Datasetpublic

mmrech/icskg-br-processed

ICSKG-BR: Index of Cities' Smartness & Knowledge for Global Surgery — Brazil Processed data layer for the ICSKG-BR longitudinal ecological panel study. Cross-references the IESE Cities in Motion Index (CIMI) urban development framework against Lancet Commission on Global Surgery (LCoGS) indicators across all 5,570 Brazilian municipalities (2015-2023). This dataset is currently private during the BMJ Global Health pre-submission window. It will become public (CC-BY-4.0) upon… See the full description on the dataset page: https://huggingface.co/datasets/mmrech/icskg-br-processed.

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Dataset Card

ICSKG-BR: Index of Cities' Smartness & Knowledge for Global Surgery — Brazil

Processed data layer for the ICSKG-BR longitudinal ecological panel study. Cross-references the IESE Cities in Motion Index (CIMI) urban development framework against Lancet Commission on Global Surgery (LCoGS) indicators across all 5,570 Brazilian municipalities (2015-2023).

This dataset is currently private during the BMJ Global Health pre-submission window. It will become public (CC-BY-4.0) upon publication.

Provenance

—Source code: https://github.com/matheus-rech/ICSKG
—Dataset version: v0.1.0
—Schema version: 1.1
—Generated at: 2026-04-07T15:59:23Z
—Source DuckDB: icskg_br_export.duckdb (sha256 473d5ad523dab966)
—Compression: zstd level 6

Cookbook scope (v0.1.0)

This release implements the LCoGS-side of the ICSKG-BR Technical Cookbook v1.0 (March 2026). The canonical panel/municipal_health.parquet is derived at publish time from the source tables via the cookbook §5 merge pipeline (50,130 rows = 5,570 mun × 9 years, 49 columns).

Cookbook §3 sources INCLUDED in v0.1.0:

—IBGE population master (§3.3)
—DATASUS SIH surgical aggregates (§3.1)
—DATASUS CNES SAO + bellwether (§3.2)
—IBGE SIDRA GDP (§3.3)
—Atlas Brasil HDI (§3.4)
—FIRJAN IFGF (§3.5)
—ANS TABNET insurance (§3.9)
—Census 2022 sanitation (§3.8 partial)
—Mobility (vehicle fleet)

DEFERRED to v0.2.0 (extractors not yet built — see project phase 12):

—ANATEL broadband (§3.6) — needed for CUDS Technology dimension
—RAIS employment (§3.7) — needed for CUDS Economy/Workforce dimension
—SNIS sanitation proper (§3.8) — Census 2022 is a partial proxy
—SIOPS health spending (§3.10) — needed for CUDS Governance dimension
—International comparators (§3.11) — WHO/World Bank/UNDP
—CUDS composite + dimension scores (§6) — blocked by missing sources above

The v0.2.0 release will add the missing 5 extractors and the cookbook §6 CUDS composite (PCA weighting + geometric mean aggregation), and will move the municipal_health derivation into the build pipeline so it's stored as a base table in the source DuckDB instead of being computed at publish time.

Totals

Metric	Value
Tables	23
Total rows	20,037,428
Compressed size	232.80 MB

Tables by namespace

`panel/`

Table	Rows	Columns	Size
`municipal_health`	50,130	49	2601.98 KB

`lcogs/`

Table	Rows	Columns	Size
`lcogs1_access`	5,571	6	43.50 KB
`v_lcogs_latest`	5,570	30	367.28 KB
`v_lcogs_panel`	50,130	30	2442.88 KB
`v_lcogs_summary`	49,717	26	1394.48 KB

`source_tables/`

Table	Rows	Columns	Size
`ans_cobertura`	5,594	6	60.58 KB
`bellwether_hospitals`	765	7	23.84 KB
`censo2022_saneamento`	5,570	4	47.88 KB
`cnes_professionals_raw`	218,461	6	129.47 KB
`frota_veiculos`	5,571	3	44.96 KB
`gdp`	50,130	4	418.16 KB
`idhm`	5,564	7	53.21 KB
`idhm_historical`	16,692	7	113.26 KB
`ifgf`	50,112	7	581.19 KB
`mobility`	5,535	3	35.73 KB
`municipalities`	5,571	12	154.55 KB
`national_indicators`	51	5	3.08 KB
`pipeline_log`	490	9	9.07 KB
`population`	50,130	3	207.30 KB
`sao_workforce`	49,753	8	383.22 KB
`sih_municipal`	50,170	8	442.42 KB
`sih_raw`	19,355,506	25	228820.14 KB
`v_sao_annual`	645	6	5.82 KB

Loading

python

from huggingface_hub import snapshot_download
import duckdb

local = snapshot_download(
    repo_id='mmrech/icskg-br-processed',
    repo_type='dataset',
    revision='v0.1.0',
    allow_patterns=['**/*.parquet', 'manifest.json'],
)

# Read the main panel directly:
panel = duckdb.sql(f"SELECT * FROM '{local}/panel/municipal_health.parquet'").df()

Citation

If you use this dataset, please cite the project:

Rech, Matheus M. (2026). ICSKG-BR: Index of Cities' Smartness & Knowledge
for Global Surgery — Brazil. https://github.com/matheus-rech/ICSKG

Production Zenodo DOI will be minted at BMJ acceptance.

License

Source data is public under Brazilian Lei de Acesso à Informação (Law 12.527/2011). This processed dataset is released under CC-BY-4.0 after BMJ Global Health publication.