@inproceedings{grezes-etal-2026-tracs,
title = "{TRACS}: Telescope Reference Astronomy Categorization Dataset and Shared Task",
author = "Grezes, Felix and
Lockhart, Kelly and
Accomazzi, Alberto and
Bartlett, Jennifer Lynn and
Seefried, Ethan and
Pandiri, Anjali and
Ghosal, Tirthankar",
editor = "Accomazzi, Alberto and
Ghosal, Tirthankar and
Grezes, Felix and
Lockhart, Kelly and
Alkan, Atilla Kaan",
booktitle = "Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wasp-1.16/",
pages = "142--153",
ISBN = "979-8-89176-456-9",
abstract = "To assess the scientific impact of observational facilities, Astronomers analyze publications which have made use of the data by the facility. This requires bibliographies annotating and linking data products with the literature, allowing bibliometrics to measure impact of the data. Creating bibliographies is laborious, involves specialists searching the literature for names, acronyms and identifiers, then determining how/if observations were used in those publications.\par These bibliographies are useful beyond metrics: from the perspective of a research scientist, the data-literature connections provide a critical path to data discovery and access. Thus, by leveraging the efforts of librarians and archivists, we can use telescope bibliographies to support the scientific inquiry process. To this end, we introduce the TRACS: Telescope Reference and Astronomy Categorization dataset and Shared task, consisting of over 89K public English texts from space telescope bibliographies, categorized using a new, simple taxonomy based on discussion with established bibliographers.\par We hope TRACS will be used to train modern Machine Learning and Artificial Intelligence models that in turn will help data curators build bibliographies. We evaluate how off-the-shelf Large Language Models perform on automatic bibliography curation as a baseline analysis. Results show the difficulty of the task and the need for custom tools."
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<abstract>To assess the scientific impact of observational facilities, Astronomers analyze publications which have made use of the data by the facility. This requires bibliographies annotating and linking data products with the literature, allowing bibliometrics to measure impact of the data. Creating bibliographies is laborious, involves specialists searching the literature for names, acronyms and identifiers, then determining how/if observations were used in those publications.\par These bibliographies are useful beyond metrics: from the perspective of a research scientist, the data-literature connections provide a critical path to data discovery and access. Thus, by leveraging the efforts of librarians and archivists, we can use telescope bibliographies to support the scientific inquiry process. To this end, we introduce the TRACS: Telescope Reference and Astronomy Categorization dataset and Shared task, consisting of over 89K public English texts from space telescope bibliographies, categorized using a new, simple taxonomy based on discussion with established bibliographers.\par We hope TRACS will be used to train modern Machine Learning and Artificial Intelligence models that in turn will help data curators build bibliographies. We evaluate how off-the-shelf Large Language Models perform on automatic bibliography curation as a baseline analysis. Results show the difficulty of the task and the need for custom tools.</abstract>
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%0 Conference Proceedings
%T TRACS: Telescope Reference Astronomy Categorization Dataset and Shared Task
%A Grezes, Felix
%A Lockhart, Kelly
%A Accomazzi, Alberto
%A Bartlett, Jennifer Lynn
%A Seefried, Ethan
%A Pandiri, Anjali
%A Ghosal, Tirthankar
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%Y Alkan, Atilla Kaan
%S Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-456-9
%F grezes-etal-2026-tracs
%X To assess the scientific impact of observational facilities, Astronomers analyze publications which have made use of the data by the facility. This requires bibliographies annotating and linking data products with the literature, allowing bibliometrics to measure impact of the data. Creating bibliographies is laborious, involves specialists searching the literature for names, acronyms and identifiers, then determining how/if observations were used in those publications.\par These bibliographies are useful beyond metrics: from the perspective of a research scientist, the data-literature connections provide a critical path to data discovery and access. Thus, by leveraging the efforts of librarians and archivists, we can use telescope bibliographies to support the scientific inquiry process. To this end, we introduce the TRACS: Telescope Reference and Astronomy Categorization dataset and Shared task, consisting of over 89K public English texts from space telescope bibliographies, categorized using a new, simple taxonomy based on discussion with established bibliographers.\par We hope TRACS will be used to train modern Machine Learning and Artificial Intelligence models that in turn will help data curators build bibliographies. We evaluate how off-the-shelf Large Language Models perform on automatic bibliography curation as a baseline analysis. Results show the difficulty of the task and the need for custom tools.
%U https://aclanthology.org/2026.wasp-1.16/
%P 142-153
Markdown (Informal)
[TRACS: Telescope Reference Astronomy Categorization Dataset and Shared Task](https://aclanthology.org/2026.wasp-1.16/) (Grezes et al., WASP 2026)
ACL
- Felix Grezes, Kelly Lockhart, Alberto Accomazzi, Jennifer Lynn Bartlett, Ethan Seefried, Anjali Pandiri, and Tirthankar Ghosal. 2026. TRACS: Telescope Reference Astronomy Categorization Dataset and Shared Task. In Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications, pages 142–153, Hengqin, Zhuhai, China. Association for Computational Linguistics.