waterdatacollaborative Goto Github PK
Name: Berkeley Water Data Collaborative
Type: Organization
Bio: Pooling water based data science projects and resources from the California Water Data Challenge and beyond.
Location: UC Berkeley
Name: Berkeley Water Data Collaborative
Type: Organization
Bio: Pooling water based data science projects and resources from the California Water Data Challenge and beyond.
Location: UC Berkeley
Main repository for my contributions to the California Safe Water Challenge
Dammed If You Do A map and infographics visualizing dam hazard in California Inspiration Dams are important, but not often thought about by members of the public, until something goes wrong. This project communicates the potential risk, with an objective of building support for dam safety in California. What it does Identifies and communicates the status of dams in the California division of dam safety database How I built it Built by Daniel Siegal, Jessica Rahman Colin Bailey, and tag along with Dan Constable ; ) Challenges We ran into TBD... What We learned Dam data is becoming more available, but still not complete. Assessing the potential water supply impacts from a dam damage or failure is difficult given current data availability. What's next for Dammed if You Do Potential interactive features using d3, incorporation of water supply data and potential impacts.
Examples and resources for intial steps of data curation
team cleaned datasets
2018 Project by Rich Pauloo at UC Davis
Lead Levels in San Francisco Schools San Francisco Unified School District tested the school's drinking water for lead last year. This map shows the highest lead levels detected at each school based on multiple sample sites and dates. There is no safe level of lead, especially for children. Even small amounts can lower IQ. Damage from lead exposure is irreversible. Take action with CALPIRG's "Get The Lead Out:Back to School Toolkit."
CWEE's entry into the 2016 CA Water Board Data Innovation Challenge.
Water data analysis
Saving staff time by building a model that identifies Electronic Annual Reports (EARs) with errors? Can we learn more about contributing factors through geographic visualization? By Anna Waldron and Elena Smith, Winner of Imagine H2O Award in 2018 California Water Data Challenge
The California Dept of Public Health estimates that 85 percent of California’s community public water systems, supplying more than 30 million residents, rely on groundwater for at least part of their drinking water supply. Because many community water systems are entirely reliant on groundwater for their drinking water supply, contamination of this resource can have far-reaching consequences. Many groundwater basins throughout California are contaminated with either naturally occurring or anthropogenic pollutants, or both. When a groundwater source is contaminated, community water systems must use costly treatment systems to ensure that the water is safe to drink. Where treatment and alternative water supplies are not available, some community water systems serve contaminated groundwater until a solution is implemented. Contamination of the state’s groundwater resources results in higher costs for ratepayers and consumers due to the necessity of additional treatment and can pose a threat to public health for community water systems that cannot afford the necessary treatment systems. The identification of a relationship between ambient groundwater quality and community water systems that rely on a given groundwater source may help focus available efforts and resources to ensure the provision of safe drinking water. Due to the enormity of the data available, the scope of this project was selected to pertain to samples collected from Kern County from 2012 - present. This is a follow up to a legislative report published in 2013 about communities in California that rely on contaminated groundwater as a water source, identifying Kern County as having the most community water systems that are 100% reliant on groundwater.
This repository documents the Open Water Rate Specification (OWRS), a machine-readable format for specifying and sharing water rate information. OWRS is designed for analysts, economists, and software developers interested in analyzing water rates. OWRS attempts to fully encode a water utility's rate structure and pricing schedules in a form that is easy to store, share, modify and apply programmatically.
Frontend Map that makes it easy to see the severity of contamination level
Data from scraping SDWIS Water System Details pages, distributed in the Datapackage format.
Wiki to discuss current issues and next steps of collaborative effort of students, government agencies, and non-profits
Better Data Quality for Better Water Equity - Transforming EAR data
Website for the Berkeley Water Data Collaborative
Winner of the 2016 CA Water Data Challenge
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.