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...

Dataset for the Field of Research Classification shared task hosted at NSLP 2025.  More information available: https://nfdi4ds.github.io/nslp2025/docs/forc_shared_task.html

Summary From 28 to 30 October 2024, ZB MED Information Centre for Life Sciences organized a hackathon in Cologne within the scope of NFDI4DataScience with the purpose of identifying core elements for a machine-actionable Data Managemnt Plan (maDMP) across the German National Research Data Infrastructure (NFDI). We used as a starting/reference point the maDMP application profile from the Research Data Alliance DMP Commons Working Group. An additional element, ManagementPlan, was also...

Lesson at the NFDI4DS Summer school day AIKG (Artificial Intelligence and Knowledge Graphs for Scholarly Data) at Leuphana 2024 Overview, vision, task areas, and current activities

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.

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...

The presentation was delivered by Zeyd Boukhers at the FDO Conference 2022 in Leiden. This work explores the implementation of a fast-track methodology for integrating FAIR Digital Objects (FDOs) within the health and data science sectors. It discusses a test case from the NFDI4DS project, focusing on machine learning data components such as images used for training, source code, and metadata. The presentation highlights the challenges of managing often not machine-readable metadata and...

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...

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...

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...

"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...

Join us for an insightful webinar exploring the intersection of dataPACT’s pioneering AI-driven framework and the VELES Excellence Hub, a major initiative for establishing a Regional Smart Health Data Space in Southeast Europe. The dataPACT project is revolutionizing healthcare data management by integrating compliance, ethics, and environmental sustainability into AI-powered health data pipelines. Through its application in CAREPATH, dataPACT ensures secure, ethical, and...

Name: An introduction to (webby) FAIR Digital Objects - A machine-actionable approach to FAIR Description: This presentation tutorial introduces the concept of FAIR digital object including some examples on how to implement them based on common web-based technologies, i.e., webby FDOs. Keywords: FAIR, machine-actionability, FDOs, webby FDOs Guiding questions: What are the FAIR principles? What is a FAIR digital object? What is the difference between FAIR and FDOs? What are webby...

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...

Lesson at the NFDI4DS Summer school day AIKG (Artificial Intelligence and Knowledge Graphs for Scholarly Data) at Leuphana 2024 Overview, vision, task areas, and current activities

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,...

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...

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...

Lesson at the NFDI4DS Summer school day AIKG (Artificial Intelligence and Knowledge Graphs for Scholarly Data) at Leuphana 2024 Overview, vision, task areas, and current activities