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The COVID Information Commons (CIC) is an open website portal and community to facilitate knowledge-sharing and collaboration across various COVID research efforts, funded by the NSF Convergence Accelerator and the  NSF Technology, Innovation and Partnerships Directorate. The CIC serves as an open resource for researchers, students, and decision-makers from academia, government, not-for-profits and industry to identify collaboration opportunities, to leverage each other's research findings, and to accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic. The CIC was developed as a collaborative proposal led by the Northeast Big Data Innovation Hub, hosted by Columbia University, in collaboration with the Midwest Big Data Innovation Hub, South Big Data Innovation Hub, and West Big Data Innovation Hub.  It was funded by the NSF Convergence Accelerator (NSF #2028999) in May  2020 and launched in July 2020.  The initial focus of the CIC website was on the 723 NSF-funded COVID Rapid Response Research (RAPID) projects funded in 2020. The CIC-E: COVID Information Commons Extension for Pandemic Recovery project was proposed and funded in 2021 (NSF #2139391) by the CIC project team with the goal to increase researcher collaboration across NSF and NIH awardees and with global collaborators, as we continue to combat the novel coronavirus, and glean learnings for future uses of innovations developed for COVID response and recovery, including potential insights which can be leveraged for future pandemics. The CIC extension launched on June 30, 2022 increasing the corpus of awards from just NSF to include NIH-funded COVID related awards, both present and past, through all funding vehicles, in pertinent areas of COVID research, response and recovery. The CIC-extension provides more opportunity for multi-agency and multidisciplinary research collaboration as all the Principal Investigators (PIs) for awards in the CIC are invited to present their research and collaborate on CIC Research Lighting Talk Webinars and Collaboration Sessions. The COVID Information Commons (CIC) archive in Dryad includes the NSF and NIH COVID-related awards in the CIC, as well as links to valuable COVID-related datasets, groups, guides and artifacts including videos and transcripts of the CIC researcher lightning talks. The CIC archive was created in Dryad in 2024 at 84.24 MB and is updated annually. The 2025 update to the CIC archive in Dryad is 91.94 MB. It can be cited as follows: Hudson, Florence et al. (2024). COVID information commons archive [Dataset]. Dryad. https://doi.org/10.5061/dryad.37pvmcvqp 

This study analyzes and compares how the digital semantic infrastructure of U.S. based digital news varies according to certain characteristics of the media outlet, including the community it serves, the content management system (CMS) it uses, and its institutional affiliation (or lack thereof). Through a multi-stage analysis of the actual markup found on news outlets’ online text articles, we reveal how multiple factors may be limiting the discoverability and reach of online media organizations focused on serving specific communities. Conceptually, we identify markup and metadata as aspects of the semantic infrastructure underpinning platforms’ mechanisms of distributing online news. Given the significant role that these platforms play in shaping the broader visibility of news content, we further contend that this markup therefore constitutes a kind of infrastructure of visibility by which news sources and voices are rendered accessible—or, conversely—invisible in the wider platform economy of journalism. We accomplish our analysis by first identifying key forms of digital markup whose structured data is designed to make online news articles more readily discoverable by search engines and social media platforms. We then analyze 2,226 digital news stories gathered from the main pages of 742 national, local, Black, and other identity-based news organizations in mid-2021, and analyze each for the presence of specific tags reflecting the Schema.org, OpenGraph, and Twitter metadata structures. We then evaluate the relationship between audience focus and the robustness of this digital semantic infrastructure. While we find only a weak relationship between the markup and the community served, additional analysis revealed a much stronger association between these metadata tags and content management system (CMS), in which 80% of the attributes appearing on an article were the same for a given CMS, regardless of publisher, market, or audience focus. Based on this finding, we identify the organizational characteristics that may influence the specific CMS used for digital publishing, and, therefore, the robustness of the digital semantic infrastructure deployed by the organization. Finally, we reflect on the potential implications of the highly disparate tag use we observe, particularly with respect to the broader visibility of online news designed to serve particular US communities. 

Monitoring voltage dynamics in defined neurons deep in the brain is critical for unraveling the function of neuronal circuits but is challenging due to the limited performance of existing tools. In particular, while genetically encoded voltage indicators have shown promise for optical detection of voltage transients, many indicators exhibit low sensitivity when imaged under two-photon illumination. Previous studies thus fell short of visualizing voltage dynamics in individual neurons in single trials. Here, we report ASAP2s, a novel voltage indicator with improved sensitivity. By imaging ASAP2s using random-access multi-photon microscopy, we demonstrate robust single-trial detection of action potentials in organotypic slice cultures. We also show that ASAP2s enables two-photon imaging of graded potentials in organotypic slice cultures and in Drosophila. These results demonstrate that the combination of ASAP2s and fast two-photon imaging methods enables detection of neural electrical activity with subcellular spatial resolution and millisecond-timescale precision.