Linking Literature and Data: Status Report and Future Efforts

In the current era of data-intensive science, it is increasingly important for researchers to be able to have access to published results, the supporting data, and the processes used to produce them. Six years ago, recognizing this need, the American Astronomical Society and the Astrophysics Data Centers Executive Committee (ADEC) sponsored an effort to facilitate the annotation and linking of datasets during the publishing process, with limited success. I will review the status of this effort and describe a new, more general one now being considered in the context of the Virtual Astronomical Observatory.Comment: 9 pages, 2 figures, to appear in: Future Professional Communication in Astronomy II (FPCA-II

Decades of Transformation: Evolution of the NASA Astrophysics Data System's Infrastructure

The NASA Astrophysics Data System (ADS) is the primary Digital Library portal for researchers in astronomy and astrophysics. Over the past 30 years, the ADS has gone from being an astronomy-focused bibliographic database to an open digital library system supporting research in space and (soon) earth sciences. This paper describes the evolution of the ADS system, its capabilities, and the technological infrastructure underpinning it. We give an overview of the ADS's original architecture, constructed primarily around simple database models. This bespoke system allowed for the efficient indexing of metadata and citations, the digitization and archival of full-text articles, and the rapid development of discipline-specific capabilities running on commodity hardware. The move towards a cloud-based microservices architecture and an open-source search engine in the late 2010s marked a significant shift, bringing full-text search capabilities, a modern API, higher uptime, more reliable data retrieval, and integration of advanced visualizations and analytics. Another crucial evolution came with the gradual and ongoing incorporation of Machine Learning and Natural Language Processing algorithms in our data pipelines. Originally used for information extraction and classification tasks, NLP and ML techniques are now being developed to improve metadata enrichment, search, notifications, and recommendations. we describe how these computational techniques are being embedded into our software infrastructure, the challenges faced, and the benefits reaped. Finally, we conclude by describing the future prospects of ADS and its ongoing expansion, discussing the challenges of managing an interdisciplinary information system in the era of AI and Open Science, where information is abundant, technology is transformative, but their trustworthiness can be elusive.Comment: 10 pages, 3 figures, submitted to the ADASS 2023 proceeding

Telescope Bibliographies: an Essential Component of Archival Data Management and Operations

Assessing the impact of astronomical facilities rests upon an evaluation of the scientific discoveries which their data have enabled. Telescope bibliographies, which link data products with the literature, provide a way to use bibliometrics as an impact measure for the underlying data. In this paper we argue that the creation and maintenance of telescope bibliographies should be considered an integral part of an observatory's operations. We review the existing tools, services, and workflows which support these curation activities, giving an estimate of the effort and expertise required to maintain an archive-based telescope bibliography.Comment: 10 pages, 3 figures, to appear in SPIE Astronomical Telescopes and Instrumentation, SPIE Conference Series 844