Thursday 9 October 2025

Digital Science, a technology company serving stakeholders across the research ecosystem, has made a series of 23 recommendations for Australia’s research future in a report published today into the use of persistent identifiers (PIDs) in research.

The report is the Australian National Persistent Identifier (PID) Benchmarking Toolkit, available now on Figshare.

Commissioned by the Australian Research Data Commons (ARDC), Digital Science was tasked with developing a comprehensive PID benchmarking framework, and to conduct a benchmarking process that could be used to monitor the effectiveness of Australia’s National PID Strategy over time. The report, developed collaboratively with the ARDC, also benefited from consultation and engagement with the Australian research community. 

The lead author of the report, Digital Science’s VP of Research Futures, Simon Porter, will discuss the findings at two upcoming events in Brisbane, Australia: International Data Week (13-16 October) and the eResearch Australasia Conference (20-24 October).

A unique opportunity for Australian research

“This is the first time Australia’s National PID Strategy has been benchmarked, and it represents a unique opportunity for the Australian research system to benefit from that process,” Simon Porter said.

“What we’ve seen from the benchmarking is that Australia’s adoption of ORCID for research publications across the research sector has been extremely successful – and Australia is now third in the world for including DOI (Digital Object Identifier) links with dissertations published online.

“Workflows between publishers, institutional research information systems, and ORCID are also sufficiently strong, and we can see that Australia is well placed for a more comprehensive use of the ORCID infrastructure.

“However, our comprehensive review gave Australian research a mixed score card and recommended several changes and interventions to help strengthen the national strategy,” Mr Porter said.

“One of the key issues we’ve seen is that although Australian researchers are more engaged than the global average in the practice of data citation, they trail significantly behind their European peers.

“And while ORCID and ROR adoption has been strong for publications, the use of persistent identifiers with data sets and non-traditional research outputs (NTROs) remains the exception rather than the norm. As significant publishers of NTRO items in their own right, institutions should hold themselves to the same standards that they expect from publishers – all creators should ideally be described with an ORCID, and affiliation id (ROR).”

Natasha Simons, Director of National Coordination at the ARDC, congratulated Digital Science on the release of the National PID Benchmarking Toolkit. “The Australian Persistent Identifier Strategy is a critical national initiative to benefit the Australian people by strengthening our digital information ecosystem, the quality of our research and our capacity for effective research engagement, innovation and impact,” she said. “So it is essential to develop robust benchmarks that can track our progress and measure outcomes. The Toolkit provides us with exactly what’s needed.”

Recommendations to strengthen Australia’s research future

Some of the 23 recommendations made in the report include:

• Australian research has progressed to the point where ORCIDs should now be mandatory for all researchers; Australian Institutions should require ORCID registration within their institutional research information management systems.

• Australian research institutions should adopt the best practices of publishers to ensure that all authors are described by ORCIDs and affiliations via ROR.

• Australia should join international pressure to ensure that all publishers both record ORCID records and push the associated metadata into Crossref, and to avoid publishers that do not support ORCID workflows.

• Australia should consider a national policy for publishing dissertations with DOIs in institutional repositories, formalizing the use of ORCIDs for authors and their supervisors.

• Reports published by universities and their research centres should ideally be published in institutional repositories, with associated identifiers.

• Ongoing benchmarking analysis of PIDs should not ignore closed access material. (e.g., ignoring closed-access publications would result in missing 35% of Australia’s research output in 2024.)

• RAiDs (Research Activity Identifiers) should be added from “day one” of the creation of a funding grant.

• Grants funding organizations should create persistent identifiers “as soon as is practical” – including complete metadata – to enable research funding to be visible and tracked earlier.

“We welcome the opportunity to have led this benchmarking process, and we hope our recommendations will lead to some meaningful improvements within Australian research,” Mr Porter said.

“Importantly, we’ve also demonstrated that it is possible to produce a benchmarking toolkit for PIDs, and our work may have implications for other nations and their roadmaps towards a persistent identifier future.”

Background: The importance of PIDs

Persistent identifiers (PIDs) are unique numbered references to individual researchers and their work, which are connected to digital outputs and resources. They help connect researchers, projects, outputs, and institutions, and have become critical for:

• Making research inputs and outputs FAIR (findable, accessible, interoperable, and reusable)
• Enabling research outputs to be identified, tracked and cited
• Analyzing research impact
• Supporting national-scale research analytics

Widely used PIDs include ORCID iDs, DOIs, RORs, and emerging identifiers include DOIs for grants, and identifiers for projects (RAiDs).

Note: In the report, Simon Porter declares that he is also a member of the ORCID Board.

Discover more at International Data Week (13-16 October) and the eResearch Australasia Conference (20-24 October).

About Digital Science

Digital Science is an AI-focused technology company providing innovative solutions to complex challenges faced by researchers, universities, funders, industry and publishers. We work in partnership to advance global research for the benefit of society. Through our brands – Altmetric, Dimensions, Figshare, IFI CLAIMS Patent Services, metaphacts, OntoChem, Overleaf, ReadCube, Symplectic, and Writefull – we believe when we solve problems together, we drive progress for all. Visit digital-science.com and follow Digital Science on Bluesky, on X or on LinkedIn.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

The post Australian research well placed for adoption of National Persistent Identifier (PID) Strategy appeared first on Digital Science.

Danny uses agriculture as one example of an area of research vital to the survival of humanity where we aren’t doing enough research. Any fellow BBC Countryfile fan will know that farmers work incredibly hard tending to their agricultural land and responding to the dynamic needs placed on them by the changing climate and other factors. Though they may like to, they often don’t have time to do experiments and contribute to the corpus of research information in this space in a way they would like to.

However, if we start to collect data from the automation of the mechanisation farmers used to work the land, we can allow these “robots” to conduct a series of experiments that humans don’t have the time to do.

Danny believes that in the future these machines will also contribute to planning future experiments to explore such research spaces. He believes that the future of automated science will be done by AI – allowing humans to increase the number of experiments they can conduct, increase the amount of data gathered, and increase the number of hypotheses being tested.

Subscribe now to be notified of each weekly release of the latest TL;DR Short, and catch up with the entire series here. 

If you’d like to suggest future contributors for our series or suggest some topics you’d like us to cover, drop Suze a message on one of our social media channels and use the hashtag #TLDRShorts.

The post TL;DR Shorts: Dr Danny Hillis on the automated future of research appeared first on Digital Science.

In this week’s TL;DR Shorts episode we’re revisiting Dr Etosha Cave, a mechanical engineer who wants to catalyse more collaborative creativity to solve the big problems our global society is facing.

Dr Cave is co-founder and Chief Scientific Officer of Twelve, a chemical technology company that is working on converting excess carbon dioxide into useful materials such as sustainable fuels and plastics. We recorded her thoughts at last year’s Science Foo Camp, or Sci Foo – an unconference that brings together people from numerous different backgrounds, levels and types of experience to see which hot topics and future collaborations arise.

Given the Sci Foo setting, it was perhaps no surprise that when Etosha was asked about what the future of research might look like, she talked about collaboration and groups of diverse make-up working together to solve problems. She hopes that in the future we can benefit from the creation of more innovation playgrounds to catalyse creative thinking for big teams of diverse minds that are focused on solving a specific problem. This space for creativity is certainly an ethos that Etosha applies to her and her teams’ work at Twelve.

Subscribe now to be notified of each weekly release of the latest TL;DR Short, and catch up with the entire series here. 

If you’d like to suggest future contributors for our series or suggest some topics you’d like us to cover, drop Suze a message on one of our social media channels and use the hashtag #TLDRShorts.

The post TL;DR Shorts: Dr Etosha Cave on a collaborative future for research appeared first on Digital Science.

If we were living in a simulation, in order for humanity to continue its drive out towards longer, happier lives, every now and then something drastic should happen. We should get a serendipitous paradigm shift at the most desperate time. The next paradigm shift is AI. AI may be the technological Shangri-La we were crying out for in order to stop the heating of the planet and ultimately, the end of humanity. This may also be the case with drug discovery. The way in which we find and create new drugs may be about to transform forever.

Drug discovery has come a long way. It started with natural remedies and saw landmark serendipitous discoveries like penicillin in 1928. The mid-20th century introduced rational drug design, targeting specific biological mechanisms. Advances in genomics, high-throughput screening, and computational methods have further accelerated drug development, transforming modern medicine. However, despite these advances, fewer than 10% of drug candidates succeed in clinical trials (Thomas, D. et al. Clinical Development Success Rates and Contributing Factors 2011–2020 (BIO, QLS & Informa, 2021)). Challenges like pharmacokinetics and the complexity of diseases hamper progress. While we no longer fear smallpox or polio and have effective treatments for bacterial infections and Hepatitis C, today’s most damaging diseases are complex and hard to treat due to our limited understanding of their mechanisms.

Nature 627, S2-S5 (2024) https://doi.org/10.1038/d41586-024-00753-x

Cue paradigm shift. DeepMind’s AlphaFold has revolutionized biology by accurately predicting protein structures, a task crucial for understanding biological functions and disease mechanisms. The economic prowess of Deepmind is also creating some mind-blowing figures. The estimated replacement cost of current Protein Data Bank archival contents (the dataset from which the AlphaFold models were built) exceeds US$20 billion (assuming an average cost of US$100,000 for regenerating each of the >200,000 experimental structures). AlphaFold has subsequently generated a database of more than 200 million structures. Some back of the envelope maths infers that this would have cost us $20,000,000,000,000 using the original methods.

Of course, there are many simultaneous attempts to move the research needle using AI. A team from AI pharma startup Insilico Medicine, working with researchers at the University of Toronto, took 21 days to create 30,000 designs for molecules that target a protein linked with fibrosis (tissue scarring). They synthesized six of these molecules in the lab and then tested two in cells; the most promising one was tested in mice. The researchers concluded it was potent against the protein and showed “drug-like” qualities. All in all, the process took just 46 days. Scottish spinout Exscientia has developed a clinical pipeline for AI-designed drug candidates.

Not only does the platform generate highly optimized molecules that meet the multiple pharmacology criteria required to enter a compound into a clinical trial, it achieves it in revolutionary timescales, cutting the industry average timeline from 4.5 years to just 12 to 15 months. These companies have the technical know-how to build the models, and most likely some internal data with which to train them on. But they need more.

The power of existing data

Platforms like the Dimensions Knowledge Graph, powered by metaphactory, demonstrate the potential of structured data. With over 32 billion statements, it delivers insights derived from global research and public datasets. Connecting internal knowledge with such vast external data provides a trustworthy, explainable layer for AI algorithms, enhancing their application across the pharma value chain.

AI is not all there is to be excited about in drug discovery. A further technological, serendipitous paradigm shift could amplify the results of AI alone. Once trained, machine-learning models can be updated as and when more data become available. With ‘federated learning’, separate parties update a shared model using data sets without sharing the underlying data. Advances in federated learning allow for collaborating across organizations without sharing sensitive data, maintaining privacy while pooling diverse datasets. Federated learning is a machine learning technique that allows models to be trained across multiple companies holding local data samples. Instead of sending data to a central server, each device sends its model updates (e.g., weight changes) to the central server. This allows further reduction in time and cost in the drug discovery process by improving predictive models, without leaking private company held datasets. Public data can augment local datasets held in corporate R&D departments, enriching the training process. Public data with similar characteristics can help in creating more comprehensive models. This is why we need more, better described open academic research data.

Pharmaceutical companies of the world should be engaging further with both open academic data aggregators in order to assist in the improvement of metadata quality and highly curated linked datasets like the ones supported by the Dimensions Knowledge Graph and metaphactory. The limiting factor is not the AI capabilities, it is the amount of high-quality, well described data that they can incorporate into their models. They need to:

1. Acquire: Gather data from diverse sources, including internal external datasets. Make use of federated learning.
2. Enhance: Enrich data with metadata and standardized formats to improve utility and interoperability.
3. Analyse: Use new models to establish patterns, trends and drug candidates.

You may be thinking that this isn’t a serendipitous leap. This is the fruition of decades of research moving us to a point where these technologies can be applied. You may be right. Either way, the timing of these paradigm changing tools does feel serendipitous. Without AI and federated learning, we could not tackle today’s complex diseases in such an efficient manner. There is a long way to go, but by continuing to curate and build on top of academic data, we can push the boundaries of what’s possible in modern medicine.

This is part of a Digital Science series on research transformation. Learn about how we’re tracking transformation here.

The post The next serendipitous paradigm shift for drug discovery appeared first on Digital Science.

In this chat, Alice tells us about research infrastructure, persistent identifiers (PIDs), why metadata matters, and what she would like to see from the future of information management in the research community, ahead of the first-ever PIDFest, a conference that focuses on persistent identifiers for research information and infrastructure, taking place at the National Library of Technology in Prague, Czechia next week. If Alice’s interview inspires you to learn more or engage with some of the topics scheduled for discussion, you can still register to attend virtually.

The importance of community engagement

Alice talks about the importance of building research solutions with the community in mind. Whether it is a new standard or a brand new process, consulting with researchers and future users of a new standard or process, understanding their needs, and trying to meet them as well as possible means that they feel seen, heard, and involved, and have a certain sense of buy-in to making the solution’s adoption within the research ecosystem a reality. As Alice says, if you don’t bring communities along the journey with you, it will be much harder to succeed.

In order to better support different segments and groups within research to further develop ways in which research can be made more open, inclusive, collaborative and impactful, Alice and three other friends from across the research landscape came together to form the MoreBrains Cooperative. A consultancy working across all segments of research, the research-related Avengers at MoreBrains are academic librarian Josh Brown, technical product wizard and former Digital Science colleague Phil Jones, academic publisher and data scientist Fiona Murphy, and Alice, a community engagement professional who has helped transform research by engaging with the research community to help support the adoption of new and better ways of doing research. MoreBrains’s values of community engagement and involvement even extend to their cooperative status.

PIDs and metadata

Given their name, persistent identifiers (or PIDs) in research information may not always be as well known or indeed as persistent as we would like them to be. As a former academic myself, I often admit that I did not know much about the world of PIDs until moving to Digital Science and peeking behind the curtain. Yet I was far from alone in that lack of understanding. PIDs allow researchers to claim work as their own, tie it to their institution, and share that work with citeable credit. One way that community engagement can help encourage the adoption of systems and processes that lead to greater findability of research information is through building confidence in the use of new systems and helping people understand the value of getting involved. For example, by spending a little time carefully selecting your keywords and fields of research when publishing a paper, you can increase the chances of people finding that paper and building on your work, allowing your research to reach its fullest potential as a contribution to the ever-growing corpus of knowledge.

But it doesn’t just stop with PIDs for researchers, institutions, equipment, etc. as in order to be a good data citizen and put information into the system that is useful for ourselves and others, we must understand the value of metadata. Good information about information leads to data that can be easily navigated, while also adding context for users of that information, whether it is a published paper, a geological sample, or a particular microscope.

Take, for example, a well-curated digital music library. It would be easy to throw all of your music into the library and not care about preserving information that would help disambiguate one artist from others with similar names, or assign a song to an album, or even label the genre of music. If this info-about-info, or metadata, hasn’t initially been included with songs, it can be a daunting task to manually add it for every individual song, and so this process can be overlooked. In musical terms, a lack of rich and accurate metadata could mean searching for Taylor Swift’s It’s Nice To Have A Friend from the album Lover (Taylor’s Version) and being presented with James Taylor’s You’ve Got A Friend from the album Mud Slide Slim and the Blue Horizon. While both songs are great, the lack of metadata prevents the user from finding exactly what they want, when they want it, from the sea of information they can access. If that metadata had been included from the moment those songs entered the digital environment, they would already be disambiguated from one another. In the same way, if researchers can add detailed metadata to the research information they put into the system, they are contributing to a community built on good data citizenship and can hope to find research information within the system that is just as easy to discover too.

Why metadata matters

Good metadata helps researchers find information relevant to them. It also helps to make connections between otherwise disparate pieces of research information. As Alice says in our chat, a lot of good research relies on making connections between information that already exists, and building on the shoulders of giants, as the saying goes. Research information is being produced at an unprecedented rate, and keeping on top of it all is an uphill battle. This means that we need systems that allow us to quickly recall the most relevant and reliable research information. Metadata powers this.

Good metadata can also break down silos of knowledge by making connections across fields of research by linking observations and effects that are appropriately labelled with the right metadata. This allows researchers to discover information from complementary fields of research that they may not yet know they were missing out on. This becomes particularly useful in our urgent quest for sustainable solutions to our looming global challenges.

PIDs and trust in Open Research

One challenge we have discussed at length here at TL;DR and within the community is the need to ensure robustness and integrity in the research we build on, especially in a more open research culture. Openness is important in the world of research information too. PIDs and the metadata used to describe them must be openly accessible. Creating a culture of good open metadata can go a long way towards addressing our research integrity needs, as metadata can reveal whether research information, research affiliations, and so on, have been checked so that the user can better determine whether or not that piece of research can it be trusted.

In this way, metadata and persistent identifiers offer another way of demonstrating the provenance of research. This is particularly important as we build new research technologies on this information. In order to build strong solutions, we must ensure that the information we build these with is trustworthy.

PIDs also help researchers get acknowledgement and credit for the work people they doing. One way that we support this at Digital Science is to help researchers secure DOIs or digital object identifiers, perhaps the best known PID for researchers, when they share their work on figshare, which can be cited by other researchers. This can go some way to shaking up the types of research people are able to get recognition for.

We’re all In this together

Given Alice’s community-minded values, it is perhaps no surprise that one of her hopes for the future of research infrastructure is a more joined-up approach. Alice explains that if there is no overarching organisation wrangling researchers’ infrastructure needs, everyone does their own thing with little to no support, and the entire landscape can become more fragmented. However, we all know that the best research is done collaboratively and collectively. If solutions can be created that help as many people as possible, and outcomes and impact can be shared across groups, the community can use these best practices and the standards that arise from bringing the community together, so that everyone can collectively benefit from them, and use them to make bigger and faster developments in research.

However, not everyone has equal access to such support. Engaging with research infrastructure, training, and investment, all require a lot of resources, and not all organisations and institutions have the same availability of expertise, time and money. It is important to engage with and include the entire global research community in these conversations.

We also need to consider the sustainability of information. We don’t want to lose information and knowledge, especially when we don’t know when it could be useful in future. However, through strong two-way community engagement, there is an opportunity to be more inclusive and create frameworks around research information that will ensure sustainable access to this knowledge for future researchers.

Through Alice’s previous work and now with her team at MoreBrains, Alice continues to champion community engagement with research infrastructure and showcase the value of this vital piece of the research puzzle that enables so much research and development to be built on the knowledge that already exists. Watch the full interview on our YouTube channel.

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 Huw James from Science Story Lab for filming and co-producing this interview. Thanks also to Alice for her time and for the excellent coffee.

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Update: Video recording of the session now available.

Well, Falling Walls certainly lived up to expectations! It’s six years since I was originally slated to attend but had to hand presentation duties over to my cofounder due to the birth of my youngest daughter, Annabelle.

I was fortunate to be able to attend in person this year, and today started with a wonderful panel session on the “Implications of AI for Science: Friend or Foe?”, chaired by Cat Allman who has recently joined Digital Science (yay!) and featuring a brilliant array of panellists:

• Alena Buyx, Professor of Ethics in Medicine and Health Technologies and Director of the Institute of History and Ethics in Medicine at Technical University of Munich. Alena is also active in the political and regulatory aspects of biomedical ethics; she has been a member of the German Ethics Council since 2016 and has been its chair since 2020.
• Sudeshna Das, a Postdoctoral Fellow at Emory University and with a PhD from the Centre of Excellence in Artificial Intelligence at the Indian Institute of Technology Kharagpur. Her doctoral research concentrated on AI-driven Gender Bias Identification in Textbooks.
• Benoit Schillings, X – The Moonshot Factory’s Chief Technology Officer, with over 30 years working in Silicon Valley holding senior technical roles at Yahoo, Nokia, Be.Inc and more. At X, Benoit oversees a portfolio of early-stage project teams that dream up, prototype and de-risk X’s next generation of moonshots.
• Henning Schoenenberger, Vice President Content Innovation at Springer Nature, who is leading their explorations of AI in scholarly publishing. He pioneered the first machine-generated research book published at Springer Nature.
• Bernhard Schölkopf, Director of the Max Planck Institute for Intelligent Systems since 2001. Winner of multiple awards for knowledge advancement, he has helped kickstart many educational initiatives, and in 2023 he founded the ELLIS Institute Tuebingen, where he acts as scientific director.

Cat Allman herself, now VP Open Source Research at Digital Science, was the perfect facilitator of the discussion – she has spent 10+ years with the Google Open Source Programs Office, and has been co-organizer of Science Foo Camp for 12+ years.

The panel session is part of Falling Walls Science Summit 2023, an annual event that gathers together inspirational people from across the globe who are helping to solve some of the world’s biggest problems through their research, new ventures, or work in their local community. I saw around 50 presentations yesterday during the Pitches day, and I’ll be sharing some of the highlights in a follow up post!

But before we go further, an important moment happened during the discussion, and Alena deserves a special mention for ensuring that Sudeshna was given the time to speak, just before the panel answered questions in the Q&A section.

Sudeshna had been unfortunately cut off due to timekeeping, and although it had been well-intentioned — to ensure the Q&A section didn’t get lost — Alena did the right thing in stepping in. Alena’s polite but firm interjection was appreciated by everyone in the room, and it’s this kind of thoughtfulness during the discussion, which was on show throughout, that made it a very enjoyable panel debate to attend.

Onto the session itself, and in their opening remarks, each panellist was encouraged to state whether they felt AI was a friend or foe to science. Of course, that is a binary way to view a complex and ever evolving topic, and the responses reflected this — they radiated a generally positive view of the potential for AI to help science, but with caution on how it’s important to focus on specific examples and situations, to try to be precise both in terms of what the AI is and what it’s intended to do.

Benoit expanded on this need to be precise by giving a couple of specific examples of how he’s been experimenting with AI, both of which fall into the broader category of AI acting a personal assistant. 

In one experiment, Benoit fed a model his reading list and asked for a personalised list of research recommendations and summaries. He was essentially taking to a more personalised level the types of recommendation engine that many websites use to (try to) encourage us to consume more content. What came across was his optimism that such a way of filtering / tailoring the literature — as an aid to a practising researcher — could help deal with the mountain of scientific content. He expects these types of systems to be common within the next few years, and it will be interesting to see who manages to create (or integrate) such a system successfully.

Whilst his first example could be seen as using an AI assistant to narrow down a broad selection of options, his second example is the reverse — when starting out on a new research topic, he often asks Bard for fifteen ideas for avenues to explore on that topic (I forget the exact phrase he used, sorry!). Although not all fifteen suggestions make sense, what comes back is usually useful at stimulating his further thought and ideas on the topic — it’s a great way to get started, and to avoid getting too deep or narrow too soon on a new project.

This issue with AI assistants giving incorrect or nonsensical answers also prompted the conversation to move onto that topic; Bernhard and his team are working on how future models could have some sense of causation, rather than just correlation, to try to help address this gap in current AI systems. 

He gave a particular example where machine learning models had been trained to identify patients with a particular type of illness (I didn’t catch the name); when trained, the model appeared to give excellent detection rates, and appeared to be able to determine with high accuracy whether or not a given patient suffered from this illness. 

However, when it was used in a clinical setting on new patients (presumably as a first test of the system), it failed — what had gone wrong? It turned out the model had spotted that patients with a thoracic (chest) tube had the illness, but those without the tube didn’t — as once a patient is being treated for the illness, they have such a tube fitted. As all the training data was based on known patients, it had used the presence of the tube to determine whether they had the illness. But of course new, unknown patients do not have a tube fitted, and hence the model failed. If models could have some sense of causation, this type of issue might be avoided.

This brings me onto one of the most interesting points raised during the discussion — Alena, who is a trained medical doctor, made the case that, rather than looking to AI assistants to help with potentially complex medical diagnoses, a real, tangible benefit to doctors all around the world would be for AI to help with all note-taking, paperwork, and admin tasks that eat up so much of a doctor’s time and energy.

She made the point that there are other problems with having AI / automated diagnosis machines, namely that you end up with a layering of biases. 

• First there is the algorithmic bias, from the machine learning model and its training data. For example, in medicine there are issues with training data not being gender balanced, or being dominated by lighter skin tones, making the results less reliable when applied to a general population. 
• And secondly, there is the automation bias — that causes humans to trust the answer from a machine, even when it contradicts other observations — which adds a further bias on top. This combination of biases is not good for doctors, and not good for patients! 

As an aside: there was a discussion on how the term “bias” is now often used almost exclusively to refer to algorithmic bias, but there is also inductive bias, which perhaps needs a new name! 

Sudeshna, whose PhD was in identifying gender bias in textbooks, was asked to comment on the issue of biases in AIs. She emphasised that results from AI models reflect biases present in the training data which generally reflect biases in human society. These biases can be cultural and/or driven by data-quality (garbage in -> garbage out), but also stem from the data tending to be from the Global North, where they lack local data from the rest of the world. 

Henning gave an example where his team had seen a similar issue when testing a model on answering questions about SDGs; the answers were extracted from the literature which is predominantly focused on SDGs from a Global North perspective. Henning and I were speaking to Carl Smith in the hallway after the talk, and Carl mentioned how in psychology research this type of issue is often termed the WEIRD bias; another term I learned today!

Having local data — at different scales — is important for AI models to generate answers in context, and without that data, it’s hard to see how local nuance and variety won’t be lost. However, there’s no simple solution to this, and whilst a comment was made that improving data quality (labelling, accuracy, etc) — and training models based on high quality data — was one of the best routes to improving performance, it was acknowledged that it can’t by itself fix the issues of datasets only representing a small fraction of the world’s population.

Overall the tone of the discussion was one of cautious optimism though, and the examples given by the panellists were generally positive instances of people using this new technology to help humans do things better, or quicker, or both.

Earlier in the session, Henning had referred to a book recently published by Springer which was generated using GPT, but which crucially had three human authors/editors who took responsibility (and accountability) for the published work. 

“This book was written by three experts who, with the support of GPT, provide a comprehensive insight into the possible uses of generative AI, such as GPT, in corporate finance.”

Translated opening sentence from the book’s description

Henning made a point of highlighting how current responsible uses of AI all have “humans-in-the-loop”, emphasising that AI is helping people produce things that they might not have the time or resource to. In this specific example, the book was written in approximately two to three months, and published within five — much shorter than the usual twelve months or more that a book usually takes.   

There was time towards the end for a small number of audience questions, and the first was whether we had (or could) learn something from the previous time new technology was unleashed on the public via the internet and had a transformative effect on the world; namely the rise of social media and user generated content and interaction, often dubbed Web 2.0.

It was at this point that Alena stepped in and gave Sudeshna the time to add her thoughts on the previous topic, that of how to address bias in the large language models.

Sudeshna made the very important comment that there is no fixed way in how we should look to address biases, because they aren’t static; the biases and issues we are addressing today are different from the ones of five or ten or twenty years ago. She mentioned her PhD study, on gender bias, and how today she would take a broader approach to gender classification. And so whatever methods we determine for addressing bias should reflect the fact that in ten years we will very likely see different biases, or see biases through a different lens.

Alena then gave a brilliant response to the question of whether anything was different this time vs when Facebook et al ushered in Web 2.0.

She said that back then, we’d had the unbridled optimism to say “go ahead, do brilliant things, society will benefit” to those tech companies. Whereas today, whilst we still want to say “go ahead, do brilliant things” to those companies, the difference is that today we – society / government / the people — are in the room, and have a voice. And that hopefully, because of that, we will do things better.

As the panel wrapped up, Bernhard made the observation that early predictions of the internet didn’t necessarily focus on the social side, and didn’t predict how social media would dominate. He suggests we view our predictions on AI in a similar way; they are likely to be wrong, and we need to keep an open mind.

Finally, Henning closed out the session with a reminder that it is possible to take practical steps, at first an individual then organisational level, which set the approach across a whole industry. His example was that of the SpringerNature policy of not recognizing ChatGPT as an author, which came about because they saw ChatGPT start to be listed as an author on some papers, and very quickly concluded that, because ChatGPT has no accountability, it cannot be an author. Other publishers followed suit, and the policy was effectively adopted across the industry. 

It makes you wonder what other steps could we take as individuals and organisations to bring about the responsible use of AI we all hope to encourage.   

Disclaimer: The write up above is based on my recollection of the panel discussion and some very basic notes I jotted down immediately afterwards! I have focused on those points that stood out to be, and it’s not meant to be an exhaustive summary of what was discussed. I have also  probably grouped things together that were separate points, and may have things slightly out of order. But I’ve strived to capture the essence, spirit and context of what was said, as best I can — please do ping me if you were there and think I’ve missed something!

Double disclaimer: For completeness I should point out that — as you can probably tell — I work at Digital Science, alongside Cat. Digital Science is part of Holtzbrinck, as is Springer Nature, who supported the session. But the above is entirely my own work, faults and all.

The post Implications of AI for science: Friend or foe? An impressive opening to the Falling Walls Circle 2023 appeared first on Digital Science.

We are on tour!

Recently, we had the chance to make a mini exhibit of our ‘What does a University Look like project’ at the International Conference of Science of Science and Innovation in Chicago. Placed prominently in the main auditorium, the posters have triggered many interesting conversations around the structure of institutions – particularly how different universities undertake interdisciplinary collaboration. I have noticed that whilst many people encounter the posters as art, people linger longest when it is their own university pointing out structural, and historical reasons why a cluster might look a certain way.

For the conference, we chose seven Universities. Northwestern University and University of Chicago were selected as universities in the area. The remaining five universities were chosen to illustrate different university shapes. Harvard University was chosen to represent a medically-focused university, and also showed off its enormous scale of 62,000 affiliated researchers. MIT and ETH Zurich represented the shapes of institutes of technology. The University of Oxford and Peking University were chosen as two different examples of comprehensive universities. Both universities show a balanced set of disciplines, however their shapes are quite different. Oxford exhibits a common shape of disciplines wrapped around a clinical sciences core. The University of Peking on the other hand, is a tale of two halves, with almost two independent clinical and technological sides.

Examples of medically focused universities

Examples of comprehensive universities

Examples of technology focused universities

Access all seven posters and more

You can find all seven posters as additions to the ‘What does a university look like’ project in figshare.

The post What does a university look like? On tour at the ICSSI conference… appeared first on Digital Science.

The news has been greeted with much anticipation – and not a little humour – from users and the latest clash between Twitter’s Elon Musk and Threads’ Mark Zuckerberg. But will the new channel pack a punch for academics who might use it in their research? Social media and research communications expert Andy Tattersall provides the tale of the tape.

How will Threads square up to Twitter in the social media arena? Do academics need another platform to disseminate their research?

When Facebook’s parent company Meta announced it was launching its own microblogging rival to Twitter, it felt inevitable but also sent a shudder down the spine of many people living in my part of the world. Whilst Threads might seem like a suitable, if not cliched name for the platform, given Twitter’s use of threaded updates, it also conjures up dystopian images. Firstly as those of a certain age will remember, Threads was a British-Australian BBC produced TV film that depicted a fictional nuclear war, at a time when this felt like a real possibility. It was set in Sheffield, near to where I grew up and currently work. Whilst the newest social media kid on the block is unlikely to result in that kind of devastation, it does appear to be spurred on by an increasingly public spat between the two tech giants Elon Musk and Mark Zuckerberg. And at first glance on launch day, Threads appears remarkably similar to its established rival in terms of functionality, although there is no Direct Message function. In addition, it does not have a desktop version, which for some might seem progressive, but for professionals it implies the whole thing has been rushed. 

What lies ahead for Threads?

The latest addition to the researcher’s communications toolkit is unlikely to gain large numbers of followers from academia overnight. When Musk took over Twitter last year many from the academic community saw it as the final straw due to the platform’s increasingly toxic environment. Mastodon was one of the winners from the exodus with an estimated 200,000 new users in those first few days. The number jumped to over two million new subscribers in the following weeks. I was one of them and like many reminisced as Mastodon felt very much like Twitter a decade earlier, fresher, friendlier and more focused. Yet it did not have the critical mass due to the siloed nature of Mastodon’s servers, known as Instances. Despite the Twitter backlash it was much harder for organisations to make the switch and leave behind carefully constructed audiences. Also, Twitter was widely acknowledged as the number one communications tool for academics, largely due to its ease of use (it is easy to use, harder to use it well), but also because the institutions, media, funders and public were all on there. The initial weeks after Musk’s takeover I found myself juggling both platforms, initially using cross-posting tools until Musk intervened to turn off access to helpful independent platforms that allowed that kind of functionality. Twitter’s changes in policy and direction also led me to use LinkedIn a bit more, where I have seen increased activity across my network, whilst also endeavouring to engage in specialist groups more.  

Where Threads might be different

Twitter is a tool in isolation, it has no associated social media platforms to lean on to for leverage. Threads is different, in that it will rely heavily on its social media siblings Facebook and Instagram to help with the launch. Their combined user base far outstrips that of Twitter, the question will be whether fans of those two platforms will adopt it and how well will they work as a suite of tools. For it to be a useful academic tool it needs the public, the organisations, publishers, funders and the public on board. Where it is likely to be different from Twitter is how it is openly controlled by the owners. Twitter is seen by many as Musk’s plaything which he uses to flirt with conspiracy and controversy. Whilst Facebook, also collectively guilty of various internet misdemeanours, does not have a large personality publicly shaping the platform on the fly. Having a major tech company behind you is no guarantee that your new platform will take off. One only has to look at Google’s various attempts and subsequent failures with their forays into social media. On a personal level, as someone who had given up Instagram, it was annoying that I had to revive my Instagram credentials to sign up for a Threads account. This in itself may be a major barrier to many new users, especially as you are stuck using your Instagram account name by default. This is problematic if you have a personal identity (where you use a fictitious name) and want your academic Threads profile to have your real name. As an aside, it could mean ultimately Instagram gains millions of new users as a by-product, whether they engage is another thing. Whilst its launch has been delayed in the EU, which hardly helps connecting academics together. 

What does this mean for academia?

For those academics communicating their research it means another platform to consider. This in itself is problematic, as with too much choice the easiest option is to just ignore them all or stick with what you know. Communicating one’s research is not only a good thing to do, it is increasingly regarded as an important part of the research lifecycle. It can help increase citations, form collaborations, generate impact and project your work to those who may not be aware of it but find it beneficial. The demands on academics’ time and attention means there is little or no room to explore new platforms. Not only are there a plethora of general and specialist social media platforms, but there are also other mediums to consider. Blogging, podcasts, videos, animations and discussion forums provide valuable ways to reach out to different audiences. Academics do not have the time to critically appraise and  learn this growing suite of technologies, which is something I try to do, which is far from easy. Hence why so many researchers and aligned professionals either pay to learn about which tools to use properly, or outsource the work altogether to external consultants. 

Facebook is the number one social media platform but one that the academic community has never truly taken advantage of. To a large extent, this is a shame as it is global, has a decent demographic spread between young and middle-aged adults, and has good functionality, especially in relation to groups and pages. It is used by academics and groups, in particular for reaching groups and communities or by targeted adverts. However, on an individual level it has struggled to strike a balance between professional and personal identities. Twitter is much easier to navigate between multiple accounts and networks. So if academics can look beyond that and see Threads as a whole new platform it may be useful. No doubt whatever happens, it will highlight even more tensions between Musk and Zuckerberg, how much of it is real or for show, nobody knows. Nor can anyone predict what Musk will do as a result, some have long predicted Twitter’s demise and there is a possibility that one of the contenders could knock the other one out, in the ring or on the web.  

The post Will researchers try new Threads? appeared first on Digital Science.

Dr Jessica Miles recently participated in the SSP2023 panel on AI and the Integrity of Scholarly Publishing, the writeup from which has just been published on the Scholarly Kitchen.

I caught up with Jessica to chat about how she came to be on the panel, her background in microbiology, and her thoughts on the future of scholarly publishing in an AI world. I got a Barack Obama impression for free 😊

Quick links

• From school science fair to SSP2023
• What does Mary Shelley’s Frankenstein have to do with AI?
• Generative AI – the new normal
• Barack Obama?
• What is the publishing industry’s moat?
• The value of getting things done

From school science fair to SSP2023

John Hammersley: Tell us how you came to be on the SSP2023 panel on AI

Jessica Miles:

It seems so surprising, right? (JH: Not at all!!) I asked myself that same question the other day — I was at the SpringerNature office and I met somebody who works on submissions who I’d connected with on LinkedIn. He stopped me and said: “I looked at your LinkedIn, your career, and it seems …he didn’t say bizarre, but went with ‘very interesting!’…and I’d love to chat about it.”

And it made me think — in respect to the SSP AI debate and my participation – that one of the reasons I was invited is precisely because I don’t have the normal profile of someone that you might expect to be participating in an AI debate. But if that’s the case, what’s interesting enough about my career that makes my perspective on this noteworthy? And the conclusion I came to is that, beyond my experience in scholarly publishing, it’s quite a bit to do with my experience with (and enthusiasm for!) science communication.  I’ve been interested in science communication for a long time – when I went to school, the university I picked specifically had a program in science communication.

From an early age I learned about Michael Faraday (and his role as a science communicator) and I thought that would be something really cool to do, even though I didn’t know what that was at first! I thought I maybe wanted to do science journalism, eventually landed into research and so when I think about a lot of the different things that I’ve been interested in or done, they really fit kind of nicely within that space of science communication that I’ve been following for a long time.

So getting back to the SSP debate, if you think about the audience being in the scholarly technology space, not an audience of experts on AI, this idea of communicating science in a different way but with this sort of publishing lens comes to mind. And so I think that’s why I was really excited to get the opportunity to do this and why I think it makes sense because it’s not like I’m giving a talk at Google or to a group of AI experts, it’s really about communicating science, but at the intersection of something else, something new.

What does Mary Shelley’s Frankenstein have to do with AI?

John: Yes, I agree it’s really important that different perspectives are included (in these discussions), and that we don’t reduce AI to just being about the technology because it’s totally not just that! Chat GPT’s explosion into the public consciousness is a great example — it wasn’t so much the technology but the interaction with it that caught the imagination. So I think it’s good when forums do try to include lots of different viewpoints. But I also know what you mean in terms of not feeling qualified, because your background isn’t in the technology side of it.

Jessica:

Exactly — my PhD is in microbiology, not machine learning, and I certainly wouldn’t call myself an AI expert. So, yeah, it’s a natural question people asked of me (after the debate). And that’s the answer that I came up with after some reflection! I think having that perspective informs the way I’m thinking about these technologies. For example, I think about one of the courses that I took where we read Mary Shelley’s Frankenstein. The text of Frankenstein is not necessarily considered particularly difficult and some might ask, “Why is this a college level text?” But one of the main ideas of her work is that she’s interrogating how society is thinking about new technologies — it’s a commentary on science, and I was thinking about that recently because it feels like we’re in another similar moment where we’re having to grapple with this new technology and some people are quite fearful and some people are quite excited.

And some people worry that it will take away the mystery of the world if we can recapitulate human thought, human intelligence.”

Where does that leave us from a philosophical perspective? We’re starting (or continuing) to ask if anything is sacred anymore? There are a lot of really interesting questions, and having a historical perspective feels like a nice way to approach this moment so that it’s not so overwhelming. It’s like “okay there are historical parallels, and yes this might buck those trends but at least you have some kind of grounding to approach all of this.”

We spent a lot of time on Frankenstein — we must have spent several months on it — and it’s not necessarily something you would think would require that level of rigor. But when you consider it from the context of society and all the other things that were happening around that time — industrial revolution, and the rapid pace of change that brought — there is very much an allegory meaning behind it that belies its reputation as a simple text.

Generative AI – the new normal

John: Frankenstein is a nice example of the fear a new technology can generate. Yet, we’re very good at adapting to the new normal as new things are developed – stuff that would have been seen as magical and amazing previously is then seen as expected, trivial commonplace, once it’s been (repeatedly) demonstrated.

Generative AI – for text, images, and more – is quickly becoming the new normal. So I’m curious, because I didn’t go to SSP this year and I haven’t had a chance to look over the sessions –  what did you find most interesting at SSP, either from the sessions or just from the chats you had whilst you were there?

Jessica:

One topic that particularly resonated was that of the scholarly intellectual output of humans — that the ideas aren’t machine generated, and that manifests itself in terms of the written text. So you can imagine, especially in humanities, there’s a very heavy focus on making sure that the text isn’t machine generated, or at least that the ideas and the text are very much those of a human being.

On the science side however, it seemed to be not so much a concern that the text itself isn’t from humans, but what was seen as more worrying was that there’s even more active fraud with respect to data outputs. The opening plenary was from Elisabeth Bik, whom you might know from her work in ensuring scientific integrity and thoroughly investigating image manipulation.

John: Yes, Elisabeth Bik is a legend – I don’t know how she finds the energy in the face of ever more papers!

Jessica:

She talked about image manipulation and her efforts as a whole, and then focused on the potential implications for these new technologies – not so much on the text side, but on the image side with respect to making synthetic outputs. From her talk, it sounds like we’re in a challenging moment because although the general consensus was that we’re a little bit too early on with those technologies to really see the impact, everyone feels like there’s a brewing storm in terms of all the people who have had time to use these tools, and learn these tools – that we will see nefarious actors (e.g. paper mills) start to incorporate them in ways that we haven’t seen before. And that we’re ill-equipped as a publishing community to deal with it.

John: It’s interesting you mention that. Tim Vines shared a tweet today…

Jessica: Tim was my debate partner!

John: It’s a small world indeed! I know Tim from when he founded Axios Review, back when Overleaf was also a new kid on the block 😊 He shared this tweet and it really highlighted how close we are to researchers being able to use AI to generate plausible scientific papers.

Jessica:

This feels almost fully autonomous, not fully, but with a sort of minimal intervention. And obviously I haven’t looked at this in great detail, but it’s a huge step forward from AI “simply” helping to fine-tune something a researcher has written, like we’ve had before with grammar tools.

Barack Obama?

John: Exactly. It feels like we’re almost at the point where a researcher can ask the AI to write the paper, like e.g. a CEO could say to Chat GPT “please write five paragraphs on explaining why the company retreat has to be canceled this year and make it apologetic and sound like Barack Obama.”

Jessica: (in a deep voice) Folks, the company retreat…

John: …

Jessica: I’ve just been watching his new documentary on Netflix so I have his voice in my mind…

John: Okay.

Jessica: It’s actually quite good — he interviews people who have different roles at the same companies and internally tries to get at “what is working, what is a good job”? It’s very US focused but I thought it was quite interesting. Anyway, pivot, but we can go back to AI!

John: This is a nice aside! I saw a video excerpt of him speaking recently, where he was asked for the most important career advice for young people and I believe he said “Just learn how to get stuff done.” Because there are lots of people that can always explain problems, who can talk and talk about stuff, but if you’re the one that can put your hand up and say “That’s all right, I’ll sort that, I can handle that”, it can get you a long way.

What is the publishing industry’s moat?

John: But yes, back to AI, some of the new generative image stuff is a bit crazy – being able to use it to expand on an existing image, rather than just generate something standalone, suddenly makes it useful for a whole load of new things. And I see Google has now also released a music generator, which generates music for you based on your prompt. Everything seems to be happening faster, at a bigger scale than before, and I can see why scholarly publishing is trying to figure out how to ride this tsunami…

Jessica:

How are we going to keep up? Yeah…to borrow a phrase, “What is our moat?”

I think a lot of people are thinking about that, especially given that there’s not only Chat GPT but also all of the smaller models that proficient people can now train on their own. As a scholarly publisher, you’re serving a population that has an over-representation of people—your core demographic—who are going to be really fluent in these models, so what can you offer to them? What can you offer this group that they can’t kind of already do on their own? That’s the million dollar question.

John: Publishers would say they try to help with trust in science, and research integrity – for example, through peer review and editorial best-practices. But they also have an image problem, because there is also a tendency to chase volume, because volume generally equals more revenue, and if you’re chasing volume then you’re going to accept some papers that prove to be controversial. It’s an interesting dynamic between volume, trust and quality.

Jessica:

The volume question is always really interesting because there’s that perspective where people assume publishers have these commercial incentives to grow volume, which has some validity in an OA context of course, but there’s the other viewpoint which is that science is opening up and becoming more inclusive) and that almost by definition means publishing more papers from more authors. But restricting what people can publish…should that be up to the publishers? I do think there is a sense that publishers don’t want to be in the business of telling people they can or can’t publish. Because it’s one thing to say at a journal level, “we don’t think this paper meets the editorial bar”, it’s quite another thing to say, “we don’t think this paper ought to be published anywhere, ever”, right? In fact, I think many publishers would say the opposite: “any sound paper ought to be published somewhere.” We see this view borne out by the increasing investments publishers are making to support authors even before they submit to a journal and also to find another journal, if a paper isn’t accepted. 

Another consideration that was also mentioned at the conference is that writing papers is the way that scientists communicate with each other. Dr. Bik was saying that hundreds of thousands (I forget the exact number) of papers were published on COVID research in the last three years. And she said, “Why do we need that many papers?” Well, in retrospect it’s very easy to say that, but if you are an editor working during COVID, as I once was, for which paper are you going to say “we don’t actually need this one”? Everything was happening so fast, you didn’t know what papers would be the crucial ones in the long run – how can you make that judgment? So I do think more gets published, in response to perceived need.

To be clear: you’re not going to publish anything you don’t think is scientifically sound, but most of us are  not going to try to set the bar at “will this be valuable three or five years later”? That is quite a different bar than “is this just methodologically sound”?

And I don’t know if we’ve gone too far from the question on AI, but with higher volume comes this need to curate–which we’ve needed for a long time–and as the volume increases, the need to curate increases. This curation is another value-add for publishers, but also again something that AI can potentially be very good at given reasonable inputs. I want to be careful not to set up a false dichotomy of “publisher-curation” versus “AI-curation”, because publishers are very much already using AI for things like summarization and recommendation engines, but there is the question of whether publishers, moving forward, continue to drive this curation.

John: One reason you write papers as early career researchers is because you’re learning how to write a paper — you’re publishing some results that aren’t necessarily all that ground-breaking but it’s a record of what you’ve done, why you say your methodology is sound, and so forth. And in doing so you learn how to write a paper. It raises the question of how much e.g. undergraduate work should be published to give say third and fourth year students the opportunity to go through the process of getting their work published.

Jessica:

It is a fascinating question because not only is the pedagogy piece real–that early trainees, undergrads, etc need to learn how to write–but also because publishing is about putting your ideas out there and becoming known to the community.

You can present at conferences (which is another skill), but the scale of impact from that is typically much smaller, and typically it can be difficult to get to the point where you would present at a conference without having published. If you aren’t able to publish, then you’re not able to establish yourself within the community. So publishing is critical for early career researchers to get that first step on the ladder.

The value of getting things done

John: That brings us back almost full circle to where we started – you talked about Michael Faraday as a science communicator, and that you were into science as a kid…

Jessica: I definitely was — I did science fairs at school and that’s actually how I learned about Michael Faraday. And I hadn’t really thought about the communication of science as being distinct from the research itself but for some reason the communication aspect specifically really appealed to me. I have always been quite curious and into learning and storytelling, and being able to communicate ideas through stories.

John: As we’re nearly at time, perhaps that’s a good point to end on — the value of science fairs, and the value of doing something for yourself and getting that experience is one thing AI is not going to replace; it might be able to create the poster for you, or it might write a paper for you, but if you’re the one that’s got to stand there and say what you’ve done, it’s usually pretty obvious if you know what you’re talking about or not, and there’s no AI substitute for that.

Jessica: Yet!

Jessica Miles holds a doctorate in Microbiology from Yale University and now leads strategic planning at Holtzbrinck. I (John Hammersley) am one of the co-founders of Overleaf and now lead community engagement at Digital Science.

The post Dr Jessica Miles: From Michael Faraday to microbiology to AI and beyond appeared first on Digital Science.

CERN is an inspiration to people worldwide — not only scientists, researchers and engineers but students, children and their parents too. As I count myself as belonging to many of those categories, I’m always excited to hear the latest on CERN’s plans for the future. So with that as context, here’s my take on CERN FCC Week 2023 🙂

Looking to the Future: CERN FCC Week 2023

Last week I was in London for CERN FCC Week 2023, an event which brings together the people – scientists, engineers, researchers and more – working on CERN’s Future Circular Collider (FCC) project to present and discuss the latest details from the ongoing feasibility studies and planning assessments.  

You’ve probably heard of CERN, which was founded in 1954, and may well have heard of the Large Hadron Collider (LHC), the world’s largest and most powerful particle accelerator, built underground and which forms part of CERN’s accelerator complex sitting astride the Franco-Swiss border near Geneva. The LHC started operation in 2008 and is arguably most famous for confirming the existence of the Higgs Boson, the discovery of which was announced by CERN on the 4th July 2012, 48 years after it first appeared in the scientific literature. 

Ok, so that’s CERN and the LHC…so what’s the FCC? The FCC is the ambitious proposal to explore higher energy collisions in a new underground laboratory similar in principle to the LHC but at a larger scale; where the LHC is a 27km ring of superconducting magnets, the FCC ring would be approximately three times longer! The latest geological and civil engineering assessments suggest a collider circumference of 91km is feasible.

The timescales for operation of the FCC are not short; construction is not estimated to start until 2030, with first operation of FCC-ee (electron-positron collisions) slated for 2045, and first operation of FCC-hh (proton-proton collisions) in 2070. 

It is this second stage of the FCC, FCC-hh, which is the 100 TeV hadron collider intended to push the energy frontier an order of magnitude higher than that of the LHC.

But before we get onto the FCC, and why it’s such a unique opportunity despite (or perhaps because of) the long timescale involved, it’s worth revisiting the LHC and that moment in 2012 from the perspective of someone who was there, namely Professor Jon Butterworth. Jon was a guest on the panel for the week’s main public engagement event, “Giant Experiments, Cosmic Questions” held on the 8th June at The Royal Society. The panel featured fellow particle physicist Dr Sarah Williams, alongside radio astronomer Professor Anna Scaife and gravitational wave astronomer Dr Laura Nuttall, and was hosted by the brilliant Robin Ince.

It was lovely to hear Jon’s brief retelling of the story of the discovery, and how those first few years of the LHC were the best and most exciting of his professional life. As a child he undertook the important task of writing a book about the planets and moons in our solar system, and recalls the need to continually update it as astronomers discovered new moons of the outer planets. These astronomers and their exploration of the solar system inspired Jon to become an explorer of physics on the microscopic scale (and beyond). 

In his talk he makes the point that particle physics colliders are tools for exploring the universe, not simply “detectors” as they are often labelled. The collisions they generate provide a view on physics at the small scale in the same way telescopes (of all kinds) help us explore the early universe and physics on a much larger scale.

What does this have to do with the FCC, a particle physics collider? Well, in much the same way that we use telescopes such as JWST — and Hubble before it — along with radio astronomy telescopes such as the Square Kilometre Array to explore the universe looking outward, we use particle physics accelerators to look at small scales. As Jon put it in his talk, we are often in uncharted waters and don’t know what we’ll find until we look.

Jon spoke passionately of being inspired as a child by astronomers discovering new moons in the solar system – this gave him the aspiration to discover something new, to be an explorer, which led him to CERN and the LHC, where he (and others of course!) have inspired the next generation of early career researchers to continue to explore high energy physics to better understand the universe we live in.

This brings me nicely onto the Early Career Researchers (ECRs) session at CERN FCC Week. Hosted by Dr Sarah Williams — who I had the fortune of being seated next to at the conference dinner the evening before — it gave the panel of ECRs, including: Abraham Tishelman-Charny (Brookhaven National Laboratory), Andrey Abramov (CERN), Armin Ilg (University of Zurich), Emily Howling (Univ. of Oxford University College), Julia Gonski (Columbia University), and Tevong You (King’s College London), the chance to discuss their hopes, fears and experiences to date with the FCC project. 

(as an aside, I’d also bumped into Armin at the conference dinner, where we briefly discussed Overleaf! His feature request was for push notifications to e.g. Slack, or at least the APIs to make that possible — something which is indeed on the long list, albeit not yet on the immediate roadmap. Still, there’s lots of cool new stuff coming soon 🙂)

After Sarah’s introduction, Armin and Julia both gave excellent presentations to kick things off, and each panellist then spoke very eloquently as to how they’d come to be involved in the FCC project. A theme that ran throughout their stories and the way they answered the questions from the audience was one of exploration and the desire to probe the boundaries of what’s possible — to be at the cutting edge of research, and to be part of the community making that happen. 

There was also a notable degree of pragmatism; they all appreciated the complexities and challenges in getting a project such as FCC approved, and in their answers highlighted the successes and opportunities, ranging from the amazing support they’ve already seen from the local communities where the collider will be built, to the wider benefits of funding large scale projects that manifest themselves in many different ways throughout the life of a project such as this. And in spite of some of the concerns only natural with the project still in its early stages, the session had a real air of optimism and enthusiasm about it, and a hope for the future.

In my eyes, this represents the true success of CERN – not in the discoveries, but in the inspiration and excitement it generates in each new generation. And that is why, to me, their next laboratory to explore the unknown — Future Circular Collider — represents a unique and compelling opportunity.

Firstly, and as was discussed extensively during the week, the assessments show that the economic case is extremely well-founded – designing, building and installing the FCC adds significant value in many areas, from the jobs it creates to the skills it develops and hones in the people building it. And this is just one example. By any measure, there is a good economic case for building the FCC.

The main question around the FCC is then not the economic cost but the opportunity cost: there are always more experiments to build than you have the resources / support for, and you have to ask whether there is something else you could and/or should be focusing on.

Arguments against the FCC make the case that the scientific and engineering resources it requires would be better spent elsewhere, especially given the long timeframe and scale of the project. There are also questions about whether the approach of building a larger collider like the FCC to explore higher energy collisions is the right one; essentially, will it be able to probe a large enough part of Jon’s map of the unknown to justify the (opportunity) cost.

But I don’t see this as a zero-sum decision; having the FCC as a flagship project will inspire more students (and perhaps more importantly, school children!) to become interested in physics and engineering, leading to more resources and more opportunities for other projects alongside the FCC.

Furthermore, CERN is one of the few organisations worldwide that can do this – it combines the ability to bring together an international community of scientists in a large-scale collaboration while at the same time being able to connect with the public in its educational outreach in a way perhaps second only to NASA.

CERN is a brilliant organisation whose scientists, researchers and engineers are exploring the boundaries of our understanding and, in doing so, are inspiring future generations to be explorers in their own way. That’s the real value of the FCC, and it’s a value that, to me, is hard to argue against.

The post CERN 2070 – The next generation appeared first on Digital Science.

Really fun and friendly atmosphere, great opportunity to learn about all sorts of new tech and meet interesting people.”

Tish Mehta

Warwick University

Our write-up of the night

We take a look back at a successful FuturePub event, our first since the pandemic. FuturePub took place on May the Fourth (be with you) at The Royal Institution in London, the historic home of engaging people with research. Read on to learn about the event, discover who gave a lightning talk and how you can watch them on-demand, and check out our photo gallery.

FuturePub’s Back, Alright!

Thursday May the 4th, also known as Star Wars Day, saw the much-anticipated return of #FuturePub! After a short pandemic-induced break, we were thrilled to be back celebrating and showcasing the best of research innovation. This time we were at the Royal Institution (Ri), the spiritual home of science communications owing to the venue’s long history of engaging the public with the latest scientific breakthroughs. With 15 Nobel Prize winners and 10 elements associated with the Ri, alongside a whole host of novel scientific discoveries that have been made at the Ri, it felt like the perfect place to bring our community together to network, natter, and learn about some of the new technologies that are changing the way we not only do research but also how we disseminate its findings.

Why is it called FuturePub?

While the “Future” part is fairly self-explanatory, the “Pub” bit makes people wonder. Does it stand for “publication” or for “public house”? Delightfully, the answer is both! We often have talks about the future of scholarly publishing tech (amongst other things), and after the talks we end up at the pub! Win-win, or should that be pub-pub? 🙂

What is FuturePub like?

FuturePub is our way of bringing people from different segments of the research community working in different roles together in one room to discuss the latest in research, communications and engagement in an informal setting, and this FuturePub event was no exception. Tickets had gone quickly — we were fully booked well in advance of the night — and nearly everyone who booked was able to make it (although we did miss those who messaged to say their plans had had to change at the last minute).

So with almost 100 attendees we were close to capacity on the night, and it created a fun, bustling atmosphere! Many people commented on how great it was to be back, and a quick survey at the start of the lightning talks conducted using the highly scientific “cheerometer” revealed that around a third of attendees were returning FuturePubbers while the remainder were attending for the first time. A great balance! 🙂

Fantastic event for connecting with people from across the scientific and scientific publishing world. A highly stimulating environment, covering both fun and intellectual discussion.”

Chris Arthurs

Hadean (speaker)

It can be hard to convey the vibe of a FuturePub event… so we recorded it! Here’s a lovely video montage created by our photographers/videographers on the night, Dragos and Miki (if the video doesn’t show below, you may need to accept cookies, or simply click here to view directly on YouTube):

This year also saw the launch of some very snazzy FuturePub branding! Our large monograms could be seen on the team’s T-shirts, on our banners, on the holding slides, and most importantly on our laptop stickers available on the swag table. The monograms may all have looked the same but attendees enjoyed spotting the difference between them all – a subtle nod to the fact that the future of research is interdisciplinary and features so many different but equally important fields of study.

Who did we hear from?

Lightning talks are a key feature of all our FuturePub events; short, interesting and exciting intros to cool new things that people are building, ideally related in some way to scholarly communication 🙂 We particularly love demos of things people have built themselves — you’ll find a lot of start-up founders speaking at FuturePub! 

This time we had five talks on a range of topics: 

Our first talk was from Andrew Preston, founder of Cassyni, a platform that stores research talks and makes the talk and the content easily accessible, easy to navigate, and easy to cite. It is no wonder therefore that you can, in a delightfully meta way, access ALL the talks from the evening on Cassyni here.

Next up we heard from Ivy Cavendish who founded TooWrite, a tool which helps researchers and students to plan, structure and execute research communications in a more engaging, less stressful way. Watch Ivy’s talk in full on Cassyni here.

Our third talk from PeerRef founder Elliott Lumb discussed the pros and cons of a system of peer-reviewing research publications independently of journals. This talk generated a lot of discussion during the Q&A with lots of interest in the area. Watch Elliott’s talk in full on Cassyni here.

This led perfectly to our fourth talk on how to measure success for Open Science, delivered by Iain Hyrnaszkiewicz of PLOS. Iain’s lightning talk gave the audience a lot of food for thought, and showcased how the answer requires engagement from so many different research stakeholders to truly define and measure success in this important quest. Watch Iain’s talk in full on Cassyni here.

Our final talk captured the futuristic element of FuturePub perfectly as Chris Arthurs and Mimi Keshani — from Hadean — shared their thoughts on how scientific research will be impacted by the metaverse. Watch Chris and Mimi’s talk in full on Cassyni here. 

Thanks again to all our speakers, who each gave a brilliant talk and generated a lot of discussion in the room and afterwards. If you feel inspired by their talks and would like to speak at a future FuturePub event, please submit your lightning talk proposal here! 

And finally, it’s worth adding that The Royal Institution was the perfect place to be discussing novel engagement methods for science and research — the Ri is the home of the Christmas Lectures and one of London’s first one-way streets, assigned as such purely to manage the high volumes of traffic caused by the masses of public visitors to the Ri that wanted to hear about the latest research. Indeed, what is even more special is that most of these visitors were women, who at the time weren’t allowed to attend any public lectures at other prestigious London venues. The Ri helped open up science to all of society, and today it’s an inspiring yet at the same time cosy and friendly building, perfect for FuturePub 🙂 

What did our attendees think?

We received overwhelmingly positive feedback on the event, with attendees loving the location and venue, and also engaging with the lightning talks and added extras, such as a heritage tour of the building and a classic Ri demo show while we mopped up the last of the food and drink and met new people. 

We also have some helpful tips for next time — clearer labelling on the food, slightly less photography, more time for networking! Oh, and more capacity — we had a number of people who couldn’t attend because it was fully booked. So for the next FuturePub London we will aim for a slightly bigger venue, whilst keeping the cosy, friendly feel!

It was great to be back FuturePubbing. We’ll take on board all feedback received, to make sure we keep doing more of the good stuff. Our next event will be in the Autumn, and if you’d like to know more about what the Digital Science team are up to in the meantime, keep your eyes peeled on TL;DR where we’ll keep our ears to the ground and share the latest in research in informal and easy to digest content from our internal experts and leading voices from within our community.

Tag, you’re it!

Check out our photo gallery from the night and see whether you can spot yourself!

See you at the next FuturePub

Our next FuturePub London will be held in Sept/Oct 2023 — you can subscribe to the FuturePub video series on Cassyni to receive an automatic update ahead of the next event, and we’ll be sure to announce it widely when the date and venue are confirmed!

If you’d like to speak at a future FuturePub event, please submit your lightning talk proposal here! 

The post FuturePub is back! Here’s what happened on May the 4th appeared first on Digital Science.

University research data looks like something from outer space – let’s zoom in and see what’s there

Research institutions need the right tools to discover their strengths and weaknesses, to plan for the future, and to make a greater impact for the communities of tomorrow. In this post, Digital Science’s VP Research Futures, Simon Porter, uses a digital telescope to view the ‘galaxies’ of research within our best and brightest institutions – and explains why that matters.

When we see new images of our universe through the lens of the James Webb Space Telescope (JWST), we’re left in awe of the unique perspective we’ve witnessed, and something about our own universe – even the perception of our own existence – has altered as a result.

What we see are entirely new galaxies, and worlds of possibility.

That’s also what I see when I look at the research data spanning our many universities and research institutions globally. Each one of these institutions represents its own unique universe of research within them.

For me, Dimensions – the world’s largest linked research database and data infrastructure provider – is like the JWST of research data. It enables us to see data in ways we hadn’t thought possible, and it opens up new worlds of possibility, especially for research strategy and decision-making.

What does a university look like?

We began our What does a University Look Like? project in 2019 and it’s rapidly evolved thanks to developments in 3D visualization technology, the expansion of data availability, and the combination of data sources, such as Dimensions and Google BigQuery.

By modelling data from Dimensions into a 3D visualization tool called Blender, we’ve been able to see right into the detail of university research data and capture it in a way that is analogous to the process of taking raw data from JWST and processing it to make a high-quality snapshot of space from afar.

To do this, we’ve used the 2020 Field of Research (FoR) codes, which were developed for research classification in Australia and New Zealand, and we’ve designated a color to each one of those codes (see Figure 2). Each single point of color represents an individual researcher coded by the 2-digit FoR they’re most associated with; researchers are depicted by a sphere, and the size of the sphere is based on the number of publications that researcher has produced.

We then apply algorithms developed by CWTS at Leiden University to determine research clusters – co-authorship networks – within a specific university. These clusters are then layered on top of each other by discipline, with Clinical Science clusters at the bottom, then moving up through Health Sciences, then Science and Engineering, and Linguistics at the top. This is the result.

In Figure 1, we see a 3D visualization of the University of Melbourne, a leading Group of Eight (Go8) research university in Australia. Within this image are 234 research clusters, comprising connections between more than 18,000 co-authored researchers affiliated to the University of Melbourne from 2017-2022.

The high-quality nature of this visualization means we can zoom right into the level of the individual sphere (ie, researcher), or pull back to see the bigger picture of the research environment they’re connected to or surrounded by. We can see every research field and every individual or team they’re collaborating with at the university.

If the university has a biological sciences cluster, we can see whether there’s a mathematician interacting with that cluster, clinical scientists, engineers, or someone from the humanities or social sciences. It opens up a new level of understanding about the research landscape of an institution and its individuals.

On our Figshare, you can also watch a video that takes you through the various research clusters found at the University of Melbourne. You can also follow the “What does a university look like?” Figshare project here.

At Digital Science, we’ve created six of these visualizations – five universities from Australia and one from New Zealand – to help demonstrate Dimensions’ unparalleled capabilities to assist with analyzing research data. While many institutions have similarities, some are completely different in research collaboration structures (see Figure 4).

To see a brief video where I walk through all six of the visualizations, visit the Digital Science YouTube.

Looks great – but why does it matter?

These 3D visualizations aren’t just about producing a pretty picture; they’re an elegant and useful way of representing the richness of research data contained about each institution in Dimensions. This is particularly true for university administrations where the ability to measure and promote internal institutional collaboration is just as important as measuring international collaboration.  

To illustrate this point, consider the differences between the collaboration structures of the Australian National University (see Figure 4) and the University of Melbourne. Beyond the immediate difference of network size and discipline focus (the University of Melbourne is larger, and  has a much larger medical and health sciences footprint), the two universities have very different collaboration shapes, with disciplines more distinctly separate in the ANU graph. That two prestigious research institutions can have such different shapes suggest there are different external forces at play that influence the shape of collaboration.

Figure 4 represents the Australian National University (ANU), with more than 5,600 co-authored researchers from 2017-2022 and 75 research clusters identified in the data. 

Two reasons that might significantly contribute to the different shape of ANU are its funding model and physical campus shape. ANU’s funding model is unique within Australian higher education, having been endowed with the National Institutes Grant, providing secure and reliable funding for long-term pure and applied research. A key focus of the grant is maintaining and enhancing distinctive concentrations of excellence in research and education, particularly in areas of national importance to Australia. This concentration of excellence is also perhaps reflected in the relative discipline concentration within the visualisation. ANU is also a relatively spread out campus at roughly three times the size of the University of Melbourne’s Parkville campus, making the physical collaboration distance between disciplines larger.

By beginning to identify how factors such as size of campus and funding models can influence the collaboration structures provides key insights for universities, governments and funders. The relative ease of creating these models based on Dimensions data opens the possibility of creating collaboration benchmarks able to be correlated with other external factors. These insights can in turn help shape interventions that maximise local collaboration, in line with the culture of the institution. As with stargazing, the more you look into the past, the better you can see the future. 

Note: Simon Porter first shared these visualizations at the Digital Science Showcase in Melbourne, Australia (28 February to 2 March 2023).

About Dimensions

Part of Digital Science, Dimensions is the largest linked research database and data infrastructure provider, re-imagining research discovery with access to grants, publications, clinical trials, patents and policy documents all in one place. www.dimensions.ai 

The post Discovering ‘galaxies’ of research within universities appeared first on Digital Science.