The consortium NFDI4DS supports researchers along all stages of the research data lifecycle to conduct their research in line with the FAIR principles. By conducting interviews and surveys, NFDI4DS continuously identifies the needs and challenges of researchers from various disciplines regarding data science and artificial intelligence, keeping ethical, legal, and social aspects in mind. Those identified needs and challenges are continuously addressed by picking up existing services,...
NFDI4DS at a Glance
A presentation of OpenCitations held during an internal meeting with the NFDI4DS project.
OpenCitations, an open infrastructure organization for bibliographical data
©NFDI4DataScience (NFDI4DS) is a consortium to support researchers in all stages of the research data lifecycle to conduct their research in line with the FAIR principles. The developed infrastructure targets researchers from a wide range of disciplines in data science and AI. We present the ideas of the NFDI4DS gateway and the NFDI4DS portal. Two approaches to navigate digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) from various NFDI4DS resources...
NFDI4DS Gateway and Portal
Due to the ever increasing importance of Data Science and Artificial Intelligence methods for a wide range of scientific disciplines, ensuring transparency and reproducibility of DS and AI methods and research findings have become essential. The NFDI4DS project promotes the findability, accessibility, interoperability, and reusability in DS and AI by developing an open integrated research data infrastructure in which all artefacts (e. g., papers, code, models, datasets) will be interlinked...
NFDI4DS Transfer and Application
NFDI4DataScience (NFDI4DS) is a consortium founded to support researchers in all stages of the research data lifecycle in order to conduct their research in line with the FAIR principles. The infrastructure developed targets researchers from a wide range of disciplines working in the field of data science and artificial intelligence. NFDI4DS contributes to systematically understanding the needs and challenges of researchers in various disciplines regarding data science and artificial...
NFDI4DS Infrastructure and Services
©NFDI4DataScience (NFDI4DS) is a consortium to support researchers in all stages of the research data lifecycle conduct their line with FAIR principles. The developed infrastructure targets from wide range disciplines science and AI. We present ideas NFDI4DS gateway portal. Two approaches navigate digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) various resources such as ORKG, DBLP database, other knowledge graphs (KGs). Transparency, reproducibility,...
NFDI4DS Gateway and Portal
In the past years, scientific research in Data Science and Artificial Intelligence has witnessed vast progress. The number of published papers digital objects (data, code, models) is growing exponentially. However, not all these artifacts are findable, accessible, interoperable reusable (FAIR), contributing to a rather low level reproducibility experimental findings reported scholarly publications (reproducibility crisis). this paper, we focus on Open best practices, i.e., set...
Open Science Best Practices in Data Science and Artificial Intelligence
Key to NFDI4DS’s success is an active and vibrant community as establishing a common data culture and practice relies on the community’s participation and acceptance. We address this challenge by leveraging the network of NFDI4DS partners to raise awareness for topics around FAIR data and establish international standards. By identifying requirements, we improve our services and develop new strategies for building and finding user communities.
Community and Training in NFDI4DS
In the NFDI4DS project, as part of the “Research Knowledge Graphs” task area, there is already a recommendation to share tasks across measures via working groups that could start as soon as possible with the project realization. In this context, we can target an artifact collection and model it via FDOs. This should give us the means to explore the adoption of this model, as well as the assumptions or issues we will face in the process, something that we can, in turn, give feedback to the...
FAIR Digital Objects research and adoption: Let's pretotype
NFDI4DS Transfer and Application.
NFDI4DS Infrastructure and Services.
NFDI4DataScience registry for reproducible Data Science and Artificial Intelligence Leyla Jael Castro 1, 3 [0000-0003-3986-0510], Zeyd Boukhers 3 [0000-0001-9778-9164], Olga Giraldo 1, 3 [0000-0003-2978-8922], Adamantios Koumpis 2, 3, Oya Beyan 2, 3 [0000-0001-7611-3501], Dietrich Rebholz-Schuhmann 1, 2, 3 [0000-0002-1018-0370] 1 ZB MED Information Centre for Life Sciences 2 Faculty of Medicine, University of Cologne 3 NFDI4DataScience consortium Abstract Scientific advances are built...
NFDI4DataScience registry for reproducible Data Science and Artificial Intelligence
(en)Conversations on AI Ethics Episode 1: In our first episode Judith Simon and Jaana Müller-Brehm explain to us what trustworthy AI actually is and why it is so important.
What is trustworthy AI?
(en)Conversations on AI Ethics Special Episode: Check out this special episode to get to know the experts we had the pleasure of interviewing.
Getting to know the experts
NFDI4DSO: Towards a BFO Compliant Ontology for Data Science.
Research Knowledge Graphs in NFDI4DS.
Community and Training in NFDI4DS.
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Software Management Plans (SMPs) help formalize a set of structures and goals that ensure the research software is accessible and reusable in the short, medium and long term. Although not as common as the Data Management Plans, SMPs are gaining attention, with different communities providing examples and guidance on around it (e.g., ELIXIR SMPs, eScience Center in the Netherlands SMP Guidance and the Max Plank Digital Libraries SMP).Machine-actionable SMPs (maSMPs) aim at providing a...
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Metadata crosswalks for software management plans at NFDI4DS hackathon maSMP 2023 |
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The Methods Hub extends and builds upon Notebooks. The components of GESIS Notebooks (execution, place, and pontent) will become part of the Methods Hub.Presented at the NFDI4DS Consortium Meeting, May 17, 2024, in Hannover.
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From GESIS Notebooks to Methods Hub |
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The presentation will introduce the Ethical Data Initiative. The Ethical Data Initiative provides a neutral space to bring together diverse actors and stakeholders, shaping the future of data governance. In doing so, we aim to increase equality and inclusivity in the data space; building data confidence and empowering the digital citizens of tomorrow. The presentation will also share information about the Campaign for Data Ethics in Education. The Campaign advocates for the integration of...
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The Ethics of AI and Data in Higher Education |
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Machine learning (ML) has enabled and accelerated frontier research in the life sciences, but democratised access to such methods is, unfortunately, not a given. Access to necessary hardware and software, knowledge and training, is limited, while methods are typically insufficiently documented and hard to find. Furthermore, even though modern AI-based methods typically generalize well to unseen data, no standard exists to enable sharing and fine-tuning of pre-trained models between different...
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Artificial Intelligence for Image Data Analysis in the Life Sciences |
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ROCK-IT aims to develop a demonstrator for automation and remote-access to beamlines of synchrotron radiation facilities. Remote access experiments for demanding in-situ and operando experiments is not available at the moment. The four participating Helmholtz centers DESY, HZB, HZDR, and KIT have identified catalysis operando experiments as a pilot development. So far, no automation exists for such experiments and since the optimization of catalysts requires to evaluate a large parameter...
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Research Data Management and Data Flow in ROCK-IT – Demonstrator for Automation and Remote-Access to Synchrotron Beamlines |
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"Software is eating the world" [1] - The famous quote and article by Marc Andreesen, founder of Netscape, is now 12 years old and it is fair to say: He was right, software is the driver of modern economy and pervasive throughout all industries. Taking it one step further, we argue that while software has eaten the world, open source is eating software. Open source makes up 80% - 90% of applications and if we think about it, it is clear that the modern IT industry would not be where it is...
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Software ate the world - and Open Source is eating software! |
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Software Management Plans (SMPs) help formalize a set of structures and goals that ensure the research software is accessible reusable in short, medium long term. Although not as common Data Plans, SMPs are gaining attention, with different communities providing examples guidance on around it (e.g., ELIXIR SMPs, eScience Center Netherlands SMP Guidance Max Plank Digital Libraries SMP). Machine-actionable (maSMPs) aim at semantic layer top form metadata schemas. Based inspired by work done...
Software Management Plans (SMPs) help formalize a set of structures and goals that ensure the research software is accessible reusable in short, medium long term. Although not as common Data Plans, SMPs are gaining attention, with different communities providing examples guidance on around it (e.g., ELIXIR SMPs, eScience Center Netherlands SMP Guidance Max Plank Digital Libraries SMP). Machine-actionable (maSMPs) aim at semantic layer top form metadata schemas. Based inspired by work done...
The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) Artificial Intelligence (AI) by connecting digital artifacts ensuring they adhere FAIR (Findable, Accessible, Interoperable, Reusable) principles. To this end, poster introduces NFDI4DS Ontology, which describes resources in DS AI models structure consortium. Built upon NFDICore ontology mapped Basic Formal Ontology (BFO), serves as foundation for knowledge...
This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS as foundational framework, offers unified and intuitive interface for querying various scientific databases using federated search. RAG-based QA, powered by Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities fostering conversational engagement Gateway...
Abstract This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS as foundational framework, offers unified and intuitive interface for querying various scientific databases using federated search. RAG-based QA, powered by Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities fostering conversational engagement...