---
title: "nsf.gov in the Machine Relations Index"
description: "nsf.gov appeared as a cited source in 4 of 37,851 monitored answer runs (0.01%) from 2026-05-10 to 2026-11-09."
canonical: "https://machinerelations.ai/index/domains/nsf.gov"
---

# nsf.gov in the Machine Relations Index

Canonical URL: https://machinerelations.ai/index/domains/nsf.gov
Canonical domain: nsf.gov
Source role: Academic and government source
Release mri_score_v2.1+2026-11-09+d7ef5411c915; methodology mri_score_v2.1; generated 2026-11-09; window 2026-05-10 to 2026-11-09; artifact 6cd5e904f35f.

nsf.gov appeared as a cited source in 4 of 37,851 monitored answer runs (0.01%) from 2026-05-10 to 2026-11-09.

## Overall Evidence

- Citation rate: 0.01%
- Cited runs: 4 of 37,851
- Days cited: 4 of 177
- Engine breadth: 2 observed engines (claude, perplexity)
- Confidence: Collecting
- Standing: Collecting: no rank until it clears the evidence floor

## Observed Segments

| Category | Question shape | State | Cited runs | Observed runs | Run dates | Rate | Standing |
|---|---|---|---:|---:|---:|---:|---|
| [AI Infrastructure](https://machinerelations.ai/index/categories/ai-infrastructure) | [Problem-first research](https://machinerelations.ai/index/categories/ai-infrastructure/problem_first) | published | 3 | 299 | 14 | 1.00% | #106 of 476 |
| [Deep Tech & Hardware](https://machinerelations.ai/index/categories/deep-tech-hardware) | [How buyers choose](https://machinerelations.ai/index/categories/deep-tech-hardware/how_choose) | published | 1 | 272 | 13 | 0.37% | #233 of 287 |

## Citation History

nsf.gov was first cited in a monitored answer run on September 30, 2026, and most recently on October 31, 2026, with cited runs in 2 of the 3 months from then to this release. The release window opens 2026-05-10, so an earlier citation would not show here.

A month's rate is the share of all monitored answer runs that month that cited this domain. The latest month is still collecting.

| Month | Cited runs | Observed runs | Rate |
|---|---:|---:|---:|
| September 2026 | 1 | 4,971 | 0.02% |
| October 2026 | 3 | 16,496 | 0.02% |
| November 2026 (to 2026-11-09) | 0 | 3,368 | 0.00% |

## By Answer Engine

An engine's rate is its cited runs over every monitored run that engine answered in the window. Engines answer different numbers of runs, so compare rates, not counts.

| Engine | Cited runs | Engine's observed runs | Rate |
|---|---:|---:|---:|
| [Perplexity](https://machinerelations.ai/index/engines/perplexity) | 3 | 9,075 | 0.03% |
| [Claude](https://machinerelations.ai/index/engines/claude) | 1 | 8,995 | 0.01% |
| [ChatGPT](https://machinerelations.ai/index/engines/chatgpt) | 0 | 8,956 | 0.00% |
| [Gemini](https://machinerelations.ai/index/engines/gemini) | 0 | 7,316 | 0.00% |
| [Google AI Mode](https://machinerelations.ai/index/engines/google_ai_mode) | 0 | 2,172 | 0.00% |
| [Google AI Overviews](https://machinerelations.ai/index/engines/google_ai_overviews) | 0 | 1,337 | 0.00% |

Since August 31, 2026, nsf.gov is cited in none of 966 observed Google AI Mode runs. Google AI Mode's collection changed between August 10, 2026 and August 30, 2026, so its row above pools both sides of the change.

Since October 7, 2026, nsf.gov is cited in 3 of 4,306 observed Perplexity runs (0.07%). Perplexity's collection changed between October 6, 2026 and October 7, 2026, so its row above pools both sides of the change.

## Peers

nsf.gov holds its best segment rank, #106 of 476, in AI Infrastructure / Problem-first research, at 1.00%. 36 sources on that leaderboard share that rate, and their order inside the tie is not a measured difference. Listed here: the nearest sources cited more and less often than it, and the sources beside it that share this best segment. Leaderboard: https://machinerelations.ai/index/categories/ai-infrastructure/problem_first

| Rank | Source | Source role | Segment citation rate | Against this source |
|---|---|---|---:|---|
| #75 of 476 | [knowledgesdk.com](https://machinerelations.ai/index/domains/knowledgesdk.com) | Other observed source | 1.34% | +0.34 pts |
| #76 of 476 | [mastra.ai](https://machinerelations.ai/index/domains/mastra.ai) | Other observed source | 1.34% | +0.34 pts |
| #77 of 476 | [mesour.com](https://machinerelations.ai/index/domains/mesour.com) | Other observed source | 1.34% | +0.34 pts |
| #105 of 476 | [northeastern.edu](https://machinerelations.ai/index/domains/northeastern.edu) | Academic and government source | 1.00% | Same rate |
| #107 of 476 | [opsresolve.com](https://machinerelations.ai/index/domains/opsresolve.com) | Other observed source | 1.00% | Same rate |
| #165 of 476 | [Impressive Magazine](https://machinerelations.ai/index/domains/impressivemagazine.com) | Editorial publication | 0.67% | -0.33 pts |
| #166 of 476 | [infoq.com](https://machinerelations.ai/index/domains/infoq.com) | Other observed source | 0.67% | -0.33 pts |
| #167 of 476 | [luminaryera.com](https://machinerelations.ai/index/domains/luminaryera.com) | Other observed source | 0.67% | -0.33 pts |

## Citation

Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai. nsf.gov, 0.01% citation rate, 4 of 37,851 monitored answer runs, 2026-05-10 to 2026-11-09. Release mri_score_v2.1+2026-11-09+d7ef5411c915. https://machinerelations.ai/index/domains/nsf.gov. CC BY 4.0.

APA

Parrott, J. (2026). nsf.gov in the Machine Relations Index (Version mri_score_v2.1+2026-11-09+d7ef5411c915) [Data set]. Machine Relations Research. https://machinerelations.ai/index/domains/nsf.gov https://doi.org/10.5281/zenodo.23652423

BibTeX

```bibtex
@misc{parrott2026mrinsfgov,
  author = {Parrott, Jaxon},
  title = {nsf.gov in the Machine Relations Index},
  year = {2026},
  version = {mri\_score\_v2.1+2026-11-09+d7ef5411c915},
  publisher = {Machine Relations Research},
  url = {https://machinerelations.ai/index/domains/nsf.gov},
  doi = {10.5281/zenodo.23652423},
  license = {CC-BY-4.0},
  note = {Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai}
}
```

DOI: https://doi.org/10.5281/zenodo.23652423

Citation file: https://machinerelations.ai/CITATION.cff

## Badge

Badge image: https://machinerelations.ai/index/domains/nsf.gov/badge.svg

[![nsf.gov in the Machine Relations Index: cited in 0.01% of monitored AI answers (4 of 37,851 runs), Nov 9, 2026 release. Machine Relations Research.](https://machinerelations.ai/index/domains/nsf.gov/badge.svg)](https://machinerelations.ai/index/domains/nsf.gov)

Put this badge on your site to show how often AI answers cite it. It links to this profile and updates with every new release; if a later release no longer includes this domain, the badge stops loading. To keep today's numbers fixed, save the SVG and host it yourself.

## Use the data

The public JSON is https://machinerelations.ai/data/machine-relations-index.json. The release manifest is https://machinerelations.ai/data/mri-release-manifest.json. The domain CSV is https://huggingface.co/datasets/machine-relations/machine-relations-index/resolve/main/domains.csv. The Zenodo concept DOI is https://doi.org/10.5281/zenodo.23652423.

```bash
curl -fsSL "https://machinerelations.ai/data/machine-relations-index.json" -o machine-relations-index.json
curl -fsSL "https://machinerelations.ai/data/mri-release-manifest.json" -o mri-release-manifest.json
curl -fsSL "https://huggingface.co/datasets/machine-relations/machine-relations-index/resolve/main/domains.csv" -o domains.csv
curl -fsSL -L "https://doi.org/10.5281/zenodo.23652423" -o mri-zenodo.html
```

```python
import json
from urllib.request import urlopen

index_url = "https://machinerelations.ai/data/machine-relations-index.json"
with urlopen(index_url) as response:
    index = json.load(response)
print(index["generated"], index["total_domains"])
```

## Public Boundary

The public dataset reports citation rates, rankings, and evidence counts. It excludes internal query identifiers, raw cited URLs, and answer-engine provider payloads. MRI measures observed root-domain citations in monitored prompts; it does not measure all AI answers, recommendation quality, website traffic, or commercial performance. A citation does not prove source support, and MRI confidence is not an Answer-Source Fidelity grade.

## Related Sources

- [Machine Relations Index](https://machinerelations.ai/index)
- [Public JSON artifact](https://machinerelations.ai/data/machine-relations-index.json)
- [Release manifest](https://machinerelations.ai/data/mri-release-manifest.json)