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.

This analysis is intended as a starting point for understanding where FemTech is growing and where major gaps remain. Using Dimensions AI data, I analyzed grant funding, patent filings, and clinical trials from the past 10 years. I assigned results to broad women’s health categories using keyword matching in titles and abstracts. 

Some grants, patents, and trials may appear under multiple categories if they are relevant to more than one area (e.g., a menstruation tracking app could be tagged as relevant to fertility and PCOS management). As a result, category totals should not be summed together to avoid double-counting. Instead, the charts provide a relative comparison of activity across FemTech areas and show which topics receive more research, investment, and commercialization efforts. Additionally, while patents indicate where commercialization is happening, they do not guarantee market success or regulatory approval.

Where is FemTech Research Growing?

Fertility Innovation Leads Research and Commercialization

Over the past 10 years, global funding agencies such as NIH, the European Commission, and NIHR have awarded $1.06 billion in grants for FemTech-related research. The top-funded areas include:

• Fertility –  43.1% of total funding
• Pregnancy –  31.4% of total funding
• Breast cancer – 10.9% of total funding.

Notably, menopause remains an underfunded area, receiving just 0.55% of total research dollars. This pattern of underfunding in menopause research may reflect broader research biases in women’s health, which leads to non-reproductive aging conditions receiving less attention. Endometriosis and menopause remain starkly underrepresented, reinforcing the pattern of underinvestment in midlife women’s health.

Are We Seeing Innovation? (Patents & Clinical Trials Trends)

Patents Show Market Interest, But Only in Select Areas

While research funding drives exploration, patent filings can indicate where private sector innovation is happening. My analysis finds that like grant funding, FemTech patents are unevenly distributed across key areas of women’s health:

• Fertility – 48% of total patents. This reflects strong market demand, heavy private investment, and ongoing consumer interest in reproductive health solutions.
• Pregnancy – 32% of total patents, reinforcing continued industry attention on maternal health
• Breast cancer – 11.5% of total patents, indicating continued investment but less dominance compared to fertility and pregnancy-related innovations.

Clinical Trials Reveal an Innovation Gap

Clinical trials provide a window into what is actually being tested for real-world use, and the results show clear misalignments:

• Fertility – 41.8% of all clinical trials
• Pregnancy – 29.7% of clinical trials 
• Breast cancer 11% of clinical trials
• Sexual health is significantly under-tested, representing only 3.4% of trials, despite strong consumer interest in solutions for sexual wellness.

Bridging the Gap Between Research, Innovation, and Commercialization in FemTech

While research funding drives exploration, patent filings indicate where private sector innovation is happening. Our analysis finds that FemTech commercialization is highly concentrated, but there are some differences between innovation-heavy areas and those struggling to reach clinical validation.

• Fertility technology has a far higher ratio of patents to clinical trials than any other category (4.21 patents per trial). This patent volume  reflects strong market demand, heavy private investment, and rapid technological advancements in assisted reproduction. However, the gap between patents and trials suggests companies may be prioritizing direct-to-consumer models over clinical validation, or facing regulatory hurdles in testing fertility innovations.
• Pregnancy-related innovation has a balanced ratio (0.25 patents per trial), indicating steady research and commercialization efforts moving in parallel.
• Breast cancer technology follows a similar trend (0.24 patents per trial), showing consistent investment across both innovation and clinical validation.
• Menopause (0.18 patents per trial) and gynecological cancers (0.16 patents per trial) see even less commercialization, reinforcing concerns that midlife women’s health remains a lower priority for investment.
• Sexual health and contraception have some of the lowest patent-to-trial ratios (0.07 and 0.03, respectively) which may indicate that commercialization is still lagging behind research.
• Endometriosis has clinical trials but almost no patents, confirming that diagnostic and treatment innovation remains stagnant.

Implications:

• Are regulatory pathways slowing down fertility tech adoption?
• Are companies prioritizing patents in high-demand markets (like fertility) while ignoring other health needs?
• Should funding agencies reallocate resources toward under-commercialized areas like endometriosis, menopause, and contraception, or will demographic trends inevitably increase commercial investment in some of these areas?

Technology Trends in FemTech: AI, Wearables, and Digital Health

Beyond funding and commercialization trends, an analysis of patents and clinical trials reveals which technologies are shaping FemTech innovation. Three key areas dominate:

AI-Driven Diagnostics

Machine learning and AI are being integrated into fertility tracking, ovulation prediction, and personalized reproductive health solutions. Patents in AI-based reproductive health are on the rise, reflecting growing interest in automated health insights. Breast cancer detection AI is another expanding area, with startups and researchers developing algorithmic screening tools.

Wearable Devices for Women’s Health

FemTech wearables can range from cycle-tracking smart rings to pelvic floor training sensors and are emerging as a major category in innovation. Key areas of innovation include fertility tracking, pregnancy monitoring, and menopause symptom management. While patents for wearable tech are increasing, clinical trials remain limited, indicating that many of these products have not yet reached large-scale medical validation. 

Telemedicine & Digital Health

Telemedicine is playing an increasing role in FemTech commercialization, particularly in clinical research. Examples range from virtual genetic counseling, menopause support apps, and AI-powered health coaching for reproductive and sexual health.

Implications:

• AI-driven diagnostics and wearable devices are growing fast in patents but lagging in clinical trials, which may suggest regulatory or adoption challenges.
• Telemedicine is further along in implementation, likely due to faster regulatory pathways for software-based interventions compared to hardware or pharmaceuticals.

Regulatory, Privacy, and Access Challenges in FemTech

FemTech is advancing rapidly, but policy frameworks are not keeping pace. Many innovations in AI-driven diagnostics, wearable health devices, and digital therapeutics face fragmented regulatory approval pathways that differ by region.

Privacy Concerns are a key issue, as many FemTech solutions collect sensitive health data. This raises concerns about data ownership, consent, and third-party sharing. Access Disparities are also a concern. Low-income populations face significant barriers to accessing affordable, approved, and accessible FemTech solutions.

These issues have been explored in the broader mHealth research movement in recent years, and prior work in these areas may be useful for informing policy and regulatory issues in FemTech. Moving forward, policymakers, researchers, and industry leaders must work together to ensure that FemTech innovations are ethically developed, widely accessible, and properly regulated.

Conclusion & Call to Action

FemTech is at a high-visibility moment. Fertility solutions are flourishing, while other critical areas like menopause, sexual health, and non-hormonal contraception continue to be neglected. The imbalance in research funding, innovation, and clinical validation reflects both structural funding priorities and market incentives.

At the same time, women’s health funding in the U.S. is facing new uncertainties. It is clear that changes in public health funding priorities will impact the future of research and commercialization in the FemTech space as well as women’s health more broadly .

Ensuring sustained investment will be critical to keeping FemTech innovation moving forward. History has shown that fluctuations in funding can significantly impact the pace of innovation. As policymakers, researchers, and industry leaders navigate these changes, cross-sector collaboration will be key.

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About the Author

Emily Alagha, Senior Director of Research Analytics & Support | Digital Science

Emily Alagha is a Senior Director of Research Analytics & Support at Digital Science, where she leverages AI-powered platforms like Dimensions to support data-driven strategies to optimize research funding and enhance research management practices. With a background in medical librarianship, she is passionate about health literacy and ensuring research is accessible to all. She is also a neurodivergent self-advocate committed to amplifying autistic voices and increasing autistic representation in research.

The post Tracking the FemTech Research Boom: What Data Tells Us About Innovation & Gaps 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.

Author’s note about identify-first language

In this post, I am using identity-first language (e.g., ‘autistic person’) to honor the preference of many in the autism community who embrace their identity as an integral part of who they are. This approach reflects the values of empowerment and self-identification.

The Rise of Participatory Research

There’s a growing recognition in the research community that individuals with lived experience of a condition or phenomenon can offer unique and valuable insights to the design of scientific studies. This collaborative approach is often referred to as participatory research and actively involves individuals with lived experience in all stages of the research process. Dimensions data (visualized below) reveals a steady increase in research articles using terms related to participatory research, suggesting a growing embrace of this approach within the scientific community. This shift reflects a move towards more inclusive research practices that empower individuals and communities to actively participate in knowledge creation that is directly relevant to the needs and priorities of those it aims to serve.

This post examines recent trends in a specific subset of participatory research that highlights lived experience contributions, as identified through publication authorship and acknowledgments. Focusing on autism research, I will delve into this trend by leveraging Dimensions data to analyze autistic authorship and acknowledged collaborative support. I’ll also compare the trajectory of this movement to similar trends in mental health and chronic illness research. Finally, I’ll discuss the implications of these findings for research impact and visibility and advocate for greater inclusion of those with lived experience in shaping future studies.

Characterizing Autistic Contributor Representation in Autism Research Articles

Methodology

Individual contributions to research studies are most often represented by the author and acknowledgements sections of publications. To investigate how autistic contributions are characterized in the literature, I leveraged the capabilities of the Dimensions database to search within the raw affiliation and acknowledgements fields of research publications. I used a combination of search strategies to focus on publications related to autism research and specifically targeted publications that either:

• Included autistic or neurodiverse authors in the raw affiliations section OR
• Acknowledged autistic people, patient networks, or advisory groups in their acknowledgments section AND
• Mentioned autism-related keywords in their full text

I examined author affiliations and acknowledgments to identify the most common language used to represent contributions from autistic people. I also explored bibliometric indicators such as citation counts, Field Citation Ratio (FCR), and Altmetric Attention Scores to assess the impact and reach of autism research with autistic contributors compared to the broader field of autism studies. Finally, I applied the same approaches to explore how lived experience contributions are characterized in other fields to identify avenues for potential future growth of autistic representation in research.

The Rise of Autistic Authorship

To understand how autistic authors represent themselves, I conducted a qualitative review of author affiliations in participatory autism research to identify common phrases and terms. These range from explicit identifiers like “Autistic Researcher” or “Independent Autistic Scholar,” to affiliations with advocacy organizations such as the Autistic Self Advocacy Network, and roles emphasizing lived experience like “Expert by Experience” or “Lived Experience Professional.” While the number of publications authored by self-identified autistic individuals is currently limited (231), these publications offer valuable insights into the unique perspectives and contributions of autistic researchers.

This network visualization represents a preliminary attempt to identify leading neurodivergent researchers engaged in autism and neurodiversity scholarship. While the search terms were designed to highlight self-identified neurodivergent researchers and allies, it’s important to note that this method may not be fully accurate, and not all individuals included may identify as neurodivergent. The visualization highlights key figures like Sonia Johnson, Fiona Ng, and Dora Madeline Raymaker, who are known for their work in this area and could provide valuable leadership on best practices for autistic inclusion in research.

Highlighting specific examples of impactful, autistic-led research with high citation counts and Altmetric Attention Scores (a measure of online attention and engagement) demonstrates the influence of these authors on the broader research conversation.

Top Cited Research Article among Autistic Lived Experience Authors:

• Nicolaidis, C., Raymaker, D., McDonald, K., Dern, S., Boisclair, W. C., Ashkenazy, E. & Baggs, A. (2013). Comparison of Healthcare Experiences in Autistic and Non-Autistic Adults: A Cross-Sectional Online Survey Facilitated by an Academic-Community Partnership. Journal of General Internal Medicine, 28(6), 761–769. https://doi.org/10.1007/s11606-012-2262-7  

This study compares the healthcare experiences of autistic and non-autistic adults through an online survey, uncovering significant disparities for autistic people. Autistic collaboration involves authors from the Autistic Self Advocacy Network and the Academic Autistic Spectrum Partnership in Research and Education (AASPIRE). The high citation count of this study underscores its impact on shaping subsequent research around healthcare access and equity for autistic people.

Top Altmetric Score and Field Citation Ratio among Autistic Lived Experience Authors:

• Pearson, A. & Rose, K. (2021). A Conceptual Analysis of Autistic Masking: Understanding the Narrative of Stigma and the Illusion of Choice. Autism in Adulthood, 3(1), 52–60. https://doi.org/10.1089/aut.2020.0043  

This conceptual analysis investigates autistic masking as a response to stigma. Collaborators include Kieran Rose of The Autistic Advocate and Infinite Autism. The high Altmetric score and Field Citation Ratio (a measure of a study’s influence within its specific field) highlight the broad reach and impact of this work on online platforms and in further research.

These examples illustrate the power of autistic-led research to generate new insights and draw attention to often overlooked topics. Having examined the influence of key autistic researchers, it’s essential to explore the broader scope of autistic involvement in research, beyond authorship.

Broadening the Scope: How do Papers Characterize Autistic Contributions Beyond Authorship? 

While authorship provides a clear indicator of direct contribution, it doesn’t capture the full spectrum of autistic involvement in research. I expanded the analysis to include the acknowledgments section of publications to gain additional insight into how autistic people contribute to and shape research. Acknowledgments often reveal a wider range of roles and contributions, such as participation in advisory boards or community networks.

Expanding the analysis to include publications that acknowledge autistic or neurodiverse people, patient networks, or advisory groups in the acknowledgments section significantly broadened the dataset to 703 publications (as of September 25, 2024). Throughout this post, I use the term ‘autistic-contributor research’ to describe these studies where autistic individuals are explicitly acknowledged or listed as co-authors. This term represents a narrower subset of participatory autism research, specifically focusing on visible contributions through acknowledgments or authorship, rather than all potential forms of participatory involvement.

As the chart below illustrates, this expanded search demonstrates that autistic contributions extend beyond authorship and can be recognized in several different capacities.

Patient Representation: The term “patient” emerges as a frequent descriptor in research acknowledgments. It can encompass diverse roles like “patient partner,” or refer to administrative functions related to patient involvement. However, the meaning of “patient” in the author affiliation and acknowledgements section can be ambiguous, sometimes signifying autistic individuals themselves, other times denoting individuals with different conditions within the study.

While widely used, “patient” has limitations in autism research. It centers on pathology and potentially overlooks the broader spectrum of autistic experiences beyond the clinical realm. Not all autistic people identify with this label, as it may imply illness or deficit. While “patient” may suggest autistic involvement in healthcare research, it also highlights the need for more precise language that recognizes the multifaceted roles of autistic people beyond the traditional patient-provider dynamic.

Independent Researchers and Advocates: The presence of terms like “advocate,” “self-advocate,” “lived experience,” and “independent researcher” highlights several ways autistic people contribute to research both as individuals and as part of broader groups of expertise. The use of “independent researcher” in affiliations suggests a recognition of the contributions made by autistic researchers working outside traditional academic institutions.

Group Advisory Roles: The prevalence of terms like “advisory board,” “advisory panel,” “community network”, and “working group” underscores the importance of structured mechanisms to ensure that autistic perspectives and lived experiences directly inform research design and implementation. These groups may not always be composed of autistic people, but they often have close ties to communities with lived experience and aim to represent those perspectives. 

How do studies integrate autistic voices into the study design? Autistic-contributor research is more likely to use qualitative or mixed-methods approaches

Autistic-contributor studies in this dataset are significantly more likely to employ qualitative or mixed-methods approaches when compared to all autism research. Qualitative methods, such as interviews and focus groups, allow autistic people to express their unique perspectives and insights in their own words. Some examples of how studies may integrate autistic voices include co-creating research questions with autistic people, adapting methods to be more accessible, including autistic researchers on the team, and involving autistic participants in data analysis and communication of findings. These collaborative approaches can help studies be more directly relevant to the autism community.

Who is leading in these types of autistic-contributor collaborations? 

It can be useful to explore leading organizations in this dataset to understand where and how investments in autistic-contributor collaborations are happening. Affiliation, funding, and geographic data in Dimensions highlight the United Kingdom’s prominent role in fostering research collaborations involving autistic people. The National Institute for Health and Care Research (NIHR) and the Department of Health and Social Care (DHSC) are the leading funders, while University College London and King’s College London are at the forefront of institutions publishing participatory approaches in this field. These data suggest a strong commitment within the UK to promoting inclusive research practices. However, it’s important to acknowledge that this analysis primarily reflects English-language publications, and there may be additional contributions in other languages that use different terminology to acknowledge autistic participation.

Comparing Autistic-Contributor Autism Research with all Clinical Autism Research

What topics are addressed by autism research that acknowledges autistic partners in the author or acknowledgements fields?

In a concept analysis of autistic-contributor research literature, I found a clear emphasis on lived experience, health services, and support systems. Instead of primarily asking “What causes autism?” or “How can we diagnose autism?”, this research asks “How can we improve the lives of autistic people?”. This emphasis is reflected in the prominence of terms like “improve access” and “health system” in the autistic-contributor research network visualization above. 

This focus contrasts with broader clinical autism research, which emphasizes cognitive and behavioral aspects of autism. In the clinical autism concept network above, the strongest themes are diagnosis, social skills, and behavior. 

The distinction is further reinforced by how research is categorized. Clinical autism research falls under Field of Research (FoR) classifications of Psychology and Biomedical Sciences, while autistic-led research leans towards Health Sciences and Health Services. This highlights a fundamental difference in priorities. 

It’s also worth considering the potential impact of age on these research approaches. Autistic-led research may naturally involve more adults, given the complexities of participating in research design. This could lead to a greater focus on issues relevant to autistic adults, an area often overlooked in traditional research.

How does impact look compared to all autism research?

Data sourced: 25 September 2024.

Though still in its early stages, autistic-contributor research shows promising signs of greater impact in both academic citations and public reach. 

Citation, Field Citation Ratio (FCR), & Citation Rate: The average Field Citation Ratio (FCR) for autistic-contributor research is 5.30, compared to 2.31 for all autism research. The citation rate for autistic-contributor autism research (76.65%) is slightly higher than the overall citation rate for autism research (65.57%). Additionally, autistic-contributor research demonstrates a comparable average number of citations per publication (22.76) to the broader field of autism research (23.28). These figures indicate that autistic-contributor research is cited more frequently within the scientific community.

Altmetric Attention Score & Societal Impact: Autistic-contributor research in autism exhibits an average Altmetric Attention Score of 8.6, notably higher than the average of 4 for all autism research. This indicator shows that autistic-contributor autism research sparks more conversations outside of academia than broad autism research.

Translation into Policy, Practice & Innovation: Autistic-contributor research in autism has a higher rate of citation in policy documents (4.7%) compared to the broader field of autism research (2.0%). It also maintains a comparable rate of citation in clinical trials (0.7% vs. 1.2%). However, when it comes to citations in patents, autistic-contributor research lags behind with only 0.4% of publications cited compared to 2.2% in the broader field. These figures suggest that while involving autistic people in research may lead to findings that are more readily translatable into policies and clinical practices, there’s room for growth in terms of fostering innovation and generating patentable discoveries.

Autistic-contributor research in autism represents a small subset of the overall autism literature, but its higher FCR scores and Altmetric Attention Score, comparable citation averages, and stronger translation into policy collectively show the value and influence of research that actively involves autistic people. 

Learning from Other Fields: Comparison to Chronic Illness and Mental Health Research Literature with Lived Experience Contributions

Both chronic illness and mental health research fields have a strong track record of including people with lived experience as active contributors. We can gain valuable insights to enhance autistic representation in research by analyzing language used to acknowledge lived experience contributions in these fields. If we were to standardize language used to describe these collaborations, would it be easier to measure these types of collaborations? What terms would be best to use across fields?

“Patient” and “patient advocates” are some of the most highly used terms across both mental health and chronic illness participatory research, but may present challenges in the context of autism research where some participants do not want to pathologize autism. An emphasis on “lived experience” as an authorship and acknowledgement phrase is also common across all three fields, and may be a better approach to recognize contributions in autism research. Another structure sometimes used in the author affiliation fields is “with [condition]”, such as “researcher with chronic illness” or “advisor with bipolar disorder”. This structure is difficult to standardize across research areas and may make it harder to discover experts with relevant lived experience.

Additionally, there is an emphasis on group collaborators across all three fields. The prevalence of working groups and advisory panels demonstrates the effectiveness of these structures in facilitating meaningful participation and ensuring that diverse perspectives are heard. 

Implications and Recommendations

Despite the promising rise in participatory autism research, it still constitutes a small fraction of the overall autism literature. Much of the research remains rooted in clinical or mechanistic approaches and often overlooks the contributions of those with lived experience. To address this gap, funders, researchers, and institutions must prioritize participatory research approaches that actively incorporate autistic perspectives at every stage of the research process. 

Recommendations:

• Funders and Institutions: Prioritize funding and support for participatory research initiatives that actively involve autistic people in all stages of the research process.
• Researchers: Embrace collaborative approaches and methodologies, establish meaningful partnerships with autistic and neurodivergent communities, and ensure that research designs and methodologies are inclusive and accessible.
• Publishers: Consider metadata fields which standardize how participatory collaborations are described, in collaboration with the research community Consistent language can improve the discoverability of lived experience collaborators.
• Autistic Individuals: Seek out opportunities to participate in research, share your expertise and insights, and advocate for greater representation and inclusion within the research community.

By actively involving autistic people in the research process, researchers in the field can improve the relevance of their work and address the real-world challenges and needs of the community. This evidence can inform policy decisions and advocacy efforts that lead to more equitable and supportive systems for autistic people and foster a deeper understanding of autism.

Special thanks to Holly Wolcott, Ph.D., Senior Vice President of Research Analytics at Digital Science, for her insightful feedback on this blog post.

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About the Author

Emily Alagha, Senior Director of Research Analytics & Support | Digital Science

Emily Alagha is a Senior Director of Research Analytics & Support at Digital Science, where she leverages AI-powered platforms like Dimensions to support data-driven strategies to optimize research funding and enhance research management practices. With a background in medical librarianship, she is passionate about health literacy and ensuring research is accessible to all. She is also a neurodivergent self-advocate committed to amplifying autistic voices and increasing autistic representation in research.

The post Embracing Lived Experience: A Data-Driven Look at Autistic Involvement in Autism Research 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.

After a welcome and introduction from Digital Science’s MD of Publisher Sales, Helen Cooke, we kicked off the day with a keynote from Mark Hanhel, our VP of Open Research. Mark shared where he predicts experimentation will lead in the ever-changing global academic publishing landscape, and what Digital Science can do to support publishers with data, tools and insights. 

Following Mark’s keynote, we had a series of lightning talks to share product updates and roadmaps for our publisher solutions. Amye Kedall, VP of Product, presented exciting updates from Dimensions and Altmetric, and explained how Digital Science is adopting AI in our product development plans to help publishers drive discovery of content and do more with less. 

Claire Turner, SVP Commercial, shared Figshare’s updated roadmap format. She explained how the Figshare team plans to expand their capabilities to support capturing engaging content, streamline researcher experiences, enhance administrative workflows, and update core capabilities.

Next up was Juan Castro, CEO and Co-Founder of Writefull, who presented on Writefull’s AI-powered language and metadata solutions for publishers, and how they have streamlined the workflow of a leading chemistry publisher. 

After a networking break, we held our first panel of the day. The panel was moderated by Cathy Holland from Digital Science, who was joined by Andreea Moldovan from Sage, Jon Treadway from Great North Wood Consulting, Becky Moakes from Maverick, and Ian Potter from Frontiers. The panel had a lively discussion on the journey of adapting to AI in scholarly publishing – weighing up the positive and negative impacts, and looking ahead to see whether AI is, or will ever be, the new normal.

The next panel (pictured below) was moderated by Tyler Ruse of Digital Science. Pooja Aggarwal from Bloomsbury, James Butcher from Journalology, Ritu Dhand from Springer Nature, and Lisa Walton from BMJ. They discussed how the UN’s Sustainable Development Goals (SDGs) are affecting publishing and editorial strategies, how to measure success in the SDGs, the importance of the SDG Publishers Compact, and what the path to 2030 could look like. 

Following a publishing industry quiz, we had our third and final panel of the day, which was moderated by Digital Science’s Nigel Thompson. Nigel was joined by Hannah Barnsley from the Royal Society of Chemistry, Simon Boisseau from Accucoms, Bernie Folan from OASPA, and Rhodri Jackson from Oxford University Press. The panel spoke about open access trends and models, the role wider initiatives play in supporting OA, and where they think the OA movement is heading.  

As the day drew to a close, Jessica Miles from Holtzbrinck Publishing Group delivered a thought-provoking keynote, reflecting on the past, present, and future of STM publishing workflows. She spoke about how the workflows have been shaped by distinct periods of digital transformation: from establishing infrastructure for digital content creation in the move from print to digital, to the expansion of the publishing workflow influenced by AI and machine learning, to how AI will impact the fundamental value proposition of STM publishing going forward.

We concluded the day by enjoying some networking drinks. Once again, we’d like to send our heartfelt thanks to everyone who attended and spoke at this year’s Publisher Day, and we look forward to next year’s event.

Want to learn more about our solutions for publishers? Visit our webpage, or get in touch with the publisher team at: publishing@digital-science.com

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 publisher marketing activities and strategy across the Digital Science publisher solutions, including Writefull, Altmetric, Dimensions and Figshare. Before joining Digital Science, Olivia worked in journals marketing at Sage Publishing.

The post Publisher Day 2024: The road ahead for scholarly publishing 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.