> ## Documentation Index > Fetch the complete documentation index at: https://developers.openalex.org/llms.txt > Use this file to discover all available pages before exploring further. # Analyze a Funder's Research Portfolio > Map what a funding agency invests in by field, track shifts over time, and find top recipient institutions What does a funder actually invest in? How has their portfolio shifted over the past decade? This recipe maps a funder's research output by field, detects shifting priorities, and identifies the top institutions receiving funding. We'll use the US National Science Foundation as the example. ## Step 1: Find the funder Search by name to get the funder ID: ```bash theme={"dark"} https://api.openalex.org/funders?search=national+science+foundation&per_page=3&select=id,display_name,works_count,awards_count ``` ```json theme={"dark"} [ {"id": "https://openalex.org/F4320306076", "display_name": "National Science Foundation", "works_count": 1483062, "awards_count": 812094}, {"id": "https://openalex.org/F4320332161", "display_name": "National Natural Science Foundation of China", "works_count": 3567561, "awards_count": 840334} ] ``` NSF is `F4320306076` — 1.5M funded works and 812K awards. ## Step 2: Map the portfolio by field Group funded works by research field to see where the money goes: ```bash theme={"dark"} https://api.openalex.org/works?filter=awards.funder_id:F4320306076&group_by=primary_topic.field.id&per_page=10 ``` ```json theme={"dark"} [ {"key": "https://openalex.org/fields/22", "key_display_name": "Engineering", "count": 54753}, {"key": "https://openalex.org/fields/31", "key_display_name": "Physics and Astronomy", "count": 45894}, {"key": "https://openalex.org/fields/17", "key_display_name": "Computer Science", "count": 36559}, {"key": "https://openalex.org/fields/13", "key_display_name": "Biochemistry, Genetics and Molecular Biology", "count": 27962}, {"key": "https://openalex.org/fields/23", "key_display_name": "Environmental Science", "count": 27954} ] ``` ## Step 3: Track output over time Group by year to see overall funding trends: ```bash theme={"dark"} https://api.openalex.org/works?filter=awards.funder_id:F4320306076,publication_year:2015-2025&group_by=publication_year ``` ```json theme={"dark"} [ {"key": "2015", "key_display_name": "2015", "count": 19636}, {"key": "2018", "key_display_name": "2018", "count": 28959}, {"key": "2021", "key_display_name": "2021", "count": 30387}, {"key": "2023", "key_display_name": "2023", "count": 31144} ] ``` NSF-funded output grew from \~20K works in 2015 to \~31K in 2023. ## Step 4: Detect shifting priorities Run the same field breakdown for two time windows and compare the shares: ```bash theme={"dark"} # Recent (2023–2025): 75,728 funded works https://api.openalex.org/works?filter=awards.funder_id:F4320306076,publication_year:2023-2025&group_by=primary_topic.field.id&per_page=10 # Historical (2015–2017): 74,214 funded works https://api.openalex.org/works?filter=awards.funder_id:F4320306076,publication_year:2015-2017&group_by=primary_topic.field.id&per_page=10 ``` Award dollar amounts aren't available at this grouping level, so shares are by funded work count — still a strong signal of where a funder directs effort. Compare each field's share of total output across the two periods:
Field2015–172023–25Change
Engineering16.9%18.0%+1.1%
Physics and Astronomy13.9%15.4%+1.5%
Computer Science11.9%12.1%+0.2%
Environmental Science9.1%8.3%-0.8%
Materials Science8.2%7.1%-1.1%
Biochemistry / Mol. Bio.8.2%7.4%-0.8%
Earth and Planetary Sciences6.7%5.8%-0.9%
Mathematics3.1%3.8%+0.7%
Social Sciences—3.5%new
Physics and Engineering grew their share while Materials Science and Earth Sciences shrank. Social Sciences entered the top 10 — consistent with NSF's expanding investment in human-centered research. ## Step 5: Find top recipient institutions Drill into a specific field to see which institutions receive the most funding: ```bash theme={"dark"} https://api.openalex.org/works?filter=awards.funder_id:F4320306076,primary_topic.field.id:22&group_by=authorships.institutions.id&per_page=5 ``` ```json theme={"dark"} [ {"key": "https://openalex.org/I130701444", "key_display_name": "Georgia Institute of Technology", "count": 1902}, {"key": "https://openalex.org/I27837315", "key_display_name": "University of Michigan", "count": 1436}, {"key": "https://openalex.org/I219193219", "key_display_name": "Purdue University West Lafayette", "count": 1410}, {"key": "https://openalex.org/I157725225", "key_display_name": "University of Illinois Urbana-Champaign", "count": 1343}, {"key": "https://openalex.org/I63966007", "key_display_name": "Massachusetts Institute of Technology", "count": 1230} ] ``` ## Full script This script maps any funder's portfolio, computes field share shifts, and identifies top institutions per field. ```python theme={"dark"} import requests BASE = "https://api.openalex.org" FUNDER = "F4320306076" # NSF def api(endpoint, params): return requests.get(f"{BASE}/{endpoint}", params=params).json() # Portfolio by field — recent vs. historical recent = api("works", { "filter": f"awards.funder_id:{FUNDER},publication_year:2023-2025", "group_by": "primary_topic.field.id", "per_page": 15, }) historical = api("works", { "filter": f"awards.funder_id:{FUNDER},publication_year:2015-2017", "group_by": "primary_topic.field.id", "per_page": 15, }) recent_total = recent["meta"]["count"] historical_total = historical["meta"]["count"] recent_shares = {g["key_display_name"]: g["count"] / recent_total for g in recent["group_by"]} historical_shares = {g["key_display_name"]: g["count"] / historical_total for g in historical["group_by"]} all_fields = sorted(set(recent_shares) | set(historical_shares), key=lambda f: recent_shares.get(f, 0), reverse=True) print(f"{'Field':<45} {'2015–17':>8} {'2023–25':>8} {'Change':>8}") print("-" * 71) for field in all_fields: old = historical_shares.get(field, 0) new = recent_shares.get(field, 0) delta = new - old sign = "+" if delta > 0 else "" print(f" {field:<43} {old:>7.1%} {new:>7.1%} {sign}{delta:>6.1%}") ```