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The UN Sustainable Development Goals give a shared framework for classifying research by societal impact. OpenAlex tags works with SDGs automatically, so you can map the global landscape with a handful of group_by calls. We’ll compare India and Brazil to show how national research priorities diverge.

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Step 1: See the global distribution

Group all works by SDG to see where the world’s research effort goes: 
https://api.openalex.org/works?group_by=sustainable_development_goals.id&per_page=5

[
  {"key": "https://openalex.org/sdgs/3", "key_display_name": "Good health and well-being", "count": 22709013},
  {"key": "https://openalex.org/sdgs/2", "key_display_name": "Zero hunger", "count": 13933476},
  {"key": "https://openalex.org/sdgs/4", "key_display_name": "Quality education", "count": 11897329},
  {"key": "https://openalex.org/sdgs/7", "key_display_name": "Affordable and clean energy", "count": 11005161},
  {"key": "https://openalex.org/sdgs/10", "key_display_name": "Reduced inequalities", "count": 9476374}
]
Health dominates globally — SDG 3 accounts for more tagged works than the next two combined.

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Step 2: Compare two countries

Filter by country code and group by SDG. Run one call per country: 
# India
https://api.openalex.org/works?filter=authorships.institutions.country_code:IN&group_by=sustainable_development_goals.id&per_page=10

# Brazil
https://api.openalex.org/works?filter=authorships.institutions.country_code:BR&group_by=sustainable_development_goals.id&per_page=10
Each call returns the same shape — SDGs ranked by output. The rankings reveal different national priorities:
#IndiaBrazil
1Good health and well-beingGood health and well-being
2Affordable and clean energyQuality education
3Zero hungerZero hunger
4Clean water and sanitationLife on land
5Life on landPeace, justice, and strong institutions
Both countries lead with health and food security, but India’s #2 is energy while Brazil’s is education. Brazil’s emphasis on “Life on land” (#4) reflects its Amazon-related research.

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Step 3: Track a specific SDG over time

Pick an SDG and group by year to see growth trends. Here’s SDG 13 (Climate Action): 
https://api.openalex.org/works?filter=sustainable_development_goals.id:https://metadata.un.org/sdg/13,publication_year:2015-2025&group_by=publication_year

[
  {"key": "2015", "key_display_name": "2015", "count": 163264},
  {"key": "2018", "key_display_name": "2018", "count": 179558},
  {"key": "2021", "key_display_name": "2021", "count": 156949},
  {"key": "2024", "key_display_name": "2024", "count": 208595}
]
Climate research output grew steadily from 2015 to 2019, dipped briefly, then surged to a new high in 2024.

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Step 4: Find leading institutions

Group by institution within an SDG to find who’s producing the most research: 
https://api.openalex.org/works?filter=sustainable_development_goals.id:https://metadata.un.org/sdg/13&group_by=authorships.institutions.id&per_page=5

[
  {"key": "https://openalex.org/I19820366", "key_display_name": "Chinese Academy of Sciences", "count": 34901},
  {"key": "https://openalex.org/I1294671590", "key_display_name": "Centre National de la Recherche Scientifique", "count": 24791},
  {"key": "https://openalex.org/I4210165038", "key_display_name": "University of Chinese Academy of Sciences", "count": 11505},
  {"key": "https://openalex.org/I74801974", "key_display_name": "The University of Tokyo", "count": 9228},
  {"key": "https://openalex.org/I4210164339", "key_display_name": "Oldham Council", "count": 8833}
]

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Full script

This script compares any two countries’ SDG profiles side by side: 
import requests

BASE = "https://api.openalex.org"

def api(endpoint, params):
    return requests.get(f"{BASE}/{endpoint}", params=params).json()

def sdg_profile(country_code):
    """Get a country's SDG distribution as {sdg_name: count}."""
    data = api("works", {
        "filter": f"authorships.institutions.country_code:{country_code}",
        "group_by": "sustainable_development_goals.id",
        "per_page": 17,
    })
    return {g["key_display_name"]: g["count"] for g in data["group_by"]}

# Compare two countries
country_a, country_b = "IN", "BR"
profile_a = sdg_profile(country_a)
profile_b = sdg_profile(country_b)

all_sdgs = sorted(set(profile_a) | set(profile_b),
                  key=lambda s: profile_a.get(s, 0) + profile_b.get(s, 0),
                  reverse=True)

total_a = sum(profile_a.values())
total_b = sum(profile_b.values())

print(f"{'SDG':<45} {country_a:>8} {country_b:>8}")
print("-" * 63)
for sdg in all_sdgs:
    share_a = profile_a.get(sdg, 0) / total_a
    share_b = profile_b.get(sdg, 0) / total_b
    print(f"  {sdg:<43} {share_a:>7.1%} {share_b:>7.1%}")
Works can be tagged with multiple SDGs, so percentages across all goals will sum to more than 100%. The shares still show relative emphasis — a country with 15% in energy vs. 5% is investing proportionally more research effort there.