Hosted by TechSoup on February 13, 2023. https://events.techsoup.org/e/mykxzr/ Nonprofit organizations can use data to help communities and funders better understand their work. But how do you know which data to use? And where do you find it? And critically: once you have data to share, how can you use it to tell a story about your organization? TechSoup is collaborating with DataCommons.org and Tech Impact’s Data Innovation Lab to help answer these questions. We know that organizing the data you need in a meaningful way can be difficult, especially if the data comes from many different places. In this webinar, you will learn how DataCommons.org helps to address this challenge, and how we are working together to make it as easy as possible for small organizations to use public data to share stories about their work and impact.

1. February 13, 2023
How Can Public Data Help Your
Organization?
An introduction to
DataCommons.org

2. To build a dynamic bridge that leverages technology
to enable connections and innovative solutions
for a more equitable planet.

3. 3 © TechSoup Global. All Rights Reserved.
Guests
Héc Maldonado-Reis
Tech Impact
Ramanathan Guha
Google

4. 4 © TechSoup Global. All Rights Reserved.
Héc Maldonado-Reis
Director, Research Development & Analytics at the
Tech Impact Data Innovation Lab
Tech Impact

5. How Can Public Data Help
Your Organization?
An Introduction to DataCommons.org

6. Héc Maldonado-Reis
Director, Data Innovation Lab, Tech Impact
Héc oversees Research Development and Analytics at Tech Impact's
Data Lab, levering data and technology for social good.
The Data Lab convenes non-profit, government and industry partners
across diverse sectors (from education and healthcare to housing and
workforce development) on matters spanning from data governance &
strategy to data modeling & reporting.
6

7. 7
Development Operations
Engagement
Social
Policy
---------------------- Programs ----------------------

8. Prescribe
Predict
Describe
Diagnose
8

9. 9
Data collection
Process enhancement
Operating
Evaluating outcomes
Tracking project
progress
Reporting
Resource allocation
New ventures
Targeting

10. 10
New e-mail
From
Survey
tool?
Subject
contains
”Chronic"?
Save to
SharePoint
Terminate
E-mail
confirmation
Terminate
Yes Yes
No No
Operating

11. 11
Reporting

12. 12
Targeting

13. Héc Maldonado-Reis (hec@techimpact.org)
Thank you!
13

14. 14 © TechSoup Global. All Rights Reserved.
Ramanathan Guha
Google Fellow
Google

15. Data Commons
https://datacommons.org

16. Context
There is a lot of data …
From census (US, India, UK, …), RBI, USGS, BLS, BEA, UDISE, World Bank,
Eurostat, NOAA, EPA, EIA, NCIS, IPCC, CDC, HHS, HUD, HURDAT, …
This data is essential
For science, journalism, policy and our users
Using this data is extremely painful
Forage for data, track down assumptions, clean, normalize, join, costly
data wrangling repeated over and over again

17. Analogy with Satellite Imagery and Google Earth/Maps

18. From search for datasets,
download, clean,
normalize, join …
to
Just ask in Natural
Language
Our goal for data
CA counties most
at food risk from
climate change?

19. Census
BLS
HURDAT
NOAA
CDC
WHO
IPCC OECD
Aggregated
Normalized
Knowledge
Graph
World Bank
EIA
EPA
APIS
Journalists
Students Researchers
Search, News Cloud

20. What we have built: Code
Open source:
- GCP infrastructure for creating, storing, serving, KG
- Visualization tools
In Google’s Search Stack: Integration with Google search

21. What we have built: Data
People & places, …
- Demographics: Census (US, India, …) , Eurostat, …
- Economics: BLS, BEA, WorldBank, …
- Health: CDC, DEA, WHO, ICD, …
- Food, Crime, Education, Elections, Trade, …
Climate Change & Sustainability:
- Climate: IPCC, EPA, HURDAT, NOAA
- Energy: EIA, NREL, …
- Water, Agriculture: USGS, WRIS (India), USDA, FAO
- Emissions: EPA, EIA, ..
3.5+ Billion time series
1.7 trillion triples
2.4 M places
100+ k variables
3x search KG
5x FRED

22. Application: In Google Search

23. NL interface in Google search
Energy use per capita in India
CO2 emissions in Sweden
Number of unemployed in California
Population growth rate in Germany
Fertility rate in bangladesh
Number of poor hispanic women in Santa Clara

24. Application: Helping Community Service Organizations

25. Two facets
The Physical World: shelters, food distribution, …
The Information World
Understanding who is most at what kind of risk
Reducing waste
Carbon accounting

26. There are many inequities

27. Fraction of
population
with
Disabilities

28. Percentage
of population
with Diabetes

29. Without
Health
Insurance

30. New factors such as climate change
Climate change is not as
simple as 1.5 vs 2 degree.
Climate deltas vary widely
across places

31. Jobs
Temperature,
Humidity
Air Quality
Disasters
Employment,
Wages, …
Energy
Farming,
Water
BLS, BEA
Census, HUD,
Fannie Mae
EIA,
NREL
Census
USDA, FWS,
USGS
NOAA
NCWG
HURDAT
IPCC, NASA, NOAA, EPA
CDC,
DEA
NCES
Food
Health
Shelter
Housing
Pre-existing
health
conditions
Demographics
Education
To identify who is most
at risk, we need data
about data about
health, food, farming,
water, climate,
employment, …
Messy Data Ecosystems
Migration

32. The Mission
- `Organize and make easily accessible’ all this data for everyone
- researchers, policy folks, administrators, students … all of our users!
- Data Commons is the base data layer for many applications
- Different interfaces, data augmentations, ML tools and more
- From many different companies, universities, …
- This ‘base data layer’ is open and built together with other players from
industry, academia and government (like html, schema.org, etc.)

33. Many topics already in and many more coming
Climate, Water, Agriculture, Air quality
Fuels, Energy Production
Natural Disasters (Storms, fires, floods, …)
Mortality causes, Poverty, Social Benefits
Education (K-12 and college)
Employment & labor markets, Businesses
Housing, Commute, Insurance, Disabilities, Immigration, Crime

34. Example

35. Todd & Oglala Lakota
counties, SD
McKinley, NM
Heat vs Cardiac conditions --- who is in most danger?
Todd & Oglala Lakota
counties, SD

36. Oglala Lakota County --- other vulnerability factors
High rate of uninsured High Level of Poverty Fewer homes per capita

37. Oglala Lakota County --- the bigger picture
Low labor force participation Low levels of college edu. Very young!

38. Many Data Commons, one schema, one API
- different topics, different ACLs, free vs paid, …
An overlay, on top of which
both the overlay and base
Data Commons can be
accessed with the same,
single API - overlaid data
could be private or
semi-public or …

39. Feeding America Data Commons

40. CA counties most at food risk from rising temperatures
Imperial
Siskiyou
Trinity
San Mateo
Santa Clara
Humboldt
Del Norte

41. Learn more … datacommons.org
Collaborate with us!

42. Q&A

43. 43 © TechSoup Global. All Rights Reserved.
● Learn more:
https://blog.techsoup.org/posts/introducing-data-commons-to-civil-society
● Join the conversation:
Email us at datacommons@techsoup.org
How can Data Commons work for you?