Albert Einstein College of Medicine launches a $7M data science institute to help researchers harness large sets of data that can lead to medical breakthroughs.

Join us for a conference on Biomedical Data Science in Maine! This event unites researchers, data enthusiasts, and innovators. Experience engaging scientific talks, an interactive poster session, and roundtable discussions that promote collaboration and innovation in the field.

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As biomedical research becomes increasingly data-driven, the need for coherent, high-quality training in research data management (RDM) has never been more pressing [1]. A well-trained workforce — comprising both researchers and data stewards — is essential to ensure that data is FAIR [2], secure, and usable across disciplines and institutions. While many National Research Data Infrastructure (NFDI) consortia have independently developed training activities, these efforts often remain fragmented, leading to duplication, gaps, and inconsistent learning experiences. In response, NFDI's Section Training and Education (EduTrain) emerged to develop a framework for the NFDI as a whole [3]. Regarding the biomedical domain context, several NFDI consortia have begun to coordinate and align their RDM training strategies, share resources, and co-create a unified curriculum tailored to the needs of the biomedical research community. This initiative is based on three core objectives: (i) aligning training activities and pedagogical approaches across consortia, (ii) co-developing a modular, community-driven curriculum framework for data stewardship/RDM, and (iii) building on existing educational resources through systematic evaluation, reuse, and collaborative content development. These activities are also closely linked to EduTrain, the DALIA (DAta LIteracy Alliance) framework and the newly launched RDMTraining4NFDI base service [4]. Alignment of training activities involves identifying overlapping goals and audiences among consortia such as NFDI4Health, GHGA, NFDI4BioImage. Through the recently formed Biomedical Interest Group [5], these consortia - and others are welcome to join - are mapping existing training efforts, identifying good practices and facilitating communication between trainers and stakeholders. The goal is to reduce redundancy while increasing interoperability and visibility of training resources across national and international infrastructures. Co-creation of a common RDM curriculum lies at the heart of this effort. Rather than prescribing a one-size-fits-all program, the initiative is developing a modular, biomedical-focused curriculum framework that can be adapted to career stages and specific disciplinary needs. It encompasses both foundational RDM concepts and domain-specific modules, addressing the training needs of researchers, data stewards, and infrastructure providers [6]. A co-creation model ensures that content is grounded in real-world use cases and developed collaboratively by domain experts, instructional designers, and training coordinators from the consortia. A recent example is the ASSURED [7] service developed by KonsortSWD-NFDI4Society, BERD4NFDI and GHGA, which is being tested in the other biomedical consortia for usability, adaptability and new content. The initiative emphasizes leveraging and contributing to existing resources. Many high quality materials already exist within individual consortia, but are under-utilised beyond their original scope and, unfortunately, not shared according to the FAIR principles [8]. By piloting joint training sessions, integrating materials into common platforms, such as OERSI and DALIA, and exploring peer review processes, the community aims to assess, improve, and extend these assets. In addition, contributors will be encouraged to openly license new materials, ensuring widespread reusability and fostering a culture of open education. We present a shift from isolated training activities to a sustainable, community-driven ecosystem for biomedical Data Literacy education. Together, consortia can more effectively support the growing demands of data-centric research and contribute to a future where high-quality RDM is the norm—not the exception.

Tumor boards are multidisciplinary teams of healthcare professionals that are working together to encompass the full spectrum of care around diagnosing, plan...

Boehringer Ingelheim has announced expansion of its global Computational Innovation footprint with a new centre dedicated to AI & ML in the UK.

Abstract. The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There i

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