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Cybersyn Public Domain Pro

Complete package of all Cybersyn data sourced from public domains. Includes point-in-time history


Cybersyn Public Domain Pro contains all Cybersyn data products sourced from public domains. In addition to core tables that reflect the best known value as of today, this product includes point-in-time tables of all data historically published across Cybersyn's public data products.
Example sources:
  • Federal Deposit Insurance Corporation (FDIC)
  • U.S. Census Bureau (USCB)
  • U.S. Securities and Exchange Commission (SEC)
  • Data Commons
  • FRED
  • GitHub
  • Internet Corporation for Assigned Names and Numbers (ICANN)
  • National Oceanic and Atmospheric Administration (NOAA)
Cybersyn Public Domain Pro includes all tables from the below products as well as point-in-time history for these tables where applicable. Any additional public data released by Cybersyn in the future will be added.

Data Products Included

Documentation and data dictionaries for each underlying table and source included in Cybersyn Public Domain Pro can be found here:
All Cybersyn products follow the EAV (entity, attributes, value) model with a unified schema. Entities are tangible objects (e.g. geography, company). Entities may have characteristics (i.e. descriptors of the entity) in an index table and values (i.e. statistics, measure) in a timeseries table. Data is joinable across all Cybersyn products that have a GEO_ID. Refer to Cybersyn Concepts for more details.

Notes & Methodology

Point-in- time history

Refer to this Cybersyn Concepts page for more information about point-in-time history tables.

Examples & Sample Queries

View values as of a specific date and time
Use Cybersyn’s point-in-time history table to look back at what FRED data was published as of October 31, 2023.
FROM cybersyn.bureau_of_labor_statistics_employment_timeseries_history
WHERE _effective_start_timestamp <= '2023-10-31 00:00:00'
AND (_effective_end_timestamp >= '2023-10-31 00:00:00' OR _effective_end_timestamp IS NULL);
Pull all historical revisions to a particular data point
Check the history of reported nonfarm employees in the United States for a given date. Pull historical estimates that may have been revised in subsequent monthly releases.
FROM cybersyn.financial_fred_timeseries_history
WHERE variable_name = 'All Employees, Total Nonfarm, Seasonally adjusted, Monthly, Persons'
AND date = '2023-09-30'
ORDER BY _effective_start_timestamp;
Get timestamps for all updates to Cybersyn’s data
Select all distinct times any new data was inserted or any existing data was modified in the FX rate table. This will create a log of all timestamps where Cybersyn ran a job that made any changes to the underlying data in the table.
SELECT DISTINCT _effective_start_timestamp
FROM cybersyn.fx_rates_timeseries_history
ORDER BY _effective_start_timestamp;
FDIC deposit exposure
Determine which banks have the highest percentage of uninsured deposits.
WITH big_banks AS (
SELECT id_rssd
FROM cybersyn.financial_institution_timeseries
WHERE variable = 'ASSET'
AND date = '2022-12-31'
AND value > 1E10
SELECT name,
1 - value AS pct_uninsured,
FROM cybersyn.financial_institution_timeseries AS ts
INNER JOIN cybersyn.financial_institution_attributes AS att ON (ts.variable = att.variable)
INNER JOIN cybersyn.financial_institution_entities AS ent ON (ts.id_rssd = ent.id_rssd)
INNER JOIN big_banks ON (big_banks.id_rssd = ts.id_rssd)
WHERE = '2022-12-31'
AND att.variable_name = '% Insured (Estimated)'
AND att.frequency = 'Quarterly'
ORDER BY pct_uninsured DESC;
Track house prices and gross income inflow in a specific city
Compare Phoenix house prices vs gross income inflow.
WITH county_map AS (
FROM cybersyn.geography_relationships
WHERE geo_name = 'Phoenix-Mesa-Scottsdale, AZ Metro Area'
AND related_level = 'County'
), gross_income_data AS (
geo_name AS msa,
SUM(value) AS gross_income_inflow
FROM cybersyn.irs_origin_destination_migration_timeseries AS ts
JOIN county_map ON (county_map.related_geo_id = ts.to_geo_id)
WHERE ts.variable_name = 'Adjusted Gross Income'
GROUP BY geo_id, msa, date
), home_price_data AS (
SELECT LAST_DAY(date, 'year') AS end_date, AVG(value) AS home_price_index
FROM cybersyn.fhfa_house_price_timeseries AS ts
JOIN cybersyn.fhfa_house_price_attributes AS att
ON (ts.variable = att.variable)
WHERE geo_id IN (SELECT geo_id FROM county_map)
AND att.index_type = 'purchase-only'
AND att.seasonally_adjusted = TRUE
GROUP BY end_date
FROM gross_income_data AS gid
JOIN home_price_data AS hpi ON ( = hpi.end_date)
ORDER BY date;

Releases & Changelog

See "Release & Changelog" section on the respective product pages listed under "Data Products Included" above.
11/7/23 - Added point-in-time history for 45 tables
Added point-in-time history tables for the following Cybersyn datasets.
  • American Community Survey
  • Canada Statcan
  • Carbon Credit Purchases
  • Carbon Intensity
  • Company Index and Characteristics
  • Data Commons
  • Domain Index
  • FHFA
  • Geography Characteristics
  • Home Mortgage Disclosures
  • IMEI
  • IRS
  • NOAA Weather Stations and Metrics
  • OpenFIGI Security Index
  • PermID Security Index
  • Points of Interest (POIs)
  • Urban Crime
  • US Addresses
  • USDA
  • US Treasury
  • USPS Address Changes


The data in this dataset is sourced on the individual product documentation pages linked here. Links to provider terms and disclaimers are provided where appropriate on the individual product pages.
Cybersyn is not endorsed or affiliated with any of these providers. Contact [email protected] for questions.
Last modified 10d ago