National Data Science Organizers Lightning Talks From Around the Country Dr. Brand Niemann Founder and Co-Organizer Federal Big Data Working Group Meetup Director and Senior Data Scientist/Data Journalist Semantic Community Federal Big Data Working Group Meetup Data Science November 5,

Because I am a Data Scientist and Data Journalist Who we are? What we do? Where we do it? When we do it? Why we do it? How we do it? Specific example: Data Science for the Map of Federal Crowdsourcing and Citizen Science Projects for the NDSO Challenge 2 Poynter: A Global Leader in Journalism: 6 questions that can help journalists find a focus, tell better stories6 questions that can help journalists find a focus, tell better stories

Who we are?: Definitions Federal: Supports the Federal Big Data Initiative, but not endorsed by the Federal Government or its Agencies; Big Data: Supports the Federal Digital Government Strategy which is "treating all content as data", so big data = all your content; Working Group: Data Science Teams composed of Federal Government and Non-Federal Government experts producing big data products; and Meetup: The world's largest network of local groups to revitalize local community and help people around the world self-organize like MOOCs (Massive Open On-line Courses) now endorsed by the White House 3

What we do?: October 19 th Meetup This Meetup was organized for: Robin Thottungal, Chief Data EPA, and Division Director, EAD, OIAA, OEI, Robin Thottungal Greg Godbout, Chief Technology Officer, Environmental Protection Agency, and former Executive Director and Co-Founder of 18F, and Greg Godbout Jay Benforado, Director, National Center for Environmental Innovation at EPA, and Co-Chair, Federal Community of Practice for Crowdsourcing and Citizen ScienceFederal Community of Practice for Crowdsourcing and Citizen Science for the National Data Science Organizers Workshop on November 5-6, 2015, as an example of:National Data Science Organizers Workshop data science for curated data sets, user-centric digital services focused on the interaction between government and the people and businesses it serves, and a Federal Community of Practice on Crowdsourcing and Citizen Science of Big Data that meets bi-monthly to share lessons learned and develop best practices for designing, implementing, and evaluating crowdsourcing and citizen science initiatives. 4 See Recording and Agenda:

Where we do it?: Locations Xcelerate Solutions 8405 Greensboro Dr., Suite 930, McLean 22102, VA National Science Foundation 4201 Wilson Blvd, Arlington, VA Eastern Foundry 2011 Crystal Drive, 4th Floor, Arlington 22202, VA Marriott Wardman Park 2600 Woodley Road NW, 20008, Washington, DC Conferences, Workshops, etc. 5

When we do it?: Meetup Calendar Schedule September 28 th, Climate Change & Genomic Data - Data Science Meetup of Meetups October 5th, Data Science for EPA & USGS Fracturing & Fracking Data (Dr. Sophia Liu, USGS and USGS Staff). See July 13 th Meetup: Data Science for USGS Minerals Big DataData Science for USGS Minerals Big Data October 19th, Sensing Our Air: The Quest for Big Data About Our Air Quality (EPA’s New Chief Data Scientist, Robin A Thottungal, Invited)Robin A Thottungal November 2 nd, Data Science for Random Forests: TIBCO Enterprise Runtime for R. See June 1 st Meetup: Data Science for Homeless Data: QlikView. Tableau, & Spotfire BakeoffData Science for Homeless Data: QlikView. Tableau, & Spotfire Bakeoff November 5-6 th, OSTP/NSF Data Science Meetup of Meetups, Ballston, VA November 16 th, Data Science for the DataAct Datathon December 7 th, Data Science for DoD Joint Doctrine January 4 th, 2016, Data Science for Semantics: MarkLogic and Cray Graph Appliance Update February 1st, 2016, Data Science for Census American Community Survey 6

Why we do it?: Use Federal Big Data Examples and Technology Federal Big Data Examples: White House Climate Change and Precision Medicine NIH Genomic EPA Air Quality USGS Water Quality Department of Commerce Census Treasury DataAct DoD Joint Doctrine Major Big Data Technologies: TIBCO Enterprise Runtime for R (TERR) MarkLogic Semantics Cray Graph Appliance 7

How we do it?: Like the NIH Data Commons FAIR Principles: Findable Accessible Interoperable Reusable Cloud: Data Software Results Federal Science Policy: OSTP Public Access to Scientific Data Memo (February 2013) New Program: Big-Data-to- Knowledge (2013) New Position: Associate Director of Data Science (2014) Digital Enterprise (2015): Data Commons Metadata Open APIs Digital Objects Containers A NIH – Semantic Medline Data Science Data Publication Commons 8

How we do it?: OSTP/NSF National Data Science Organizers Workshop Week of November 2 nd : NSF Data Science/Big Data Principal Investigators (About 300) NSF Data Hubs (4) Organizers of Largest Data Science/Big Data Meetups (About 65) Pipeline for Return on Investment: PIs put their data, tools and research results in the Data Hubs Data Hubs provide those data, tools, and research results to the world, but especially to the Data Science/Big Data Meetups Data Science/Big Data Meetups collaborate with PIs and Data Hubs to increase usage and feedback 9

How we do it?: We Already Do This! Semantic Community: Provides a Community Sandbox that is like a GitHub, Data Hub, Data Commons, etc. Metadata (MindTouch) Open APIs (MIndTouch) Digital Objects (MindTouch) Containers (Spotfire) Organize the Federal Big Data Working Group Meetup Support Agencies and Programs in Crowdsourcing Their Data Sets Mentor Data Scientists (Tutorials and MOOCs) and Entrepreneurs (Eastern Foundry) Federal Big Data Working Group Meetup: Federal: Supports the Federal Big Data Initiative, but not endorsed by the Federal Government or its Agencies; Big Data: Supports the Federal Digital Government Strategy which is "treating all content as data", so big data = all your content; Working Group: Data Science Teams composed of Federal Government and Non- Federal Government experts producing big data products; and Meetup: The world's largest network of local groups to revitalize local community and help people around the world self- organize like MOOCs (Massive Open On-line Classes) now embraced by the White House. 10

How we do it?: Data Mining - Science - Questions - Publication Process Data Mining Process: Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment Data Science Process: Data Preparation Data Ecosystem Data Story Data Science Questions: How was the data collected? Where is the data stored? What are the data results? and Why should we believe the data results? Data Science Data Publication: Knowledge Base Spreadsheet Index Web & PDF Tables to Spreadsheet Data Browser Dynamically Linked Adjacent Visualizations 11

How we do it?: Collaboration for Data Science Win-Wins USDA Open Government Data Training, Innovation Competition, and Online Course in Data-Driven Farming: _Farming_Business#Story _Farming_Business#Story Many Curated Government Data Sets and Data Science Products: Pick an Agency and/or a Data Set and Look for a Meetup on That: Mentor Startups Partnership with Eastern Foundry: Group/events/ / Group/events/ / 12

Specific Example: Data Science for the Map of Federal Crowdsourcing and Citizen Science Projects for the NDSO Challenge The National Data Science Organizers (NSDO) are looking for a set of meta- design categories for each challenge model so teams can find, gather, and share data. The Federal Crowdsourcing and Citizen Science Toolkit provides both a set of meta-design categories and agency partners to help teams find, gather, and share data. The Map of Federal Crowdsourcing and Citizen Science Projects has been converted to a data set of 102 projects that can be used by the NDSO teams for the upcoming OSTP/NSF NSDO Workshop, November 5-6, 2015, and for going forward for the rest of This work also demonstrates a simple data science project for a hackathon challenge that shows how this map was created in Excel and visualized in Spotfire.

CCSInventory.xlsx

NOAA has the most projects: 26 Web Player