The FAIRness of CHeCLOUD, the Cultural Heritage Linked Open Data Cloud

"The FAIRness of CHeCLOUD, the Cultural Heritage Linked Open Data Cloud" raises a common problem for Cultural Heritage data practitioners, i.e. the absence of centralized repositories to access CH datasets. Additionally, they evaluate the FAIRness of the CHeCLOUD datasets using a methodology inspired by systematic literature review principles for dataset identification and established quality assessment frameworks.
CHeCLOUD indexes 192 datasets and 16 ontologies. Domain-specific sub-clouds exist (Linguistic LOD Cloud, Life Sciences LOD Cloud), but none specifically target CH data. The SLR principles-based methodology for dataset discovery is novel, employing a systematic three-phase approach (identification, assessment, maintenance) that is both systematic and reproducible. The comprehensive FAIRness evaluation is of course novel as well, with the detailed FAIR-to-quality mapping framework (Table 1) representing a methodological contribution that extends beyond the CH domain. The authors provide actionable insights as well: reusability is well-addressed (0.76 mean score) due to good licensing practices, while interoperability (0.50) and findability (0.60) require community improvement. I can personally see the results being useful for both the CH and SW practitioners and the community as a whole - even if for just finding new LOD datasets.

As for clarity, the paper is generally well-structured and comprehensive. The authors put effort in making FAIR principles clear to readers but some sections are overly filled with abbreviations - the tables would be sufficient (for instance, subsection 3.3. LOD Cloud Data Quality Assessment is a bit difficult to read for this). The GitHub repository provides reproducibility of the author's results, and I tested the web application with good results, even finding datasets I was not personally aware of.

This paper makes a valuable contribution by addressing a genuine infrastructure gap in cultural heritage data. The methodology is sound and the results provide actionable insights for the community. The web application is a tool I can foresee being used by the community. The Limitations section self-reports the shortcoming of the CHECLOUD and its over reliance on manual data control.

- Page 6 presents some formatting issues in the table
- Line 123: in the following [section?]
- Line 132: Accessiblity → Accessibility
- Line 137: Interoberability → Interoperability
- Line 305: Fix spacing of footnote 11

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Originality. This paper illustrates a rigorous methodology for assessing the quality of Linked Open Data under the lens of the FAIR paradigm, applied to the Cultural Heritage domain through the CHeCLOUD case study, an index of selected datasets from the Cultural Heritage community. While the approach is domain-specific, the proposed framework is explicitly designed to be reusable across other LOD sub-clouds. The analysis highlights key strengths of the Cultural Heritage community, particularly in terms of data reusability and accessibility but at the same time it reveals weaknesses in findability and interoperability. By providing a comprehensive and FAIR-aligned assessment of Cultural Heritage (CH) Linked Open Data, this study positions the CH community in direct comparison with other more established LOD sub-clouds, offering an original cross-domain perspective. Overall, the study offers both a critical assessment and practical insights to improve FAIR data sharing practices in Cultural Heritage.

Significance. This paper effectively highlights the strengths and weaknesses of the Cultural Heritage community in FAIR data sharing, also through a meaningful comparison with other LOD sub-clouds. However, it does not systematically clarify which differences among datasets (such as size, fragmentation, or institutional source) most significantly impact quality outcomes. While the qualitative metrics across FAIR dimensions are clearly presented, a deeper discussion of the underlying factors shaping these scores would strengthen the analysis, for example exploring whether qualitative differences (dataset size) correlate with specific subdomains or types of institutions. Such an analysis would also enhance the policy relevance of the work as a tool for assessing and guiding FAIR data production strategies in the Cultural Heritage domain.

Quality of writing. The paper is well organized and generally easy to read, despite the high level of technical detail. However, some elements currently presented in Section 6, such as the annotation aspects described in Section 6.2, could be introduced earlier to better support the methodological description. In addition, a more explicit discussion of the significance of the proposed methods and results in relation to the ECCH infrastructure currently under development would be highly desirable.
Bibliographic reference links don’t seem to work, which makes the consultation of tables and supporting material sometimes difficult.