# Events and press
## Gun Violence Data Hub: Atlas of American Gun Violence
[**The Trace**](https://www.thetrace.org/)
The Gun Violence Data Hub is working to become the most comprehensive gun violence data resource in the United States. Built on Redivis infrastructure, they are building and maintaining an interactive data library that provides visualizations and entry points for further analysis.
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## Imagining a new future for scientific communication
[**Continuous Science Foundation**](https://continuousfoundation.org/)
Redivis joined a diverse group of researchers, infrastructure builders, funders, open-science advocates and change-makers in a workshop to develop future modalities for scientific communication.
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## Temporal Coverage for Awards, Instruments and Projects in DataCite
[**Metadata Game Changers**](https://metadatagamechangers.com/)
Redivis is excited to offer new DataCite identifiers as their schema evolves, such as the addition of [Coverage](https://datacite-metadata-schema.readthedocs.io/en/4.6/appendices/appendix-1/dateType/#coverage) to the dateType vocabulary.
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## Analyzing the Data Citation Corpus on Redivis
[**Make Data Count.**](https://makedatacount.org/)
Redivis is featured on the Make Data Count blog, where Stanford's Director of Open Scholarship Strategy Zach Chandler uses Redivis to explore and analyze data citations.
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## Datapages for interactive data sharing using Quarto
**Mika Braginsky | Stanford**
Mika Braginsky built an interactive data dashboard that uses the Redivis platform as a backend for hosting the underlying data.
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Datapages is built by [Mika Braginsky](https://mikabr.io/) and [Mike Frank](https://web.stanford.edu/~mcfrank/) at Stanford University
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## Simplifying sharing of national voting data with Redivis
[**UCLA Library**](https://redivis.com/UCLALibrary) **| FORCE11 '24 Conference**
Redivis CEO Ian Mathews co-presented with Graham Straus from UCLA about the way UCLA Library has worked to curate and distribute the L2 Voter File dataset in an accessible, secure format. Redivis allowed us at UCLA to compile 3.4 terabytes of national voting data into a single table that undergraduates, graduates, and faculty can access and easily work with in a secure environment.
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Mathews, I., & Straus, G. (2024, August 1). Simplifying sharing of national voting data with Redivis. Zenodo.
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## Redivis: A Scalable Web Platform for Business Research
[**Stanford Graduate School of Business** ](https://redivis.com/StanfordGSBLibrary) **| PEARC 2024 Conference**
Redivis CEO Ian Mathews co-presented with Alex Storer from Stanford GSB Library about their paper written on the use case administering data for business school researchers.
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PEARC '24: Practice and Experience in Advanced Research Computing 2024: Human Powered Computing Article No.: 58, Pages 1 - 4
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[Link to slides](https://docs.google.com/presentation/d/1Rjv8kLl0JnUHL1TsTdy8vQPVSkGCNa-ErQLFEBHJzfw/edit#slide=id.g27827c9454c_0_14)
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## FAIR Enough: Building an Academic Data Ecosystem to Make Real-World Data Available for Translational Research
[**Stanford Center for Population Health Sciences**](https://redivis.com/StanfordPHS) **| Journal of Clinical and Translational Sciences**
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Chu I, Miller R, Mathews I, Vala A, Sept L, O’Hara R, and Rehkopf DH. FAIR enough: Building an academic data ecosystem to make real-world data available for translational research. Journal of Clinical and Translational Science 8: e92, 1–9. doi: 10.1017/ cts.2024.530
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## Data Analysis and Machine Learning Workflows on Redivis
[**Stanford Center for Population Health Sciences**](https://redivis.com/StanfordPHS) **| Workshop**
Redivis CEO Ian Mathews presented the Redivis platform as a tool for data discovery and analysis, with a small exploration of ML workflows.
> Learn how to utilize Redivis, a data platform used to store and query data on the PHS Data Portal, for every stage of your analytical workflow. This presentation will showcase common methodologies in working with large claims datasets, including scalable cohort generation and analytical workflows in R, Python, Stata and SAS. The session will conclude with an exploration of using modern ML techniques to classify patient notes and other unstructured data.
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## Running ML and AI Models on Redivis
[**Stanford Graduate School of Business Library**](https://redivis.com/StanfordPHS) **| Workshop**
Redivis CEO Ian Mathews co-presented with Alex Storer from the Stanford GSB's [Data, Analytics, and Research Computing](https://www.gsb.stanford.edu/faculty-research/darc) (DARC) group about running ML and AI Models on Redivis.
This training includes a walkthrough on how to get started coding ML and AI workflows.
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## Expanding Data Horizons with Redivis
[**Carnegie Mellon Libraries**](https://redivis.com/CMULibraries) **| Workshop**
The [Carnegie Mellon Libraries](https://www.library.cmu.edu/) shared how they are using Redivis to offer datasets and data hosting across campus.
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## EIDC in the Classroom: Teaching with the Environment
[**Georgetown Environmental Impact Data Collaborative**](https://redivis.com/EIDC) **| Workshop**
The [Massive Data Institute](https://mdi.georgetown.edu/) (MDI) in Georgetown's McCourt School of Public Policy harnesses modern data and computing power to produce cutting edge social science, computer science, and data science research that improves public policy decision making.
The [Environmental Impact Data Collaborative](https://mdi.georgetown.edu/eidc/) group within MDI hosted an event to talk about their mission and the resources they'd built using Redivis to accomplish it.
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