster with findings from their deep dive on conflict of interest (COI) statements. Entitled Conflict of Interest: A data driven approach to categorisation of COI statements, the initial goal was to look at COI statements with a view to creating a binary model that determines whether a Conflict of Interest statement is present or not in an article. 

However, all was not as it seemed. While some articles had no COI and some had one present, those present covered a number of different areas, which led the team to think COIs might represent a spectrum rather than binary options.

Gold standard

Conflict of interest is a crucial aspect of academic integrity. Properly declaring a COI statement is essential for other researchers to assess any potential bias in scholarly articles. However, those same researchers often encounter COI statements that are either inadequate or misleading in some way even if they are present. 

The Digital Science team – all working on research integrity with Dimensions – soon realized the data could be leveraged further to better explore the richness inherent in the nuanced COI statements. After further research and analysis, it became clear that COI statements could be categorized into six distinct types:

1. None Declared
2. Membership or Employment
3. Funds Received
4. Shareholder, Stakeholder or Ownership
5. Personal Relationship
6. Donation

This analysis involved manually annotating hundreds of COI statements with Natural Language Processing (NLP) tools. The aim was to create a gold standard that could be used to categorize all other COI statements, however despite the team’s diligence a significant challenge persisted in the shape of ‘data skewness’ – which can be defined as an imbalance in the distribution of data within a dataset that can impact data processing and analytics.

Fatal flaw

One irresistible conclusion to the data skewness was a simple one – that authors weren’t truthfully reporting their conflicts of interest. But could this really be true?

The gold standard approach came from manually and expertly annotating COI statements to develop an auto-annotation process. However, despite the algorithm’s ability to auto-annotate 33,812 papers in just 15 minutes, the skewness that had been initially identified persisted, leading to the false reporting theory for authors (see Figure 1 of COI Poster). 

To firm up this hypothesis, when the Retraction Watch database was analyzed, the troubling trend, including the discrepancy between reported COI category and retraction reason, became even more apparent (see Figure 2 of the COI Poster). 

Moreover, when the team continued with the investigation, they found there were 24,289 overlapping papers in Dimensions GBQ and Retraction Watch, and among those papers, 393 were retracted due to conflict of interest. Out of those 393 papers, 134 had a COI statement, however 119 declared there was no conflict to declare.

Conclusion

Underreporting and misreporting conflict of interest statements or types can undermine the integrity of scholarly work. Other research integrity issues around paper mills, plagiarism and predatory journals have already damaged the trust the public has with published research, so further problems with COIs can only worsen the situation. With the evidence of these findings, it is clear that all stakeholders in the research publication process must adopt standard practices on reporting critical trust markers such as COI to uphold the transparency and honesty in scholarly endeavors. 

To finish on a positive note, this research poster was awarded second-place at the 2024 World Conference on Research Integrity, showing that the team’s research has already attracted considerable attention among those who seek to safeguard research integrity and trust in science.

You can find the poster on Figshare: https://doi.org/10.6084/m9.figshare.25901707.v2

Partial data and the code for this project are also available on Figshare.

For more on the topic of research integrity, see details of Digital Science’s Catalyst Grant award for 2024, which focuses on digital solutions around this topic.

About the Author

Simon Linacre, Head of Content, Brand & Press | Digital Science

Simon has 20 years’ experience in scholarly communications. He has lectured and published on the topics of bibliometrics, publication ethics and research impact, and has recently authored a book on predatory publishing. Simon is an ALPSP tutor and has also served as a COPE Trustee.

The post Shining a light on conflict of interest statements appeared first on Digital Science.

To AI or not AI?

The use of AI technologies has always been susceptible to charges of potential bias due to skewed datasets large language models have been trained on. But surely firms are making sure those biases have been ironed out, right? Sadly, when it comes to AI and recruitment, not all applications of the technology are the same so firms need to tread carefully. In other words – if you don’t understand it, don’t use it.

Since the launch of ChatGPT at the end of 2022, it has been difficult to read a newspaper, blog or magazine without some reference to the strange magic of AI. It has enthused and concerned people in equal measure, with recruiters being no different. From every gain in being able to understand and work with huge amounts of information, there appears to be negatives around data bias and inappropriate uses. 

Scismic is part of the larger company Digital Science, and both have been developing AI-focused solutions for many years. From that experience comes an understanding that responsible development and implementation of AI is crucial not just because it is ‘the right thing to do’, but because it simply ensures better solutions are created for customers. Customers who in turn can trust Digital Science and Scismic as partners during a period of such rapid change and uncertainty.

AI in focus

The potential benefits of using AI in recruitment are quite clear. By using Generative AI such as ChatGPT, large amounts of data can be scanned and interpreted quickly and easily, potentially saving time and money during screening. In turn, the screening process may also be improved by easily picking up key words and phrases in applications, while communications about the hiring process can be improved by using AI-powered automated tools.

But, of course, there is a downside. Using AI too much seems to take the ‘human’ out of Human Resources, and AI itself is only as good as the data it has been trained on. A major issue with AI in recruitment has been highlighted by the recent brief issued by the US Equal Employment Opportunities Commission (EEOC), which supported an individual who has claimed that one vendor’s AI-based hiring tool discriminated against them and others. The EEOC has recently brought cases against the use of the technology, suggesting that vendors in addition to employers can be held responsible for the misuse of AI-based technology. 

When should we use AI?

In general, if you don’t understand it, do not use it. Problems arise for both vendors and recruiters alike when it comes to the adoption of AI tools at scale. While huge data sets offer the advantages set out above, they also introduce biases over and above human biases that employers and employees have been dealing with for years. Indeed, rather than extol the virtues of using AI, it is perhaps more instructive to explain how NOT to use this powerful new technology.

As a responsible and ethical developer of AI-based recruitment solutions, colleagues at Scismic were surprised to see a slide like the one below at a recent event.  While it was designed to show the advantages of AI-based recruitment technology to employers it actually highlights the dangers of ‘layering’ AI systems on top of each other. This means the client company will lose even more visibility on who and how the system is selecting – increasing the risk of bias, missing good candidates and, ultimately, the risk of legal challenge. 

In this scenario, with so many technologies layered onto each other throughout the workflow, it is almost impossible to understand how the candidate pipeline was developed, where candidates were excluded, and at which points bias has caused further bias in the selection process!

While the list of AI tools used in the process is impressive, which is less so from a recruitment perspective is the layer upon layer of potential biases these tools might introduce to the recruitment process.

At Scismic, they offer a different approach. AI is used to REMOVE biases in datasets, so that all of the advantages of using automated processes are protected by introducing mitigating processes, thus ensuring a fairer and more ethical recruitment program for employers. 

Positive Discrimination?

Scismic’s technology focuses on objective units of qualifications – skills. We use AI to reduce the bias of terminology usage associated with describing skills. Now we have two ways in which we reduce evaluation bias:

1. Blinded candidate matching technology that relies on objective units of qualifications – skills
2. Removing bias of candidates terminology to describe their skill sets.

What type of AI is being used?

To help explain how Scismic does this, we can split AI into subjective (or Generative) AI like ChatGPT, and objective AI. Subjective AI is, broadly, a contextual system that makes assumptions on what to provide the user based on the user’s past interactions and its own ability to use context. This system can work well for human interactions (such as ChatBots) which is what it was designed for. 

However, when applied to decision making about people and hiring (which is already an area fraught with difficulty) subjective and contextual systems can simply reinforce existing bias or generate new bias. For example, if a company integrates a GenAI product into its Applicant Tracking System (ATS) and the system identifies that most of the people in the system share a particular characteristic then the system will assume that’s what the company wants. Clearly if the company is actually trying to broaden its hiring pool this can have a very negative effect, which can also be challenged in court. 

Objective AI works differently as it does not look at the context around the instruction given but only for the core components it was asked for. This means it doesn’t make assumptions while accumulating the initial core results (data) but can provide further objective details on the data set.  In many ways it is a ‘cleaner’ system but because it is focused and transparent it is the better choice for removing unintended bias.

AI is a tool and, as with so many jobs that require tools the question is often; what is the best tool to use? In short, we recommend that a tool that produces better results with less bias is the answer in a hiring process.

Case by case

To show how well some cases can turn out when using ‘objective AI’ responsibly and astutely, here are three case studies that illustrate how to arrive at some genuinely positive outcomes:

1. The right AI: With one customer, Scismic was hired to introduce a more diverse pool of talent as the company was 80% white males, and those white males were hiring more white males to join them. After introducing Scismic’s recruitment solution, the percentage of diverse applicants across the first five roles they advertised rose from 48% to 76%
2. The right approach: One individual who had been unlucky in finding a new role in life sciences for a very long time finally found a job through Scismic. The reason? He was 60 years old. With an AI-based hiring process, his profile may well have been ignored as an outlier due to his age if a firm typically hired younger people. However, by removing this bias he finally overcame ageism – whether it had been AI- or human-induced – and found a fulfilling role with a very grateful employer
3. The right interview: Another potential hire being helped by Scismic is neurodivergent, and as a result appears to struggle to be successful in interviews. An AI-based scan of this person’s track record might see a string of failed interviews and therefore point them to different roles or levels of responsibility. But the lack of success is not necessarily down to this, and human intervention is much more likely to facilitate positive outcomes than using AI as a shortcut and misdiagnose the issue.

When not to use AI?

One aspect highlighted in these case studies is that while AI can be important, what can be equally as important is when NOT to use it, and understand it is not a panacea for all recruitment problems. For instance, it is not appropriate to use AI when you or your team don’t understand what the AI intervention is doing to your applicant pipeline and selection process. 

Help in understanding when and when not to use AI can be found in a good deal of new research, which shows how AI is perhaps best used as a partner in recruitment rather than something in charge of the whole or even part of the process. This idea – known by some as ‘co-intelligence’ – requires a good deal of work and development on the human side, and key to this is having the right structures in place for AI and people to work in harmony. 

For example, market data shows that in the life sciences and medical services, employee turnover is over 20%, and in part this is due to not having some of the right structure and processes in place during recruitment. Using AI in the wrong way can increase bias and lead to hiring the wrong people, thus increasing this churn. However, using AI in a structured and fair way can perhaps start to reverse this trend.

In addition, reducing bias in the recruitment process is not all about whether to use or not use AI – sometimes it is about ensuring the human element is optimized. For instance, recent research shows that properly structured interviews can reduce bias in recruitment and lead to much more positive outcomes. 

With recruitment comes responsibility

It is clear that AI offers huge opportunities in the recruitment space for employees and employers alike, but this comes with significant caveats. Both for recruiters and vendors, the focus on developing new solutions has to be how they can be produced and implemented responsibly, ethically and fairly. This should be the minimum demand of employers, and is certainly the minimal expectation of employees. The vision of workplaces becoming fairer due to the adoption of ethically developed AI solutions is not only a tempting one, it is one that is within everyone’s grasp. But it can only be achieved if the progress of recent decades in the implementation of fairer HR practices are not lost in the gold rush of chasing AI. As a general rule, recruiters and talent partners should understand these components of the technologies they are using:

1. What is the nature of the dataset the AI model has learnt from? 
2. Where are the potential biases and how has the vendor mitigated these risks?
3. How is the model making the decision to exclude a candidate from the pipeline? And do you agree with that premise?

Understanding the steps involved in creating this structure can be instructive – and will be the focus of our next article, ‘Implementing Structured Talent Acquisition Processes to Reduce Bias in your Candidate Evaluation’. In the meantime, you can contact Peter Craig-Cooper at Peter@scismic.com to learn more about our solutions.

See also our announcement: STEM skills-based economy focus for Scismic’s new Chief Commercial Officer

About the Author

Simon Linacre, Head of Content, Brand & Press | Digital Science

Simon has 20 years’ experience in scholarly communications. He has lectured and published on the topics of bibliometrics, publication ethics and research impact, and has recently authored a book on predatory publishing. Simon is an ALPSP tutor and has also served as a COPE Trustee.

The post Scismic and Objective, Skills-focused, AI-driven Recruitment in STEM appeared first on Digital Science.

Figshare hosts an annual “State of Open Data (SOOD)” Report where it aims to provide insight into researchers around the world regarding their attitudes towards and experiences of open data containing 6,091 usable data. For this project, we decided to take the time to dig deeper into the results of the reports to see where the trends were not consistent based on different demographics. 

In analyzing answers to questions about the researchers themselves, research data collection and curation, support for research, we began to notice the data between the top 10 countries showed a consistent divergence with some common names repeatedly popping up. As such, this report is “The Global Lens: Highlighting national nuances in researchers’ attitudes to open data”.

You can begin to see the trends and the differences in some of the data we started to uncover below. When researchers answered the question on the survey “Thinking about the country in which you are currently working, how supportive are you of the idea of a national mandate for making research data more openly  available?” – In Ethiopia, 48% of researchers strongly favour, and 77% support, a national mandate. In the U.S., 61%  support, with 18% opposed. In Japan, 14% are strongly in favour, and 42% support, but a majority (58%) are neutral or against, with 17% opposed.

When creating a visual representation of the statistics, a common pattern emerged, the contrast between Ethiopia, Japan and USA on their view on Open Data. 

Our team tries to uncover the “why” behind various countries’ perspectives on open data. We look at each country’s history of research practices, funding, and policies. Exploring these factors individually was not sufficient to gain a thorough understanding. Thus, using the Digital Science network, we consulted experts that have worked or have experience in each country to validate our findings and deepen our insights into the world of researchers and open data.

About the Author

Mark Hahnel, Vice President Open Research | Digital Science

Mark Hahnel is the VP Open Research at Digital Science. He is the founder of Figshare, which he created while completing his PhD in stem cell biology at Imperial College London. Figshare currently provides research data infrastructure for institutions, publishers and funders globally. He is passionate about open science and the potential it has to revolutionize the research community. Mark sits on the board of DataCite and the advisory board for Directory of Open Access Journals (DOAJ). He was on the judging panel for the National Institutes of Health (NIH), Wellcome Trust Open Science prize and acted as an advisor for the Springer Nature master classes.

The post Three Countries. Three Different Views on Open Data. appeared first on Digital Science.

Last week, one of the world’s largest charitable organizations, the Bill & Melinda Gates Foundation, changed its policy on open access. In a major shift, it decreed that from 2025 it would no longer fund authors’ article processing charges (APCs) to be published in open access journals. Instead, it will mandate authors to make their articles available as ‘preprints’, which are available to read by everyone but require no fee to post online in a repository. 

Quite clearly, this move is designed to build on the advantages of making medical research openly accessible, as well as capturing the frustration that many share of not being able to discover key information about potentially life or death medical issues – whether that is due to paywalls on articles, or the sometimes hefty APCs that are charged. It was these advantages and frustrations that resulted in the open access (OA) movement forming in the 1990s, and as we detailed here last year fuelled the growth in OA over the last quarter of a century. 

But as we celebrate National Public Health Week and World Health Day on 7th April, what has been the impact of OA in opening up research to the public at large?

Research Transformed

Articles supported by the Bill & Melinda Gates Foundation (BMGF) make up a sizeable corpus of texts in the medical literature – according to Dimensions, in 2023 there were 4,494 publications that acknowledged funding from the Foundation,  appearing in journals published by major publishers such as Elsevier (855 articles funded by BMGF), Springer Nature (780) and Wiley (347). While there are already substantial numbers of articles published in major journals funded by BMGF – and the new mandate does not appear to stop them being published in such journals subsequent to posting as preprints – we may see some changes as a result of the ‘preprint first’ policy.

Changing our focus to looking back at how health research such as that funded by BGMF has been made available to all as open access articles, we can see from the chart below that there has been a marked increase in the amount of medical research that is now openly accessible over the last 20 years or so. And significantly, we can also see this using the free web app of Dimensions.

In 2003 there were 1.66 million article publications, according to Dimensions, with just a quarter of them available as open access articles. We can see in the chart that some of the main health categories made up a sizable number of these OA articles in 2003, which was just three years after the Bill & Melinda Gates Foundation was first formed. 

Fast forward 10 years, and medical research had started to transform in terms of its accessibility to the public. In 2013 there were 3.1 million articles published, of which 1.3 million, or 42%, were now OA. Looking at health research specifically, the percentages were much bigger as adoption in these fields outpaced others: In Biomedical and Clinical Sciences 48% of articles were OA, in Clinical Sciences it was 45% and in Biological Sciences it was already over half at 57%.

Further acceleration in the adoption of open access in the last decade has seen the accessibility of health research grow even further. Not only has the total number of articles published increased by well over 50%, but the proportion of articles in medical research that are open access are well over 60%, and nearly 70% in the case of Biological Sciences. 

New perspectives

Since its inception, the Bill & Melinda Gates Foundation has made grant payments totalling over $71 billion to support gender equality, global development and global health programs. Its aim has been to create a world where each individual has the opportunity to lead a healthy, productive life, and you can see from its commitment to OA that it views access to the most current research as being part of that mission. As we reflect on and celebrate National Public Health Week and World Health Day, it is clear how important access to data is in supporting underserved communities to take advantage of the benefits that access to health research brings. To learn more about how research impacts society, see our latest TL;DR campaign on Research Transformation.

About the Author

Simon Linacre, Head of Content, Brand & Press | Digital Science

Simon has 20 years’ experience in scholarly communications. He has lectured and published on the topics of bibliometrics, publication ethics and research impact, and has recently authored a book on predatory publishing. Simon is an ALPSP tutor and has also served as a COPE Trustee.

The post Healthy increase in access to Medical Research appeared first on Digital Science.

At Digital Science, we recognize that the journey toward AI adoption is as unique as the organizations and individuals we support. From bench researchers to medical affairs professionals to research offices, our approach is grounded in collaboration and deep understanding.

Since 2013, we’ve been investing in advanced AI methodologies, expanding our technical and analytical capabilities, and assembling a global team of AI experts. To us,  AI isn’t a one-size-fits-all solution; it encapsulates a range of both new and existing capabilities and approaches that when thoughtfully applied, can significantly enhance capabilities and streamline workflows. Our commitment continues to be focused on working closely with our partners, deeply understanding their unique challenges and aspirations, to deliver innovative and responsible AI capabilities that enhance human intelligence, drive progress, and unlock the full potential of the research community.

Our Capabilities

For the last decade, we have focused around machine learning innovations with Dimensions.ai, investment in Writefull and development of different LLMs. Building on this AI lineage, 2024 will see a continuous flow of new releases, starting with Dimensions Research GPT Enterprise and Dimensions Research GPT.

Dimensions in ChatGPT

Available via OpenAI’s GPT Store, the new products aim to provide users looking to use ChatGPT for research-related questions with generative answers they can trust – grounded in scientific evidence from Digital Science’s Dimensions database.

Key features of Dimensions Research GPT Enterprise – available to Dimensions customers with a ChatGPT Enterprise licence – include: 

• Answers to research queries with publication data, clinical trials, patents and grant information
• Set up in the client’s private environment and only available to client’s end users
• Notifications each time content generated is based on Dimensions data, with references and citation details
• Possible for clients to have custom features (following prior discussion with Dimensions).

For Dimensions Research GPT, answers to research queries are linked to tens of millions Open Access publications, and access to the solution is free to anyone with a Plus or Enterprise subscription to OpenAI’s GPT Store.

Next-generation search experience

Dimensions has introduced a new summarization feature to support the user in their discovery process for publications, grants, patents and clinical trials. It has integrated AI-driven summarization capabilities into the Dimensions web application to enable all users to accelerate the identification of the most relevant content for their research questions. Short, concise summaries are now available for every record in a given search result list with a single click, providing users with AI-generated insights quickly. The Dimensions team has used feedback from members of the research community – including academic institutions, industry, publishers, government, and funders – to develop this summarization feature in the Dimensions web app.

Smarter searching in Dimensions

Other AI solutions will follow shortly from Digital Science, all of which seek to surface AI capabilities to support users with specific, relevant functionalities where AI in particular can offer improved results. Just as importantly, they have been developed with a grounding in reliability and responsibility so that users can trust them as they do with all our other products. 

Connecting your Data

The Dimensions Knowledge Graph, powered by metaphactory, aims at helping customers harness the synergy of global research knowledge and their internal data, and enable AI-powered applications and business decisions.

AI-Powered Writing Support

Writefull uses big data and Artificial Intelligence to boost academic writing. With language models trained on millions of journal articles, it provides the best automated language feedback to date leading the next generation of research writing help.

Deeper Understanding of Scholarly Papers

Available within ReadCube Enterprise Literature Management & Papers Reference Management, our beta AI Assistant is designed to enhance research efficiency by providing real-time, in-depth analysis, summarization, and contextual understanding of scholarly articles within a researcher’s literature library.

Our latest AI insights

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How does Digital Science use AI? We ask ChatGPT

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The post Digital Science and Artificial Intelligence appeared first on Digital Science.

For many researchers, the ongoing hype around recent developments with Generative AI (GAI) has left them feeling nonplussed, with so many new, unknown solutions for them to use. Added to well-reported questions over hallucinations and responsibly-developed AI, the advantages that GAI could offer have been offset by some of these concerns.

In response, Digital Science has developed its first custom GPT solution, which combines powerful data from Dimensions with ChatGPT’s advanced AI platform; introducing Dimensions Research GPT and Dimensions Research GPT Enterprise. 

Dimensions Research GPT’s answers to research queries make use of data from tens of millions of Open Access publications, and access is free to anyone via OpenAI’s GPT Store; Dimensions Research GPT Enterprise provides results underpinned by all publications, grants, clinical trials and patents found within Dimensions and is available to anyone with an organization-wide Dimensions subscription that has ChatGPT enterprise account. Organizations keen to tailor Dimensions Research GPT Enterprise to better meet the needs of specific use cases are also invited to work with our team of experts to define and implement these.

These innovative new research solutions from Dimensions enable users of ChatGPT to discover more precise answers and generative summaries by grounding the GAI response in scientific data – data that comes from millions of publications in Dimensions – through to the increasingly familiar ChatGPT’s conversational interface. 

These new solutions have been launched to enable researchers – indeed anyone with an interest in scientific research – to find trusted answers to their questions quickly and easily through a combination of ChatGPT’s infrastructure and Dimensions’ well-regarded research specific capabilities. These new innovations accelerate information discovery, and represent the first of many use cases grounded in AI to come from Digital Science in 2024.

How do they work?

Dimensions Research GPT and Dimensions Research GPT Enterprise are based on Dimensions, the world’s largest collection of linked research data, and supply answers to queries entered by users in OpenAI’s ChatGPT interface. Users can prompt ChatGPT with natural language questions and see AI-generated responses, with notifications each time any content is based on Dimensions data as a result of their queries on the ChatGPT platform, with references shown to the source. These are in the shape of clickable links, which take users directly to the Dimensions platform where they can see pages with further details on the source records to continue their discovery journey. 

Key features of Dimensions Research GPT Enterprise include: 

• Answers to research queries with publication data, clinical trials, patents and grant information
• Set up in the client’s private environment and only available to client’s end users
• Notifications each time content generated is based on Dimensions data, with references and citation details.

What are the benefits to researchers?

The main benefit for users is that they can find scientifically grounded, inherently improved information on research topics of interest with little time and effort due to the combination of ChatGPT’s interface and Dimensions’ highly regarded research specific capabilities. This will save researchers significant time while also giving them peace of mind by providing easy access to source materials. However, there are a number of additional key benefits for all users in this new innovation:

• Dimensions AI solutions makes ChatGPT research-specific – grounding the answers in facts and providing the user with references to the relevant documents
• It calls on millions of publications to provide information specific and relevant to the query, reducing the risk of hallucination of the generative AI answer while providing an easy route to information validation
• It can help overcome challenges of sheer volume of content available, time-consuming tasks required in research workflows and need for trustworthy AI products.

What’s next with AI and research?

The launch of Dimensions Research GPT and Dimensions Research GPT Enterprise represents Digital Science’s broader commitment to open science and responsible development of AI tools. 

These new products are just the latest developments from Digital Science companies that harness the power of AI. In 2023, Dimensions launched a beta version of an AI Assistant, while ReadCube also released a beta version of its AI Assistant last year. Digital Science finished 2023 by completing its acquisition of AI-based academic language service Writefull. And 2024 is likely to see many more AI developments – with some arriving very soon! Dimensions Research GPT and Dimensions Research GPT Enterprise, alongside all Digital Science’s current and future developments with AI, exemplify our commitment to responsible innovation and bringing powerful research solutions to as large an audience as possible. If you haven’t tested ChatGPT yet as part of your research activities, why not give it a go today?

About the Author

Simon Linacre, Head of Content, Brand & Press | Digital Science

Simon has 20 years’ experience in scholarly communications. He has lectured and published on the topics of bibliometrics, publication ethics and research impact, and has recently authored a book on predatory publishing. Simon is an ALPSP tutor and has also served as a COPE Trustee.

The post Fast forward: a new approach for AI and research appeared first on Digital Science.

11th February marks the annual International Day of Women and Girls in Science, celebrating the achievements of women in science, technology, engineering and maths (STEM) professions, and encouraging girls in school to pursue STEM-related subjects with a view to contributing to STEM-related careers.  Set up by the United Nations in 2015, the aim is to expand the pool of talented researchers and tackle gender disparity within STEM subjects. Digital Science believes in the importance of inclusive research, encouraging diverse perspectives and giving equal opportunities regardless of gender, race, ethnicity, or other dividing factors. Here you’ll find resources to shine a light on some of the issues as well as hear from others about ways in which they are supporting women and girls to progress in science.

Our aim is to bridge the divides and help get more women working in these fields in order to harness all talent in tackling challenges and driving progress for all.

New and thought-provoking research

A generational shift in reverse

For decades, the proportion of women publishing their first academic publication has increased. However, following the COVID-19 pandemic, this growth has gone in reverse for two consecutive years, with 2024 not looking better.

Read more

Sharing research and knowledge from a range of Digital Science experts highlighting, and celebrating the achievements of women in science, technology, engineering and maths (STEM).

Celebrating the women of TL;DR from Digital Science

Read

Rita Levi-Montalcini: A long life of achievement

Read

Elsie Widdowson: Long-lasting impact on health

Read

The Digital Science Speaker Series welcomes some of the most exciting and innovative movers, shakers, and doers from our research community

Our second Digital Science Speaker Series talk was from Professor Dame Athene Donald on how a career in science, technology, engineering or maths (STEM) is “Not Just For The Boys”, and how an underrepresentation of any group of society can impact the research being done.

Athene chatted with Digital Science’s Director of Researcher and Community Engagement, Dr Suze Kundu, about her research, her book, her experiences of being a woman in STEM, and the big changes she would like to see in research culture to foster greater inclusion of all genders in the STEM workforce.

Check out the video and the entire Speaker Series playlist over on YouTube. Don’t miss the TL;DR article about empowering women in STEM.

How else we are helping #BridgingTheDivides

In this TL;DR series we ask if a fragmented research ecosystem is slowing global progress?

The post Bridging the Divides for Women and Girls in Science appeared first on Digital Science.

The question, however, is just as important for academic institutions looking to check the data on their research programs: have you done your due diligence on that? If not, then a linked database such as Dimensions can help you.

Strategic Objectives

At a recent panel discussion hosted by Times Higher Education (THE) in partnership with Digital Science on optimizing research strategy, the question of due diligence was framed by looking at the academic research lifecycle and the challenges emanating from the increased amount of data now accessible to universities. More specifically, how universities could extract and utilize verified data from the ever–increasing number of sources they had at their disposal. 

Speaking on the panel, Digital Science’s Technical Product Solutions Manager Ann Campbell believes there are numerous benefits to using new modes of data to overcome problems associated with data overload. “It’s important to think holistically, of not only the different systems that are involved here but also the different departments and stakeholders,” she said. “It’s better to have an overarching data model or a perspective from looking at the research life cycle instead of separate research silos or different silos of data that you find within these systems.”

The panel recognized that self–reporting for academics could lead to gaps in the data, while different impact data could also be missed due to a lack of knowledge or understanding on behalf of faculty members. 

Digital Science seeks to address these problems by adding some power to its Dimensions linked database in the shape of Google BigQuery. By marrying this computing power to the size and scope of Dimensions, academics and research managers are empowered to identify specific data from all stages of the research lifecycle. This allows researchers to seamlessly combine external data with their own internal datasets, giving them the holistic view of research identified by Ann Campbell in the discussion. 

Data Savant

The theme of improving the capabilities of higher education institutions when it comes to data utilization has been most vividly described by Ann Campbell in her November presentation to the Times Higher Education Digital Universities conference in Barcelona in October. Memorably, she compared universities’ use of data to the plot of popular TV drama Game of Thrones. Professors as dragons? Rival departments as warring families? Well not quite, but what Ann did observe was that there are many competing elements within HEIs – research management, research information, academic culture, the library – and above them are senior management who have key questions that can only be answered using data and insights across all of them:

• Which faculties have a high impact? Should we invest more in them?
• Which faculties have high potential but are under–resourced?
• How can we promote our areas of excellence?
• How can we identify departments with strong links to industry?
• What real–world research impact can we feed back into our curriculum?
• Are we mitigating potential reputational risk through openness and transparency? 

Bringing these disparate challenges together requires a narrative, which is another reason why the Game of Thrones analogy works so well as we see that for all the moving parts of the story to work, a coherent story is required. This can be how an institution’s research culture strategy is working with a rise in early career international collaborations, how an increase in new funding opportunities followed a drive to increase interdisciplinary collaborations, or how the global reputation of a university could be seen to have improved its impact rankings position due to increased SDG–related research. 

Any good story needs to have the right ingredients, and where Digital Science can really help an institution is to bring together those ingredients from across an organization into viewable and manageable narratives. 

Telling Stories

But the big picture is not the whole story, of course. There are other, smaller narratives swirling through HEIs at any given time that reflect the different specialisms, hot topics or focus areas of the university. Three of these focus areas most commonly found in modern universities are research integrity, industry partnerships and research impact, and these were discussed recently at another collaborative webinar between THE and Digital Science: Utilising data to deliver research integrity, industry partnerships and impact. 

This panel discussion was a little more granular, and teased out some specific challenges for institutions when it came to data utilization. For research integrity, certain data relating to authorship can be used as ‘trust markers’, based around authorship, reproducibility and transparency. Representing Digital Science, Technical Product Solutions Manager Kathryn Weber–Boer went through the trust markers that form the basis of the Dimensions Research Integrity solution for universities. 

But why are these trust markers important? The panel discussion also detailed that outside universities’ realm of interest, both funders and publishers were increasingly interested in research integrity and the provenance of research emanating from universities. As such, products like Dimensions Research Integrity were forming a key part of the data management arsenal that universities needed in the modern research funding environment.  

In addition, utilization and scrutiny of such data can help move the dial in other important areas, such as changing research culture and integrity. Stakeholders want to trust in the research that’s being done, know it can be reproduced, and also see there is a level of transparency. All of these factors then influence the promotion and implementation of more open research activities.

Another important aspect of research integrity and data utilization is not just having information on where data is being shared in what way, it is also whether it is being shared as it has been recorded as, and where it is actually located. As pointed out in the discussion, Dimensions is a ‘dataset of datasets’ and allows the cross–referencing of these pieces of information to understand if research integrity data points are aligned. 

Positive Outlook

Discussions around research integrity and data management can often be gloomy affairs, but there is some degree of optimism now there are increasing numbers of products on the markets to help HEIs meet their goals and objectives in these spheres of activity. Effective data utilization will undoubtedly be one of THE critical success factors for universities in the future, and it won’t just be for the effective management of issues like research integrity or reputations. With the lightning fast development, adoption of Generative AI in the research space and increasing interest in issues like research security and international collaboration, data utilization – and who universities partner with to optimize it – has never been higher up the agenda. 

You can view the webinars here on utilizing new modes of data and delivering research integrity.

About the Author

Simon Linacre, Head of Content, Brand & Press | Digital Science

Simon has 20 years’ experience in scholarly communications. He has lectured and published on the topics of bibliometrics, publication ethics and research impact, and has recently authored a book on predatory publishing. Simon is an ALPSP tutor and has also served as a COPE Trustee.

The post Putting Data at the Heart of your Organizational Strategy appeared first on Digital Science.