Commissioned by ARDC as part of its work on the National Persistent Identifier Strategy, the toolkit provides a structured approach to assessing the adoption and maturity of PID infrastructure across Australia’s research ecosystem. It is intended to inform shared planning, highlight areas for improvement, and support long-term progress towards a more connected and interoperable research environment.

About the Toolkit

Persistent identifiers — including ORCID iDs, DOIs, RORs, and RAiDs — are now widely recognised as foundational components of modern research infrastructure. They help connect researchers, projects, outputs, institutions, and services across systems and domains.

The National PID Strategy outlines five strategic objectives that aim to increase the discoverability and reusability of research, improve reproducibility, reduce administrative burden, strengthen impact assessment, and support national capability mapping. The benchmarking toolkit aligns with these objectives by offering a set of SMART benchmarks — Specific, Measurable, Achievable, Relevant, and Time-bound —that provides a national framework for assessing progress against the Strategy whilst also providing a mechanism for institutions and sector bodies to evaluate their progress.

Each benchmark is grounded in the current state of practice and considers relevant factors such as international dependencies, expected maturity, stakeholder responsibilities, and alignment with FAIR principles (Findable, Accessible, Interoperable, and Reusable).

A Sector-Wide Resource

The toolkit covers a broad range of research inputs and outputs, including grants, research projects, research data, facilities, reports, methods, and non-traditional outputs. It also highlights the importance of connecting research activities across the full lifecycle.

As a practical demonstration of benchmarking in action, the report includes an analysis of Australia’s progress in ORCID adoption. Drawing on data from Dimensions and other sources, the report benchmarks ORCID uptake and record completeness by country, funder, discipline, publisher, and institution. Australia’s coordinated efforts over the past several years — including those led by ARDC and its partners — have resulted in one of the highest levels of ORCID adoption and integration globally.

Invitation to Contribute

This consultation draft is intended to support discussion and feedback across the sector. We invite contributions from institutions, funders, service providers, and researchers to help ensure that the final version — to be published in October 2025 — is both useful and implementable.

Over the coming months, Digital Science and ARDC will continue to refine the benchmarks and consult with stakeholders through a series of validation workshops and collaborative development activities.

Timeline

• July–August 2025: Consultation, Validation and refinement
• September 2025: Stakeholder workshops
• October 2025: Final release of the benchmarking toolkit

Access the Report

Download the consultation draft of the Australian National PID Benchmarking Toolkit: https://doi.org/10.6084/m9.figshare.29281667

Co-authored by Digital Science’s Simon Porter (VP Research Futures), Dr Juergen Wastl (VP Research Evaluation and Global Challenges) and Dr Hélène Draux (Senior Data Scientist).

For questions or feedback, please contact Simon Porter.

The post Supporting Australia’s PID Strategy: Introducing the National Benchmarking Toolkit (Consultation Draft) appeared first on Digital Science.

As someone who works at the intersection of open research and AI, I’ve found myself returning to a specific question: What kind of content truly matters to AI? I have previously spoken of an AI powered flywheel effect in research, where each cycle follows this pattern:

1. Raw data is processed by AI to generate initial research outputs
2. Knowledge extraction tools mine these outputs for higher-order insights
3. These insights form a new, refined dataset
4. AI processes this refined dataset, generating more precise analyses
5. The cycle continues, with each rotation producing more valuable knowledge

I interpret “understanding the fundamentals” as being the base layer, the raw data. Models can mimic almost any writing style and generate endless reams of text. Not all content is created equal. I have witnessed this first hand as self declared “academics” from around the globe use generalist repositories to post non peer-reviewed content written by LLMs which proves their genius.

AI systems, particularly large language models, rely on data to learn. Not just more data, but better data; data that reveals new structure in the world. Without novel input, AI models will become better at rephrasing the known, but not at understanding the unknown. At its core, science is a method for producing high-quality, structured novelty – a repeatable process for generating new facts, testing them against reality, and sharing them with the world. Basic research, often funded for its long-term potential rather than short-term applications, is the primary engine of this kind of content. AlphaFold succeeded because it was trained on grounded, empirical data from the protein databank.

If we want AI to continue advancing in a meaningful way that uncovers new knowledge, we need to prioritise access to and support for novel scientific research. That means supporting open science. It means investing in infrastructure that ensures new data and discoveries are FAIR (Findable, Accessible, Interoperable, and Reusable). It means rethinking publication practices to encourage the dissemination of negative results, replication studies, and raw data. And it means funding basic research across the globe.

China has significantly increased its investment in basic research, aiming to reduce reliance on foreign technology and achieve self-sufficiency in foundational sciences. China’s spending on basic research passed 6% of its total R&D in 2023 and continues to rise. China seems to be an outlier. Here in the UK for example, basic research through UKRI continues but often faces pressures to demonstrate short-term economic impact. In the US, the NSF and NIH continue to support basic research, but federal R&D budgets have shifted toward mission-driven, applied research. The Inflation Reduction Act and CHIPS and Science Act brought some uplift, but basic science still receives a minority share of total R&D.

The geopolitical landscape is hard to predict. But the narrative is that all countries want to compete on the AI stage. Radical abundance only will happen in your country if you have some control of the input to the models. 

At a separate SXSW London session, we also heard from former UK Prime Minister Tony Blair and The Rt Hon Peter Kyle MP, UK Secretary of State for Science, Innovation and Technology. Peter Kyle’s plans for integrating AI into the UK government are commendable, and seem to be advancing at a pace that the UK government is not famous for. A comment from Tony Blair however highlighted what is at stake if we don’t fund basic research: “It is amazing to me that we are not feeding all of the NHS data into these AI models.”

Whether you trust this government with your most sensitive data or not, we will have future governments who may not follow your best interests – in the same way that LLM models are ignoring copyright on academic publications today, they may ignore human ethics when it comes to your medical data. Feeding the NHS into LLMs is not the answer. The easiest way to generate new data for the AI models with a view to advance science and technology is to fund more basic research and insist that the outputs be made open in a FAIR manner.

There is so much to gain. The value of novel scientific research has never been higher.

The post AI Needs New Facts – The Value of Novel Scientific Research appeared first on Digital Science.

Gold Open Access—the model where authors (or their funders) pay article processing charges to make work freely available—has grown steadily over the past decade. But if you look closely, the growth is slowing. And what’s taking its place isn’t the altruistic, community-powered model many hoped for. Instead, Hybrid Open Access is filling the gap: a model where paywalled journals charge extra to make select articles open.

Diamond Open Access—where publishing is free for both authors and readers, supported by institutions rather than APCs—has been making headlines. But here’s the uncomfortable truth: we still don’t have enough data to know whether Diamond OA is actually growing or simply being talked about more.

A System Stuck in the Middle

Digging into Dimensions data layered on top of OpenAlex, a clear pattern emerges. Gold OA’s momentum is slowing. But instead of researchers turning to Green OA (self-archiving in repositories) or Diamond OA, many are choosing Hybrid OA instead.

Why?

Because researchers chase visibility, reputation, and prestige. That often means publishing in high-impact journals—many of which are owned by legacy publishers who now offer hybrid models. These options give authors a way to comply with funder mandates without sacrificing perceived academic clout.

Institutions and libraries, meanwhile, are under pressure to show open access progress. Hybrid OA, while expensive, is a politically safe way to do that. It’s the administrative equivalent of checking a box—even if it means paying twice.

Transformative agreements like Read and Publish deals have only accelerated this trend, redirecting subscription budgets to cover OA fees—effectively normalizing hybrid publishing in many disciplines.

A New Vision Emerges

But while the hybrid tide rises, a quiet revolution is underway.

In 2022, a coalition of organizations—Science Europe, cOAlition S, OPERAS, and ANR—launched a bold Action Plan to support Diamond OA. Their goal: to build a truly equitable, community-driven publishing ecosystem, where knowledge is a public good and the costs are shouldered collectively—not by individual researchers.

This vision took shape through the DIAMAS project and culminated in the creation of the Diamond OA Standard (DOAS). Think of DOAS as a blueprint: a framework to help Diamond journals measure, improve, and sustain quality.

It’s built on seven pillars:

• Legal ownership, mission, and governance
• Open science practices
• Editorial management and research integrity
• Technical service efficiency
• Visibility and impact
• Equity, diversity, inclusion, and multilingualism
• Continuous improvement

Together, these components aim to professionalize Diamond OA without compromising its values. They send a clear message: if scholarly communication is a public good, then it must be shaped and governed by the scholarly community itself.

The Missing Link: Measurable Growth

Despite this momentum, the numbers tell a more sobering story.

Early data from OpenAlex paints the picture that Diamond OA has plateaued in terms of publication volume. Enthusiasm and infrastructure have grown—but the data doesn’t reflect this.

Why the disconnect? Part of the issue is visibility. At Digital Science, we would love a better way to track Diamond Open Access growth. DOAJ lists 1,369 journals as being “without fees”, but there seem to be many edge cases resulting in a landscape that isn’t black and white.

It is also true that many Diamond journals operate with limited marketing, uncertain technical infrastructure, and fragmented funding. And despite the ideals, many researchers still don’t see them as viable options for career advancement.

What Comes Next?

There are reasons for optimism. The DIAMAS project isn’t just advocating for Diamond OA—it’s building the scaffolding. Its service portal offers templates, best practices, and technical guidance to help journals align with DOAS standards and professionalize their operations. The European Diamond Capacity Hub (EDCH) serves as a coordination center for Diamond OA stakeholders in Europe. Launched alongside the EDCH, the ALMASI Project focuses on understanding non-profit OA publishing in Africa, Latin America, and Europe.

The pieces are falling into place for equitable open access solutions. What’s needed is adoption and quantification. Funders and Institutions should include equitable OA in their promotion criteria. Researchers must see it as a credible home for their work. If anyone knows of faster ways to get clean data for Diamond, please reach out.

The path forward exists. But like any path, it only becomes clear by walking it. If you are working in this space and have this data, we would love to disseminate it through our tools at Digital Science.

The post From Gold to Diamond: Is Equitable Open Access Still a Mirage? appeared first on Digital Science.

AI can also serve as a tool to aid humans in data extraction from many papers. Consider a scenario where AI synthesizes information from hundreds of studies to create a refined dataset. That dataset then feeds back into the system, sparking new research papers.

This cycle—dataset to paper, paper to knowledge extraction, knowledge to new datasets—propels an accelerating loop of discovery. Instead of a linear research pipeline, AI enables a continuous, self-improving knowledge ecosystem.

From data to papers

I looked for interesting datasets on Figshare. The criteria was a) that I knew they would be re-usable as they had been cited several times. And b) the files were relatively small (<100MB) so as not to hit the limits of the common AI tools. 

This one fit the bill:

Rivers, American (2019). American Rivers Dam Removal Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.5234068.v12

From there I asked Claude 3.7 Sonnet “Based on the attached files, can you create a full length academic paper with an abstract, methods results, discussion and references”. Followed by “Can you convert the whole paper to latex so I can copy and paste it into Overleaf?”

The resulting paper needs a little tweaking in the layout of the results and graphs, but other than that, has done a great job.

Papers to new data/knowledge

A single paper is just the beginning. The real challenge is synthesizing knowledge from the ever-growing volume of research. This is where specialized knowledge extraction tools become crucial. How do we effectively mine this knowledge? This is where ReadCube shines. ReadCube helps researchers manage and discover scholarly literature, but its real power lies in its knowledge extraction capabilities. Imagine ReadCube as a powerful filter, sifting through countless pages to extract the nuggets of wisdom.

Tools like ReadCube can then analyze vast collections of papers, uncovering patterns and relationships that human researchers might miss. This process involves:

• Text and citation mining: AI can analyze papers to identify emerging trends, inconsistencies, or knowledge gaps.
• Automatic synthesis: AI can compare findings across thousands of studies, synthesizing insights into new, high-level conclusions.
• Hypothesis generation: By recognizing correlations between disparate research areas, AI can propose new research directions.
  

The Flywheel Effect: How the Cycle Accelerates

The true magic happens when this extracted knowledge becomes the input for the next iteration. Each cycle follows this pattern:

1. Raw data is processed by AI to generate initial research outputs
2. Knowledge extraction tools mine these outputs for higher-order insights
3. These insights form a new, refined dataset
4. AI processes this refined dataset, generating more precise analyses
5. The cycle continues, with each rotation producing more valuable knowledge

With each turn of this flywheel, the insights become more refined, more interconnected, and more actionable. The initial analyses might focus on direct correlations in the data, while later iterations can explore complex causal relationships, predict future trends, or suggest optimal intervention strategies.

This AI-driven, data-to-knowledge cycle represents a paradigm shift in research. Imagine the possibilities in fields like medicine, climate science, and economics. We’re moving towards a future where AI and human researchers work in synergy, pushing the boundaries of discovery. Rather than replacing researchers, AI acts as a force multiplier, enabling deeper faster knowledge generation.

The post The Perpetual Research Cycle: AI’s Journey Through Data, Papers, and Knowledge appeared first on Digital Science.

Although AI feels like a recent tech development, Danny reminds us that it has a long-established history. Danny worked alongside the likes of Marvin Minsky, and Claude Shannon – no, they’re not Bugsy Malone characters but are two of the team members who established the field of artificial intelligence. Working with them, Danny and the crew discovered that what they thought would be easy was much harder than expected, while what they were wary of was much easier to achieve. Pattern recognisers were developed with little effort, but creating a computer that could beat a human at Chess was much harder.

It turned out that the main barriers to success were a lack of data and, the most limiting factor of all, a lack of computational power. But that’s OK because Danny’s PhD focused on what would be required to build the biggest computer. He discussed his Thinking Machines in our Speaker Series chat which we shared last month.

Danny notes that today’s AI researchers are working on algorithms that are very close to the ones the team imagined back at the start of this area of research, however, he reminds us that we are still way off machines that can replace humans. While well-trained machines can carry out specific talks well, they are missing the critical thinking part of intelligence, however good they are becoming in mimicking intelligence, as evidenced in numerous case studies of AIs that hallucinate, or create solutions that look and sound right based on the fact that the machine has recognised patterns and attempts to apply those rules but that, without real meaning or understanding, are factually incorrect. Danny tells the story of how his granddaughter can recognise patterns in visiting contractors and become someone who sounds like an expert in moments, but scratch the surface and there is no real knowledge of the area to be able to make logical decisions. I too am reminded of the time I accidentally found myself co-piloting an island-hopper propellor plane across Belize, having curiously followed the actions of the pilot for the first two stops – but we’ll save that story for another time. The year is young, and we’ve got lots more to chat about, and many more stories to share.

Danny reflects that, while to experts it doesn’t feel like AI has moved on much since the development of supercomputational power, there is a change coming, as evidenced by the ever-increasing rate of development in the area. The difference this time around is funding, which is attracting the smartest minds in their droves, catalysing this progress by exploring the intuitive aspects of this technology.

To make this technology truly good, Danny firmly believes that a source of truth is required. One of his interests is building a knowledge graph of the provenance of information, which he further expanded on in last month’s Speaker Series. This would go some way to building technology that is as robust and trustworthy as possible, while attempting to eliminate biases or building on questionable knowledge that can bed into the foundations, creating points of future weakness and instability.

The great thing about building good technology is that it in turn starts to iteratively learn and teach itself, generating more knowledge, even about that knowledge itself. These are exciting times for AI, but public and research community engagement remains vital to ensure that developments do not double down on historically discriminatory narratives or unscientific knowledge that have no place in today’s society.

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 Evolution of AI appeared first on Digital Science.

Venki breaks his thoughts down into two categories, the first of which is around general integrity in research. Measures of robust research include whether published research is sufficiently detailed that someone could perform the experiment and observe results that reproduce the reported research outcomes. This is just one measure to help determine the quality of research being conducted and is a core tenet of peer review, by peers and competitors who would be able to expose any issues in the research. There is also the conscious or unconscious cherry-picking of observations, which can again be mitigated through community consensus and conversation. If something is proven to be wrong, this consensus can be challenged and eventually help shift our understanding of the problem.

However, Venki also discusses the much bigger and more pervasive issue of misinformation, which goes beyond the scientific community and impacts all aspects of our lives, including politics, behaviour, economics, and more. Many threads of life are susceptible to the negative impacts of misinformation, which is being accelerated further by the ease at which information can be shared, and the contribution that AI is making to the volume of misinformation available in the wild.

Venki encourages us to be alert and involved as a society in combatting misinformation through robust critical thinking, to help prevent the spread of incorrect information that could otherwise go on to misinform policy and processes that impact us every day.

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: Professor Venki Ramakrishnan on Trust in Research appeared first on Digital Science.

Joy acknowledges the need to have a term that refers to both the geographic region of the Southern hemisphere and also the economic Global South, especially when describing the differences in access to funding and research information, and opportunities to collaborate on a global scale due to a lack of visibility of research outputs or the inability to attend conferences half a world away.

However, using Africa as an example, Joy expresses concern about the fact that, while it is easy to use the four major commercial capitals of the continent to represent the entire region, the resulting outlook is underrepresenting vast swathes of communities across the continent, so that when solutions are proposed, they do not adequately serve the needs of the many, only of the few that were represented.

Do you have any ideas for how we can better represent our communities across the world? Get involved in the conversation on our many social media channels using the hashtag #TLDRShorts, and let us know if you’d like to suggest future contributors for our series or suggest some topics you’d like us to cover.

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

The post TL;DR Shorts: Joy Owango on the Global South appeared first on Digital Science.

In our first Speaker Series chat of 2025, and in a month that heralds much political interest in the role of truth and trust in society, Deborah delves into the challenges and opportunities facing science communication today. She explores the importance of storytelling, the necessity of improving scientific literacy in all, and the steps needed to build a future where science journalism drives meaningful dialogue and action. Her insights offer a vision of how journalists and scientists can work together to showcase the human side of science and ensure it serves all communities fairly and effectively.

Deborah Blum is an acclaimed science journalist, Pulitzer Prize-winning author, and Director of the Knight Science Journalism program at MIT. With a career spanning decades, she has worked tirelessly to bridge the gap between science and the public through her compelling storytelling and her commitment to advancing science literacy. Her influential books, which include The Poisoner’s Handbook and The Poison Squad, explore the intersection of science, history, and societal impact. At the Knight Science Journalism program, Deborah leads efforts to train and support journalists worldwide, fostering a global community dedicated to improving the quality of science communication and addressing pressing challenges like misinformation and declining public trust in science.

The Role of Science Journalism

Science journalism plays an important role in making connections between scientific discoveries and people’s everyday lives. Deborah describes the role that science journalists play in helping to translate complex scientific ideas into stories that resonate with readers. Good storytelling can make even the most abstract research feel relevant and engaging.

In a world increasingly driven by scientific and technological progress, this connection is more important than ever. Deborah highlights that science journalism not only informs but also inspires public interest and action. By showing how science impacts issues like health, climate change, and technology, journalists help communities see the relevance of research in shaping our future. As she puts it, “We need to write about science and its impacts, right? We need to acknowledge that it has these social and cultural impacts. We need to illuminate those in all of their social justice issues.”

The Impact of the KSJ Program

At the heart of Deborah’s work is her leadership of the Knight Science Journalism (KSJ) program at MIT, where she has been Director for a decade, a role which she will be stepping down from in July 2025. The KSJ program is somewhat of a global engine for excellence in science communication. The program provides resources, fellowships, and a thriving community for journalists to deepen their expertise and broaden their perspectives.

But the KSJ program goes beyond training – it builds a community of professionals who share a commitment to thoughtful, accurate reporting. Deborah believes this community approach is critical in a world where misinformation spreads rapidly. When journalists feel supported and connected, they are better equipped to tackle tough stories and elevate public understanding. This global impact is reflected in the program’s alumni, who are shaping conversations about science across continents.

Challenges in Science Communication

Communicating science effectively isn’t without its hurdles. Deborah points out that misinformation is casting an ever-growing shadow, compounded by public scepticism and limited access to scientific education and critical thinking skills. We now live in an age where everyone has a platform from which they can share their thoughts, but not everyone values the accuracy of those thoughts. Deborah emphasised the need for journalists to cut through the noise with credible, engaging stories.

Another challenge is the disconnect between scientists and the public. Deborah argues that many researchers struggle to communicate their work in accessible ways, leaving gaps that can be filled by misunderstanding or fear. “The more people can see scientists as actual human beings next door, the better off we all are,” she insists. Building bridges between these groups is crucial to fostering a more informed and engaged society.

The Importance of Science Literacy

Science literacy is the foundation of informed decision-making, yet many people lack the tools to critically evaluate scientific information, with many people even being fearful and actively disengaged with science. Deborah shares her thoughts about the shortcomings of educational systems. She believes that by not teaching people how to think critically about science, we are doing a disservice to society. Without this foundation, the public is more vulnerable to the potentially negative consequences of pseudoscience and misinformation.

Deborah believes that improving science literacy requires a collective effort. Journalists, educators, and policymakers must work together to ensure that everyone has access to clear and reliable information. It is not only about teaching facts but also about teaching people how to think, and how to evaluate those facts for any inherent bias. “We don’t want everyone to be a scientist, but we do want everyone to know something about science and how to make decisions about science and to recognize that every time you fry an egg or make a cup of tea or peel a banana, you’re engaging with chemistry, right, in everyday science. And it makes the world that much more interesting.” By developing people’s ability to understand and question scientific claims, society can make better choices for the future, and hold bad actors accountable for bad science.

Building Trust in Science

Trust in science has been eroded in recent years, but Deborah sees this as an opportunity for change. Trust isn’t automatically granted – it is something that must be earned. Scientists and journalists both have roles to play in this process. Deborah encourages researchers to embrace transparency and share not just their successes but also their uncertainties and failures, to humanise their motivations and actions, and to showcase the more realistic side of the scientific process.

She also highlights the importance of equity in building trust. Science needs to serve all communities, not just a select few, so addressing social justice issues in science such as unequal access to education and healthcare can help rebuild trust in science’s potential to improve lives. Through honest and inclusive communication, science can regain its role as a trusted guide for society.

The Future of Science Journalism

Looking ahead, Deborah envisions a future where science journalism is more valuable than ever. She sees the next generation of journalists as not only storytellers but also advocates for social justice. She believes that the future of science journalism lies in showcasing the human side of science, and how it impacts people and communities.

Deborah encourages young journalists to be fearless in tackling big issues, from climate change to misinformation. We need journalists who are smarter, braver, and more curious. By embracing innovation and collaboration, science journalism can continue to be a powerful force for good, shaping public understanding and inspiring meaningful action in an increasingly complex world.

Deborah’s thoughts are a powerful reminder of the critical role that science journalism plays in shaping a better-informed, more engaged society. From her leadership of the Knight Science Journalism program to her advocacy for transparency, equity, and science literacy, Deborah tangibly demonstrates how storytelling can drive meaningful change. As we face global challenges like misinformation, climate change, and declining trust in science, her call for collaboration and innovation in both journalism and science communication is more relevant than ever. By fostering a new generation of journalists who are fearless, thoughtful, and socially conscious, Deborah is helping to build a future where science journalism not only informs but also empowers us to build a better world.

You can watch the full interview with Deborah on our YouTube channel, and 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. We’ve even joined the podcast universe! Catch our 2025 Speaker Series season and our chat with Deborah on Spotify or Apple Podcasts.

With thanks to Huw James from Science Story Lab for filming and co-producing this interview. Filmed at the Knight Science Journalism offices at MIT in Cambridge, Massachusetts, USA in April 2024.

The post Science Journalism and Social Justice – meet Deborah Blum appeared first on Digital Science.

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

Read more

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

Read more

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

Venki on Apple Podcasts

Venki on YouTube

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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

Niamh on Apple Podcasts

Niamh on YouTube

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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

Alice on Apple Podcasts

Alice on YouTube

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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

Rodney on Apple Podcasts

Rodney on YouTube

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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

Eugenia on YouTube

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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

Gilbert on Apple Podcasts

Gilbert on YouTube

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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

Jenny on Apple Podcasts

Jenny on YouTube

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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

Danny on Apple Podcasts

Danny on YouTube

Read more

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

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: Venki Ramakrishnan on the Future of Biology appeared first on Digital Science.