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Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank (www.isobank.org), a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes. 

Abstract We present a calibration component for the Murchison Widefield Array All-Sky Virtual Observatory (MWA ASVO) utilising a newly developed PostgreSQL database of calibration solutions. Since its inauguration in 2013, the MWA has recorded over 34 petabytes of data archived at the Pawsey Supercomputing Centre. According to the MWA Data Access policy, data become publicly available 18 months after collection. Therefore, most of the archival data are now available to the public. Access to public data was provided in 2017 via the MWA ASVO interface, which allowed researchers worldwide to download MWA uncalibrated data in standard radio astronomy data formats (CASA measurement sets or UV FITS files). The addition of the MWA ASVO calibration feature opens a new, powerful avenue for researchers without a detailed knowledge of the MWA telescope and data processing to download calibrated visibility data and create images using standard radio astronomy software packages. In order to populate the database with calibration solutions from the last 6 yr we developed fully automated pipelines. A near-real-time pipeline has been used to process new calibration observations as soon as they are collected and upload calibration solutions to the database, which enables monitoring of the interferometric performance of the telescope. Based on this database, we present an analysis of the stability of the MWA calibration solutions over long time intervals. 

Abstract Precise instrumental calibration is of crucial importance to 21-cm cosmology experiments. The Murchison Widefield Array’s (MWA) Phase II compact configuration offers us opportunities for both redundant calibration and sky-based calibration algorithms; using the two in tandem is a potential approach to mitigate calibration errors caused by inaccurate sky models. The MWA Epoch of Reionization (EoR) experiment targets three patches of the sky (dubbed EoR0, EoR1, and EoR2) with deep observations. Previous work in Li et al. (2018) and (2019) studied the effect of tandem calibration on the EoR0 field and found that it yielded no significant improvement in the power spectrum (PS) over sky-based calibration alone. In this work, we apply similar techniques to the EoR1 field and find a distinct result: the improvements in the PS from tandem calibration are significant. To understand this result, we analyse both the calibration solutions themselves and the effects on the PS over three nights of EoR1 observations. We conclude that the presence of the bright radio galaxy Fornax A in EoR1 degrades the performance of sky-based calibration, which in turn enables redundant calibration to have a larger impact. These results suggest that redundant calibration can indeed mitigate some level of model incompleteness error.