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arxiv: 2606.30663 · v1 · pith:74RHXVTUnew · submitted 2026-06-17 · 💻 cs.CY · cs.SE

FAIR+S: A validation study of a framework for sustainable research data and software

Danila Valko , Jan S\"oren Schwarz , Jorge Marx G\'omez , Ralf Isenmann This is my paper

Pith reviewed 2026-07-01 07:40 UTC · model grok-4.3

classification 💻 cs.CY cs.SE
keywords FAIR principlessustainabilityresearch data managementgreen softwareenvironmental impactexpert surveyopen sciencecarbon footprint
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The pith

Expert survey validates that extending FAIR with sustainability metrics is relevant for research data and software but shows low awareness of green practices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes FAIR+S as an extension of the existing FAIR principles that incorporates environmental factors such as energy consumption and carbon emissions into the description, sharing, and reuse of research data and software. It tests this extension through a cross-disciplinary expert survey focused on feasibility, relevance, and acceptance by stakeholders. The results affirm the framework's practical importance while highlighting gaps in current researcher knowledge about sustainable software development. This approach matters because it attempts to layer sustainability reporting onto already widely adopted open-science structures without requiring entirely new systems.

Core claim

FAIR+S embeds carbon-footprint and energy-use considerations directly into FAIR-aligned metadata schemas, workflows and development specifications so that research infrastructures can report, compare, and audit environmental implications in a measurable and interoperable manner. Validation through the expert survey confirms the framework's importance and practical relevance across disciplines while revealing current gaps in researchers' awareness of green software practices.

What carries the argument

FAIR+S framework, which extends the FAIR principles by weaving environmental accountability into metadata, workflows, and specifications for digital research artefacts.

If this is right

  • Research infrastructures could begin reporting and comparing environmental impacts of data and software using existing FAIR metadata structures.
  • Reproducible research workflows could simultaneously support open-science goals and measurable decarbonisation targets.
  • Development specifications for research software could include explicit energy and carbon criteria without breaking interoperability.
  • The identified awareness gaps indicate a need for targeted education on green software practices within research communities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Institutional policies or grant requirements might eventually reference FAIR+S-style reporting as a condition for funding.
  • Concrete tools for calculating and storing carbon metrics within standard metadata formats would be a logical next implementation step.
  • The framework could connect to existing life-cycle assessment methods already used in engineering domains.
  • Wider adoption would require addressing how smaller research groups without dedicated sustainability expertise can comply.

Load-bearing premise

A cross-disciplinary expert survey provides sufficient validation of the framework's feasibility, relevance, and acceptance across stakeholders and disciplines.

What would settle it

A larger survey of active researchers finding that most view the added sustainability reporting as impractical or irrelevant to their work would undermine the validation results.

Figures

Figures reproduced from arXiv: 2606.30663 by Danila Valko, Jan S\"oren Schwarz, Jorge Marx G\'omez, Ralf Isenmann.

Figure 1
Figure 1. Figure 1: The expert panel characteristics. 5.4% Other 48.6% Computer Science 29.7% Engineering 2.7% Life Sciences 13.5% Social Sciences Note. n = 27, representing the number of complete respondent replies used in this figure [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The expert’s main area of research. GSP are not yet widely adopted in research software and data development. This observation further indicates that the proposed framework is timely. Overall, the [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Experts’ familiarity with relevant concepts. beyond practical experience and is grounded in the conceptual knowledge required to validate the proposed framework. 3.7% More than 3 times a year 25.9% 2-3 times a year 11.1% Never 40.7% Once a year 18.5% Once every few years Note. n = 27, representing the number of complete respondent replies used in this figure [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The expert’s review activities. Additionally, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Importance and adoption of FAIR principles. 4.2 Framework Validation Results The evaluation of FAIR+S focused on three complementary validation dimensions: per￾ceived importance, added value, and practical feasibility. 4.2.1 Perceived Importance Perceived importance was assessed using principle-level importance ratings of the FAIR+S dimensions (S1–S5, see, Q27 in Appendix A) [PITH_FULL_IMAGE:figures/full_… view at source ↗
Figure 6
Figure 6. Figure 6: FAIR+S principles importance. 4.2.2 Added Value Beyond perceived importance, validation also requires assessing whether FAIR+S is seen as delivering concrete benefits to research practice. The added value of the framework was assessed through survey questions target￾ing perceived trust enhancement, transparency, and the usefulness of sustainability￾related disclosures. Trust-related value was primarily eva… view at source ↗
Figure 7
Figure 7. Figure 7: FAIR+S principles added value [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FAIR+S principles practical feasibility. While experts generally viewed sustainability reporting as feasible in principle (Fig. 8a), feasibility ratings were consistently higher for execution context attributes than for en￾ergy efficiency attributes, and overall slightly lower than the perceived importance of transparency in this regard (see Fig. 7a). This discrepancy suggests a clear implemen￾tation gap, … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper proposes FAIR+S as an extension of the FAIR principles that incorporates environmental sustainability metrics (energy use, carbon footprint, life-cycle impacts) into metadata schemas, workflows, and development specifications for research data and software. It reports validation of the framework's feasibility, relevance, and acceptance via a cross-disciplinary expert survey, which the authors state confirms the framework's importance while highlighting gaps in researchers' awareness of green software practices.

Significance. If the survey evidence is methodologically sound, the work would be significant for bridging open-science infrastructure with decarbonization goals in computer science and engineering domains; the integration of sustainability into existing FAIR-aligned systems offers a scalable entry point without requiring entirely new standards.

major comments (1)
  1. [Survey Methodology] Survey Methodology section (or equivalent): the validation claim rests on a cross-disciplinary expert survey, yet the manuscript provides no details on survey design (questions, scales, or instruments), sampling frame, response rate, disciplinary coverage, or analysis methods to rule out selection bias or low statistical power. This directly undermines the central assertion that the survey demonstrates feasibility and acceptance across stakeholders.
minor comments (1)
  1. [Abstract/Introduction] The abstract and introduction use 'FAIR+S' and 'FAIR+Sustainability' interchangeably without an explicit definition on first use.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the survey methodology. We agree that additional details are necessary to support the validation claims and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Survey Methodology] Survey Methodology section (or equivalent): the validation claim rests on a cross-disciplinary expert survey, yet the manuscript provides no details on survey design (questions, scales, or instruments), sampling frame, response rate, disciplinary coverage, or analysis methods to rule out selection bias or low statistical power. This directly undermines the central assertion that the survey demonstrates feasibility and acceptance across stakeholders.

    Authors: We accept the referee's assessment that the manuscript currently lacks these methodological details, which weakens the presentation of the validation results. In the revised version we will insert a dedicated 'Survey Methodology' subsection that specifies: the survey instrument (full list of questions and response scales), the sampling frame and recruitment approach, the response rate, the disciplinary breakdown of the expert respondents, and the analysis procedures (including any steps taken to assess bias or statistical power). These additions will allow readers to evaluate the robustness of the feasibility and acceptance findings. revision: yes

Circularity Check

0 steps flagged

No circularity: validation rests on external survey data

full rationale

The paper presents a framework extension (FAIR+S) and validates it via a cross-disciplinary expert survey. No equations, derivations, fitted parameters, or self-citation chains appear in the load-bearing claims. The central assertion—that the survey confirms importance, relevance, and gaps in awareness—depends on external respondent data rather than reducing to internal definitions or prior author work by construction. This is a standard empirical validation structure with no self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical content; the framework and survey validation introduce no free parameters, axioms, or invented physical entities.

pith-pipeline@v0.9.1-grok · 5771 in / 926 out tokens · 23332 ms · 2026-07-01T07:40:32.952598+00:00 · methodology

discussion (0)

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