New ADS Functionality for the Curator

Accomazzi, Alberto; Kurtz, Michael J.; Henneken, Edwin A.; Grant, C. S.; Thompson, Donna M.; Chyla, Roman; McDonald, Steven; Shaulis, Taylor J.; Blanco-Cuaresma, S.; Shapurian, Golnaz; Hostetler, Timothy W.; Templeton, Matthew R.

doi:10.1051/epjconf/201818608001

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Astronomy and astrophysics: The SAO/NASA Astrophysics Data System (ADS, https://ui .adsabs.harvard.edu) is a digital library for research in astronomy and astrophysics operated by the Smithsonian Astrophysical Observatory under a NASA grant. It is the main discovery platform for scientific literature used by the community of astronomers and astrophysicists, providing both disciplinary completeness and enriched data features (Accomazzi, Kurtz, et al, 2018). The ADS maintains three bibliographic databases covering publications in astronomy and astrophysics, physics, and the arXiv e-prints.…”

Section: Bibliographic Recordsmentioning

confidence: 99%

Authorship in top-ranked mathematical and physical journals: Role of gender on self-perceptions and bibliographic evidence

Mihaljević

Santamaría

2020

Quantitative Science Studies

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Despite increasing rates of women researching in math-intensive fields, publications by female authors remain underrepresented. By analyzing millions of records from dedicated bibliographic databases zbMATH, arXiv, and ADS, we unveil the chronological evolution of authorships by women in mathematics, physics, and astronomy. We observe a pronounced shortage of female authors in top-ranked journals, with quasi-stagnant figures in various distinguished periodicals in the first two disciplines and a significantly more equitable situation in the latter. Additionally, we provide an interactive open-access web interface to further examine the data. To address whether female scholars submit fewer articles for publication to relevant journals or whether they are consciously or unconsciously disadvantaged by the peer review system, we also study authors’ perceptions of their submission practices and analyze around 10,000 responses, collected as part of a recent global survey of scientists. Our analysis indicates that men and women perceive their submission practices to be similar, with no evidence that a significantly lower number of submissions by women is responsible for their underrepresentation in top-ranked journals. According to the self-reported responses, a larger number of articles submitted to prestigious venues correlates rather with aspects associated with pronounced research activity, a well-established network, and academic seniority.

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Section: Bibliographic Recordsmentioning

confidence: 99%

Authorship in top-ranked mathematical and physical journals: Role of gender on self-perceptions and bibliographic evidence

Mihaljević

Santamaría

2020

Quantitative Science Studies

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“…service that recommends articles related to the one the user is currently reading, however they do not provide information about the recommender algorithms used [58]. [59] is another example of a system recommending astronomical articles.…”

Section: Introductionmentioning

confidence: 99%

Using Research Literature to Generate Datasets of Implicit Feedback for Recommending Scientific Items

2019

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In an age of information overload, we are faced with seemingly endless options from which a small number of choices must be made. For applications such as search engines and online stores, Recommender Systems have long become the key tool for assisting users in their choices. Interestingly, the use of Recommender Systems for recommending scientific items remains a rarity. One difficulty is that the development of such systems depends on the availability of adequate datasets of users' feedback. While there are several datasets available with the ratings of the users for books, music, or films, there is a lack of similar datasets for scientific fields, such as Astronomy and Life and Health Sciences. To address this issue, we propose a methodology that explores scientific literature for generating utility matrices of implicit feedback. The proposed methodology consists in identifying a list of items, finding research articles related to them, extracting the authors from each article, and finally creating a dataset where users are unique authors from the collected articles, and the rating values are the number of articles a unique author wrote about an item. Considering that literature is available for every scientific field, the methodology is in principle applicable to Recommender Systems in any scientific field. The methodology, which we call LIBRETTI (LIterature Based RecommEndaTion of scienTific Items), was assessed in two distinct study cases, Astronomy and Chemistry. Several evaluation metrics for the datasets generated with LIBRETTI were compared to those derived from other available datasets using the same set of recommender algorithms. The results were found to be similar, which provides a solid indication that LIBRETTI is a promising approach for generating datasets of implicit feedback for recommending scientific items.

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“…Similarly, the AAS journals allow users to directly specify links to software and datasets which are properly tagged as such in the annotated digital documents. This means that it is now possible for the ADS to automatically identify links to data products found in these sections of the full-text documents, adding these links to the collection of the ones curated by the archives [8].…”

Section: Linked Resourcesmentioning

confidence: 99%