©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,...

10.52825/cordi.v1i.391 article EN cc-by Proceedings of the Conference on Research Data Infrastructure 2023-09-07

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...

10.52825/cordi.v1i.299 article EN cc-by Proceedings of the Conference on Research Data Infrastructure 2023-09-07

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...

10.48550/arxiv.2408.08698 preprint EN arXiv (Cornell University) 2024-08-16

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...

10.48550/arxiv.2406.07257 preprint EN arXiv (Cornell University) 2024-06-11

Composing training data for Machine Learning applications can be laborious and time-consuming when done manually. The use of FAIR Digital Objects, in which the is machine-interpretable -actionable, makes it possible to automate simplify this task. As an application case, we represented labeled Scanning Electron Microscopy images from different sources as Objects compose a set. In addition some existing services included our implementation (the Typed-PID Maker, Handle Registry, ePIC Data Type...

10.3897/rio.9.e108706 article EN cc-by Research Ideas and Outcomes 2023-08-22

Abstract Search and harvesting use cases on harmonised metadata play an important role in several activities National Research Data Infrastructures (NFDI). The working group Harvesting of the NFDI section (meta)data, terminologies provenance works a common understanding user needs (for search) service requirements harvesting), analysis data sources landscape, recommendations concerning specific needs, e.g., for spatial or sensitive data. Here, we present search gaps challenges across...

10.1007/s13222-024-00480-7 article EN cc-by Datenbank-Spektrum 2024-06-28

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...

10.5281/zenodo.7129715 PUBLICATION cc-by-4.0 2022-09-30

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...

10.5281/zenodo.7244257 PUBLICATION ENG cc-by-4.0 2022-10-24

The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To this end, this poster introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the NFDI4DS consortium. Built upon the NFDICore ontology and mapped...

10.48550/arxiv.2408.08698 Datacite PUBLICATION ENG OPEN 2024-01-01

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,...

10.18420/inf2023_100 Datacite PUBLICATION ENG UNKNOWN 2023-01-01

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...

10.18420/inf2023_103 Datacite PUBLICATION ENG UNKNOWN 2023-01-01

The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To this end, this poster introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the NFDI4DS consortium. Built upon the NFDICore ontology and mapped to...

10.5445/ir/1000174861 Datacite PUBLICATION ENG OPEN 2024-01-01

10.1109/ic-etite58242.2024.10493275 Crossref PUBLICATION UND CLOSED 2024-02-22

©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...

10.52825/cordi.v1i.391 Crossref PUBLICATION UND OPEN 2023-09-07

10.1109/ickecs61492.2024.10616403 Crossref PUBLICATION UND CLOSED 2024-04-18

10.1109/icpects56089.2022.10047434 Crossref PUBLICATION UND CLOSED 2022-12-08
Displaying top 20 publications out of 108

Alternate Names
Affiliations German Research Centre for Artificial Intelligence
0009-0004-8720-0116
Alternate Names
Affiliations Deutsches Klimarechenzentrum Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS Freie Universität Berlin Institut für Meteorologie
0000-0002-9218-5676
Alternate Names
Affiliations Aarhus University Deutsches Forschungszentrum für Künstliche Intelligenz Saratov State University
0000-0002-3447-9860
Alternate Names
Affiliations Hamburg University of Technology Protelecom Universidad del Valle de Guatemala Universität Hamburg
0009-0008-1582-2417
Alternate Names
Affiliations
0000-0002-2476-8335
Alternate Names
Affiliations University Hospital Cologne University of Passau University of St. Gallen
0000-0002-7047-1057
Alternate Names
Affiliations
0000-0001-9778-9164
Alternate Names
Affiliations Goethe University Frankfurt Hochschule für den öffentlichen Dienst in Bayern Ilia State University Johannes Gutenberg Universität Mainz Ludwig-Maximilians-Universität München Max Planck Digital Library Technical University Munich Universitetet i Oslo University of Art and Design Offenbach University of Glasgow Université Blaise Pascal
0000-0002-2880-8947
Alternate Names
Affiliations Fraunhofer Society Technische Universität Berlin Universität der Bundeswehr München Weizenbaum Institute for the Networked Society
0000-0002-8786-7250
Alternate Names
Affiliations Fraunhofer IAIS Leipzig University Leuphana University of Lüneburg Paderborn University University Hamburg
0000-0002-0191-7211

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...

10.5281/zenodo.10275895 DATASET ENG cc-by-4.0 2023-12-06

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...

10.5281/zenodo.10275895 dataset EN cc-by Zenodo (CERN European Organization for Nuclear Research) 2023-12-06

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...

10.5281/zenodo.10275894 dataset EN cc-by Zenodo (CERN European Organization for Nuclear Research) 2023-12-06

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...

10.1007/978-3-031-65794-8_1 book-chapter EN cc-by Lecture notes in computer science 2024-01-01

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.

10.5281/zenodo.11567240 PRESENTATION ENG cc-by-4.0 2024-06-11

Presentation for the 1st Base4NFDI User Conference Each NFDI consortium works on establishing research data infrastructures tailored to its specific domain. To facilitate interoperability across different domains and consortia, the NFDIcore ontology was developed and serves as a mid-level ontology for representing metadata about NFDI resources such as individuals, organizations, projects, data portals, etc. Recognizing the diverse needs of consortia, NFDIcore establishes mappings to a wide...

10.5281/zenodo.14185853 PRESENTATION ENG cc-by-4.0 2024-11-19

"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...

10.5281/zenodo.10259442 LESSON ENG cc-by-4.0 2023-12-05

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...

10.5281/zenodo.10721179 LESSON cc-by-4.0 2024-02-28

AI systems that learn from data present a unique challenge for safety, as there is no specific design artifact, model, or code to analyse and verify. The safety assurance challenges become even more complex in cooperative intelligent systems, like collaborative robots and autonomous vehicles. These systems are often loosely interconnected, allowing them to form and dissolve configurations dynamically. Evaluating the consequences of failures in largely unpredictable configurations is a...

10.5281/zenodo.14223982 LESSON cc-by-4.0 2024-11-26

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...

10.5281/zenodo.10563415 LESSON ENG cc-by-4.0 2024-01-24

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...

10.5281/zenodo.10064021 POSTER ENG cc-by-4.0 2023-11-01
NFDI4DS at a Glance

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,...

Fraunhofer-Publica OTHER ENG UNKNOWN 2023-01-01
NFDI4DS Infrastructure and Services

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...

Fraunhofer-Publica OTHER ENG UNKNOWN 2023-09-01
NFDI4DSO: Towards a BFO Compliant Ontology for Data Science

The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. To this end, this poster introduces the NFDI4DS Ontology, which describes resources in DS and AI and models the structure of the NFDI4DS consortium. Built upon the NFDICore ontology and mapped to...

Fraunhofer-Publica OTHER ENG UNKNOWN 2024-09-01
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