dewey

Contents

• What Dewey Is
• Credential Enforcement
• Download Enforcement
• Access Method Decision Table
• Authentication
• Quick Reference: Featured Datasets
• SafeGraph Global Places Quick Reference
• Additional Resources

What Dewey Is

Dewey Data is an academic data marketplace — one institutional Platform Subscription unlocks a catalog of ~300 datasets from ~40 providers (foot traffic, POI, mobility, consumer transactions, real estate, labor). UVA Library and NYU both hold the institutional subscription; SafeGraph and most providers are free under it.

Dewey is not a SQL warehouse like WRDS. Data is delivered as partitioned Parquet/CSV.gz files downloaded via an API key. You discover datasets, read metadata, sample, filter (by date partition + columns), then download. Think "S3 of presigned Parquet links," not "PostgreSQL."

	WRDS	Dewey
Data	Finance/accounting	POI, foot traffic, mobility, consumer, real estate
Access	PostgreSQL / SAS on the grid	File download (Parquet/CSV.gz) via API key
Query engine	server-side SQL	DuckDB over the files (local or remote presigned URLs)
Licensing	per-vendor, negotiated	one platform subscription unlocks the catalog
AI access	none	MCP server (api.deweydata.io/mcp)

Credential Enforcement

IRON LAW: NEVER GUESS, INVENT, OR HARDCODE THE API KEY

The Dewey API key belongs to the **user's** account (`app.deweydata.io` → Connections → Add Connection → API Key). It is shown **once**. You do not have it and cannot derive it.

• ALWAYS ask the user for the key before any real data pull. No exceptions.
• NEVER write a placeholder like apikey = "your_api_key" and run it — it will 401 and waste a round trip. Read from DEWEY_API_KEY env var or a gitignored file (~/.config/dewey/apikey).
• NEVER commit the key, echo it back, or paste it into a script that gets committed.

Guessing or hardcoding the key is NOT HELPFUL — every call 401s, and a committed key is a security incident the user must rotate.

Each product (dataset) has its own product path / project ID (prj_…), obtained from the dataset page: Get Data → (Skip filtering) → Connect to API / Bulk API → API URL. One API key, many product paths. If you don't have the product path, discover it via the MCP server (search_datasets) rather than guessing.

Download Enforcement

IRON LAW: NO BULK DOWNLOAD WITHOUT METADATA + SAMPLE + FILTER FIRST

Before downloading ANY Dewey dataset, you MUST:

1. IDENTIFY the product path and what partitions/columns you actually need
2. META — call get_meta (deweydatapy) / get_download_info (MCP) to learn partition columns, date range, file count, total size
3. SAMPLE — pull 100 rows (read_sample / MCP sample_dataset) and INSPECT the schema before committing to a full pull
4. FILTER — restrict by date partition (partition_key_after/before) AND columns; for selective pulls use DuckDB COPY TO over the presigned URLs, never download the whole catalog
5. DOWNLOAD the filtered subset, then verify row counts / NULLs / date range on disk

This is not negotiable. Skipping the sample-and-filter step is NOT HELPFUL — Dewey datasets are routinely hundreds of GB to multiple TB; an unfiltered pull burns hours of bandwidth and disk for data you'll immediately throw away.

Rationalization Table — STOP If You Think:

Excuse	Reality	Do Instead
"I'll just download everything and filter in pandas"	SafeGraph Patterns is multi-TB; you'll fill the disk	DuckDB COPY TO with WHERE on remote parquet — pull only the rows/cols you need
"I don't need to check the schema first"	Column names differ by provider/release (naics_code vs NAICS_CODE, opened_on may not exist)	read_sample(nrows=100) BEFORE the full pull
"No date filter needed, I want all of it"	Most datasets are date-partitioned; "all" = every weekly file ever	Set partition_key_after/before to your study window
"The download started, so it's correct"	A started download ≠ the right columns	Inspect a sample on disk before claiming success
"Presigned links are fine for a long job"	Links expire in 24h (download_files0)	Use download_files1 (page-by-page, refreshes links) for large multi-day pulls
"I'll hardcode the product path I think it is"	Wrong prj_ → 404 or someone else's data	Get it from Connect to API, or MCP search_datasets

Red Flags — STOP Immediately If You're About To:

• Call download_files* without first calling get_meta + read_sample → STOP. Meta + sample first.
• Download a dataset with no start_date/end_date / partition filter → STOP. Scope the date range.
• Load a whole remote dataset into a DataFrame → STOP. Use DuckDB COPY TO … (FORMAT PARQUET, PARTITION_BY …) to persist a filtered subset to disk.
• Run a pull with apikey="your_api_key" or any guessed key → STOP. Ask the user; read from env/file.
• Write the API key into a script you'll commit → STOP. Env var or gitignored file only.

Access Method Decision Table

Need	Method	Reference
Discover/search datasets, check schema, sample — from inside Claude	MCP server (api.deweydata.io/mcp)	references/mcp.md
Scripted Python bulk download	deweypy (recommended) or deweydatapy (legacy, product_path API)	references/deweypy-client.md
Selective pull — specific columns/rows from huge datasets	DuckDB over presigned URLs (read_parquet($urls) + COPY TO)	references/duckdb.md
R workflow	deweyr (download_dewey())	references/deweypy-client.md
One-off, dataset < 2.0 GB	UI CSV download (platform → project)	references/access-options.md
Analyze data already on disk	DuckDB / pandas / polars over *.parquet or *.csv.gz	references/access-options.md

Authentication

Get the key once from app.deweydata.io → Connections → Add Connection → API Key. Store it out of source control:

mkdir -p ~/.config/dewey && echo 'YOUR_KEY' > ~/.config/dewey/apikey && chmod 600 ~/.config/dewey/apikey
# or: export DEWEY_API_KEY=...   (add to .envrc, which should be gitignored)

import os, pathlib
apikey = os.environ.get("DEWEY_API_KEY") or pathlib.Path("~/.config/dewey/apikey").expanduser().read_text().strip()

Institutional login (to browse the catalog / create the key) is via UVA NetBadge (use your UVA email) or NYU SSO. The Platform Subscription is what makes SafeGraph etc. free — see references/datasets.md.

Quick Reference: Featured Datasets

Provider	Dataset(s)	What it is
SafeGraph	Global Places (POI), Geometry, Spend, Patterns	POI master, building footprints, card spend, foot-traffic visit patterns
Advan Research	Monthly/Weekly Patterns, Home Panel	Foot traffic aggregated to place & census-block
dataplor	POI	Global POI, strong emerging-markets coverage
Veraset	Movement	Device-level mobility (institutional license only)
PassBy	Foot Traffic	Per-POI foot-traffic analytics
Consumer Edge / PDI	Spend / transactions	Card & product-level purchasing
LinkUp	Job postings	Labor-market activity
ATTOM / Dwellsy / RentHub	Real estate	Property records, rentals

Full catalog (all ~250 datasets): references/catalog.md — every dataset grouped by category with time coverage, row count, size, and download access (machine-readable: references/catalog.csv). Featured-dataset detail + discovery workflow: references/datasets.md.

SafeGraph Global Places Quick Reference

Core POI schema — columns are UPPERCASE, NAICS_CODE is a string, BRANDS is a JSON-array string (extract with json_extract_string(BRANDS,'$[0].safegraph_brand_name')). Always sample before filtering.

Column	Meaning
PLACEKEY	Stable unique POI id (join key across SafeGraph products)
LOCATION_NAME	POI name
BRANDS	JSON array: [{"safegraph_brand_name":"…"}] — not plain text
STREET_ADDRESS,CITY,REGION,POSTAL_CODE,ISO_COUNTRY_CODE	Address (REGION=US state)
LATITUDE,LONGITUDE	Coordinates
NAICS_CODE,NAICS_CODE_2022	6-digit NAICS (string)
TOP_CATEGORY,SUB_CATEGORY	Category labels
OPENED_ON,CLOSED_ON,TRACKING_CLOSED_SINCE	Open/close dates (exist but sparsely populated — NULL for BTMs)

Resolved empirically: crypto/Bitcoin ATMs do exist as standalone POIs under NAICS_CODE='522320'; all major operators are present. But OPENED_ON/CLOSED_ON are NULL for BTMs in the current release → it's a cross-section, not a time series. Full details, the 7 BTM operators, and the worked example: references/safegraph-places.md and examples/btm_safegraph_pull.py.

Additional Resources

Reference Files

• references/access-options.md — all download methods (UI, deweypy, deweydatapy, DuckDB, MCP, R), 24h link expiry, partitioning, reading data on disk
• references/deweypy-client.md — deweypy (modern CLI + auth/download) and deweydatapy (get_meta, get_file_list, read_sample, download_files0/1) function reference; deweyr for R
• references/duckdb.md — selective remote-Parquet pulls, COPY TO … PARTITION_BY pattern, querying downloaded files
• references/mcp.md — Dewey MCP server URL, JSON config, the 9 tools, discovery → schema → sample workflow
• references/datasets.md — featured-dataset catalog, UVA NetBadge / NYU institutional access, discovery workflow
• references/catalog.md + catalog.csv — full enumerated catalog (~250 datasets / 39 partners) by category, with coverage / rows / column count / size / access
• references/schemas.json — full column schemas for all ~250 datasets (keyed by slug → columns[] with name/type/description; 11,264 columns). Look up a dataset's columns here before pulling, instead of a live get_dataset_schema call
• references/linkage.md — cross-dataset join-key map (placekey, ticker, cusip/cik, domain, person id, lat/long, fips, zip…) — which datasets combine and on what spine
• references/safegraph-places.md — Global Places schema, NAICS 522320, BTM operator brands, opened_on/closed_on, the Bitcoin-ATM worked example

Example Files

• examples/btm_safegraph_pull.py — acceptance test: filter SafeGraph Global Places to the 7 BTM operator brands + NAICS 522320, verify standalone-POI / open-close coverage, export the US subset to ~/projects/batm/