The Unified Astronomy Thesaurus: Semantic Metadata for Astronomy and Astrophysics

Frey, Katie; Accomazzi, Alberto

doi:10.3847/1538-4365/aab760

Cited by 8 publications

(6 citation statements)

References 10 publications

(6 reference statements)

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“…These terms were chosen largely due to their adherence with the Unified Astronomy Thesaurus (UAT; Frey & Accomazzi 2018), which could allow for a simple, hierarchical connection of related subtopics, or from one topical categorization to the next. This is put into practice in the recruitment of panelists who select from options of Scientific Categories and Keywords, similar to Table 3 of Frey & Accomazzi (2018), to narrow in (at a finer level) on their areas of expertise, to more directly match by hand to a similar keywords and categorization selections provided by proposers. We have only modestly tested categorizing with PACMan at such a refined level, the results of which, as shown in Figure 5 of Strolger et al (2017), tends to show a much higher misclassification (or failure) rate that does not significantly improve with the size of the training pool.…”

Section: Improved Proposal and Reviewer Categorizationmentioning

confidence: 99%

PACMan2: Next Steps in Proposal Review Management

Strolger

Pegues

King

et al. 2023

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With the start of a new Great Observatories era, there is renewed concern that the demand for these forefront facilities, through proposal pressure, will exceed conventional peer-review management’s capacity for ensuring an unbiased and efficient selection. There is need for new methods, strategies, and tools to facilitate those reviews. Here, we describe PACMan2, an updated tool for proposal review management that utilizes machine-learning models and techniques to topically categorize proposals and reviewers, to match proposals to reviewers, and to facilitate proposal assignments, mitigating some conflicts of interest. We find that the classifier has cross-validation accuracy of 80.0% ± 2.2% on proposals for time on the Hubble Space Telescope and the James Webb Space Telescope.

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Section: Improved Proposal and Reviewer Categorizationmentioning

confidence: 99%

PACMan2: Next Steps in Proposal Review Management

Strolger

Pegues

King

et al. 2023

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“…Tag articles with relevant data content keywords from the Unified Astronomy Thesaurus (UAT). 50 The UAT is an open and community-supported project that formalizes astronomical concepts (Frey & Accomazzi 2018). It is adopted as a standard by the AAS journals 51 and the broader astronomical community, including ADS, to tag astronomical work with accurate, broadly adopted concepts.…”

Section: Data Content Abstractmentioning

confidence: 99%

Best Practices for Data Publication in the Astronomical Literature

Chen¹,

Schmitz²,

Mazzarella³

et al. 2022

ApJS

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We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multidimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union for publication of nomenclature, data, and metadata. A convenient Checklist of Recommendations for Publishing Data in the Literature (Appendix A) is included for authors to consult before the submission of the final version of their journal articles and associated data files. We recommend that publishers of journals in astronomy and astrophysics incorporate a link to this document in their Instructions to Authors.

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“…Furthermore, the process is documented in some wiki pages. We chose the widely used platform GitHub, because -aside from being a suitable repository and archive -its issue tracking has been shown to support a transparent and inclusive editorial process of vocabulary management [3]. implementation Different tools were used to create the SKOS form than those used for MSC 2010.…”

Section: Tools For Implementation and Our Proceduresmentioning

confidence: 99%

10 Years Later: The Mathematics Subject Classification and Linked Open Data

Arndt¹,

Ion²,

Runnwerth³

et al. 2021

Preprint

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Ten years ago, the Mathematics Subject Classification MSC 2010 was released, and a corresponding machine-readable Linked Open Data collection was published using the Simple Knowledge Organization System (SKOS). Now, the new MSC 2020 is out. This paper recaps the last ten years of working on machine-readable MSC data and presents the new machine-readable MSC 2020. We describe the processing required to convert the version of record, as agreed by the editors of zbMATH and Mathematical Reviews, into the Linked Open Data form we call MSC2020-SKOS. The new form includes explicit marking of the changes from 2010 to 2020, some translations of English code descriptions into Chinese, Italian, and Russian, and extra material relating MSC to other mathematics classification efforts. We also outline future potential uses for MSC2020-SKOS in semantic indexing and sketch its embedding in a larger vision of scientific research data.

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The Unified Astronomy Thesaurus: Semantic Metadata for Astronomy and Astrophysics

Cited by 8 publications

References 10 publications

PACMan2: Next Steps in Proposal Review Management

PACMan2: Next Steps in Proposal Review Management

Best Practices for Data Publication in the Astronomical Literature

10 Years Later: The Mathematics Subject Classification and Linked Open Data

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