Pipeline release! nf-core/drugresponseeval v1.1.0 - Drugresponseeval 1.1.0 - Humongous Zapdos!
Please see the changelog: https://github.com/nf-core/drugresponseeval/releases/tag/1.1.0
#celllines #crossvalidation #deeplearning #drugresponse #drugresponseprediction #drugs #fairprinciples #generalization #hyperparametertuning #machinelearning #randomizationtests #robustnessassessment #training #nfcore #openscience #nextflow #bioinformatics
Cost for research — how cost data of research can be included in open metadata to be reused and evaluated
#AlexanderWagner #BiancaSchweighofer #ChristophBroschinski #ColinSippl #CorneliaLang #DirkPieper #FAIRPrinciples #GernotDeinzer #JuliaBartlewski #LisaMarieStein #OpenAccess #OpenMetadata #ResearchFunding #SilkeWeisheit #transparency
Making Reproducibility a Reality by 2035? Enabling Publisher Collaboration for Enhanced Data Policy Enforcement
#AllysonLister #FAIRPrinciples #MatthewCannon #RebeccaTaylorGrant #ReproducibilityOfResearch #ResearchData #SusannaAssuntaSansone
Get hands-on with data classification and discover how OpenAIRE tools and services like ARGOS, Explore, and AMNESIA support FAIR practices across the research lifecycle. Don’t miss this opportunity to learn, play, and connect! Register here: https://shorturl.at/EOOI8
#OpenAIRE #HorizonEurope #DataManagement #FAIRprinciples #DataLifecycle
📢Besoin de faire un Plan de Gestion de données pour votre projet de recherches ? Venez ce 19/05 17h à la #formation "Le plan de gestion de données du projet de recherche (PGD)" à la BDL ➡️ https://www.bibliotheque-diderot.fr/formations/le-plan-de-gestion-de-donnees-du-projet-de-recherche-pgd
Évènement PrintempsDeLaDonnée 2025 #PDLD2025
#openscience #datamanagement #DMP #DataManagementPlan #FAIRprinciples #DMPOpidor #Lyon #DATALystE
A prominent example of what can happen if institutions are dependent on commercial enterprises sitting in the USA: The Trump administration made Microsoft block the email account of the #ICC prosecutor:
https://apnews.com/article/icc-trump-sanctions-karim-khan-court-a4b4c02751ab84c09718b1b95cbd5db3
Given this recent example and the circumstance that this administration is in a constant quarrel with scientific institutions and also science in general, it is quite scary how dependent many - not all - German universities are for their core IT infrastructure on Microsoft services.
I hope this is a wake-up call for the IT service departments of our universities? We are increasingly encouraged to publish scientific findings (data, articles) according to #FAIRPrinciples in #OpenScience , but shouldn't we also think more about the vulnerability of our whole workflow, if the underlying IT can be shut down simply by an order of someone on the other side of the planet? Open alternatives do exist!
🔊 𝗘𝗮𝗴𝗲𝗿 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗢𝗽𝗲𝗻 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗼𝗯𝗷𝗲𝗰𝘁-𝗯𝗮𝘀𝗲𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵?
In an upcoming #WiNoDa webinar, we will illustrate how the 𝗙𝗔𝗜𝗥 (Findable, Accessible, Interoperable, Reusable) and 𝗖𝗔𝗥𝗘 (Collective Benefit, Authority to Control, Responsibility, Ethics) 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 can transform your research — making it more 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲, 𝗲𝘁𝗵𝗶𝗰𝗮𝗹, and 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹.
Join us online on May 20th!
👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄: winoda.de/en/event/webinar-open-science-fair-and-care/
📢 We’re looking forward to participating in the Mini-Conference on Open and FAIR Practices in Natural & Engineering Sciences, taking place 22–23 May in Utrecht.
🔗 https://community.data.4tu.nl/2025/02/05/open-and-fair-in-nes/
#FAIRdata #OpenResearch #inggrid #FAIRprinciples #4TUResearchData
New Whitepaper published: "Measuring the Value of (Research) Data" 📊📚
Data is more than just the "new oil"—it’s a unique economic asset with special characteristics that make its value challenging to measure. But how can businesses and research institutions quantify the actual value of their data?
📄 Read the full whitepaper here: https://zenodo.org/records/14944087
#ResearchData #DataEconomy #DataValue #FAIRPrinciples #Innovation #NFDI #BusinessValue #DataSharing
[Reading] passionnant, et si bien écrit : "Di Cosmo R., Granger S., Hinsen K., Jullien N., Le Berre D., Louvet V., Maumet C., Maurice C., Monat R. et Rougier N. P., « CODE beyond FAIR »=> https://inria.hal.science/hal-04930405
#FAIRprinciples #researchdatamanagement #openscience #researchsoftwares #reproductibility #science #FLOSS #digitalpreservation
[veille] #ebooks Kosmopoulos Chr. et Schöpfel J., "Publier, partager, réutiliser les données de la recherche : les data papers et leurs enjeux" Pr. Univ. du Septentrion=> https://www.septentrion.com/fr/book/?GCOI=27574100316700
#openscience #datapapers #researchdatamanagement #FAIRprinciples #FAIRdata #publishing
The INFLIBNET Centre, with financial support under DataCite's GAF, has made significant strides in promoting an #openscience ecosystem with special reference to research data sharing & #PIDs in India. Read how this collaboration is transforming the Indian research landscape:
https://doi.org/10.5438/khj0-3784
#ResearchData #FAIRPrinciples #PID #PersistenIdentifier
@Mohamadmostafa
[FAIR Data] 📢FAIRfest: Celebrating advancements of FAIR solutions in EOSC | Online; aujourd'hui et demain ! => https://fair-impact.eu/form/fairfest-celebrating-advancement
#today #eosc #FAIRprinciples #FAIRisation #researchdata #europe #FAIRimpact
Our #FAIRsharingCommunityChampions #MarkMcKerracher has created a short video "Data tips: #FAIR principles in 60 seconds" as part of his work at the SDS repository at #UniversityofOxford, where he also recommends @fairsharing
Take a look at https://doi.org/10.25446/oxford.28323506.v2, and at the entire series of videos is available at https://portal.sds.ox.ac.uk/SDS_self_help
We're excited to be supporting the upcoming
✨ BERD Unconference Workshop Series ✨
Meet peers and collaborate: Build connections and collaborate with like-minded peers from business, economics, and related research fields on #FAIRprinciples, #AI, data and more.
https://www.berd-nfdi.de/berd-academy/berd-unconference-workshop-series/
Pipeline release! nf-core/drugresponseeval v1.0.0 - 1.0.0!
Please see the changelog: https://github.com/nf-core/drugresponseeval/releases/tag/1.0.0
#celllines #crossvalidation #deeplearning #drugresponse #drugresponseprediction #drugs #fairprinciples #generalization #hyperparametertuning #machinelearning #randomizationtests #robustnessassessment #training #nfcore #openscience #nextflow #bioinformatics
🚨 Final call! Last few days left to apply for PSDI funding for your event. Don't miss this opportunity to bring your ideas to life and make an impact.
Apply now 👉 https://t.ly/87deF
#EventFunding #ScientificData #CollaborationInScience #WorkshopFunding #DataInfrastructure #InnovationInScience #PSDI #FAIRPrinciples
Domain Ontologies: Indispensable for Knowledge Graph Construction
AI slop is all around and increasingly extraction of useful information will face difficulties as we start to feed more noise into the already noisy world of knowledge. We are in an era of unprecedented data abundance, yet this deluge of information often lacks the structure necessary to derive meaningful insights. Knowledge graphs (KGs), with their ability to represent entities and their relationships as interconnected nodes and edges, have emerged as a powerful tool for managing and leveraging complex data. However, the efficacy of a KG is critically dependent on the underlying structure provided by domain ontologies. These ontologies, which are formal, machine-readable conceptualizations of a specific field of knowledge, are not merely useful, but essential for the creation of robust and insightful KGs. Let’s explore the role that domain ontologies play in scaffolding KG construction, drawing on various fields such as AI, healthcare, and cultural heritage, to illuminate their importance.
Vassily Kandinsky, 1913 – Composition VII (1913)At its core, an ontology is a formal representation of knowledge within a specific domain, providing a structured vocabulary and defining the semantic relationships between concepts. In the context of KGs, ontologies serve as the blueprint that defines the types of nodes (entities) and edges (relationships) that can exist within the graph. Without this foundational structure, a KG would be a mere collection of isolated data points with limited utility. The ontology ensures that the KG’s data is not only interconnected but also semantically interoperable. For example, in the biomedical domain, an ontology like the Chemical Entities of Biological Interest (ChEBI) provides a standardized way of representing molecules and their relationships, which is essential for building biomedical KGs. Similarly, in the cultural domain, an ontology provides a controlled vocabulary to define the entities, such as artworks, artists, and historical events, and their relationships, thus creating a consistent representation of cultural heritage information.
One of the primary reasons domain ontologies are crucial for KGs is their role in ensuring data consistency and interoperability. Ontologies provide unique identifiers and clear definitions for each concept, which helps in aligning data from different sources and avoiding ambiguities. Consider, for example, a healthcare KG that integrates data from various clinical trials, patient records, and research publications. Without a shared ontology, terms like “cancer” or “hypertension” may be interpreted differently across these data sets. The use of ontologies standardizes the representation of these concepts, thus allowing for effective integration and analysis. This not only enhances the accuracy of the KG but also makes the information more accessible and reusable. Furthermore, using ontologies that follow the FAIR (Findable, Accessible, Interoperable, Reusable) principles facilitates data integration, unification, and information sharing, essential for building robust KGs.
Moreover, ontologies facilitate the application of advanced AI methods to unlock new knowledge. They support both deductive reasoning to infer new knowledge and provide structured background knowledge for machine learning. In the context of drug discovery, for instance, a KG built on a biomedical ontology can help identify potential drug targets by connecting genes, proteins, and diseases through clearly defined relationships. This structured approach to data also enables the development of explainable AI models, which are critical in fields like medicine where the decision-making process must be transparent and interpretable. The ontology-grounded KGs can then be used to generate hypotheses that can be validated through manual review, in vitro experiments, or clinical studies, highlighting the utility of ontologies in translating complex data into actionable knowledge.
Despite their many advantages, domain ontologies are not without their challenges. One major hurdle is the lack of direct integration between data and ontologies, meaning that most ontologies are abstract knowledge models not designed to contain or integrate data. This necessitates the use of (semi-)automated approaches to integrate data with the ontological knowledge model, which can be complex and resource-intensive. Additionally, the existence of multiple ontologies within a domain can lead to semantic inconsistencies that impede the construction of holistic KGs. Integrating different ontologies with overlapping information may result in semantic irreconcilability, making it difficult to reuse the ontologies for the purpose of KG construction. Careful planning is therefore required when choosing or building an ontology.
As we move forward, the development of integrated, holistic solutions will be crucial to unlocking the full potential of domain ontologies in KG construction. This means creating methods for integrating multiple ontologies, ensuring data quality and credibility, and focusing on semantic expansion techniques to leverage existing resources. Furthermore, there needs to be a greater emphasis on creating ontologies with the explicit purpose of instantiating them, and storing data directly in graph databases. The integration of expert knowledge into KG learning systems, by using ontological rules, is crucial to ensure that KGs not only capture data, but also the logical patterns, inferences, and analytic approaches of a specific domain.
Domain ontologies will prove to be the key to building robust and useful KGs. They provide the necessary structure, consistency, and interpretability that enables AI systems to extract valuable insights from complex data. By understanding and addressing the challenges associated with ontology design and implementation, we can harness the power of KGs to solve complex problems across diverse domains, from healthcare and science to culture and beyond. The future of knowledge management lies not just in the accumulation of data but in the development of intelligent, ontologically-grounded systems that can bridge the gap between information and meaningful understanding.
References
#AIInHealthcare #ArtificialIntelligence #BiomedicalOntologies #CulturalHeritageData #DataIntegration #DataInteroperability #DomainOntologies #DrugDiscovery #ExplainableAI #FAIRPrinciples #GraphDatabases #KnowledgeGraphs #KnowledgeManagement #LLMs #Ontology #OntologyDesign #OntologyDevelopment #OntologyDrivenAI #SemanticRelationships #SemanticWeb
⏳ Time is running out! The deadline to apply for PSDI funding for your event is approaching. Submit your application by 2 February!
Get up to 80% funding for events that align with PSDI goals. Apply now 👉 https://t.ly/qF9TG
#EventFunding #ScientificData #CollaborationInScience #WorkshopFunding #DataInfrastructure #InnovationInScience #PSDI #FAIRPrinciples
