FAIR+S: A validation study of a framework for sustainable research data and software
- URL: http://arxiv.org/abs/2606.30663v1
- Date: Wed, 17 Jun 2026 09:30:47 GMT
- Title: FAIR+S: A validation study of a framework for sustainable research data and software
- Abstract summary: FAIR principles already structure how digital research artefacts are described, shared, and reused.<n> FAIR+S weaves carbon-footprint and energy-use considerations directly into FAIR-aligned metadata.<n>This creates a foundation for reproducible research that simultaneously advances open science goals and decarbonisation objectives.
- Score: 0.9449650062296823
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The FAIR principles (Findable, Accessible, Interoperable, Reusable) have transformed research data management, but they do not address the environmental impact of creating and using research software and data, such as energy consumption, carbon emissions, and life-cycle impacts that become central to computer science and engineering-related domains. To bridge this gap FAIR+Sustainability or FAIR+S, an extension of the FAIR framework that embeds environmental accountability as a core element, was introduced. Because FAIR principles already structure how digital research artefacts are described, shared, and reused, they offer an effective entry point for embedding sustainability considerations at scale. FAIR+S weaves carbon-footprint and energy-use considerations directly into FAIR-aligned metadata schemas, workflows and development specifications. In doing so, it enables research infrastructures to report, compare, and audit the environmental implications of data and software in a measurable, interoperable, and transparent manner. This creates a foundation for reproducible research that simultaneously advances open science goals and decarbonisation objectives. However, integrating environmental accountability into established research workflows raises questions of feasibility, relevance, and acceptance across stakeholders and disciplines. In this work we validated the framework through a cross-disciplinary expert survey. The evaluation confirms its importance and practical relevance, but also reveals current gaps in researchers' awareness of green software practices.
Related papers
- AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research [2.527316655254016]
This paper presents a domain-expert evaluation of faithfulness for AquiLLM, an open-weight, offline RAG-LLM platform.<n>We report results from an astronomy case study evaluating AquiLLM across retrieval and scientific analysis tasks.
arXiv Detail & Related papers (2026-09-15T02:09:45Z) - AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery [184.2309450554096]
AutoResearch is defined as the developmental spectrum of AI-powered scientific workflow automation.<n>We analyze how research systems redistribute control, evidence, execution, validation, and accountability across and organize the field.<n>We propose five evaluation dimensions--novelty, impact, reliability, and provenance--and show that AutoResearch autonomy is domain-conditioned.
arXiv Detail & Related papers (2026-05-22T03:40:30Z) - The Reproducible Research Platform establishes a unified open science environment bridging data and software lifecycles across disciplines, from proposal to publication [0.0]
We developed the open-source Reproducible Research Platform (RRP), which unifies research data management with version-controlled, containerized computational environments.<n>RRP enables anyone to execute, reuse and publish fully documented, FAIR research without manual retrieval or platform-specific setup.<n>We demonstrate RRP's impact by reproducing results from diverse published studies, including work over a decade old, showing sustained usability.
arXiv Detail & Related papers (2025-12-04T22:02:19Z) - Toward Sustainable Generative AI: A Scoping Review of Carbon Footprint and Environmental Impacts Across Training and Inference Stages [2.2758077237273846]
Generative AI is spreading rapidly, creating significant social and economic value.<n>The cumulative environmental footprint generated during large-scale service operations has received comparatively less attention.<n>This study conducts a scoping review of methodologies and research trends in AI carbon footprint assessment.
arXiv Detail & Related papers (2025-11-21T11:59:34Z) - Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions [4.258678191793365]
NFDI4DataScience is developing and providing Research Knowledge Graphs (RKGs)<n>RKGs aim to capture and connect complex datasets, models, software, and scientific publications.
arXiv Detail & Related papers (2025-08-04T11:11:51Z) - Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
arXiv Detail & Related papers (2025-04-01T13:53:11Z) - On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data.<n>This paper thoroughly investigates recent advances in element-wise ZSIR and provides a basis for its future development.
arXiv Detail & Related papers (2024-08-09T05:49:21Z) - FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training? [3.0406004578714008]
The rapid evolution of Large Language Models highlights the necessity for ethical considerations and data integrity in AI development.
While FAIR principles are crucial for ethical data stewardship, their specific application in the context of LLM training data remains an under-explored area.
We propose a novel framework designed to integrate FAIR principles into the LLM development lifecycle.
arXiv Detail & Related papers (2024-01-19T21:21:02Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Assessing Scientific Contributions in Data Sharing Spaces [64.16762375635842]
This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions.
To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter.
Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index.
arXiv Detail & Related papers (2023-03-18T19:17:47Z) - A survey on applications of augmented, mixed and virtual reality for
nature and environment [114.4879749449579]
Augmented reality (AR), virtual reality (VR) and mixed reality (MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing.
However, the possibilities that AR, VR and MR offer in the area of environmental applications are not yet widely explored.
We present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues.
arXiv Detail & Related papers (2020-08-27T09:59:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.