Presented at the BioFAIR Showcase, 2026, below are 19 insightful posters highlighting ongoing projects, collaborative efforts, and recent progress from across our community. These presentations showcase practical applications of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in the life sciences, offering a closer look at the tools and methodologies driving the future of biological research. We invite you to explore the gallery to discover the vital work and diverse perspectives shared by this year’s contributors.
| No. | Lead Author | Title | Abstract |
| 02 | David Barry | Promoting open and reproducible workflows for bioimage analysis | Microscopy has been a cornerstone of biological research for centuries. However, while technological advancements have made microscopy increasingly quantitative, the analytical approaches used by many researchers still often lack reproducibility. In tandem with these technological advances, the role of the “bioimage analyst” has emerged. Often associated with microscopy core facilities, these experts possess the unique combination of skills and knowledge to optimise computational analyses of microscopy images. At the Francis Crick Institute, our team endeavours to do this primarily via training and method/workflow development, adhering to FAIR principles wherever possible. However, the barriers to fully implementing FAIR workflows in bioimaging remain significant. Many bio researchers lack computational skills and require extensive training and support to implement reproducible pipelines to analyse their own data. The data itself can be challenging, even for seasoned analysts, as microscopy file formats are many and varied, despite much progress on standardisation from groups such as the Open Microscopy Environment. Furthermore, modern microscopy systems allow the routine acquisition of large, complex, multi-dimensional datasets, requiring substantial computational resources to analyse, which many researchers do not have access to. These are not Crick-specific challenges – they are systemic across the life sciences and represent an area where programmes like BioFAIR could have a transformative impact. Here we present an overview of our efforts to tackle these challenges. These include the development of open-source analysis workflows, training programmes aimed at building computational literacy among bench scientists, and community-building initiatives to connect knowledge silos distributed around the UK and beyond. |
| 03 | Anthony Brookes | Café Variome: Advanced Software for Federated Discovery of Data & Samples | Collaboration in research can be promoted and facilitated by enabling the discovery of shareable data and samples beyond local and limited networks. The standards-based, TRE-compatible, Café Variome software (www.cafevariome.org) addresses this challenge by providing a flexible, secure, web-based platform for decentralized, federated, responsible data discovery. It can support a single discovery node, through to a fully-decentralized network, to help researchers identify the existence of relevant assets, whilst ensuring that data custodians retain full control over what resources are discoverable by whom, and what information they are then provided with (e.g., yes/no, counts, links, handoffs). International adopter communities have used the system to find metadata, data, samples, patients, trial subjects, healthcare records, and drug toxicities across many disease areas, including dementia and rare diseases. By sharing only approved or obfuscated information and limiting query responses, the platform promotes wider collaboration while maintaining privacy and data security. |
| 04 | Eva Caamano Gutierrez | BIOMEDASA: Driving Innovation in Biomedical Data Science Through Collaboration | Biomedical Data Science (BMDS) is central to modern biomedical discovery, yet its full potential remains constrained by fragmented training pathways, skills shortages, limited diversity, and barriers to reproducible, FAIR-aligned research. An MRC expert review highlighted these systemic challenges and called for new, collaborative models that embed data science more effectively within biomedical research ecosystems. BIOMEDASA is an MRC-funded pilot initiative responding directly to this challenge across the Liverpool City Region and Greater Manchester. Built around five integrated work packages—Enhance, Nurture, Reskill, Analyse, and Inspire—the programme embeds data scientists within NHS research teams, delivers open and accessible training in experimental design, data curation, AI and FAIR principles, supports reskilling and leadership development for biomedical professionals, evaluates national master’s-level BMDS provision, and engages underrepresented young people to widen participation in data science careers. Now at its midpoint, BIOMEDASA has generated tangible outputs, including embedded team-science models, interdisciplinary training events, outreach resources, and early evidence to inform workforce investment and policy. Crucially, the project also leverages shared data science research facilities and captures career development opportunities through the University of Liverpool’s Research Technical Professional Pathway, addressing sustainability beyond the pilot phase. This presentation serves as both a progress update and a call to action. We invite the community to help crystallise BIOMEDASA’s legacy—identifying scalable elements, transferable training models, and future collaborations. By aligning closely with BioFAIR priorities in skills development, FAIR data adoption, and community-led training, BIOMEDASA offers practical insights for strengthening BMDS capacity nationally and beyond. Co-authors: Emily Johnson, Christina McDonald (nee Birch), Anthony Evans, David Hughes, Elisabeth Deja, Alasdair Ivens, Andrew Jones. |
| 05 | Eli Chadwick | RO-Crate: Capturing FAIR research outputs in bioinformatics and beyond | RO-Crate is a mechanism for packaging research outputs with structured metadata, providing machine-readability and reproducibility following the FAIR principles. It enables interlinking methods, data, and outputs with the outcomes of a project or a piece of work, even where distributed across repositories. Researchers can distribute their work as an RO-Crate to ensure their data travels with its metadata, so that key components are correctly tracked, archived, and attributed. Data stewards and infrastructure providers can integrate RO-Crate into the projects and platforms they support, to make it easier for researchers to create and consume RO-Crates without requiring technical expertise. Community-developed extensions called “profiles” allow the creation of tailored RO-Crates that serve the needs of a particular domain or data format. Current uses of RO-Crate in bioinformatics include: Describing computational workflows registered with the WorkflowHub platform Creating FAIR exports of workflow executions – supported by workflow engines including Galaxy, Nextflow, WfExS, CWL, and more Capturing metadata from federated learning processes within Trusted Research Environments Capturing plant science experiments as Annotated Research Contexts (ARC), complex objects based on RO-Crate which describe workflows, workflow executions, inputs, and results defining RO-Crate conventions for biodiversity genomics metadata This poster highlights the most prominent applications of RO-Crate within bioinformatics, with the aim of increasing awareness and sparking new conversations and collaborations within the BioFAIR community. |
| 06 | Christina Ernst | Empowering FAIR Functional Genomics: Data Infrastructure, Curation, and Analysis at EMBL-EBI | The Functional Genomics team at EMBL-EBI provides the essential infrastructure for the submission, archiving, analysis, and visualisation of high-throughput functional genomics experiments. Our core mandate is to promote FAIR data principles, ensuring data are Findable, Accessible, Interoperable, and Reusable, across a spectrum of technologies, including bulk and single-cell RNA-Seq, epigenetic profiling, and emerging spatial transcriptomics. We manage the submission and curation of datasets into the ArrayExpress collection via the Annotare tool, ensuring robust metadata capture that facilitates deep scientific reuse. These curated data power our downstream knowledgebase Expression Atlas, which provides interactive, standardised visualisations of gene and protein expression across species, tissues, and biological conditions. Beyond this, we support major international initiatives, including the Human Cell Atlas, by maintaining essential data repositories and contributor portals. Our team integrates biological expertise with scalable software engineering to deliver reproducible analysis pipelines on HPC and cloud platforms. We are actively evolving our infrastructure to accommodate new data types and are integrating AI-driven approaches to enhance metadata annotation and pattern discovery. Through the development of community standards and commitment to open science, we empower the global research community to derive novel biological insights from complex molecular data. |
| 07 | Paola Galdi | SPLICE: A Scientific Platform for Life sciences Data Integration, Collaboration and Exploration | SPLICE is a data platform being developed by the Data Science & Data Management Team at CRUK Scotland Institute to enhance data management and analysis in life sciences research. By combining metadata harmonisation, automated workflows and high-performance computing within a single platform, SPLICE supports end-to-end research processes across multi-omics, imaging and other experimental modalities. Built on FAIR principles and implemented using an infrastructure-as-code strategy, SPLICE enables reproducible deployment across local systems, national-scale facilities, commercial cloud platforms and trusted research environments, ensuring flexibility, security and sustainability. SPLICE is being adopted to meet the data management and analysis needs of the MRC National Mouse Genetics Network, and the CRC-STARS and PREDICT-Meso projects. Its design is driven by a commitment to make data integration and collaboration simpler, more robust and more widely accessible. Key features include: automated analysis and reporting pipelines, data collection and curation aligned with FAIR best practices, support for sensitive data within trusted research environments, advanced search and visualisation capabilities for building custom datasets, and support for generating high-quality training datasets for AI applications. We report on the first deployment of the platform at the Edinburgh International Data Facility, which is supporting the development of backend components and programmatic interfaces. The platform architecture includes four modules: data transfer and quality control, data storage and access, data analysis and visualisation, and data export. Together, these components form a coherent framework that supports high-throughput data movement, scalable analysis and streamlined research workflows. |
| 08 | Carole Goble | WorkflowHub – a workflow registry for the BioFAIR community | WorkflowHub (https://workflowhub.org) is an international, free, open-source registry built to support the sharing and discovery of computational workflows, standard operating procedures, and their associated research assets. It boosts collaboration and scientific transparency by making computational workflows easier to co-create, discover, cite, and share – helping research become more reproducible and aligned with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The platform is agnostic of workflow language and format, currently registering 1500+ workflows in 28 formats, with more than 400 teams contributing across bio-sector disciplines in life sciences, health, chemistry, and biodiversity. It has premium support for Galaxy and nextflow with access to their curated canonical workflow collections. Supporting widely adopted standards such as the Common Workflow Language, RO-Crate, Bioschemas, FAIR Signposting, and GA4GH’s TRS API, WorkflowHub integrates with other platforms and tools. Its RO-Crate submission API allows research communities to programmatically create and update workflow entries from their native git repositories including nextflow’s nf-core and Galaxy’s IWC. WorkflowHub connects with many external services to enhance metadata: e.g. LifeMonitor to assess workflow health and availability and the ELIXIR bio.tools catalogue. Users can register ‘draft’ workflows to co-develop through team collaboration, which may be cross-institutional, and control who can see the workflows. Finalised workflows can be assigned (free) DOIs, with versioning, enabling proper citation. WorkflowHub serves an international community, includes champions from the BioFAIR fellowship, and is coordinated by ELIXIR in partnership with the Australian BioCommons. Its development is shaped by its users through regular community events, and biweekly WorkflowHub Club calls. |
| 09 | Charlie Harrison | AIRBDS: Quantifying AI-readiness in bioscience data | The rapid rise of mainstream interest in AI has been accompanied by discussion of what it means for systems, organisations, and particularly data to be “AI-ready.” In the biosciences, where vast volumes of data are produced across diverse domains and modalities, this notion remains ill-defined. Bioscience data is heterogeneous, spans layered systems across spatial scales, and is stored in a plethora of formats and infrastructures, ranging from fully featured knowledge bases with integrated analysis tools to stand-alone datasets deposited in thematic or general-purpose repositories. This landscape raises a central question: what does it mean for bioscience data to be AI-ready? To address this, we established the AI-Ready Bioscience Data (AIRBDS) working group and developed a quantitative, domain-agnostic metric for assessing AI-readiness. Our approach draws inspiration from existing data-quality and openness frameworks while incorporating the group’s collective experience in AI, data stewardship, and bioscience research. We aim to map and evaluate bioscience datasets and resources across this diverse landscape, grading AI readiness and providing implicit recommendations that encourage more cohesive, AI-ready data ecosystems such as the ELIXIR Core Data Resources. By building on existing metadata standards to capture the essential features of datasets, then cataloguing and exposing these features, we seek to further increase the utility of bioscience data for both human and machine researchers. In this poster we present our draft AIRBDS Dataset Metric, the main themes and scopes, our process for constructing and refining it, and the results of our assessments of a number of established bioscience data resources. |
| 10 | Alex Henderson | FAIRSpectra: Enabling the FAIRification of Spectroscopy and Spectrometry | There is a growing requirement for data to be shared openly. This is now mandated by funding agencies including UKRI, Horizon, Wellcome and equivalent organisations around the world. Sharing data means that files must be readable by others, perhaps using different software packages, and their contents understood, ideally by machines in addition to humans: ‘machine actionability’. The FAIR Guiding Principles – that data should be Findable, Accessible, Interoperable, and Reusable – are now recognised to be one of the cornerstones of Open Research. Organisations such as ELIXIR and the Research Data Alliance have published materials on how to generate FAIR data, but there remains an operational gap between these recommendations, and how one should actually go about the task, on the ground. To tackle this impasse, we set up the FAIRSpectra Initiative, a community driven enterprise to discover what is missing in the pipeline: from data acquisition to shared resource. FAIRSpectra’s role is to open a discussion about what the chemical analysis field requires in terms of file format support, and specifically the imaging modalities of spectroscopy and spectrometry. It aims to tackle issues with interoperability and reusability. This has been broken down into two areas of activity: open file formats and metadata vocabulary. This presentation will cover what currently exists, what is still required, and offers an invitation to all interested parties to get involved, share expertise, and become part of the solution. Join us at http://fairspectra.net |
| 11 | Anke Husmann | Arrayed CRISPR screening at the MRC-AZ-University of Cambridge Joint Functional Genomics Screening Laboratory (FGSL) | The FGSL, a joint venture between the Milner Therapeutics Institute at the University of Cambridge, AstraZeneca and the Medical Research Council (MRC), aims to combine the strengths of academia and industry to accelerate the development of biomarkers and therapeutics through functional interrogation of the genome at scale. As part of the UKRI’s Human Functional Genomics Initiative, the FGSL is forming collaborations with UK-based researchers to identify novel gene targets in healthy development and disease. The FGSL leverages the unique features of arrayed screening, whereby individual genes are targeted via CRISPR/Cas9 in a plate-based format, to uncover the complexity of developmental and disease signatures using human in vitro models. The laboratory is equipped with a high-throughput screening platform that enables automated liquid handling and acquisition of high-content endpoints including spatial imaging and flow cytometry. Academic researchers, small- and medium-sized enterprises (SMEs), and industries from across the UK can propose arrayed CRISPR screening projects in complex human cell models outside of oncology such as organoids, co-cultures, primary and iPSC-derived cells. A bi-annual call for proposals for CRISPR screens is open with deadlines in May and November. Successful applicants will enter into a collaboration agreement with the University of Cambridge and in some cases with AstraZeneca to execute the screen, which will be carried out by FGSL scientists on our automated arrayed CRISPR screening platform. All successful proposals led by UK-based academics will be funded. All data will be owned by the lead applicant. |
| 12 | Rahele Kafieh | RETINA-FAIR – Resources to Enable the Transformation of retinal Images Networks and Archives into Findable Accessible Interoperable and Reusable data | Many serious illnesses (heart disease, diabetes, cancers) develop quietly, without symptoms, until they become much harder to treat. However, eye scans can reveal the earliest signs of these diseases (as well as eye disease), often years before symptoms develop. Eye scanning technologies are widely available generating hundreds of millions of eye images each year. However, eye scans are produced by many different machines, each using its own file types and formats. These systems do not ‘talk’ to each other, the information stored with the images is often incomplete or inconsistent, and many research tools cannot even open the files. As a result, researchers struggle to combine data from different hospitals or clinics inhibiting research could help develop new tools to predict and detect disease earlier. This project aims to remove these barriers. We will build an easy-to-use, open-access toolkit that can: 1) Recognise what type (file format, imaging modality and scan protocol) of eye scan is being presented. 2) Open and extract information from different file types used across the majority of eye-care services 3) Convert the images into a single, widely used medical format so they can be combined and analysed together 4) Check the quality and completeness of the date before it is used for research. By creating this shared ‘translation layer’ for eye-scan data, we will make it possible for researchers to work with images in a consistent, reliable way. This will support better research, enable large multi-centre studies, and accelerate the development of safe and effective artificial intelligence tools that can detect disease earlier and improve patient care. |
| 13 | Emma Karoune | Advancing Careers and Team Science in Biomedical Data Science | The rapid evolution of biomedical research increasingly relies on data-intensive methodologies, necessitating a workforce proficient in data science. However, the recognition and advancement of careers remains inconsistent, often hindered by unclear role definitions, unstructured technical career pathways, and the undervaluation of team-science approaches. Advancing Biomedical Data Science Careers is a two-year project, funded by the Medical Research Council and jointly led by The Alan Turing Institute and EMBL’s European Bioinformatics Institute (EMBL-EBI), that aims to document skills, roles, career pathways, and team-science approaches in biomedical data science. The project is currently at its midpoint, allowing us to present preliminary findings. These include an extensive stakeholder map of the UK biomedical data science ecosystem, developed by the research team and informed by stakeholder input gathered through engagement activities and open calls for feedback. The map provides a foundation for high-level mapping of selected competency frameworks relevant to the field, supporting the community in navigating the landscape and identifying skills gaps. The poster will also present early insights from an ongoing qualitative study examining interdisciplinary biomedical data science practices across organisations of different types and scales. This work aims to document key challenges, emerging best practices, and future needs for initiating and sustaining effective team-science. By fostering a structured understanding of competencies and collaborative dynamics, the project aims to strengthen the visibility, sustainability, and inclusivity of careers in biomedical data science. Ultimately, this research contributes to building a resilient, well-supported workforce equipped to meet the demands of modern data-driven biomedical research. |
| 14 | Tong Li | Building FAIR Nextflow Modules for Bioimage Analysis: A Multi-Institute Community Effort | Bioimage analysis workflows are often developed as monolithic pipelines tailored to individual labs, making them difficult to share, reproduce, or adapt across institutions. This fragmentation leads to duplicated effort, inconsistent processing, and limited reuse of validated analysis components. We present an ongoing community initiative spanning eight institutes — Wellcome Sanger Institute, Francis Crick Institute, Janelia Research Campus, VIB, EMBL Heidelberg, University of Heidelberg, Human Technopole, and SciLifeLab — to develop a shared ecosystem of FAIR Nextflow modules for bioimage analysis. Rather than building end-to-end pipelines in isolation, we decompose common analysis tasks into atomic, reusable modules — from illumination correction and denoising to segmentation and feature extraction — each with rigorously defined schemas for both data and metadata. Each institute maintains its own repository while adhering to shared schema conventions, enabling modules to be discovered and composed across the ecosystem. A central aggregator site (https://bioimagetools.github.io/nf-module-aggregator/) catalogues modules from all participating repositories, providing a unified view of available components. Module interfaces currently standardise around OME-TIFF, with active discussion on adopting OME-Zarr as workflows scale to larger datasets. All modules will be containerised, version-controlled, and follow the community governance model established by nf-core for genomics. Looking ahead, we are exploring whether well-typed module schemas can enable AI agents to automatically assemble analysis pipelines — selecting compatible modules, resolving schema constraints, and generating executable Nextflow workflows with full provenance tracking. We believe that community-governed, modular infrastructure is the path toward making bioimage analysis both fully reproducible and accessible at scale. |
| 15 | Andrew J. Millar and the BioRDM team | Carrot-juggling for biocurators, or metadata analysis in the chronobiology community resource BioDare2 | High-quality metadata is critical for research data re-use to scale far beyond the original data generators. Professional metadata curation is ideal for finished datasets, but not when biologists share emerging data, as they do in BioDare2, https://biodare2.ed.ac.uk (lead, Dr. Daniel Thédié). The Biological Research Data Management team (BioRDM) manages this unique analysis resource for rhythm researchers, especially for the 24-hour cycles driven by cellular circadian clocks. BioDare2 is the fastest way for many of these researchers to analyse their emerging timeseries data. The poster will outline how this added value results in users agreeing to Open their data after 3 years, such that BioDare2 now shares 13,000 Open experiments from 40 species. To streamline the user experience, BioDare2 has little mandatory metadata (species; data type; assay method; free text title and description) and optional free-text fields. We analysed the metadata that users provide, compared to original BioDare. BioDare from 2008 offered identical analysis algorithms – the same ‘carrots’ for users – but required structured technical and experimental metadata, which users interpreted as more ‘stick’. I will explain the observed balance of metadata volume against usage and the number of contributed experiments, from research of PhD candidate Juliana Rodriguez Cubillos. In preparation for assessing metadata quality at scale, she used Natural Language Processing tools to recognize semantic entities in the metadata of BioDare2 and other biological data repositories. Building on this process, the poster will outline potential scopes for AI-supported data annotation in curated and user-contributed data repositories. |
| 16 | Susanna-Assunta Sansone | Operationalising FAIR in BioFAIR: a policy-to-practice knowledge layer | A central challenge within BioFAIR is not whether FAIR matters, but how to begin and navigate an increasingly complex ecosystem of standards, repositories, and guidelines to enable informed decision-making and practical implementation. We present a set of interconnected resources that together form a FAIR knowledge layer, transforming uncertainty into actionable pathways and directly supporting the “end-to-end FAIR journey” that BioFAIR aims to deliver. FAIRsharing acts as the core intelligence and recommendation service, linking policies, standards, and databases to support FAIR compliance across the research data lifecycle, from data management planning to dataset publication. Its curated, structured knowledge graph, enriched and validated through the FAIRsharing Community Champions programme, serves as a trusted source of truth, powering downstream services, most notably the Data Stewardship Wizard (DSW), which delivers FAIR-compliant, machine-actionable Data Management Plans. FAIRassist builds on the FAIRsharing foundation by providing a measurable, generic, and extensible FAIR compliance and assistance backbone. Leveraging FAIRsharing content and community-defined FAIR profiles, it supports generation of evidence of FAIR alignment and offers actionable recommendations for improvements. Complementing and connected to these components is a FAIR literacy layer, delivered through the FAIR Cookbook and RDMkit, which provide practical, community-driven guidance, and are already part of the BioFAIR FAIR-in-action bridge Pathfinder project. These mature, interoperable FAIR-enabling resources are already embedded within the ELIXIR work programme and are increasingly adopted by European and worldwide research infrastructures, as well as in other disciplines. Together, they are well placed to deliver a scalable policy-to-practice framework for operationalising FAIR in BioFAIR. |
| 17 | Dorothea Seiler Vellame, Rachael Thompson, Sam Fletcher | Linking multi-omic data and driving discovery through FAIR principles: The Data Coordination Centre in The UK Human Functional Genomics Initiative | The UK Human Functional Genomics Initiative’s (FGx) mission is to characterise the functional consequences of variants to improve understanding of biological mechanisms and to develop new treatments. This Initiative was awarded £28.5 million funding by the Medical Research Council (MRC), in collaboration with the Biotechnology and Biological Sciences Research Council (BBSRC). Technological advances mean we can sequence the genome at unprecedented scale and accuracy, leading to vast quantities of complex, multi-omic data. The Data Coordination Centre (DCC) aims to accelerate scientific breakthroughs in functional genomics by enabling scientists to find, use, and share this data. We are a centralised team based at the University of Exeter to support researchers funded by the FGx. The DCC will work to: 1. Support data standardisation and analysis – Offering hands-on support as well as signposting to specialists in other areas of the Initiative 2. Enable the adoption of FAIR principles (Findable, Accessible, Interoperable, Reusable) – Facilitating national knowledge sharing across the FGx by centralising pipelines and workflows – Ensuring laboratory protocol and computational workflow standardisation through training, provision of metadata and documentation guidelines, and technical support for researchers 3. Ensure the legacy of data produced by the Initiative – Creating a catalogue of highly annotated datasets produced by FGx researchers to allow others to re-use data whenever possible Come and speak to us to find out more about our work and how we can collaborate. |
| 18 | Fred Ulche | The Grassroots Data Infrastructure | The Grassroots system at the Earlham Institute is designed to collect, catalogue, and manage field trial data, making it available as FAIR (Findable, Accessible, Interoperable, and Reusable) data. It captures comprehensive datasets and associated metadata using established ontologies alongside well-defined, community-driven terms to support data sharing and reuse. All data is accessible through a user-friendly web portal, which includes a map-based interface for location-driven exploration, as well as filtering by date and keyword search to enhance discoverability. In addition, the Grassroots mobile app has been developed to streamline the collection of observational data during field trials. It provides a simple and intuitive interface for efficient data entry, along with features such as AI-assisted note transcription and the ability to attach photos, enabling richer and more accurate data capture in the field. |
| 19 | Claire Witham | Improving the sharing of animal behaviour data: How do we make it FAIR? | Increasing amounts of animal behaviour data are being collected across different disciplines including animal welfare and neuroscience. Much of this data is initially collected as videos and then analysed through either automated analysis, manual behaviour scoring or a combination of both. Developing new automated methods relies on having access to good quality annotated data. However, a combination of factors make it challenging to share this data: 1) Animal rights activity: many facilities including laboratories, farms and zoos fear that sharing video and image data will lead to it being misused by animal rights activists. 2) Human caretakers: for larger species human caretakers are often present in the video. It’s difficult to share these videos due to privacy concerns and time-consuming to select videos without human presence. 3) Quantity of data: the volume of video data means that it’s difficult to fit within the size limits of data sharing sites. 4) File formats: Video and other data is often recorded in proprietary formats and can be difficult to convert and share. For annotation data it can be difficult to know which format to use. In the poster we discuss these challenges and some of the solutions we use in our work with laboratory macaques and mice to make this data FAIRer. |
| 20 | Zuzanna Zagrodzka | Supporting FAIR-from-the-Start with Electronic Laboratory Notebooks | As research becomes increasingly data-intensive, capturing experimental records in a structured, digital, and high-quality manner is essential for reproducibility, continuity, and reuse. Despite the availability of digital tools, many researchers still rely on paper laboratory notebooks, limiting the completeness, consistency, and long-term usability of research records. This creates barriers to collaboration, project continuity, and downstream data sharing, and undermines alignment with FAIR principles. Electronic Laboratory Notebooks (ELNs) provide a practical infrastructure to support the transition to digital workflows by embedding documentation directly into research processes. However, ELNs alone do not ensure FAIR compliance; the quality, consistency, and timing of data capture are critical. This is particularly important in data-rich disciplines, where reproducibility depends on detailed contextual information such as experimental design, instrument parameters, data provenance, and analytical workflows. Institutional support is key to enabling effective ELN adoption. Coordinated approaches, through shared platforms, guidance, and standardised templates, can improve consistency and promote sustainable data practices. We present the ongoing initiatives at the Cancer Research UK Manchester Institute and the planned university-wide rollout of ELN solutions at the University of Manchester highlight the role of organisational infrastructure in strengthening data quality and consistency, while embedding FAIR practices into everyday research workflows. |