Connecting Grants to Research: The Role of Open Funding Metadata

When it comes to key scientific data, being open isn’t enough — they need to be FAIR (Findable, Accessible, Interoperable, and Reusable). Watch a full walkthrough on what FAIR data are and how to publish them here: https://lnkd.in/du3P6vBv #ScientificData #OpenScience #FAIRData #DataManagement

💡 Ever wondered what’s the difference between an Institutional Repository, a Research Data Repository, and an Open Data Repository? Think of it this way: 🏛️ IR – preserves scholarly works (theses, articles, reports) 📊 RDR – manages and shares research datasets 🌍 ODR – makes data openly accessible for everyone Each plays a unique role in advancing Open Science and ensuring research visibility, accessibility, and impact. #OpenScience #ResearchData #OpenData #InstitutionalRepository #DataManagement #FAIRData #AcademicLibraries #ResearchInfrastructure #DataSharing

📈 Better data, better discovery Our PubMed integration now uses Crossref citations for more accurate research overviews. Citations determine the scale of both research areas and documents on our PubMed maps. Explore the update 👉 https://lnkd.in/d-su_KAG #OpenScience #OpenInfrastructures

Science is a process. Scientists investigate, hypothesize and experiment to answer questions. In science, a consensus is not a vote of people’s opinions. A scientific consensus occurs when many studies and experiments from different scientists produce results that support the same trend or explanation. A scientific consensus is a consensus of evidence and information, not one of opinion.

So true. Actual scientific consensus is based on the data. Not a bunch of people in a [smoke-filled] room that all agree. If scientific consensus truly meant me and my friends agreeing on something, scientific consensus would be meaningless (and involve a lot more cat videos). And yet, here we are. Time and again, in human health risk assessment, we see a bunch of like-minded individuals getting together in a smoke-filled room, agreeing on something with no actual scientific consensus, and calling it a consensus because they all decided. Typically, that smoke filled room lacks any resistance, because the organizer intentionally invited their like-minded friends. Under that consensus model, my U14 boys soccer team could reach a consensus on what food we should all eat for healthy meals -- and although I'm not against starbursts and skittles (they are delicious), I highly doubt we should eat them for every meal. But here we are, and that is a consensus. I would prefer to let good, high quality science drive that decision. Science focused on disproving favored hypotheses. Science that seeks to severely test ideas -- not science that looks to prove a favored hypothesis right.

An important point to ponder for all profiles— both for those who think objectively, and those who think they do. Our two cents as implementation scientists and pracademics: Opinions don’t have scientific validity unless they’re backed by documented evidence. Evidence doesn’t need to be peer-reviewed, though ideally it is / will be. For areas without published, peer-reviewed research, using theory / models / correlated research & findings also establishes an evidence basis. What do you think?

Bringing structure to discovery - literally! Most discovery teams don’t suffer from a lack of data. They suffer from a lack of structure in their data. Every experiment, assay, and notebook entry is a fragment of a bigger picture, but too often, those fragments live in silos. At Omnible Tech, we help teams connect the dots between molecules, results, and insights. From clean identifiers to FAIR workflows, structure isn’t overhead, it’s the foundation of discovery that scales. Let’s talk about how to make your scientific data reveal its potential. #DrugDiscovery #DataStrategy #Informatics #LIMS #ELN #FAIRData #OmnibleTech

🎇New Preprint: “SPHN Connector – A scalable pipeline for generating validated knowledge graphs from federated and semantically enriched health data.” Integrating diverse health data — from clinical records🩺 to omics🧬 — is essential for advancing personalized medicine. Within the Swiss Personalized Health Network (SPHN), we present a federated framework for implementing the FAIR principles (Findable, Accessible, Interoperable, Reusable) at a national scale.♻️ 🎓Key highlights: A federated model where hospitals and research partners semantically enrich and manage their own data locally — moving away from centralized data silos. Introduction of the SPHN Connector, a tool that: 🔹 Transforms heterogeneous data into semantically enriched RDF 🔹 Supports de-identification and validation 🔹 Enables iterative and secure data delivery in federated settings Demonstration of how this approach enables linking clinical and omics data across sites and patient populations, while maintaining local control and privacy. 📄 Read the preprint: https://lnkd.in/eMjNksuz #FAIRData #KnowledgeGraphs #SemanticInteroperability #HealthData #BiomedicalInformatics Vasundra Touré, Deepak Unni, Andrea Brites Marto, Philip Krauss, Katie Kalt, Nicola Stoira , Maximilian Pickl, SIB Swiss Institute of Bioinformatics

Funders, institutions, and publishers may ask you to create a data management and sharing plan to include in your research proposal. This reflects the growing importance of data and also the shift towards open science. Learn more: https://brnw.ch/21wSrKn #mdpi #opendata #openscience

Research is more accessible to all through OpenEmory’s new platform. An online repository of Emory University researchers’ work recently got a makeover with a new site that’s more accessible, easier to use and quicker to load. Link in comments!