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   Brown, C.; Jordan, C.; Dickey, John M.; Anderson, L. D.; Armentrout, W. P.; Balser, Dana S.; Bania, T. M.; Dawson, J. R.; Mc Clure-Griffiths, N. M.; Wenger, Trey V. (July 2017, The Astronomical Journal)
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   Marshall, H. L.; Gelbord, J. M.; Worrall, D. M.; Birkinshaw, M.; Schwartz, D. A.; Jauncey, D. L.; Griffiths, G.; Murphy, D. W.; Lovell, J. E.; Perlman, E. S.; et al (March 2018, The Astrophysical Journal)
3. Indy Survey Tool: A Framework to Unearth Correlations in Survey Data

   https://doi.org/10.1109/VIS54172.2023.00038  

   Crnovrsanin, Tarik; Di_Bartolomeo, Sara; Wilson, Connor; Dunne, Cody (October 2023, IEEE)

   Survey companion websites allow users to explore collected survey information more deeply, as well as update or add entries for papers. These sites can help information stay relevant past the original release date of the survey paper. However, creating and maintaining a website can be laborious and difficult, especially when authors might not be experienced with programming. We introduce Indy Survey Tool to help authors develop companion websites for survey papers across diverse fields of study. The tool's core aim is to identify correlations between categorizations of papers. To accomplish this, the tool offers multiple combined filters and correlation matrix visualizations that enable users to explore the data from diverse perspectives. The tool's visualizations, list of papers, and filters are harmoniously integrated and highly responsive, providing users with feedback based on their selections. Identifying correlations in survey papers is a pivotal aspect of research, as it can enable the recognition of common combinations of categorizations within the papers—as well as highlight any omissions. The versatility of Indy Survey Tool enables researchers to delve into the correlations between categorizations in survey data, an essential aspect of research that can reveal gaps in the literature and highlight promising areas for future exploration. A preprint and supplemental material for the paper can be found at osf.io/tdhqn. 

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4. Keck Infrared Transient Survey. I. Survey Description and Data Release 1

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   Tinyanont, S; Foley, R J; Taggart, K; Davis, K W; LeBaron, N; Andrews, J E; Bustamante-Rosell, M J; Camacho-Neves, Y; Chornock, R; Coulter, D A; et al (January 2024, Publications of the Astronomical Society of the Pacific)

   Abstract We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTFror ATLASobands); a volume-limited sample including all transients within redshiftz< 0.01 (D≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete toz= 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction usingPypeIt, which requires minimal human interaction to ensure reproducibility. 

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5. ALMA SPECTROSCOPIC SURVEY IN THE HUBBLE ULTRA DEEP FIELD: SURVEY DESCRIPTION

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   Walter, Fabian; Decarli, Roberto; Aravena, Manuel; Carilli, Chris; Bouwens, Rychard; Cunha, Elisabete da; Daddi, Emanuele; Ivison, R. J.; Riechers, Dominik; Smail, Ian; et al (December 2016, The Astrophysical Journal)