Every Muppets fan knows that Christmas is all about being revisited by people you’ve previously encountered. So from 25th December to 5th January we’ll be sharing our 12 Days of DSmas. Check back daily as we share a Speaker Series chat each and every day. Happy Holidays from the Digital Science Thought Leadership Team!

Merry Dr Chris Van Tulleken-mas! We chatted with Chris online about research integrity, impact, openness, and investigative research. Catch his interview here, and don’t forget to watch his Xmas Lectures on BBC for The Royal Institution this year!

Chris on Spotify

Chris on Apple Podcasts

Chris on YouTube

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Professor Dame Athene Donald joined our Speaker Series line-up in February after her Speaker Series Live talk at the Ri in London, UK. Here she is talking about why we need to empower women in STEM from the ground up.

Athene on Spotify

Athene on Apple Podcasts

Athene on YouTube

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It’s time to go to Berlin, Germany and catch Matyas Kovacs at the Falling Walls Summit. Matyas shared his thoughts on communication and innovation to impact all of society with science.

Matyas on Spotify

Matyas on Apple Podcasts

Matyas on YouTube

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As a Nobel laureate and former president of The Royal Society, Professor Venki Ramakrishnan has long played a role in shaping a more innovative, inclusive and impactful research culture, which we chatted about during his live Speaker Series lecture at the Ri. We went to Cambridge, UK to hear his thoughts on curiosity, competition and collaboration.

Venki on Spotify

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As Chief Publishing Officer at PLOS, Niamh provides business leadership for the entire PLOS portfolio to advance PLOS’s vision and mission. In this episode Niamh talks about the evolving landscape of scientific research and the push towards open science, including her journey from the early days of advocating for public access to research, to tackling current challenges like making science more inclusive and accessible.

Niamh on Spotify

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Building communities is hard, but Alice Meadows has worked hard to make it look effortless. Here she is in Boston, MA, USA, telling us about the power of persistent identifiers.

Alice on Spotify

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It’s New Year’s Eve, and a time to reflect on the past and make plans for the months ahead. When we visited the Max Planck Institute in Berlin, Germany, we added to the echoes of amazing research conversations resonating around their iconic library when we chatted about the history, philosophy and future of research with Dr Maria Avxentevskaya and Dr Ben Johnson.

Maria and Ben on Spotify

Maria and Ben on Apple Podcasts

Maria and Ben on YouTube

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Happy New Year! We caught up with pro-skater Rodney Mullen at his home in Los Angeles, USA to hear his thoughts on why we need diverse minds to innovate in all walks – and ollies – of life. And, since it’s the new year and you’re probably feeling a little “sleep deprived”, you can also follow this up with his live Speaker Series lecture at the Ri.

Rodney on Spotify

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If you’ve been eating as much cheese as this author, dearest gentle reader, you too will be experiencing a fascinatingly slippery grasp on reality – which brings us to Day 9’s featured speaker. “Is Maths Real?” was the question that Dr Eugenia Cheng posed in her live Speaker Series lecture at the Ri. I caught up with her ahead of her lecture in the iconic Faraday lecture theatre in London, UK to talk about why we need to break down barriers of knowledge in research, and reunite STEM and the humanities for impactful change.

Eugenia on Spotify

Eugenia on Apple Podcasts

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Dr Gilbert De Gregorio leads the Frontiers Planet Prize. I caught up with him in Berlin, Germany, to talk about why we need more inclusive competitions to catalyse innovation that impacts our global society.

Gilbert on Spotify

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2024 was a wild ride for global politics, and research is not immune to its changes. I caught up with Professor Jenny Reardon in Cambridge, UK, to learn more about how we can work with politics, and not against it, to provide solutions for everyone across the world, and where red tape remains to be overcome.

Jenny on Spotify

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Our final Speaker Series guest of 2024 was Dr Danny Hillis. We visited the Applied Invention offices in Cambridge, MA, USA, where innovator, inventor, and Imagineer Danny shared his thoughts on how we can use novel technology to combat novel challenges in mis- and disinformation and make the most meaningful impact from data.

Danny on Spotify

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The post The 12 Days of DSmas appeared first on Digital Science.

As part of our ongoing investigation into Research Transformation, we are delighted to present a new report, Research Transformation: Change in the era of AI, open and impact.

Within the report, we sought to understand from our academic research community how research transformation is experienced across different roles and responsibilities. The report, which is a mixture of surveys and interviews across libraries, research offices, leadership and faculty, reflects transformations in the way we collaborate, assess, communicate, and conduct research.

The positions that we hold towards these areas are not the same as those we held a decade or even five years ago. Each of these perspectives represent shifts in the way that we perceive ourselves and the roles that we play in the community. Although there is concern about the impact that AI will have on our community, our ability to adapt and change is reflected strongly across all areas of research, including open access, metrics collaboration and research security. That such a diverse community is able to continually adapt to change reflects well on our ability to respond to future challenges.

Key findings from the report:

• Open research is transforming research, but barriers remain

• Research metrics are evolving to emphasize holistic impact and inclusivity

• AI’s transformative potential is huge, but bureaucracy and skill gaps threaten progress

• Collaboration is booming, but increasing concerns over funding and security

• Security and risk management need a strategic and cultural overhaul

We do these kinds of surveys to understand where the research community is moving and how we can tweak and adapt our approach as a company. We were very grateful to the great minds who helped us out with a deep dive into what has affected their roles and will affect their roles going forward. Metrics, Open Research and AI are very aligned with the tools that we provide for academics, and the strategy we have to make research more inclusive, transparent and trustworthy.

The post Presenting: Research Transformation: Change in the era of AI, open and impact appeared first on Digital Science.

However, our second Speaker Series Live lecturer of 2024 – mathematician, concert pianist, and author Dr Eugenia Cheng – is on a mission to change that perception of the subject she loves and showcase the joy, creativity and wonder of maths, and how it underpins our everyday lives, from the food we eat to the entertainment we consume. I caught up with Eugenia ahead of her talk at the Ri in June 2024 in the spectacular Faraday Lecture Theatre to talk about the creativity, wonder and relatability of maths, and how we can all engage with it in ways that bring us joy.

^{*or math, if you’re American and your letter S went to celebrate Labor Day along with your letter U – and, apparently, the skirt-portion of my dress. Always big-chair-test your summer sartorial choices, friends. Even my 100mm Crossfliketas couldn’t brace me in that massive chair so my dress stayed in place, and my only back-up outfit was supplied by the Time Variance Authority, so let’s all agree to just let it go and never mention it again, kthxbye}

Not Just a Mathematician

If you’ve come across Eugenia’s work, you like me will know that she is far from being “just a mathematician” – Eugenia uses her maths knowledge and her educational experience to teach maths at an art school in Chicago, as well as write books and deliver many public speaking engagements. She is also a musician, pianist, composer, and artist. She also somehow still still finds time to conduct research in category theory – the most abstract part of mathematics that focuses on finding patterns within maths and across concepts and situations. Brilliantly, a little like mathsception, “the thing that explains category theory is category theory” – or the “deepest point of explanation” as Eugenia puts it. Her explanation for her topic reminded me of xkcd 435 (and not 438 as I said in the interview, sorry xkcd fans, mea culpa), whereby her area of expertise really is the most fundamental of fundamental understanding.

Unearthing the Foundations of Knowledge

As humans Eugenia thinks that we understand things better if we understand them more deeply. This depth of knowledge enables us to make more connections, so that we can unite a broader range of things – much like in research where connections across subject areas strengthen the research we can do and the problems we can help overcome. In maths this requires a less linear way of thinking. Eugenia says it is all about creativity and also practice. However this is something we often attribute to being a natural characteristic. Eugenia reminds us that it is not just nature, as aspects of nurture definitely play a role too too, mostly through practicing new ways of thinking and doing. Practice really does make perfect, whether it is thinking in a different way for research, or whether it is learning transferrable skills in cooking that help you experiment with other things.

Communicating Maths to the Masses

Eugenia reflect on what a privilege it is to be able to engage with a range of audiences of all ages and interest levels. Although she has received criticism from many in the past, who have commented on what a “waste of expertise” it is for her to share her love of maths with young children, she believes that there is nothing more important to change perceptions. Discussing how challenging it must be to communicate across such a range of demographics, she says that analogy is the key to storytelling and allowing engagement with a range of complex concepts. She also reflects on the concept of practicing as a way of strengthening communication skills, and always listening, and adaptively responding to people’s reactions.

Eugenia is frustrated that the fear or dislike of maths is so widespread – though this is something that continues to motivate Eugenia. She feels that we need to be better at remembering what education is trying to achieve – and maths education in particular. If we are all working towards different goals it is hard to achieve any of them if we employ conflicting methods. Eugenia believes that teaching people how to think and how to learn is more transferable than learning how to carry out specific tasks that we always hear about when people ask why they need to learn how to do simultaneous equations, for example. She argues that maths doesn’t always have to be “useful” – it can be, and should also be, fun!

Eugenia makes a great comparison between learning and physical fitness, describing maths as the “cure strength” of learning. Maths is effective the abs and glutes of your mind. When your maths is strong through practice and training, you are in a stronger position to access other ways of thinking and making connections. A good level of confidence in maths shouldn’t be relegated to helping you calculating a grocery bill or solving an unrealistic maths problems that inexplicably always seems to involve large quantities of fruit.

Removing the Red Tape

Eugenia believes that one way to make maths more realistic could be to make it – and other subjects – less siloed. We talk about the unrealistic siloes of the sciences, reflecting on what a shame it is that physics couldn’t be more interesting at A Level** because it had to be entirely decoupled from maths A Level, but Eugenia believes that we should expand this way of thinking to include the arts and humanities, as well as financial understanding, marketing, and business. She is a great advocate for more project-based teaching to give people more holistic and realistic experiences of how the everyday world works. In undertaking such projects that mimic situations we encounter in every day life, learners can gain a more realistic appreciation of how everything in life involves a range of subjects and skills.

However, moving to such a different way of teaching and learning requires big change and global buy-in. How can we achieve this? Eugenia is realistic in that perhaps it is a big dream to have, but every Disney fan knows that dreams can come true. However, while wishing on a star may not get us very far, perhaps showcasing the value and impact in this new way of teaching and learning, as well as investing in changes by better funding education and make it less bureaucratic could lead to novel design in educational curricula. If anyone can advocate for change in this area, I feel that Eugenia is the person to do it.

**^{Hi, physics fans! We’d like to remind viewers and readers that these opinions are purely subjective to the chemist and the mathematician you see before you. We know that physics is just fine, but if you’d like to complain, please join the queue behind our CEO, who I’m sure will be first in line to defend this most noble of subjects…}

You can watch my interview with Eugenia on our YouTube channel, catch up on Eugenia’s Speaker Series talk on the Ri’s YouTube channel, and find all of Eugenia’s books in your favourite bookstore. You can also check out our Speaker Series playlist on YouTube which includes chats with some of our previous speakers, as well as our TL;DR Shorts playlist with short, snappy insights from a range of experts on the topics that matter to the research community.

With thanks to Eugenia for sharing her time with us, Huw James from Science Story Lab for filming and co-producing this interview, and the Royal Institution for hosting us in their beautiful Faraday Lecture Theatre ahead of Eugenia’s talk.

The post The Reality of Maths – Meet Dr Eugenia Cheng appeared first on Digital Science.

Why understanding the nature of partnerships is crucial to knowledge transfer programs

What are universities for? With pressures on funding, academic freedoms under scrutiny and volatile global demographic changes, the need to prove new knowledge can have tangible outputs has never been greater. Simon Linacre looks at one university in the UK which is leading the way in research transformation.

When Digital Science launched its Research Transformation campaign in early April, one of the key aspects the team behind the initiative wanted to explore was not just the ‘what’ of how research enabled change, but the nature of the connection itself between the two sides. For many of us involved in academic research or in industry, we only see one side of this transformation, but don’t stop to think about what has enabled it in the first place. 

A key element of research transformation is the ability to understand research from different perspectives, and this is often described in policy documents and commentaries in general terms without spelling out the practicalities of what is going on. And yet to fully understand how to make the shift from working within research institutions to achieving worthwhile impact in industry, it’s the practicalities that in the end really matter. 

Knowledge Transfer Partnerships

To help us better understand both the nature of the connection between research and industry and the practicalities involved, what better way than to talk to one of the UK’s leading exponents of knowledge transfer partnerships – or KTPs – in the shape of Leeds Beckett University. LBU has one of the largest student populations in the UK at around 28,000, and ever since it started life back in the early 19th century as Leeds Mechanics Institute, it has had strong links with industry. In the UK, there has been a tradition of KTPs which celebrate their 50th anniversary in 2025. They are partly funded by the government and aim to facilitate the transfer of knowledge and technical skills from universities into industry, as well as improve the skills and business awareness of new graduates.

LBU has been particularly active in this area, with a number of successful partnerships set up with local businesses. Interestingly, these partnerships are not a straightforward transfer of knowledge or expertise from one party to another just for commercial gain, but acknowledge a need on the part of the business for a shift or change in their development, aligned to the business’ wider strategic objectives. We spoke to Jo Griffiths, Head of Knowledge Transfer Partnerships at LBU, to learn more about the connection with businesses and what this shift looks like.

“I am hugely heartened by the sense that businesses see themselves as part of a wider ecosystem and force for good.”

Jo Griffiths, Leeds Beckett University

“I’ve noticed an increase in the number of organizations we’re working with that describe doing something positive and different for the wider community as a key part of their strategic aims. Whether that’s addressing a big global challenge like sustainability – or making changes at a local level to support the growth of the wider business community – I am hugely heartened by the sense that businesses see themselves as part of a wider ecosystem and force for good,” says Ms Griffiths. 

“One recent example is that of NuGreen and sister company QMedical. They are small SMEs based on the border of Yorkshire and Lancashire in the UK. Their aspiration is to turn healthcare waste (all that bagged waste you see in hospitals and clinics) into substitute aggregates, principally sand, for use in the construction sector. Working in partnership with material scientists and engineers from the university via the KTP funding, they’re testing and bringing to market new products by converting the waste into something useful.

“There are many other discussions and projects like this being developed – we’re working on projects with third sector, not-for-profit and charities too. And yes, there is a positive commercial impact to the organization from the proposed partnerships (they wouldn’t get the funding without that) but absolutely key to all of them is how they support others – whether at an individual level, at a community level or as part of a wider shift in societal attitudes and insight.”

Benefits

The advantages of such relationships are clear, and are neatly summarized in the graphic below, where there is a symbiotic relationship between a qualified graduate (known as an ‘associate’) who leads the change, the university which creates the teaching or research, and the organization which sees improved performance as a result of the arrangement. Add in to that environmental benefits as seen in the example shared by Jo Griffiths above, it’s clear why such programs have been embraced by so many organizations and universities in the UK.

But is there also a deeper transformation at play, where the implementation itself can inform further research down the line? One high profile academic at LBU has been involved in a number of KTPs, some of which have yielded significant results for the research he conducts. Dr Jim Morgan is Principal Lecturer at LBU, specializing in Human Factors and Occupational (Health) Psychology, and he has been involved in a number of successful KTPs while working at LBU.

For example, he was involved in a project between major infrastructure services and engineering firm Amey, which partnered with LBU on the Target Zero SafetySmart Project. This came about as Amey was facing a challenge regarding one of its employee commitments, which was to create zero harm for them in what were often safety-critical environments. The senior management team at Amey worked with LBU’s Psychology Applied to Safety and Health (PASH) research group on the KTP, filling the recognized need for formal psychological and behavioral knowledge and skills among Amey colleagues to implement and embed behavioral safety strategies and solutions.

The result after a three-year project with Amey’s Consulting and Rail division and LBU was an agreed approach that included both quantitative and qualitative psychological research methods that created a flexible, long-term framework. This, alongside a more detailed understanding of accident risk saw a decrease in incidents, and also led to some cost reductions linked to accidents and incidents.

Case study

LBU has had not one but two successful KTP case studies working with the well-established rail infrastructure companies, Amey and VolkerRail. The following details are drawn from LBU’s REF Impact Case Study in 2021.

BACKGROUND: Human safety is understandably critical to the railway maintenance industry. The UK Network Rail workforce safety statistics for the five years up to 2013/2014 show that major injuries rose by a quarter, and lost time to injuries more than doubled in that time. In addition to personal suffering, the financial cost of workplace injuries was estimated to be nearly £5bn at this time.

At LBU, the Psychology Applied to Safety and Health (PASH) research group – led by Dr Jim Morgan and Dr Matteo Curcuruto – are involved in a research program focused on “helping safety-critical organizations to translate Organizational Psychology, Human Factors, and Health Psychology research knowledge into enhanced behavioral safety management practices”. This research has been funded by, among other sources, two Knowledge Transfer Partnerships (KTPs) in collaboration with industry partners, namely Amey and VolkerRail.

RESEARCH: The research employed in the programs used a ‘bottom-up approach’ to directly involve workers from the start. This approach aimed to generate improved communication, trust and a better culture of safety, with research conducted with workers at the two rail companies, as well as other workers from similar industries.

IMPACT: The result of the SafetySmart project (with Amey) has been clear, with rail operations reduced by a third in lost time injuries and a quarter in non-lost time injuries. Overall, the company estimates as a result of SafetySmart turnover will increase by over £1.3m in the three years after the project finished.

The programs have also achieved industry recognition, with the Amey KTP awarded the highest rating of “Outstanding” by Innovate UK assessors based on impact for the firm, and second highest rating of “Very Good” for the VolkerRail YourAIM project. 

Additionally, VolkerRail estimates that as a result of the YourAIM project turnover increased by a quarter of a million pounds, with a further £1m in the three years following the project being completed.

SUMMARY: Research by PASH has undoubtedly developed the safety culture at both Amey and VolkerRail, with demonstrable improvements in hours worked and turnover. Moreover, both companies have now implemented robust behavioral safety protocols to keep an eye on safety-critical workflows, further improving safety. The KTP program therefore not only improves human working conditions and business outcomes, but is also part of a symbiotic relationship with research, providing rich data for further research at universities like LBU.

See Jim and Matteo’s KTP-funded research articles in Dimensions here.

Future REFerence

The ability for universities to show impactful work outside labs and field studies is particularly important in the UK due to its system of research funding, known as the Research Excellence Framework or REF. Billions of pounds of research funding from the government is determined by how universities and their departments perform in their research programs, and KTPs are a key plank in an institution’s strategy to show how their research can have a positive impact on lives and social wellbeing. In Dr Morgan’s words, KTPs are “simply brilliant for the REF”, as they show the impact research programs at universities can have, as well as helping with the recruitment and work experience of postgraduate students, many of whom find employment with KTP partners when their studies have been completed.

So, there are numerous reasons why a university such as LBU should pursue KTPs, and rightly be proud of what they have achieved with the numerous projects they have supported and delivered. What is interesting from a research perspective is the importance of the role people play in the projects, both in terms of the interactions between the university and external organizations, as well as the outcomes and how they positively impact individuals’ lives. In addition, it is also clear that the individual researchers themselves gain a huge amount of wider knowledge from their work over and above the project focus. All in all, given the right structure and opportunities to make a difference through research, researchers can do just that.

The post What a difference making a difference makes appeared first on Digital Science.

Introduction

The Altmetric 500 is a new initiative that digs into the context of research output and the detailed measures of attention per source. We know Altmetric delivers insight into the wider impact and influence of academic articles, but how does this new initiative work behind the scenes?

New in 2024

This year, we decided to use Altmetric to investigate more closely global attention to research; we looked at articles and books with the most attention by geographic region and by a number of categories from news media, blogs, social media, policy documents, Wikipedia, and patents, as well as research that had broad attention across multiple mention source categories (the calculation is described below).

And, thanks to the recent release of Altmetric on Google’s BigQuery (GBQ), where this deep-dive report was put together, it can be rebuilt, tweaked or customized by anyone with a licence to the data. By introducing additional filters, a whole new set of insights has been created:

• publishers can use the data to understand how, where, and by whom their published content is being shared; 
• funders can learn where their funds made the biggest splash; 
• research institutions can focus on the research produced by themselves and similar institutions. 

Because we believe strongly in data availability, we have shared the code we used on Figshare to be accessed, copied, and reused by others (for non-commercial purposes, of course).

It is our belief that there are no choices in the study of science that come without exclusion; the purpose here is not to provide a definitive list of the “best” papers published in 2023 (December 2022-November 2023). The list (like this post) shows that “attention” can mean many things, and what really matters is the question one is asking. We’d like to open the conversation, and look forward to seeing what the research community does with the data.

How we put this report together

Initially we broke publications down by field of research, using the ANZSRC 2020 Fields of Research (FoRs); the United Nations Sustainable Development Goals (SDGs); and exclusively regional collaboration. The results show in brilliant colour what we already knew to be true: the deeper you dig, the more remarkable science turns out to be!

Through the magic of machine learning, Dimensions categorizes research output, assigning Sustainable Development Goals (SDGs) and fields of research (FoRs) to the appropriate publications. (More information about how that magic works can be found here for SDGs, FoR identification, and the Dimensions Data Approach in general.)

Global regions (listed in Table 1) were assigned with the aim of democratising our attention to the research landscape: for the regional highlights, we limited our results to those papers produced in collaboration within these regions (excluding those where there was collaboration outside the region). This approach to geographic distribution is deliberate but open to discussion. It was important for us to display a more equitable distribution of publications and not have data from some regions be overshadowed by countries that have an extremely high number of research publication outputs. Therefore, for example, Mexico is grouped with Latin America and the Caribbean instead of North America, and Oceania excludes Australia and New Zealand. 

Methods

We defined our criteria for inclusion: publications that have been published between December 2022 and November 2023, with the type of chapter, article, or book. In calculating mentions and citations, we excluded any which occurred after December 1, 2023 (or were identified by Altmetric after June 25, 2024). 

Attention

We counted the number of social media profiles (from Reddit, Facebook, X/Twitter) which had mentioned the publication (‘social_media_attention_sources’ in the breadth of attention calculation), the number of mentions in news or blog sources, and the number of citations in policy documents, patents, or Wikipedia pages.

Categories

For each publication, we used the Dimensions-assigned fields of research, any related UN Sustainable Development Goals, and the countries of the research organizations affiliated with each publication (a total of 555 categories, including the overall tops). Some publications were the top mentioned in more than one category or attention type (there are 362 unique articles in the Altmetric 500 of 2023). 

Breadth of Attention

Figure 1 represents the calculation used to highlight breadth of attention. This calculation begins by identifying the highest number of social media attention sources, news/blog mentions, policy mentions, and scientific citations within each category (geographic region, research field, and SDG). The mentions received by each research output are then divided by the maximum number of mentions per category. These adjusted values are summed and averaged by four, to determine a “breadth of attention indicator” for each paper within each category. Each paper receives multiple breadth of attention indicators as it is evaluated across three different parameters: geographic region, research field, and SDGs.

Some Initial Observations

An interesting feature of the attention to research is that specific research output often gains attention in one attention source category while barely acknowledged in another. This holds true for the Altmetric 500 in general, and was notable especially in comparing the most-mentioned research output by Wikipedia pages versus news mentions (Figure 3). It was also interesting to note that the publications with the most attention in these venues were not necessarily those with relatively high scientific citation.

However, despite the tendency to excel in one category of attention rather than many, we would like to highlight the publications which garnered the most attention from multiple sources: in policy documents and scientific citation (Figure 2); in policy documents and news articles (Figure 3); on Wikipedia and in the news (Figure 4); or in the mainstream and social media (Figure 5). Hover over the scatter points on these images to see the titles which were most mentioned in these attention categories.

Policy vs. scientific citations

^{Figures 2 and 3.}

Some examples we would like to highlight include:

1) One book in a series of three (Climate Change 2021 – The Physical Science Basis, Climate Change 2022 – Mitigation of Climate Change, and Climate Change 2022 – Impacts, Adaptation and Vulnerability), each of which made the Altmetric 500 for 2023, in multiple categories (with the most mentions in policy documents, on Wikipedia, and in scientific citations, in both the fields of Environmental Sciences and Economics, and the UN SDG Climate Action.

2) Introducing ACLED: An Armed Conflict Location and Event Dataset was the top of scientific citations and policy attention for publications produced within a single geographic region (in this case, the University of Oslo, in non-EU Europe), SDG 16 – Peace, Justice and Strong Institutions, and the field of Human Society.

3) Finally, COVID-19 pandemic and mitigation strategies: implications for maternal and child health and nutrition was the top for both scientific and policy citations in the UN SDG 1 – No Poverty and 6 – Clean Water and Sanitation, as well as top in scientific citations for the SDG 2 – Zero Hunger.

There are also articles which have had a relatively high level of attention from the scientific community (indicated by citations) and also received attention in policy documents (one of which has been mentioned in a patent application), but were not picked up by news media, blogs, or in social media. An example of this phenomenon is Atoms in molecules : a quantum theory.

The scatter plot of news and blog mentions relative to policy document mentions shows that most attention to the tops are either highly cited by news/blogs or policy, but the few papers which were the most highly cited in both highlight some of the major issues of 2023: GPT/LLMs and long Covid, as well as areas of perennial attention (climate change and autism). The publication Introducing ACLED had the highest number of policy mentions (in 60 policy documents from policy organizations including the World Bank, the Food and Agriculture Organization of the United Nations, Africa Portal, and the National Bureau of Economic Research), but did not have any news attention at all. 

News vs. Wikipedia and News vs. X attention

^{Figures 4 and 5.}

Finally, the European Commission (EC) had the greatest number of titles and top categories among funders; these numbers rise substantially when European Research Council (ERC) grants are also included. The titles associated with these grants are listed in Table 4. These titles are often in the top of one or more SDGs and one or more fields; they top news and blog mentions, have exceptionally broad attention and include tops of Wikipedia, X profiles, and Facebook walls.

We cannot explain here why publications gather more attention in one source than another, but Altmetric exists to provide the kind of data about attention that could be used to investigate that question.

Next Steps

Over the next few months, we’ll be exploring many facets of this dataset. The wide range of sources of attention make this an interesting dataset, no matter what kind of attention is interesting to you. Connecting the results to Altmetric on GBQ makes the results sortable and aggregatable by publisher, journal, funder, and affiliated university (see them listed below). 

Tables 4-8.

We’ll also be comparing this method to the approach of the previous Top 100 lists. To set the stage for that analysis, keep your eyes peeled for Ten Years of the Top 100, coming soon.

A note of caution

Among the Altmetric 500 are a few articles which may have attracted attention for unexpected reasons. At Digital Science, we think all research should be visible and that includes research published in non-indexed and emerging journals, or on preprint repositories. It also means that we don’t remove retracted papers from our dataset. We highlight them, include retraction notices in our publications dataset, and follow the publisher’s title edits to indicate retractions. It should come as no surprise to followers of recent news that retracted articles also garner a lot of attention. We track that, too. We think this information is worth having, and worth studying (see our own Leslie McIntosh’s posts on the misuse of research in law and identifying plagiarism. Which retracted articles get attention tells us a lot about what matters in public discourse, and the fact of article retraction tells us a lot about the challenges that confront science in our digital publishing world and in a culture of “publish or perish”. 

For example, Nature’s RETRACTED ARTICLE: Evidence of near-ambient superconductivity in a N-doped lutetium hydride is in this year’s Tops, having been discussed by the most news/blogs, the most YouTube videos, and having the greatest breadth of attention in the field of chemical sciences. There was a big buzz about the results of the paper when it was published, because the world is waiting for better solutions for conductivity. The Dimensions Research Assistant tells us that, “Near-ambient superconductivity matters because it brings the prospect of superconducting technologies operating at practical temperatures and pressures closer to reality, potentially transforming energy transmission, storage, and magnetic applications with unprecedented efficiency and reduced environmental impact (Mourachkine, 2006).” There was a second burst of attention following the retraction note published in November 2023 (which also gained substantial attention). 

Data Sources

The Altmetric 500 can be found in Figshare: https://doi.org/10.6084/m9.figshare.25047692

More about Altmetric on GBQ can be found here: https://www.altmetric.com/solutions/altmetric-on-gbq/

The post Altmetric 500: Methodology appeared first on Digital Science.

The first commitment of the Barcelona Agreement articulates that, ‘We will make openness the default for the research information that we use and produce’, but who ‘we’ are is critical in understanding all of our roles and responsibilities in the research ecosystem. Funders, publishers, infrastructure providers, institutions and researchers all have different ways of interacting with data in their contexts as producers, consumers and aggregators of data.  

The Barcelona Declaration is perhaps the first document to begin to frame community responsibility with regards to consuming open metadata. Yet, it is just that, a beginning – we believe that understanding with granular detail what should be expected from each part of our ecosystem is critical in making Barcelona actionable, to drive us forward into a more open metadata landscape. Indeed, open metadata is only important if we commit to using it in our practice, allowing it to shape the way that we interact across the research world.

A commitment to consume, however, still requires us to pay attention to the type of open metadata that we use, the contexts in which we apply it, and the expectations that we place on others when doing so. Without explicitly articulating our roles both as creators as well as consumers of research metadata, we risk creating an open, yet untrusted research landscape.

Not all metadata are the same

There is a fundamental asymmetry between production and consumption (and also aggregation). Whilst the responsibilities associated with creating metadata are relatively easy to articulate, the responsibilities around consuming and aggregating metadata are not so well thought through as, to this point in time, this has been the less proximate issue.  (Indeed, Barcelona makes it clear that we have reached a milestone in that we now need to consider this issue.)  We argue that responsibilities around consumption are contextual in nature, depending on the provenance of the metadata itself, and work needs to be put into articulating these responsibilities for each participant and use case. In the context of the recent Barcelona Declaration then, it is useful to explore some of the different ways metadata can be created and then exploring what responsibilities could result for consumers.

Within the Barcelona Declaration there are (at least) three different sorts of metadata records that are implicitly referred to:

Open metadata records

Open metadata records are those that have been created from inception with open research principles in mind. For example, a publication created under these principles will have an ORCiD associated with each researcher and a ROR ID associated with each affiliation. Within the body of the publication (and its metadata), funding organisations will be linked to their Open Funder Registry ID (or ROR ID), and the grant itself will be linking to open persistently identified grant records (for example via the Crossref grant linking system). The publication itself (along with a rich metadata representation) will be associated with a DOI, and all references that resolve to a DOI will also be openly available. When we speak about open here, we have in mind a CC0 licence for these data. Within the paper itself we might expect to see other links such as a link to a data repository, along with other trust markers that establish the provenance of the paper and situate it within the norms of good research practice. We might have similar expectations for grants, datasets, research software code, and other research objects.

Algorithmically enhanced records

Algorithmically enhanced records are metadata records that have had elements derived from algorithmic processing that was not part of the original record. The algorithm may not be open, the approach used may not be known and the probability that the metadata is correct may also not be known. (This is something of a hidden variable in many analyses today – it is generally assumed that data in an article may have statistical variances but that metadata describing an article does not.) Many publication records that have been created over time do not meet our current requirements for metadata openness. Either the technology (or identifier infrastructure) did not exist at the time that they were created, or good metadata practices have yet to take hold within the context that the record was created. For records such as these, algorithms are used to enhance the record with identifiers. Prominent examples include algorithms that are used to identify institutional affiliations, but also to reconstruct researcher identities. Algorithms can also be used to enhance the description of a record by adding links to external research classifications that would never have existed in the original metadata.

This type of data is likely to become more and more commonplace as LLMs and other AI systems are becoming more easily and cheaply available. And hence, it is likely that for some years to come metadata will have inbuilt statistically generated inaccuracies which may be ignored by the community at large, if they can be proven to be negligible in key analyses.

Institutionally enhanced metadata records

Institutionally enhanced metadata records are those enhanced through university processes for the purposes of institutional and government reporting. These records, harvested from multiple sources, or manually curated, may have additional metadata associated with them. An author on a paper might be associated with an institutional ID, new research classifications might be added with links to dataset. These institutional records might be made public through institutional profiles or syndicated to larger state or national initiatives.

What are our responsibilities when using and reusing research metadata?

The text of the Barcelona Declaration treats all three types of metadata that we have defined above to be on an equal footing: To be shared under a CC0 licence, allowing an unrestricted ability to reuse. Issues of licence aside, the way we reuse metadata should be informed by the provenance of the created information.

When considering how to implement the objectives of the Barcelona Declaration then, it is worth thinking carefully about a general approach to the responsibilities associated with reuse. As with the Barcelona Declaration, we propose these as a beginning and a discussion rather than an absolute. Refining these responsibilities will take community discussion. 

Here are three responsibilities that we think would be useful to begin the conversation:

Responsibility 1. The purpose for which a piece of metadata is intended to be used  must place a limit on both the scope (types of interpretation) and range (geographical, subject or temporal extent) under which it can be responsibly used

Beyond considerations of openness, the context of the data that is being propagated needs to be considered. Metadata is generated for a purpose, and that purpose defines the accuracy and care to which the metadata is applied. It also defines the limits and responsibilities for maintaining its accuracy.

For institutions, the Barcelona Declaration explicitly identifies Current Research Information Systems (CRIS systems) as one mechanism to make research information open. It is required that all relevant research information can be exported and made open, using standard protocols and identifiers where available. This requirement builds on a movement,  initially gaining traction around 2010 with the VIVO and Harvard Catalyst profiles projects funded by the NIH. The key use cases for these public profiles has been expertise finding, either at the institution, state, or national level. The key insight of this movement is that information collected for internal reporting and administrative purposes could also be used to create public profiles – a single source of information efficiently driving multiple uses. In some cases the approach of CRIS aggregated information has been taken further to create state-based portals such as the Ohio Innovation Exchange, or national open research analytics platforms such as Research Portal Denmark. Although successful, the nature of the provenance of these records means that there are practical limitations to the way the information can be reused beyond these applications.

Implicit in the name of a CRIS is a key limitation. CRISs are used to maintain/modify/aggregate information about ‘current’ researchers. There is (for an institution) no implied duty of care for the maintenance of public information about past staff. Indeed, from the perspective of expertise finding it may be inconvenient to have these profiles remain discoverable in the same way.  

Metadata within CRIS systems are also often collected for a politically aligned purpose such as the demonstration of value to voters (which is often presented as a national purpose in the form of government reporting), and can lead to unbalanced metadata records when used in a broader context. For instance, publications recorded for the purposes of national reporting might very accurately record the researcher affiliations within a country, but will be significantly less accurate on international affiliations for whom the reporting exercise has little bearing.

Records can become unbalanced in other ways too: research can be classified to reflect the goals of the individual reporting exercises (a point that we wrote about in detail in our article on FoR Classification) – both in terms of the classifications that are applied, and the time and effort to which those classifications are maintained, and the scope of research classified. If there is a purpose to reusing this classification metadata in a different context, the provenance under which it was recorded must be maintained and understood. 

A potential interpretation of the Barcelona Declaration could be that all metadata must be curated with the understanding that it will be used and consumed within the broader research community in perpetuity. If this is the intended interpretation, then we should be realistic about the extra effort that this requires, both in terms of effort and the structures that should be put around the codification and documentation of data curation approaches. This interpretation also instantly begs several practical questions: Does the storing, and passing on of a metadata record imply a responsibility to keep it up to date forevermore? What inequalities would this interpretation place on the broader research community? Specifically, does this interpretation advantage the “metadata rich” (those with the infrastructure to invest in improving records) and disadvantage the metadata poor (those who have poor embedded mechanisms or post hoc mechanisms for the curation of metadata)? This concern is not hypothetical, as current lack of visibility of African research has hindered efforts to comprehensively understand, evaluate and build upon African nations.

There are of course already remedies to address many of the persistence challenges associated with making institutional metadata open. One mechanism is to transfer the responsibility for the metadata from the institution to the individual researcher via their ORCiD. Within this workflow, researchers remain responsible for maintaining a public record of their outputs, and institutions can maintain responsibility for asserting when a researcher worked for them. Coupled with a national push to publish research in open access journals and repositories, the Barcelona Declaration complements the approach taken by national persistent identifier strategies as they move towards PID-optimised research cycles. 

Responsibility 2. Machine-generated metadata should not be propagated beyond the systems for which it was created, without human curation or verification

Machine-generated metadata, such as the association of an institutional identifier to an address expressed as a string, research classifications, or algorithmically determined research IDs are all generated within precision and recall tolerances. These tolerances are set by system providers, and are aligned with the requirements of their users. Individual statements, however, are not guaranteed against any particular record. What is more, algorithmically generated data can be regenerated as methods improve, potentially invalidating records from previous runs. This notion defines a hitherto overlooked metadata provenance. Without accompanying provenance, metadata can be considered to have ‘escaped’ from its originating system and runs the risk of being “orphaned”, with no ability to be updated or appropriately contextualised. To move an algorithmically generated metadata record out of the context of the system for which it was created must be to take ownership of the provenance and the statements that can result from its use.
Whilst not so much of a problem for publications (an updated version of the record can always be requested using the DOI,) this is particularly problematic for algorithmically generated researcher IDs, as (in the case of an identifier that refers to more than one person), improved algorithms could radically change the identity that the researcher that the identifier refers to. In the case of a researcher record that split because it was really two researchers, the existing researcher ID could end up pointing to a different researcher.

The Barcelona Declaration is right to focus on data sharing practices using standard protocols and identifiers where available. But here too, care must be taken to assess where metadata has come from as many algorithms associate a persistent identifier with a metadata record. For instance, if an ORCiD is used instead of an internal researcher ID to refer to a researcher, but the set of assertions that are produced have been algorithmically generated, then communicating these assertions outside of the system that they were generated breaks the model of trust established by ORCiD.

Responsibility 3. Ranking platforms should be independent of the data aggregations from which they are drawn

A key use case enabled by algorithmically generated metadata is comparative research performance assessment, often encoded in rankings systems. At a first glance, this responsibility may appear to be incompatible with responsibility 2 – if metadata should be strongly coupled to its provenance and context, why should it be divorced from the ranking use case? We regard this issue as being similar to a separation of evaluation bodies and those being evaluated. Because of the different choices that different scientometric platforms make with regards to precision on recall, the same ranking methodology can lead to different results when implemented over different scientometric platforms. However, rankings systems are often entangled with single systems, providing perverse incentives for institutions to engage (both in terms of investment and data quality feedback) with one dataset over another. 

One benefit of the focus on persistent identifiers that the Barcelona Declaration is that information assessment models can (and should) be constructed without reference to individual scientometric datasets. By decoupling data aggregations from the rankings themselves, we allow new data aggregation services to emerge without locking in single sources of truth. In this way scientometric data sources should be treated like Large Language Models LLM – extraordinarily useful, but with an ability to swap out one for another. Perhaps we need to add another R (replaceable) to FAIR data principles for scientometric datasets. 

The decoupling of data from ranking also has another effect, in that it discourages investment in the data quality of a single system, and focuses on either improving data at the source (for instance Crossref) or by improving independent disambiguation algorithms (such as those offered by the Research Organization Registry).

To develop an independent rankings infrastructure will require agreement to use not only the persistent identification infrastructure that we have, but a commitment to develop systems that refer to external classification systems. 

Can we go further? Building on a commitment to independent rankings infrastructure for instance, Is it reasonable to expect a common query language for scientometric research and analysis across scientometric systems?

The beginning of a conversation…

Finally, from the exploration above, we hope that we have made the case that our responsibilities as metadata consumers go beyond simple considerations of licence or platform. With the current state-of-the-art in research infrastructure our experiences of how to facilitate open data are not embedded in metadata and do not travel with it. How we use metadata places unclear expectations on others, and affects perceptions of trust in our analysis or in the research information system more generally. As the Barcelona Declaration moves from declaration to implementation, perhaps even blending with evolving national persistent identifier strategies, we hope that these considerations form part of the continuing conversation.

The post The Barcelona Declaration… exploring our responsibilities as metadata consumers appeared first on Digital Science.

Digital Science welcomes the Barcelona Declaration as a force to continue pushing forward not only openness and transparency but also innovation in and around the scholarly record. Following the launch of this important initiative, we reflect on Digital Science’s path and historical contributions, the economics of maintaining the scholarly record, and its future.

Dimensions is built around open data

In many senses, Dimensions is a demonstration of what can be done when data are made freely and openly available. It would not have been possible to build and maintain Dimensions without the work of initiatives such as I4OC, and the data made available by CrossRef, DataCite, PubMed, ORCID, arXiv and many others. Many pieces of the Dimensions data system leverage use of public sources, and we believe that it is only right and proper to have a version of our product that is available to the community for research purposes at no cost – hence, the free version of Dimensions that we have maintained since 2018, and which we will continue to maintain into the future.

However, service to the community was not the only reason to create a free version of Dimensions back in 2018; it was also about ensuring that researchers had access to search the scholarly record for free and about ensuring that, in an era of increasing research evaluation and increasing research on research, there would be a platform where anyone could go to check results from an analysis or evaluation exercise. At that time, we wrote a paper stating our rationale and principles behind the development of Dimensions and wrote a follow-up piece announcing and committing to continued access for academic research.

In summary, and relevant to current developments, we believe that:

• Researchers have a fundamental right to access research metadata to further their research;
• Research into bibliometrics and scientometrics, and related fields, needs to have a basis for reproducibility and we seek to participate in that ecosystem to ensure that any analysis carried out using Dimensions data is reproducible;
• Data that are used to evaluate academics or institutions should be made available in a way that allows those being evaluated to have an insight into the data on which they are being evaluated.

There is, however, an important additional component that goes beyond these principles – innovation.

A more complex picture

Before we talk about innovation, it is important to acknowledge that Dimensions is not solely built on open data. Indeed, it is a mixed environment with data of different types describing different research objects using different sources.  This leads to significant complexity in the data pipeline and in the work that needs to be done to provide “analytics-ready” data. However, for the purposes of the current discussion, it is helpful to understand a bit about the different nature of the sources of data used in our data products. These include open data from open sources. When data are published under a CC0 licence (as Digital Science did with its GRID dataset in 2017) then it is unambiguous that these data may be used in any context, commercial or noncommercial, and that they may be merged with other datasets for the purposes of creating new and better things. It is an interesting question as to whether a Digital Science “mirror” of these helps to make the research infrastructure more robust and easier to access. 

Our products also make use of licensed data. These are data for which we have an agreement that restricts its use. Examples can range from research articles; grant data from funders; and, patent documents. They can also include data licensed into products such as Altmetric, which includes data from news providers and social media platforms such as Twitter (X). These data can be expensive to acquire and can only be used and made available in our products within certain limits, even where they are already in the public domain.

All these data and data that are derived from them, even if already freely and openly available, can require substantial resources to compile and process. Examples of such derived data could include funder details, details of ethics statements, conflict of interests, data availability statements, and so on, that Digital Science has transformed, enriched and contextualised. All are activities that take significant investment and add significant value to those who use it. We expect that these types of data will increasingly become part of the Open dataset as the research ecosystem matures. Yet, as we innovate, these are also the data that cost Digital Science the largest investment to produce and maintain, including where this may be done in an automated manner. The infrastructure behind Dimensions is not simply a platform that takes data from open sources and then reserves it for users to consume; rather, it is a complex and expensive mechanism for compiling, refining and improving data so that it can be discoverable, useful and analytics ready.

Taking author contribution statements as an example, the Dimensions team has invested in the creation and curation of AIs that identify author contribution statements across the research literature. These AIs operate at a level of accuracy that still needs improvement, and hence further investment. Neither the scholarly community, nor publishers, nor standards organisations have defined or accepted a standardised data format that makes author contribution statements widely available. As such there is a significant cost to data processing. On top of this, innovations such as the CReDIT taxonomy are neither universally or evenly applied. The use of CReDIT would be of significant value to sociologists who study the research community, as well as to the evaluation community and anyone involved in tenure and promotion processes. And yet, there is no accepted structured data format that makes these data easily available. As such, the Dimensions team is working on the development of a CReDIT data structure and the creation of these data at a level of quality where they can be trusted and used in these important use cases.

As the research ecosystem matures, what should the path from algorithmically generated information back to openly available data with a defined provenance be? One option is to provide enhanced metadata back to publishers to enhance the scholarly record where gaps exist. Arguably, it is not enough for data only to be open – it should be owned by the community that created it, which includes ensuring the context and provenance of the data are maintained. This process has happened many times before, most notably during the application of DOIs to the historical scholarly record.

A model for thinking about innovation

To make sense of this complex landscape we have a mental model that we use to think about the developing world of open research metadata.

The area outside the outer circle (or horizon) can be thought of as all unpublished articles and all articles as yet unprocessed. With time the outer circle expands encompassing both more detail about the existing published literature (new fields, greater accuracy) and the detail about newly published work. At the horizon of the circle the data are mined and fall inside the circle. The fact that the circle expands is important in this model as the effort to derive the data does not expand proportionally to the volume of data refined, but it does increase. The horizon is representative of the ongoing investment in innovation that is required to derive and improve data from raw, unstructured formats. In practical terms, some cases require humans to identify data from texts; in other cases humans write and train AIs to create annotations and make them available.

The inner circle (or the “beautiful” horizon) can be thought of as open data or data that has become so inexpensive to make available as part of increases in efficiency of the production process that it is completely commoditised. These are data that either cost little to provide or are already refined to the point where little or no innovation is required to make them available. Examples include article title, journal name, page number, DOI and, most recently as a result of I4OC and I4OA, citations and abstracts.

The area between these two circles is where the friction at the heart of the Barcelona Declaration exists. A few years ago, it might be argued that there was no inner circle and yet, over the last 20 years, projects including PubMed, Crossref, I4OC, I4OA and pre-print servers such as RePEc and arXiv have slowly created a space for open data, either through community action or technological progress. Among the contributors to this effort there are some notable players including the Microsoft Academic Search project, 1science from the team at ScienceMetrix, and others.

Such a model is not unusual in other contexts, nor is it surprising that it is the natural point of friction. Determining the time for which an innovation should be profitable and the level of profit is not a trivial problem – it is sometimes left to market forces or sometimes is the result of legislation. In the context of copyright law, which was originally developed to protect creativity, the distance between the circles is determined by law to be 70 years after the death of the author in many geographies, although there are variances. Perhaps closer to home, and less legal (but nonetheless social-contract-style) agreements include humanities PhD theses, which often have an agreed two-year embargo period during which the student has the opportunity to develop and publish a book or otherwise build on top of their work.

There are other non-legislative mechanisms that also determine the distance between analogous horizons in other contexts. One might argue that the creation of a new patented invention is like the innovation horizon of the outer circle, whereas the beautiful horizon of the inner circle is the creation of parallel developments that seek to achieve the same ends as the original invention via different mechanisms. Typically, the time taken for competitors to duplicate an approach, might take several years. At some point the patent will expire, but it may already be rendered useless by the innovations of others.

Perhaps uniquely in the research information sector, Digital Science has pushed both horizons – pushing the innovation horizon:

• Connecting patent data, grant data, clinical trials and datasets in an analytics-ready dataset;
• Pioneering the creation of article level metrics with Altmetric;
• Pioneering the open sharing of research data at scale with Figshare;
• Improving the timeliness of data;
• Innovating to democratise the application of computational power to bibliographic data;
• Showing how to connect bibliographic data to wider contexts to leverage investments in open identifiers;
• Developing the first SDG categorisation;
• Developing transparent categorisation approaches using AI;
• Measuring the progress toward the adoption of OCRID against algorithmically identified research identifiers;
• Making a free ChatGPT experience available that leverages the content in Dimensions;
• Contributing to the ongoing discussions to protect the scholarly record by the open documentation of approaches to identify paper-mill activity;

as well as pushing the open data horizon:

• Making grant data freely available to funders who award less than $1m in funding per year;
• Making Dimensions available for free to Research4Life;
• Making GRID institution data available CC0;
• Making dataset searching and citations freely available in Dimensions and Figshare;
• Making ANZSRC and SDG classification freely available in Dimensions;
• Making grants data available to ORCID profiles through the UberWizard.

Taking a pragmatic position suggests that the annulus needs to be determined dynamically rather than systematically. If an individual or a company invests in pushing the innovation horizon then they are taking a chance on improving the data that researchers and other stakeholders have to make better decisions, gain deeper insights or be more efficient, and there should be an incentive to continue to invest in innovation. If the innovation is incremental or easy to replicate then the returns will be small as others should easily duplicate it. If the innovation is significant then it will be harder for others to reproduce and hence it will take a longer period before competitive forces come to bear.

A step change in technology can upset the equilibrium and change both the current competitive dynamics as well as the future focus of innovation. Machine learning was one of the key technologies that has allowed the Dimensions team to push resources into innovation over the last few years, and enhancements in the AI landscape with large language models (LLMs) will continue to fuel these developments.

At Digital Science, our belief is that by taking risks, being innovative and pushing boundaries, so that clients gain real value and significant benefit from our offerings, there should be an opportunity for an appropriate return on investment. We believe that the chance to profit is naturally kept in check by competition, which typically pushes the outer circle, by initiatives such as the Barcelona Declaration, which often advance the inner circle, and by our own mission as Digital Science to support and serve research and the community around it, where we have clearly demonstrated the ability and the will to move both circles.

The Future

Using the model above, it made sense in the past that scholarly information would be closed. In the 1950s, when Eugene Garfield started the Institute for Scientific Information, the investment required to construct the science citation index was significant. Indeed, it was Garfield’s realisation that 80% of the citations related to 20% of the literature which turned the problem of citation tracking into one that was tractable with technology contemporary to the era.

The investment that needed to be made to “mine” the publication and citation information, given the level and nature of scholarly information infrastructure at this time, was vast. Hence, it is unsurprising that the Science Citation Index was, in essence, the only such index for almost 50 years. With the digitisation of the scholarly record towards the end of the 20th Century, the bar to entry was lowered and PubMed, Crossref, Google Scholar and Scopus were all innovators, introducing competition and, ultimately, creating the Open Data Horizon.

In 2018, Dimensions made use of successive innovations from the community, such as I4OC, together with machine learning to lessen the distance between the two circles.

In the next 10 years, with technological advances in how we write and publish scholarly output, we see a world in which much of the metadata is simply available at the point of production as open data – a true realisation of the Barcelona Declaration. At this point, the distance between the two circles will be zero, with the innovation horizon and the open data horizon coinciding. The effective cost of production of the data will be zero.

So, what will be beyond Barcelona? There are still many challenges regarding research information – there will probably be a further period beyond the Barcelona Declaration’s aims in which, as we already are, we start to invest more heavily in information provenance, the integrity of research information, and in understanding sentiment and bias in the research literature. Our field of focus will shift to ensuring that we can trust the information that will be increasingly important not only in decision making but in forming the basis of AI curricula in the future.

I have confidence that in an innovative field such as research, innovation will continue to be expected of those who seek to serve the space. While Barcelona defines a beautiful horizon, that is still compatible with an endless frontier.

The post Barcelona: A beautiful horizon appeared first on Digital Science.

We begin our reflection on transformation with perhaps one of the most unremarked on, yet most pervasive changes in research – the switch between initials and full first names in the author records. As we will see, the shift from the formal to the familiar has been in flux from the start of scholarly publishing, however – particularly in the last 80 years – we can trace the influence of countries, fields of research, publishers and journal submission technology, funders and scholarly knowledge graphs on author name behaviours. In more recent history, we can observe that the shift towards full names has also been gendered, particularly in medicine, with men shifting towards full names earlier than women. 

Why does it matter? The increase in transparency afforded by first author names is not simply a curiosity. First names, in the ethnicities and genders that they suggest, provide an (albeit imperfect) high level reflection of the diversity of experiences that are brought to research. It is just as important to see ourselves reflected in the outputs of the research careers that we choose to pursue, as the voices that represent us on panels at conferences. Framed this way, the progress towards the use of first names is part of the story of inclusion in research. The ‘Initial Transformation’ is also an initial problem.

Fortunately, the use of initials as part of author names has been in steady, if gradual, decline. The full details of the “The Rise and Fall of the Initial Era” can be found in our recent paper on arXiv: https://arxiv.org/abs/2404.06500. 

Below are six observations from the paper:

The transformation from initials to full first names is part of the the broader transformation of the journal article as technology

The form of a research article itself a the technology used to encode the global norms of science. As a key building block of shared knowledge, the evolution of the form of a research article must be at once slow enough to allow the discoveries of the past to be understood today, and flexible enough to codify new patterns of behavior (such as researcher identifiers ORCiD, funding statements, conflicts of interest, authors contribution statements and other trust markers). 

Over time, not only has the structure of the content of a research article evolved, the way that authors are represented has also changed. From 1945 through to 1980, we identify a period of name formalism (referring to authors by first initial and surname). This is the only period in the history of publishing where initials are used in preference to full first names. We call this period the ‘Initial Era’. 

In the ‘Initial Era’, we suggest that accommodating a growing number of authors per paper on a constrained physical page size encouraged the formalism towards initials. From 1980, full names begin to be used more commonly than initials marking the beginning of the ‘Modern Era’. Within the ‘Modern Era’, name formalism continues a gradual decline through to the 1990s. In the period between 1990 through to 2003 – a period of significant digital transformation in which the research article was recast as a digital object, name formalism drops steeply. After 2003, the decline in name formalism is less steep, but steadily trends toward zero.

The story of the Initial transformation is one of different research cultures becoming homogenised

The US is the first country to shift towards the familiar, followed reasonably quickly by other western countries, with France perhaps holding out the longest. Slavic Countries are more formal for longer but also increasingly shift towards familiar names. At the bottom of the graph (see below) in green, are three countries in the Asia-Pacific region – Japan, South Korea and China. For these countries there is no concept of a first initial, and where names have been anglicised, full names were preferred.

The story of Initial Transformation highlights a discipline separation in research culture

How we name ourselves on papers has nothing to do with the type of research that we conduct, yet there are very clear differences in the rate of shift from name formalism between disciplines. Research does not change at a single pace, local cultures can impact change regardless of their relationship to the change itself.

Technology influenced our name formalism

The choice to use first names or initials has not always been a choice that resides with researchers themselves. Below we present an analysis of three journals that all went live with online journal systems in 1995-96. From the mid 70s through to 1995, journals still mostly employed typesetting houses that set the style of the journal. Even before the onset of online submission systems, journal styles influenced the way that first initials were represented. From the mid 70s these three journals take different approaches. Tetrahedron shifts from a majority initials approach, whereas The BMJ and the Journal of Biological Chemistry switch to typesetting that preferences initials. With the emergence of the internet in 1995, research articles began to be recast as discoverable landing pages, and here the Journal of Biological Chemistry switches all at once to a system that enforces full names, and The BMJ – a system that allows choice. In all cases where author choice is allowed, the trend away from formal names continues.

Changes in Infrastructure can affect how we understand the past as well as the present

Between 2003 and 2010, DOI infrastructure run by CrossRef was adopted by the majority of publishers. As part of the CrossRef metadata schema, a separate field for given names was assumed. Critically, during this transition most journals chose to implement their back catalogue, including full names where possible. We owe our ability to view full name data in the past to infrastructure changes in the first decade of the 2000s. 

How were publishers able to communicate first names to the crossref DOI standard? At a layer below DOIs was another language to describe the digital structure of papers. The Journal Article Tag Suite (JATS XML), now a common standard used to describe the digital form of a journal article – aiding both the presentation, and preservation of digital content - was first released in 2003, and reflected over a decade of prior work in the industry to reexpress the journal article as a digital object. Within this standard full names were also codified, and the requirement of a publisher to preserve all digital content meant that there was an imperative to apply this standard (or at least compatible earlier versions) to their complete catalogues.

Although the communication of first names seems to have occurred reasonably seamlessly to DOI metadata, the transition of first names to the scholarly knowledge graphs of the time was slower.

MedLine (and by relation pubmed) only began adding full names to its metadata records in 2002. Journals that relied on MedLine records for discovery (and chose not to implement DOIs)  did not benefit from retrospective updates. 

The difference in the adoption of first names between crossref and MedLine/PubMed also highlights a risk in adopting scholarly knowledge graphs as infrastructure. Scholarly Knowledge graphs have their own constraints on infrastructure, and make decisions on what is sustainable to present. Although enormously valuable, they are a disconnection point with the sources of truth they present. We can see this split starkly if we look at publications  from those journals that chose not to create DOIs for their articles, relying instead just on the services provided by MedLine.

The shift to full names happened at different rates for men and women, and at least for publications associated with pubmed, technology influenced the practice

With the benefit of gender guessing technology, we note that progress towards first names has occurred at different rates for men and women. This is particularly stark for publications in PubMed. 

Why is there a jump in 2002? As mentioned above, 2002 was the year that you could start to interact with author first names, with pubmed and medline incorporating it into their search. Although we cannot draw a direct causal connection, it is tempting to make the argument that this subtle shift in critical technology used by almost all medical researchers had a small but important impact on making research more inclusive. When we look at articles that have both a PubMed ID and a DOI, we can see that in 2002 the average number of first names on papers associated with women rose by 17%, and 13% for men. This jump is not present in publications that have not been indexed by PubMed. 

For medical disciplines associated with papers in pubmed, after 2002 there also is a distinct difference in the rate of first name transformation for men and women. The rate of change for men is less than half that of women, rising only 5% in 20 years, compared to 12%. For some disciplines then, this raises a methodological challenge in gender studies as (at least based on author records,) the changes in participation rates of women in science must be disentangled from changes in the visibility of women in science.

Embracing Initial Transformation

Finally, the transition from initials to first names has happened slowly and without advocacy. Whilst this has been to our advantage in identifying some of the axis along which research transformation occurs, an argument could be made that, if first names help provide us (imperfectly) access to the diversity of experiences that are brought to research, then the pace of change has not been fast enough. For instance, could more have been made of the use of ORCiD to facilitate the shift to using first names so that older works by the same researcher identified by an initial based moniker could be linked to newer works that use the researchers full first name?

The transformation away from name formalism of course does not stop at author bylines. Name formalism is also embraced in reference formats. It could be argued that even within a paper, this formalism suppresses the diversity signal in the research that we encounter. Reference styles were defined in a different era with physical space constraints. Is it time to reconsider these conventions?
Within contribution statements that use the CRediT taxonomy, initials are also commonly employed to refer to authors. Here, this convention also creates disambiguation issues when two authors share the same surname and first initials. Here too, as the digital structure of a paper continues to evolve, we should be careful not to unquestioningly embed the naming conventions of a different era into our evolving metadata standards.

The post The Initial Transformation appeared first on Digital Science.

 In this week’s fourth Spotlight on the value of social science research, we assess the final ingredient to improve UK research and innovation: why social sciences are essential to international collaboration and tackling shared global challenges.

The focus of the Academy of Social Sciences (AcSS) report Reimagining the Recipe for Research & Innovation has understandably been parochial in nature, based as it is in the UK and tackling some of the unique challenges faced by researchers operating in the UK. However, the final piece in the AcSS jigsaw in its argument for using social sciences to support STEM research looks further afield, and analyzes how social science research in general can improve collaboration and societal problems on a global scale.

There are a couple of sound contextual reasons why this argument has a strong base. Firstly, as the UK is home to some of the most highly regarded research institutions, it is well placed to offer some guidance on this aspect of research. And perhaps most importantly, with the UK having benefited from some of the more progressive higher education policies in the shape of its Research Excellence Framework (REF) and early adoption of Open Access mandates, it can offer some leadership in making recommendations for future research strategy.

Covid insights

In the AcSS report, its authors – which include Dr Juergen Wastl and Kathryn Weber-Boer from Digital Science – identify the fourth and final ingredient specifically as the ability of social sciences to enable progress through international collaboration and meeting global challenges. No global challenge has been greater in recent times than the Covid-19 pandemic, and using this example, the authors show how insights from both STEM and social science research were necessary to successfully fight the spread and control of the disease. 

We see some specific examples of this in the case study below, but to further illustrate the important role social sciences have played, the report looks at how research in all areas has supported the United Nations Sustainable Development Goals (SDGs). Looking at how both UK researchers and those internationally have focused on these global challenges, the authors used Dimensions to identify some interesting trends (see Figure 1). For example: 

• The volume of publications is greater – and the average number of citations lower – for ‘social’ SDGs when compared with the ‘economic’ and ‘environmental’ SDGs, across all of the datasets
• For UK-authored papers, about a quarter of SDG-related publications are in the social sciences – either uniquely or in collaboration with STEM colleagues – and some three-quarters are in the STEM area
• Within each of the three main subdivisions – STEM, social science and STEM/social science – UK-authored publications have much greater citation rates than the average globally
• The biggest increase in citation rates – compared with the global average – for UK-authored papers is around double in many cases
• Collaborative studies across social science and STEM account for a minority of publications however they are among the most cited research.

Impact of impact

Perhaps one of the more remarkable findings in the AcSS study is that, when analyzing the impact studies that form part of the UK’s REF program in 2021 – where universities present the wider impacts their research has had – the social sciences show a greater contribution in most of the eight categories they are broken down into. While STEM leads in Health and Technology as one might expect, social sciences lead in Societal and Economic impacts.

No global challenge has been greater in recent times than the Covid-19 pandemic… insights from both STEM and social science research were necessary to successfully fight the spread and control of the disease.

Case study

The case study used to illustrate the influence of the social sciences in the AcSS report concerns the Covid-19 pandemic and how social sciences – not on their own and in collaboration with STEM research – helped the government and health services navigate through one of the world’s most challenging episodes.

One article they cite, published in Nature Human Behaviour in April 2020, was an early attempt by a large group of scholars to share not only their collective insights in order to enable a more effective response to the pandemic, but the gaps that were evident and needed to be filled. This collaborative response to the pandemic included advice on influencing credible community sources of information and advice on how to frame public health messaging. 

The pioneering work on drug discovery obviously played a huge part in overcoming Covid-19, but much of that great work might have been wasted had social science research not played its part in how vaccines were deployed. When we look back on this and the other three ingredients – enabling whole systems thinking, critical for good policy development and underpinning smart and responsible innovation – the Covid example is emblematic of the value that social sciences can bring. Important on their own, but vital when plugged into STEM research, collaboration and solving some of the world’s most pressing problems.

It is this collaborative approach between the social sciences and STEM research that has been one of the key aspects of the Spotlight series. From the first Spotlight on seeing a more complete picture from whole systems thinking, through to effective policy making and responsible innovation, it has been notable not so much what value can be brought through collaboration between social sciences and STEM, but what can also be lost when they don’t work in harmony. Hopefully this series and the AcSS report it has highlighted can ensure fewer wasted opportunities to make a difference in the future.

Next Time

We will continue our Spotlight series next month, so please watch out for more details on Digital Science’s LinkedIn and Twitter/X accounts – as well as right here on TL;DR.

The post In the spotlight: Social sciences’ fourth key ingredient for research success appeared first on Digital Science.

A recent blog by Dimensions on “The rapid evolution of life sciences, particularly in areas like omics and next-generation sequencing (NGS)” illustrates both the promise and the challenges of modern research ecosystems. As these fields expand, generating vast quantities of data, they underscore the problem of information silos—discrete repositories of data that are accessible to few and integrated with even fewer. This fragmentation not only hinders the seamless exchange of information but also obstructs the comprehensive understanding necessary for groundbreaking discoveries and innovations. The blog outlines how the Dimensions Biotech Solution endeavours to dismantle these barriers, fostering a more integrated and accessible research landscape.

Information silos in the research ecosystem create significant obstacles. Data generated from various studies and experiments are often stored in disparate databases, accessible only to specific groups, and not readily available for cross-disciplinary collaboration. This fragmentation impedes the flow of knowledge, delaying discoveries and the application of research findings. In the fast-paced domains of omics and NGS, where the integration of data from genomics, proteomics, transcriptomics, and metabolomics is crucial, the impact of these silos is profoundly felt. Researchers are frequently left navigating a labyrinth of information, struggling to piece together a holistic view from fragmented data.

The Dimensions Biotech Solution represents a pivotal shift towards overcoming these challenges. By amalgamating data from 48 million open publications, 93 million closed publications, 158 million patents, 814 thousand clinical trials, and an impressive US$2.4 trillion in global funding data, it provides a unified platform for accessing and interpreting a wide array of research outputs. This consolidation is crucial for several reasons. Firstly, it breaks down the barriers between different types of data, allowing for a more comprehensive analysis that spans the breadth of scientific discovery and innovation. Secondly, it significantly reduces the time and effort required to access and interpret this information, accelerating the pace at which research can be applied and furthered.

The implications of such a tool are vast for the research ecosystem. For one, it facilitates a more interdisciplinary approach to research, encouraging the collaboration between fields that was previously hindered by information fragmentation. This is particularly relevant in the era of personalised medicine and complex diseases like cancer and Alzheimer’s, where understanding the interplay between various biological systems and processes is key. Additionally, by providing access to a broader range of data, including patents, clinical trials, and funding information, it enables researchers and organisations to identify emerging trends, gaps in the market, and opportunities for innovation more effectively.

Moreover, the Dimensions Biotech Solution addresses a critical gap in the market and research intelligence. Traditional reliance on open publications, which represent a fraction of the available data, has limited the scope of research and analysis. By integrating closed publications and other data sources, the platform ensures that companies and researchers have a more accurate and comprehensive understanding of the market landscape. This is invaluable for strategic planning, from market segmentation and targeting to resource allocation and product lifecycle management.

In conclusion, the Dimensions Biotech Solution offers a robust solution to meet the challenges posed by the fragmentation of the research ecosystem. By providing a centralised, accessible, and comprehensive database, it not only facilitates the swift interpretation of vast amounts of data but also enhances cross-functional strategic alignment within organisations. This, in turn, accelerates the pace of innovation and discovery in life sciences and biotechnology, pushing the boundaries of what is possible in research and development. The integration of information across silos heralds a new era of collaboration and understanding, promising significant advances in our pursuit of knowledge and the betterment of human health.

More insights and information about Dimensions Biotech Solution will be presented in an upcoming webinar slated for February 28th. It will be moderated by Lauren Black, Product Solutions Manager at Dimensions.

The post Bridging Information Gaps: Dimensions Biotech Solution’s Role in Consolidating Scientific Knowledge appeared first on Digital Science.