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NOAA GHCN-Monthly Average Temperature

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Structured monthly average-temperature (TAVG) station-year records from the U.S.

Pricing

from $13.40 / 1,000 station-year temperature records

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NexGen Watch

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🔔 NOAA GHCN-Monthly Average Temperature

Structured monthly average-temperature (TAVG) station-year records from the U.S. NOAA/NCEI Global Historical Climatology Network - Monthly (GHCN-M) v4, read directly from the official public bulk directory (www.ncei.noaa.gov/pub/data/ghcn/v4/, the ghcnm.tavg.latest.qcu bundle). One record per station-year row of the GHCN-M .dat file, carried source-native and verbatim — the 12 monthly values and their per-month DMFLAG / QCFLAG / DSFLAG exactly as NOAA publishes them. The record key is station_id + year.

Output is one station_year_temperature_record row per result; billing is pay-per-event, the value event being one station-year temperature record (a $0.02 start fee per run, then $0.02 per station-year temperature record). Source: www.ncei.noaa.gov.

No login, no API key and no CAPTCHA solving are involved: the source is read logged-out with an identified contact User-Agent.

📊 Sample Output

Real rows from run MVnBeamRmxUZDWRWS on build 0.1.5 (2026-09-19), the same input as the Quick start below — every value is as the source published it (emails masked, long text shortened):

station_id	country_code	country_name	latitude	longitude	station_name
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO
AR000000011	AR	Argentina	-30.2670	-57.6500	MONTE_CASEROS_AERO

The run finished with the status message: NORMAL: 50 unique rows (0 duplicates collapsed) from ghcnm.tavg.v4.0.1.20260917.qcu.dat. billable=50

✅ What you get

Each row is flat JSON with these fields (from the dataset schema and the sample run; a field the source does not publish for a given row is null):

record_type (string/null) — e.g. station_year_temperature_record
station_id (string/null) — e.g. AR000000011
country_code (string/null) — e.g. AR
country_name (string/null) — e.g. Argentina
year (string/null) — e.g. 1961
element (string/null) — e.g. TAVG
latitude (string/null) — e.g. -30.2670
longitude (string/null) — e.g. -57.6500
elevation (string/null) — e.g. 53.0
station_name (string/null) — e.g. MONTE_CASEROS_AERO
m01_value (string/null) — e.g. 2720
m01_dmflag (string/null) — null in every sample row
m01_qcflag (string/null) — null in every sample row
m01_dsflag (string/null) — e.g. {
m01_missing (boolean/null) — e.g. False
m02_value (string/null) — e.g. 2450
m02_dmflag (string/null) — null in every sample row
m02_qcflag (string/null) — null in every sample row
m02_dsflag (string/null) — e.g. {
m02_missing (boolean/null) — e.g. False
m03_value (string/null) — e.g. 2200
m03_dmflag (string/null) — null in every sample row
m03_qcflag (string/null) — null in every sample row
m03_dsflag (string/null) — e.g. {
m03_missing (boolean/null) — e.g. False
m04_value (string/null) — e.g. 1820
m04_dmflag (string/null) — null in every sample row
m04_qcflag (string/null) — null in every sample row
m04_dsflag (string/null) — e.g. {
m04_missing (boolean/null) — e.g. False
m05_value (string/null) — e.g. 1770
m05_dmflag (string/null) — null in every sample row
m05_qcflag (string/null) — null in every sample row
m05_dsflag (string/null) — e.g. {
m05_missing (boolean/null) — e.g. False
m06_value (string/null) — e.g. 1370
m06_dmflag (string/null) — null in every sample row
m06_qcflag (string/null) — null in every sample row
m06_dsflag (string/null) — e.g. {
m06_missing (boolean/null) — e.g. False
m07_value (string/null) — e.g. 1330
m07_dmflag (string/null) — null in every sample row
m07_qcflag (string/null) — null in every sample row
m07_dsflag (string/null) — e.g. {
m07_missing (boolean/null) — e.g. False
m08_value (string/null) — e.g. 1720
m08_dmflag (string/null) — null in every sample row
m08_qcflag (string/null) — null in every sample row
m08_dsflag (string/null) — e.g. {
m08_missing (boolean/null) — e.g. False
m09_value (string/null) — e.g. 1660
m09_dmflag (string/null) — null in every sample row
m09_qcflag (string/null) — null in every sample row
m09_dsflag (string/null) — e.g. {
m09_missing (boolean/null) — e.g. False
m10_value (string/null) — e.g. 2100
m10_dmflag (string/null) — null in every sample row
m10_qcflag (string/null) — null in every sample row
m10_dsflag (string/null) — e.g. {
m10_missing (boolean/null) — e.g. False
m11_value (string/null) — e.g. 2290
m11_dmflag (string/null) — null in every sample row
m11_qcflag (string/null) — null in every sample row
m11_dsflag (string/null) — e.g. {
m11_missing (boolean/null) — e.g. False
m12_value (string/null) — e.g. 2440
m12_dmflag (string/null) — null in every sample row
m12_qcflag (string/null) — null in every sample row
m12_dsflag (string/null) — e.g. {
m12_missing (boolean/null) — e.g. False
provenance (string/null) — e.g. NOAA/NCEI GHCN-Monthly v4 (.dat), station-year row; values and QC flags carried
derived_data_note (string/null) — e.g. QCU = quality-controlled UNADJUSTED; raw monthly values and per-month DMFLAG/QCF
source_url (string/null) — e.g. https://www.ncei.noaa.gov/pub/data/ghcn/v4/ghcnm.tavg.latest.qcu.tar.gz
record_key (string/null) — e.g. AR000000011|1961|TAVG
source (string/null) — e.g. noaa-ghcn-monthly-temperature
release_date (string/null) — e.g. ghcnm.tavg.v4.0.1.20260917.qcu.dat
observed_at (string/null) — e.g. 2026-09-19T04:51:38Z
license (string/null) — e.g. U.S. NOAA National Centers for Environmental Information (NCEI), Global Historic
attribution (string/null) — e.g. NOAA National Centers for Environmental Information (NCEI) — GHCN-Monthly v4 (ht
release_checksum (string/null) — null in every sample row
terminal (string/null) — null in every sample row
scope (string/null) — null in every sample row
note (string/null) — null in every sample row
reported_total (string/integer/null) — null in every sample row
raw_seen (string/integer/null) — null in every sample row
duplicates (string/integer/null) — null in every sample row
unique_found (string/integer/null) — null in every sample row
delivered (string/integer/null) — null in every sample row

✅ What you get

record_type (string/null) — e.g. station_year_temperature_record
station_id (string/null) — e.g. AR000000011
country_code (string/null) — e.g. AR
country_name (string/null) — e.g. Argentina
year (string/null) — e.g. 1961
element (string/null) — e.g. TAVG
latitude (string/null) — e.g. -30.2670
longitude (string/null) — e.g. -57.6500
elevation (string/null) — e.g. 53.0
station_name (string/null) — e.g. MONTE_CASEROS_AERO
m01_value (string/null) — e.g. 2720
m01_dmflag (string/null) — null in every sample row
m01_qcflag (string/null) — null in every sample row
m01_dsflag (string/null) — e.g. {
m01_missing (boolean/null) — e.g. False
m02_value (string/null) — e.g. 2450
m02_dmflag (string/null) — null in every sample row
m02_qcflag (string/null) — null in every sample row
m02_dsflag (string/null) — e.g. {
m02_missing (boolean/null) — e.g. False
m03_value (string/null) — e.g. 2200
m03_dmflag (string/null) — null in every sample row
m03_qcflag (string/null) — null in every sample row
m03_dsflag (string/null) — e.g. {
m03_missing (boolean/null) — e.g. False
m04_value (string/null) — e.g. 1820
m04_dmflag (string/null) — null in every sample row
m04_qcflag (string/null) — null in every sample row
m04_dsflag (string/null) — e.g. {
m04_missing (boolean/null) — e.g. False
m05_value (string/null) — e.g. 1770
m05_dmflag (string/null) — null in every sample row
m05_qcflag (string/null) — null in every sample row
m05_dsflag (string/null) — e.g. {
m05_missing (boolean/null) — e.g. False
m06_value (string/null) — e.g. 1370
m06_dmflag (string/null) — null in every sample row
m06_qcflag (string/null) — null in every sample row
m06_dsflag (string/null) — e.g. {
m06_missing (boolean/null) — e.g. False
m07_value (string/null) — e.g. 1330
m07_dmflag (string/null) — null in every sample row
m07_qcflag (string/null) — null in every sample row
m07_dsflag (string/null) — e.g. {
m07_missing (boolean/null) — e.g. False
m08_value (string/null) — e.g. 1720
m08_dmflag (string/null) — null in every sample row
m08_qcflag (string/null) — null in every sample row
m08_dsflag (string/null) — e.g. {
m08_missing (boolean/null) — e.g. False
m09_value (string/null) — e.g. 1660
m09_dmflag (string/null) — null in every sample row
m09_qcflag (string/null) — null in every sample row
m09_dsflag (string/null) — e.g. {
m09_missing (boolean/null) — e.g. False
m10_value (string/null) — e.g. 2100
m10_dmflag (string/null) — null in every sample row
m10_qcflag (string/null) — null in every sample row
m10_dsflag (string/null) — e.g. {
m10_missing (boolean/null) — e.g. False
m11_value (string/null) — e.g. 2290
m11_dmflag (string/null) — null in every sample row
m11_qcflag (string/null) — null in every sample row
m11_dsflag (string/null) — e.g. {
m11_missing (boolean/null) — e.g. False
m12_value (string/null) — e.g. 2440
m12_dmflag (string/null) — null in every sample row
m12_qcflag (string/null) — null in every sample row
m12_dsflag (string/null) — e.g. {
m12_missing (boolean/null) — e.g. False
provenance (string/null) — e.g. NOAA/NCEI GHCN-Monthly v4 (.dat), station-year row; values and QC flags carried
derived_data_note (string/null) — e.g. QCU = quality-controlled UNADJUSTED; raw monthly values and per-month DMFLAG/QCF
source_url (string/null) — e.g. https://www.ncei.noaa.gov/pub/data/ghcn/v4/ghcnm.tavg.latest.qcu.tar.gz
record_key (string/null) — e.g. AR000000011|1961|TAVG
source (string/null) — e.g. noaa-ghcn-monthly-temperature
release_date (string/null) — e.g. ghcnm.tavg.v4.0.1.20260917.qcu.dat
observed_at (string/null) — e.g. 2026-09-19T04:26:26Z
license (string/null) — e.g. U.S. NOAA National Centers for Environmental Information (NCEI), Global Historic
attribution (string/null) — e.g. NOAA National Centers for Environmental Information (NCEI) — GHCN-Monthly v4 (ht
release_checksum (string/null) — null in every sample row
terminal (string/null) — null in every sample row
scope (string/null) — null in every sample row
note (string/null) — null in every sample row
reported_total (string/integer/null) — null in every sample row
raw_seen (string/integer/null) — null in every sample row
duplicates (string/integer/null) — null in every sample row
unique_found (string/integer/null) — null in every sample row
delivered (string/integer/null) — null in every sample row

What you get

One station_year_temperature_record per .dat line: station_id, country_code + country_name (resolved from the station-ID prefix via ghcnm-countries.txt), station geography (latitude, longitude, elevation, station_name — enriched from the bundle's .inv inventory), year, element (TAVG), and for each month m01…m12: m##_value, m##_dmflag, m##_qcflag, m##_dsflag, m##_missing. Plus provenance, derived_data_note, source_url, record_key. An element canary (TAVG) fails loud on bundle drift.

⚙️ Sample inputs

1. Quick start — the Store example (this is what the sample above came from)

{
  "countries": [
    "AR"
  ],
  "maxRecords": 50
}

The sample run charged exactly: 2 × $0.02 apify-actor-start + 50 × $0.02 station_year_temperature_record = $1.04 on the Free tier — every delivered row was billed.

2. A smaller, narrowed run

{
  "countries": [
    "AR"
  ],
  "maxRecords": 5
}

Caps the run at 5 rows — about $0.12 on the Free tier ($0.02 start + 5 × $0.02).

3. A full-size run

{
  "countries": [
    "AR"
  ],
  "maxRecords": 2000
}

Up to 2000 rows (the schema default for maxRecords) — about $40.02 on the Free tier ($0.02 start + 2000 × $0.02) if the source has that many.

4. A filtered run

{
  "countries": [
    "AR"
  ],
  "maxRecords": 50,
  "fieldMatch": {
    "country_name": "Argentina"
  }
}

Keeps only rows whose country_name contains Argentina (a value from the sample run above), matched case-insensitively after the source is read and before the maxRecords cap, so the cap counts matches and only delivered matches are billed. searchText does the same across every column. Zero matches end the run as EMPTY_SELECTION with no row charged — if the source has moved on since that sample run, pick a value you see in your own first run. The source is still read in full — this narrows what you receive and pay for, not what the source serves.

🧾 JSON sample record

One real record from run MVnBeamRmxUZDWRWS, exactly as it lands in the dataset (emails masked, long text shortened):

{
  "record_type": "station_year_temperature_record",
  "station_id": "AR000000011",
  "country_code": "AR",
  "country_name": "Argentina",
  "year": "1961",
  "element": "TAVG",
  "latitude": "-30.2670",
  "longitude": "-57.6500",
  "elevation": "53.0",
  "station_name": "MONTE_CASEROS_AERO",
  "m01_value": "2720",
  "m01_dmflag": null,
  "m01_qcflag": null,
  "m01_dsflag": "{",
  "m01_missing": false,
  "m02_value": "2450",
  "m02_dmflag": null,
  "m02_qcflag": null,
  "m02_dsflag": "{",
  "m02_missing": false,
  "m03_value": "2200",
  "m03_dmflag": null,
  "m03_qcflag": null,
  "m03_dsflag": "{",
  "m03_missing": false,
  "m04_value": "1820",
  "m04_dmflag": null,
  "m04_qcflag": null,
  "m04_dsflag": "{",
  "m04_missing": false,
  "m05_value": "1770",
  "m05_dmflag": null,
  "m05_qcflag": null,
  "m05_dsflag": "{",
  "m05_missing": false,
  "m06_value": "1370",
  "m06_dmflag": null,
  "m06_qcflag": null,
  "m06_dsflag": "{",
  "m06_missing": false,
  "m07_value": "1330",
  "m07_dmflag": null,
  "m07_qcflag": null,
  "m07_dsflag": "{",
  "m07_missing": false,
  "m08_value": "1720",
  "m08_dmflag": null,
  "m08_qcflag": null,
  "m08_dsflag": "{",
  "m08_missing": false,
  "m09_value": "1660",
  "m09_dmflag": null,
  "m09_qcflag": null,
  "m09_dsflag": "{",
  "m09_missing": false,
  "m10_value": "2100",
  "m10_dmflag": null,
  "m10_qcflag": null,
  "m10_dsflag": "{",
  "m10_missing": false,
  "m11_value": "2290",
  "m11_dmflag": null,
  "m11_qcflag": null,
  "m11_dsflag": "{",
  "m11_missing": false,
  "m12_value": "2440",
  "m12_dmflag": null,
  "m12_qcflag": null,
  "m12_dsflag": "{",
  "m12_missing": false,
  "provenance": "NOAA/NCEI GHCN-Monthly v4 (.dat), station-year row; values and QC flags carried source-native and unmodified",
  "derived_data_note": "QCU = quality-controlled UNADJUSTED; raw monthly values and per-month DMFLAG/QCFLAG/DSFLAG are carried VERBATIM. This actor NEVER computes a trend, anomaly, normal, homogenization/bias adjustment or forecast, and never bills a derived clima",
  "source_url": "https://www.ncei.noaa.gov/pub/data/ghcn/v4/ghcnm.tavg.latest.qcu.tar.gz",
  "record_key": "AR000000011|1961|TAVG",
  "source": "noaa-ghcn-monthly-temperature",
  "release_date": "ghcnm.tavg.v4.0.1.20260917.qcu.dat",
  "observed_at": "2026-09-19T04:51:38Z",
  "license": "U.S. NOAA National Centers for Environmental Information (NCEI), Global Historical Climatology Network - Monthly (GHCN-M) v4 — U.S. Government work, public domain. Acknowledgement: data from NOAA/NCEI. No implied NOAA endorsement. Raw value",
  "attribution": "NOAA National Centers for Environmental Information (NCEI) — GHCN-Monthly v4 (https://www.ncei.noaa.gov/pub/data/ghcn/v4/)"
}

🔧 How it works

Source. The actor reads www.ncei.noaa.gov — endpoints: https://www.ncei.noaa.gov/pub/data/ghcn/v4/. Public pages and feeds only; nothing behind a login.

Transport. Plain HTTPS from the Apify platform, no proxy. robots.txt is read first and a disallowed path is never fetched. Every request carries an identified contact User-Agent. Pacing: CRAWL_DELAY=0.0.

Terminal states. A run ends NORMAL, CAPPED (your cap was reached), PARTIAL (something was withheld and the message says what), GENUINE_EMPTY (the source was read and truly had nothing in scope) or BLOCKED (the source refused or changed shape — the run FAILS loud and bills nothing). A zero-row run is never reported as a silent success.

Charging. Each station-year temperature record is charged at the moment it is pushed (station_year_temperature_record); a row that fails to charge is not delivered, so the dataset count always equals the charged count.

🔧 How it works

QCU = quality-controlled UNADJUSTED — no derived climate figures

This actor serves the QCU (unadjusted) bundle and carries the raw monthly values and QC flags exactly as published. It never computes a trend, anomaly, normal, homogenization/bias adjustment, or forecast, and never bills a derived climate figure as source data. A derived_data_note states this on every record. Missing months (source value -9999) are carried verbatim and flagged (m##_missing), never fabricated.

Filters (source monster — 1,495,274 station-year lines)

The .dat file is never emitted in full. Supply at least one narrowing filter — stationIds, countries, years, yearFrom, or yearTo. A conservative default cap (2000; hard cap 5000) applies, and a maxScanRows bound protects the run. Note: the file is sorted by station ID, so late-alphabet countries (e.g. US) sit deep in the file — raise maxScanRows to reach them.

Robots & door

www.ncei.noaa.gov/robots.txt disallows /data* and /orders*. This actor fetches only under /pub/data/ (which is not under /data* and is permitted) and never anything else. Robots is re-read every run and the actor STOPs (fails, charges nothing) on any real Disallow covering the path. NOAA/NCEI acknowledgement; no implied NOAA endorsement.

Source & licence

U.S. NOAA National Centers for Environmental Information (NCEI), GHCN-Monthly v4 — U.S. Government work, public domain. Acknowledgement: data from NOAA/NCEI (https://www.ncei.noaa.gov/pub/data/ghcn/v4/).

What is not done. No login, no cookie or CAPTCHA bypass, no private or personal-account data, no browser automation.

💰 Pricing example

Event	Free	Bronze	Silver	Gold
Actor Start (`apify-actor-start`)	$0.02	$0.02	$0.02	$0.02
Station-year temperature record (`station_year_temperature_record`)	$0.02	$0.018	$0.016	$0.0134

Worked at the live Free-tier price:

8 station-year temperature records: $0.02 start + 8 × $0.02 = $0.18
25 station-year temperature records: $0.02 start + 25 × $0.02 = $0.52
2000 station-year temperature records: $0.02 start + 2000 × $0.02 = $40.02

A run that delivers zero rows charges the $0.02 start fee only. A BLOCKED run (source refused) fails loud and charges no value event. The start fee is charged once per GB of run memory; the default run memory is 4096 MB.

Yield on the sample run: NORMAL: 50 unique rows (0 duplicates collapsed) from ghcnm.tavg.v4.0.1.20260917.qcu.dat. billable=50. maxRecords is a hard ceiling on what is delivered and billed, never a target.

⚖️ Legal & ToS

This actor reads public, logged-out pages and feeds published by www.ncei.noaa.gov. It collects only what the source publishes to any visitor, keeps to the source's robots rules (checked on every run), identifies itself with a contact User-Agent, and does not access accounts, private data or anything behind authentication. Use the output in line with the source's terms and your local law; the intended use is B2B research and monitoring.

❓ FAQ

Q: Do I need an API key or a login?
A: No. The source (www.ncei.noaa.gov) is read logged-out; the input schema has no key field and the actor carries no secrets.

Q: Why did my run return 0 rows?
A: Read the run's status message. GENUINE_EMPTY means the source was read and had nothing in scope for your input; BLOCKED means the source refused and the run failed without billing a value event — retry later or narrow the input. A zero-row run bills the start fee only.

Q: How many rows can one run return?
A: Up to maxRecords (default 2000). Raise the cap for a bigger run; you pay per delivered row.

Q: How fresh is the data?
A: Every run reads the source live at run time; nothing is cached between runs. Put it on a schedule for a continuous feed.

Q: What formats can I export?
A: The dataset downloads as JSON, CSV, Excel, XML or RSS from the run's Dataset tab or the Apify API, and any run can push to a webhook or integration.

Q: How is this different from the other NexGen Watch actors actors?
A: Same output shape and billing model; this one covers www.ncei.noaa.gov. The siblings under Related Actors cover the other sources or slices — run several on one schedule for a combined feed.

Q: Are there rate limits?
A: The actor paces itself against the source (CRAWL_DELAY=0.0) and honours its robots rules; there is no per-buyer limit beyond your Apify plan's concurrency.

🆘 Troubleshooting

Run FAILED with BLOCKED → the source refused the request or changed its page shape → nothing was billed beyond the start fee; retry after a while, and if it persists open an Issue with the run id.
Status says CAPPED → your cap (maxRecords) was reached → raise it for a bigger run.
Input validation error on start → a field is outside the schema's allowed values → start from the Quick start block and change one field at a time.
Run TIMED-OUT → a very wide request on a slow day → raise the run timeout in Run options or narrow the input; what was delivered before the timeout is still in the dataset.

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NOAA GHCN-Daily Observations — Structured daily climate observations from the U.S. NOAA/NCEI Global Historical Climatology Network - Daily (GHCN-Daily), read directly from the offi…
NOAA ISD Hourly Surface Observations — Structured hourly surface weather observations from the U.S. NOAA/NCEI Integrated Surface Database (ISD), read directly from the official public bulk…
NOAA Storm Damage & Impact Feed — A deterministic impact subset of the U.S. NOAA/NCEI Storm Events Database details file — only storm events with a published impact are emitted
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🏢 About NexGenData — NexGen Watch is NexGenData's fleet of more than 300 public data actors built on official sources, pay-per-result. Browse the catalog at apify.com/nexgenwatch.

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Menlo Ventures Portfolio Jobs Scraper

nexgenwatch/menlo-portfolio-hiring

Clean, structured job openings across the Menlo Ventures portfolio — one record per job, from the portfolio's public jobs board.

NexGen Watch

NOAA Tides & Water Level Predictions API

koalastuff/noaa-tides-water-level-monitor

Retrieve bounded NOAA CO-OPS tide predictions and water-level observations by station, date, datum, and interval for maritime and coastal data workflows.

Timo Schmidt

BambooHR Jobs Scraper - Any Employer Board

nexgenwatch/bamboohr-job-feed

Scrape job postings from any BambooHR careers board: title, location, department, posting date and apply link. One record per result. Pay per result.

NexGen Watch

Initialized Capital Portfolio Companies Scraper

nexgenwatch/initialized-portfolio-companies

One structured record per company in the Initialized Capital portfolio, read from the fund's own portfolio page (https://initialized.com/companies). Public, logged-out, robots-honoured.

NexGen Watch

USDA FDC Portion / Serving Weight Master

nexgenwatch/usda-fdc-portion-serving-weight-master

One food_portion_record per row of USDA FoodData Central's food_portion table — a food's serving/portion definition: portion ID, food FDC ID, serving quantity (amount), gram weight, measure unit, sequence, portion description and modifier.

NexGen Watch

NOAA Storm Events Locations

nexgenwatch/noaa-storm-locations

Structured storm-event location points from the U.S. NOAA/NCEI Storm Events Database, read directly from the official public bulk directory (www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/).

NexGen Watch

UK Legislation Section Retrieval — legislation.gov.uk

nexgenwatch/uk-legislation-section-retrieval

Retrieve individual sections of UK legislation from the official legislation.gov.uk service.

NexGen Watch

Insight Partners Portfolio Jobs Scraper

nexgenwatch/insight-portfolio-hiring

Clean, structured job openings across the Insight Partners portfolio — one record per job, from the portfolio's public jobs board.

NexGen Watch

EIA-861 Advanced Metering (AMR / AMI / Standard)

nexgenwatch/eia861-advanced-metering

Structured advanced-meter technology adoption records from the U.S. EIA Form EIA-861 Annual Electric Power Industry Report, read directly from the official current-year ZIP on www.eia.gov.

NexGen Watch

EIA-923 Non-Utility Source & Disposition (Annual Release)

nexgenwatch/eia923-nonutility-source-disposition

Structured electricity source-and-disposition flow ledger records from the U.S. EIA Form EIA-923 Schedules 6/7 Source and Disposition sheet, read directly from the official current-year early-release ZIP on www.eia.gov.

NexGen Watch