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Sarah Weissman

Publications and source records attributed to Sarah Weissman.

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The Impact of the MAST Data Archive

The Barbara A. Mikulski Archive for Space Telescopes (MAST) hosts science-ready data products from over twenty NASA missions, plus community-contributed data collections, and other select surveys. The data support forefront research in the ultraviolet, optical, and near-infrared wavelength bands. We have constructed bibliographies for each mission from publications in nearly 40 professional journals, and have identified more than 37,000 refereed articles where investigators made a science usage of data hosted in MAST. The publication rate over the last 50 years shows that most MAST missions have had very high productivity during their in-service lifetimes, and have remained so for years or decades afterward. Annual citations to these publications, a measure of impact on research, are robust for most missions, with citations that grow over more than a decade. Most of the citations come from about 10% of articles within each mission. We examined the bibliographies of the active missions HST and JWST in greater detail. For HST the rate of archival publications exceeded those authored by the original observing teams within a decade of launch, and is now more than 3 times higher. Early indications hint that JWST archival articles could dominate the publication rate even sooner. The production of articles resulting from any given observing program can extend for decades. Programs with small and very large allocations of observing time tend to be particularly productive per unit of observing time. For HST in general, a first publication appears within 1.5 yr for 50% of observing programs, and within 3.8 yr for 80% of programs. We discuss various external factors that affect publication metrics, their strengths and limitations for measuring scientific impact, and the challenges of making meaningful comparisons of publication metrics across missions.

astro-ph.IM

A Model for Data Citation in Astronomical Research using Digital Object Identifiers (DOIs)

Standardizing and incentivizing the use of digital object identifiers (DOIs) to aggregate and identify both data analyzed and data generated by a research project will advance the field of astronomy to match best practices in other research fields like geosciences and medicine. Increase in the use of DOIs will prepare the discipline for changing expectations among funding agencies and publishers, who increasingly expect accurate and thorough data citation to accompany scientific outputs. The use of DOIs ensures a robust, sustainable, and interoperable approach to data citation in which due credit is given to researchers and institutions who produce and maintain the primary data. We describe in this work the advantages of DOIs for data citation and best practices for integrating a DOI service in an astronomical archive. We report on a pilot project carried out in collaboration with AAS Journals. During the course of the 1.5 year pilot, over 75% of submitting authors opted to use the integrated DOI service to clearly identify data analyzed during their research project when prompted at the time of paper submission.

cs.DL

The Proposal Auto-Categorizer and Manager for Time Allocation Review at Space Telescope Science Institute

The Proposal Auto-Categorizer and Manager (PACMan) tool was written to respond to concerns on subjective flaws and potential biases in some aspects of the proposal review process for time allocation for the {\it Hubble Space Telescope} (HST), and to partially alleviate some of the anticipated additional workload from the {\it James Webb Space Telescope} (JWST) proposal review. PACMan is essentially a mixed-method Naive Bayesian spam filtering routine, with multiple pools representing scientific categories, that utilizes the Robinson method for combining token (or word) probabilities. PACMan was trained to make similar programmatic decisions in science category sorting, panelist selection, and proposal-to-panelists assignments to those made by individuals and committees in the Science Policies Group (SPG) at Space Telescope Science Institute. Based on training from the previous cycle's proposals, PACMan made the same science category assignments for proposals in Cycle 24 as did the SPG, an average of 87\% of the time. Tests for similar science categorizations, based on training using proposals from additional cycles, show that this accuracy can be further improved, to the $>95\%$ level. This tool will be used to augment or replace key functions in the TAC review processes in future HST and JWST cycles.

astro-ph.IM

Identifying Duplicate and Contradictory Information in Wikipedia

Our study identifies sentences in Wikipedia articles that are either identical or highly similar by applying techniques for near-duplicate detection of web pages. This is accomplished with a MapReduce implementation of minhash to identify clusters of sentences with high Jaccard similarity. We show that these clusters can be categorized into six different types, two of which are particularly interesting: identical sentences quantify the extent to which content in Wikipedia is copied and pasted, and near-duplicate sentences that state contradictory facts point to quality issues in Wikipedia.

cs.IR