Open Science and Interdisciplinary Studies

Have you heard of openproblems.bio? 🧬 It's kind of like Kaggle for single-cell biology. And it's a brilliant model of how the ML and bioinformatics communities collaborate. Single-cell analysis sits at a fascinating intersection: massive, tabular datasets meet cutting-edge ML meets the messy reality of biology. But here's the challenge: ML researchers love Jupyter notebooks, while biology demands robust, reproducible pipelines for processing data. Different tools, different cultures. OpenProblems.bio bridges this gap brilliantly. It transforms core single-cell challenges into living, community-run benchmarks where anyone can: ✅ Submit methods in Python or R (no pipeline expertise needed) ✅ Compare results fairly with standardized datasets and metrics ✅ Access everything openly, including published results and methods The secret sauce? A powerful three-part ecosystem: * Viash converts scripts into reproducible pipeline components * Nextflow orchestrates complex workflows * Seqera provides the platform for elastic cloud execution on AWS What excites me most: this is **FAIR open science done right** Complete transparency, version control, quality checks built in, and a framework that welcomes contributions from both communities without forcing either to abandon their tools. Tasks range from dimensionality reduction to perturbation prediction, with new benchmarks continually added by the community. If you're working in computational biology or ML for life sciences, this is infrastructure worth knowing about. The future of biomedical AI needs exactly this kind of collaborative, rigorous foundation. Thanks to Robrecht Cannoodt and the openproblems.bio community for collaborating on the blog: https://lnkd.in/gpNJNd2r #Bioinformatics #MachineLearning #SingleCell #OpenScience #Benchmarking

Most of my academic career was dedicated to working on interdisciplinary research projects. In the early days, I found there was little consideration for the inherent challenges of this type of work, particularly in managing power dynamics across the STEM/social science disciplinary divide. While there is a lot to be gained from interdisciplinary research, it can be a hard road to walk. It’s so refreshing to see interdisciplinary research gaining more prominence from funders, and critically, a more intentional approach to creating supportive spaces for people to communicate across disciplinary divides. Here are some key lessons I learnt along the (sometimes rocky) journey: 🔹Language Matters: Ensure everyone on the team understands the key terms used to articulate the research. For instance, on an antimicrobial resistance project I was working on, the term ‘driver’ meant different things to different academics. Spending time interrogating this helped the team to have a shared understanding of the objectives of the project. 🔹 Regular Knowledge-Sharing Sessions: Building an understanding of what each team member is doing to contribute to the project can help to foster a positive working environment. Facilitated sessions, where each team member presents their work, can help to foster a shared understanding. For me, learning about how microbiological sampling techniques worked was helpful when developing my own ethnographic study. 🔹 Foster a Culture of Mutual Respect: Post-docs and project administration staff, often the hardest working, sometimes see their contributions rendered invisible. I cannot stress enough how vital it is to create an environment where all disciplines and team members are valued equally. Avoid empty platitudes; good leadership comes with actively listening to each other and recognising that the project wouldn’t happen without every member of the team. 🔹 Utilise Facilitation Techniques: Don’t assume that a group will naturally come together; intentional facilitation, such as round-robin discussions, ensures everyone has a chance to contribute. This can also help to manage power dynamics and give voice to quieter members. 🔹Create a Psychologically safe space: When people do not feel safe to share their ideas, they will not take risks and experiment. Safe spaces for learning are essential; public criticism and shaming can deter people from sharing. Encourage team members to explore ideas outside their comfort zones in a way that is supportive. Creating safe spaces is the only way people will experiment together. Interdisciplinary research can indeed be a hard road to walk, but the journey is enriched with diverse perspectives and the potential for groundbreaking discoveries. It's about managing the complexities with care and ensuring every voice is heard and respected. Let's continue to push the boundaries of knowledge, together. #interdisciplinaryresearch #academicresearch #power #facilitation

Human dynamics research has evolved rapidly over the past decade, driven by interdisciplinary collaboration and technological innovation. Our new open-access opinion paper explores how GIScience, computational science, and design science are coming together to tackle major challenges like urban sustainability, disaster response, and epidemics. Check out “Advancing Translational Human Dynamics Research: Bridging Space, Mind, and Computational Urban Science in the Era of GeoAI”, co-authored with leading experts in Urban AI and GeoAI! Bin Jiang Tao Cheng Ming-Hsiang Tsou Di Zhu https://lnkd.in/gMyh6gSW

AI4Research: A Survey of Artificial Intelligence for Scientific Research How AI is Reshaping the Scientific Method: A New Blueprint for Research ... 👉 Why This Matters What if every researcher had an assistant that could read 10,000 papers overnight, design experiments autonomously, and draft manuscripts with precision? As scientific output grows exponentially—2.5 million papers published annually—the bottleneck shifts from data generation to knowledge synthesis. Current tools struggle to connect insights across disciplines or evaluate novel ideas at scale. This survey maps how AI systems are evolving from productivity tools into collaborative partners for the entire research lifecycle. 👉 What’s Inside The paper introduces AI4Research—a framework organizing AI’s role in science into five core functions: 1. Comprehension Engines   - Extract key claims from text, tables, and charts   - Resolve contradictions across studies automatically 2. Discovery Accelerators   - Generate hypotheses by combining domain knowledge   - Predict experimental outcomes before lab work begins 3. Synthesis Systems   - Build literature maps showing research trends   - Write survey papers by clustering related work 4. Writing Partners   - Draft sections while maintaining academic rigor   - Optimize figures and citations contextually 5. Peer Review Augmentation   - Match papers to ideal reviewers   - Flag methodological gaps in submissions The taxonomy reveals critical gaps: - Over 80% of current tools focus on literature review - Few systems handle interdisciplinary reasoning - Experimental automation lacks real-world validation frameworks 👉 How This Changes Research Three paradigm shifts emerge: 1. From manual workflows to AI co-authors Example: Multi-agent systems now replicate entire research cycles—one LLM proposes ideas, another designs experiments, a third critiques results—mirroring human collaboration patterns. 2. Cross-domain pollination Physics-informed neural networks help biologists simulate protein folding, while social science methods improve AI ethics frameworks. The survey identifies 37 interdisciplinary applications. 3. Open infrastructure The authors compile: - 128 datasets for training AI research assistants - 19 open-source tools for automated peer review - Benchmark tasks measuring "scientific reasoning" in LLMs 👉 Conversation Starter "Could AI-driven discovery outpace human-led research in specific domains by 2030? What safeguards would ensure its credibility?"

Happy to announce the publication of my latest article from Lindenwood University College of Arts and Humanities "The Role of Collaborative Authorship in Decentered Research Innovation" in  Information, Medium, and Society: Journal of Publishing Studies. As we pass the inflection point in this age of AI, it's clear that no one discipline can solve the wicked problems facing our world. Now more than ever, we need to break down academic silos and embrace interdisciplinary collaboration. Access the article here:  https://lnkd.in/gfpyU5FF I'm grateful to my 70+ co-authors across multiple institutions and disciplines - from computer science to psychology to sociology - for their invaluable contributions. Together we've shown how a decentered, collaborative research model can reengage faculty, provide students with critical skills, and accelerate innovation. The future is interdisciplinary! #interdisciplinary #collaboration #innovation #socialsciences #STEM #researchculture #academia

Over 400 years ago, the scientific revolution gave birth to the modern world - but today, the challenges the world faces call not for a scientific revolution, but for a revolution in science. So said Dr. Megan Kenna, at an inspirational evening dinner hosted by Megan, Schmidt Science Fellows and Schmidt Sciences to mark the launch of the inaugural Times Higher Education Interdisciplinary Science Rankings. She said: The scientific revolution "was an era of extraordinary courage and creativity, led by figures like Galileo and Newton, who dared to challenge conventions and redefine what was possible. "That revolution brought us the scientific method, disciplines like physics and chemistry, and the foundation for engineering and modern medicine. "But today, the challenges the world faces call not for a scientific revolution, but for a revolution in science.  "For a change in how science is done. "Because in recent decades, the structures of academia, research funding, and publication have rewarded an ever-increasing and narrow specialization. "While depth of expertise is essential, so too is breadth of knowledge and the ability to connect ideas across disciplines. And yet the culture that has grown up around science too often encourages researchers to stay in their lanes and to take incremental steps rather than bold leaps. "But let me remind you: that the most profound breakthroughs of the past century—the double-helix structure of DNA, the COVID-19 vaccine, and artificial intelligence—they did not emerge from scientists staying in their own lanes. They came from the scientists that were willing to cross them. "Likewise, the solutions we need for the 21st century will not come from a single discipline. They will come from the fertile intersection of fields like biology and data science or engineering and economics. "They will come from bringing together people who don’t think alike -- but who think differently, creatively, and courageously. "This is why the inaugural Interdisciplinary Science Ranking we celebrate tonight is so vital. For the first time, we are recognizing and championing the universities breaking barriers and harnessing the power of interdisciplinarity." View the rankings here: https://lnkd.in/dQTmDyet With huge thanks to Duncan Ross, Billy Wong, Sol Ramos and Jhesselle Leonor for their work to deliver this project, to Keith Burnett and Ruth Arnold for advocating for THE, to Megan, Matt Goode and all at Schmidt Science Fellows for their vision and drive, and to Wendy Schmidt and Stu Feldman and everyone at Schmidt Sciences for their world-changing philanthropy. #THEUnirankings #THEISR #THEISF