NOAA Global April Temperature Anomalies
1850–2025
Source, license and coverage
Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.
- License
- Public domain (supplier declaration)
- Source / creator
- NOAA NCEI — Climate at a Glance
- Collection method
- Supplier description: Monthly global land and ocean average temperature anomalies from 1850 to 2025, sourced directly from NOAA National Centers for Environmental Information (NCEI). Base period: 1901-2000 average. 176 annual data points showing the long-term global warming trend measured in degrees Celsius. Format: CSV, 2 columns (Year, Anomaly), 176 rows + header. Source: NOAA Climate at a Glance (https://www.ncei.noaa.gov). License: Public domain (US government data). Captured: 2026-04-13. Ideal for: climate trend analysis, time series modeling, regression tutorials, data visualization demos, and agent workflows that need authoritative temperature history.
- Coverage / snapshot
- 1850–2025
- Data last updated
- Not documented
- Update schedule
- Stored snapshot; no refresh schedule documented
NOAA source header identifies April average anomalies; the preview omits comment lines and retains the original Year,Value header. Rows are evenly spaced within the inspected input, subject to the preview byte budget; this is not a new random sample of the full dataset. Structured reuse terms reproduce the supplier’s explicit license declaration; upstream and third-party rights still apply. Coverage is stated by the supplier; it has not been verified against every record in the full file. Supplier-reported capture/snapshot date: 2026-04-13. This is not a verified source update date. The original file header identifies April average anomalies. The prior monthly/annual wording and comment-line sample header were incorrect.
Sample structure score: 100 / 100
This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.
Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.
| Check | Points | Evidence |
|---|---|---|
| Populated cells | 50 / 50 | 20 of 20 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated. |
| Consistent value types | 30 / 30 | 20 of 20 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth. |
| Consistent record shape | 20 / 20 | 10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys. |
Field-level findings and improvements
Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.
| Field | Missing cells | Most common type | Other populated types |
|---|---|---|---|
| Year | 0 / 10 | number | 0 / 10 |
| Anomaly | 0 / 10 | number | 0 / 10 |
About this data
Global land and ocean April average temperature anomalies relative to the 1901–2000 baseline, measured in degrees Celsius. Data spans yearly April observations from NOAA National Centers for Environmental Information (NCEI).
Retrieve with your agent or Python
Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.
Download the Python examplepython3 retrieve-dataset.py ebea06e3-70e8-4d4a-8dc5-4d4e513673b9 --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| Year | number | |
| Anomaly | number |
Sample Data
Preview a sample of the data before downloading.
Public sample only. Sign in to retrieve the full dataset, including free datasets.
For AI Agents
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
"mcpServers": {
"databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
}
}
# 2. Your agent can then call:
search_datasets({ query: "NOAA Global April Temperature " })
// Found: ebea06e3-70e8-4d4a-8dc5-4d4e513673b9
get_download_url({ dataset_id: "ebea06e3-70e8-4d4a-8dc5-4d4e513673b9" }) // free — sign in with MCP OAuth first# Free dataset — sign in or use your account API key: curl https://api.databazaar.io/datasets/ebea06e3-70e8-4d4a-8dc5-4d4e513673b9/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"