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NIH RePORTER Grant Award Delta

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from $100.50 / 1,000 award deltas

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NIH RePORTER Grant Award Delta

NIH RePORTER Grant Award Delta

NIH awards as they are noticed: new awards and changes to award amount, keyed on the source-native project number.

Pricing

from $100.50 / 1,000 award deltas

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NexGen Watch

NexGen Watch

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🎓 NIH RePORTER Grant Award Delta

NIH awards as they are noticed: new awards and changes to award amount, keyed on the source-native project number.

Output is one award_record row per result; billing is pay-per-event, the value event being one award delta (a $0.02 start fee per run, then $0.15 per award delta plus one $0.10 source check per run that reads the source). Source: api.reporter.nih.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

NIH RePORTER Grant Award Delta sample output — a table of real award delta rows (project_num, project_title, organization, org_state) from run i2Z4sWNpf9iE3S5S7 on build 0.1.16

Real rows from run i2Z4sWNpf9iE3S5S7 on build 0.1.16 (2026-09-17), the same input as the Quick start below — every value is as the source published it (emails masked, long text shortened):

project_numproject_titleorganizationorg_stateaward_amountaward_notice_date
5P50MH126231-05Center for Team Effectiveness to Accelerate EBP Implementation in ChilUNIVERSITY OF CALIFORNIA, SAN DIEGOCA6414472026-07-13
5P30AG021334-24Biological Mechanisms Core - RC2JOHNS HOPKINS UNIVERSITYMD1702762026-07-14
5T32AG058527-08Translational Aging Research Training ProgramJOHNS HOPKINS UNIVERSITYMD4715782026-07-09
5R01NS130876-04Identification of Regulatory Mechanisms Operating in Rare Pathogenic AUNIVERSITY OF CALIFORNIA, SAN FRANCISCOCA5737822026-07-10
5R01NS107671-07Therapeutic small molecule modulation of Kv channelsUNIVERSITY OF CALIFORNIA-IRVINECA4940452026-07-24
1R01HL179001-01A1Neural mechanisms for metabolic-ventilatory coupling during thermogeneUNIVERSITY OF VIRGINIAVA7601562026-07-06
5P20GM121322-09Evaluating the impact of TP53 mutation on the epigenetic therapy viralWEST VIRGINIA UNIVERSITYWV2513462026-07-08
1R01AG099681-01A1Targeting Novel Senescence-Associated Pathways to Combat Premature AgiNORTHWESTERN UNIVERSITYIL7946642026-07-17

The run finished with the status message: NORMAL: emitted 10 item(s)

✅ 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_key (string/null) — e.g. nih:5P50MH126231-05
  • project_num — e.g. 5P50MH126231-05
  • project_title — e.g. Center for Team Effectiveness to Accelerate EBP Implementation in Children's Men
  • organization — e.g. UNIVERSITY OF CALIFORNIA, SAN DIEGO
  • org_state — e.g. CA
  • award_amount — e.g. 641447
  • award_notice_date — e.g. 2026-07-13
  • fiscal_year — e.g. 2026
  • record_type (string) — e.g. award_record
  • delta_type (string/null) — null in every sample row

Every run also writes a RUN_RECEIPT record to its key-value store with the source checks it made and the counts it charged — diagnostics never land in the paid dataset.

⚙️ Sample inputs

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

{
"from_date": "2026-07-01",
"to_date": "2026-07-24",
"watch_mode": false,
"baseline_id": "default",
"max_items": 10
}

The sample run charged exactly: 1 × $0.02 apify-actor-start + 1 × $0.10 source-check + 0 × $0.15 award-delta = $0.12 on the Free tier — snapshot preview rows are delivered unbilled; only the source check and the start fee were charged.

2. A smaller, narrowed run

{
"from_date": "2026-07-01",
"to_date": "2026-07-24",
"watch_mode": false,
"baseline_id": "default",
"max_items": 5
}

Caps the run at 5 rows; same billing shape as the sample run above. from_date narrows what the source is asked for.

3. Watch mode on a schedule

{
"from_date": "2026-07-01",
"to_date": "2026-07-24",
"watch_mode": true,
"baseline_id": "default",
"max_items": 10
}

The first run seeds a private baseline and emits zero deltas; every later run emits one row per change, billed at $0.15 each plus the $0.10 source check; billed deltas are capped at 500 per run.

🧾 JSON sample record

One real record from run i2Z4sWNpf9iE3S5S7, exactly as it lands in the dataset (emails masked, long text shortened):

{
"record_key": "nih:5P50MH126231-05",
"project_num": "5P50MH126231-05",
"project_title": "Center for Team Effectiveness to Accelerate EBP Implementation in Children's Mental Health Services: Methods Core",
"organization": "UNIVERSITY OF CALIFORNIA, SAN DIEGO",
"org_state": "CA",
"award_amount": 641447,
"award_notice_date": "2026-07-13",
"fiscal_year": 2026,
"record_type": "award_record",
"schema_version": "1.0",
"output_mode": "preview",
"preview_cap": 25,
"preview_truncated": 0
}

🔧 How it works

Transport. Plain HTTPS from the Apify platform, no proxy. Every request carries an identified contact User-Agent.

Charging. Each award delta is charged at the moment it is pushed (award-delta); a row that fails to charge is not delivered, so the dataset count always equals the charged count. One source-check is charged per run that actually read the source.

Source

NIH RePORTER v2 project search (public, logged-out)

Public, logged-out only — no API key, login, cookie or CAPTCHA.

Snapshot mode is a PREVIEW

watch_mode: false returns at most 25 records, hard-capped in code regardless of max_items, labeled output_mode: "preview". This actor sells change detection, not bulk record export.

Watch semantics

The first watch run stores a private baseline and emits zero deltas. Later runs emit NEW / CHANGED / REMOVED keyed on the source-native id with before / after and changed_fields.

Terminal states

NORMAL · SUCCEEDED-0: GENUINE_EMPTY · SUCCEEDED-0 (no change) · PARTIAL · BLOCKED. A zero with no evidence FAILS loud rather than exiting 0 quietly.

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

⏰ Set it on a schedule

A single run is a snapshot. The value is the feed: open the actor in Apify Console → SchedulesCreate new, add this actor with input 3 above (watch_mode: true), and pick a cadence:

  • Daily0 7 * * * (07:00 UTC): catch changes within a day.
  • Weekly0 7 * * 1: a Monday digest.

The baseline persists in a named key-value store between runs, so every scheduled run compares against the last one; use a different baseline_id for independent watches. Schedules can also POST results to a webhook (Console → Integrations).

💰 Pricing example

EventFreeBronzeSilverGold
Actor Start (apify-actor-start)$0.02$0.02$0.02$0.02
Source check (source-check)$0.10$0.09$0.08$0.07
Award Delta (award-delta)$0.15$0.14$0.12$0.10

Worked at the live Free-tier price (watch mode, one delta per change):

  • 8 award deltas: $0.02 start + $0.10 source check + 8 × $0.15 = $1.32
  • 25 award deltas: $0.02 start + $0.10 source check + 25 × $0.15 = $3.87
  • 300 award deltas: $0.02 start + $0.10 source check + 300 × $0.15 = $45.12

A run that delivers zero rows charges the $0.02 start fee plus the $0.10 source check (the source was read and reported empty). 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 1024 MB.

Yield on the sample run: NORMAL: emitted 10 item(s). max_items is a hard ceiling on what is delivered and billed, never a target. Snapshot output is capped at 25 records regardless of the cap you set; watch mode is the full feed.

This actor reads public, logged-out pages and feeds published by api.reporter.nih.gov. It collects only what the source publishes to any visitor, 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 (api.reporter.nih.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 (in watch mode the first run always seeds the baseline and returns zero deltas); 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 plus one source check.

Q: How many rows can one run return?
A: Up to max_items (default 300); snapshot previews stop at 25. Raise the cap for a bigger run; you pay per delivered row.

Q: What does watch mode remember between runs?
A: A private baseline of source-native record ids in a named key-value store (baseline_id names it, so several independent watches can coexist). Each later run diffs the live source against it and emits only changes.

Q: How fresh is the data?
A: Every run reads the source live at run time; nothing is cached between runs except the watch baseline. 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 grant and research-funding watches actors?
A: Same output shape and billing model; this one covers api.reporter.nih.gov. The siblings under Related Actors cover the other sources or slices — run several on one schedule for a combined feed.

🆘 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 (max_items) 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.
  • First watch run shows no deltas → expected: it seeds the baseline; changes appear from the second run.
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  • 🏢 About NexGenData — NexGen Watch is NexGenData's fleet of 256 public monitoring and lookup actors built on official sources, pay-per-result. Browse the catalog at apify.com/nexgenwatch.

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