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:
https://api.openalex.org/funders?search =national+science+foundation & per_page = 3 & select = id,display_name,works_count,awards_count
[ { "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:
https://api.openalex.org/works?filter =awards.funder_id:F4320306076 & group_by = primary_topic.field.id & per_page = 10
[ { "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:
https://api.openalex.org/works?filter =awards.funder_id:F4320306076,publication_year:2015-2025 & group_by = publication_year
[ { "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:
# 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:
Field 2015–17 2023–25 Change Engineering 16.9% 18.0% +1.1% Physics and Astronomy 13.9% 15.4% +1.5% Computer Science 11.9% 12.1% +0.2% Environmental Science 9.1% 8.3% -0.8% Materials Science 8.2% 7.1% -1.1% Biochemistry / Mol. Bio. 8.2% 7.4% -0.8% Earth and Planetary Sciences 6.7% 5.8% -0.9% Mathematics 3.1% 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:
https://api.openalex.org/works?filter =awards.funder_id:F4320306076,primary_topic.field.id:22 & group_by = authorships.institutions.id & per_page = 5
[ { "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.
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%} " )
