ORCID Researcher Profile Search

🔎 ORCID researcher search and profile export

ORCID Researcher Profile Search collects public ORCID researcher profiles by search query, institution, discovery topic, or exact ORCID iD. Use it to turn ORCID public data into clean Apify dataset rows with names, affiliations, works, public links, identifiers, profile quality signals, and optional cohort analysis.

• ORCID researcher search for names, topics, affiliations, keywords, and ORCID Lucene queries.
• ORCID profile search when you already know exact ORCID iDs and want structured profile rows.
• ORCID scraper workflows for public names, biographies, emails, websites, keywords, external IDs, affiliations, and works.
• Institution research mapping for one institution or comparison runs across up to six institutions.
• Reviewer, expert, collaborator, and field-radar shortlists with deterministic ranking signals from returned ORCID profiles.

📦 Data you can export

Each charged dataset row is one successful public ORCID researcher profile. Fields can be null or empty when ORCID does not publish that fact for a profile.

• orcidId and matchedInputs for traceability back to your submitted targets
• names with given, family, credit, and alternate public names
• biography, country, public emails, websites, keywords, and externalIds
• currentAffiliation and affiliations for employment, education, membership, service, qualification, distinction, and invited-position facts when public
• works with source work count, optional work items, DOI, title, year, journal, URL, put code, and metadata coverage
• activity with source last-modified time, profile freshness, years active, career stage, funding count, and peer-review count
• quality with deterministic profile health, profile completeness, identity confidence, badges, recommended action, and data gaps
• Optional discovery, institutionAnalysis, fieldRadar, and relationshipMap data when those inputs request analysis rows

The Actor uses public ORCID data. It does not guess private emails, add citation impact, calculate h-index, use ORCID member-only data, or generate LLM summaries.

▶️ How to run it

1. Open the Actor input.
2. Fill one target section:
   • Search queries for names, topics, affiliations, keywords, or ORCID Lucene syntax.
   • ORCID iDs for exact public profile lookup.
   • Institutions to map one institution or compare 2-6 institutions.
   • Discovery topic or query to build expert, reviewer, collaborator, or field-radar shortlists.
3. Choose Publication detail and set Maximum works per profile.
4. Set Maximum profiles per search target to control output size and cost.
5. Optional: turn on Include relationship map when you want a cohort similarity row.
6. Start the Actor and open the dataset.

You can export the dataset as JSON, CSV, Excel, XML, RSS, or HTML. You can also run the Actor through the Apify API, rerun saved inputs, or send finished runs to webhooks and integrations.

⚙️ Input example

{
  "searchQueries": ["machine learning MIT"],
  "publicationDetail": "recentWorks",
  "maxWorksPerProfile": 3,
  "profileFormat": "standard",
  "includeRelationshipMap": true,
  "maxResultsPerTarget": 125
}

For exact lookup, use ORCID iDs instead:

{
  "orcidIds": ["0000-0002-9510-6777"],
  "publicationDetail": "fullWorks",
  "maxWorksPerProfile": 25
}

🧾 Output example

{
  "recordType": "researcher",
  "orcidId": "0000-0002-9510-6777",
  "names": {
    "given": "Goutam",
    "family": "Sarker",
    "credit": "G. Sarker",
    "other": ["Dr. Goutam Sarker"]
  },
  "biography": "Researcher focused on machine learning and data mining.",
  "country": "IN",
  "emails": ["researcher@example.edu"],
  "websites": [
    {
      "name": "Lab profile",
      "url": "https://example.edu/researcher"
    }
  ],
  "keywords": ["Machine Learning", "Data Mining"],
  "externalIds": [
    {
      "system": "Scopus Author ID",
      "value": "6603684030",
      "url": "https://www.scopus.com/authid/detail.uri?authorId=6603684030"
    }
  ],
  "currentAffiliation": {
    "organization": "National Institute of Technology Durgapur",
    "role": "Associate Professor",
    "department": "Computer Science and Engineering",
    "country": "IN",
    "startDate": "1998-07-13",
    "endDate": null
  },
  "affiliations": [
    {
      "kind": "employment",
      "organization": "National Institute of Technology Durgapur",
      "role": "Associate Professor",
      "department": "Computer Science and Engineering",
      "country": "IN",
      "startDate": "1998-07-13",
      "endDate": null
    }
  ],
  "works": {
    "totalCount": 102,
    "items": [
      {
        "title": "A Machine Learning Method for Pattern Recognition",
        "type": "journal-article",
        "year": 2025,
        "journal": "Applied Sciences",
        "doi": "10.3390/app152412967",
        "url": "https://doi.org/10.3390/app152412967",
        "putCode": 199169611
      }
    ],
    "quality": {
      "doiCoveragePct": 42.5,
      "journalCoveragePct": 70.2,
      "yearCoveragePct": 94.1,
      "recentWorksCount": 7
    }
  },
  "activity": {
    "recordLastModified": "2026-04-30T07:01:18.578Z",
    "profileFreshness": "active",
    "yearsActive": 28,
    "careerStage": "senior",
    "fundingCount": 2,
    "peerReviewCount": 1
  },
  "quality": {
    "researcherHealth": {
      "score": 84,
      "level": "good"
    },
    "profileCompleteness": {
      "score": 80,
      "level": "rich"
    },
    "identityConfidence": {
      "score": 94,
      "level": "high",
      "warnings": []
    },
    "badges": ["has-current-affiliation", "high-completeness"],
    "recommendedAction": "use-profile",
    "dataGaps": []
  },
  "discovery": null,
  "matchedInputs": ["machine learning MIT"],
  "institutionAnalysis": null,
  "fieldRadar": null,
  "relationshipMap": null
}

Analysis rows use recordType: "analysis" and keep profile fields as null. They are added only for requested institution analysis, field radar, or relationship maps.

💳 Pricing

This Actor uses pay-per-event pricing. You are charged $0.0018 for each public researcher profile saved to the dataset. Empty searches, invalid inputs, skipped profiles, failed lookups, and analysis rows are not charged as researcher profiles.

🔌 Integrations

• Call the Actor through the Apify API from scripts, apps, dashboards, or internal research tools.
• Rerun saved ORCID inputs when you want a fresh export for your own comparison workflow.
• Export datasets as CSV, JSON, Excel, XML, RSS, or HTML.
• Send finished runs to webhooks or other Apify integrations.
• Load ORCID profile rows into spreadsheets, CRMs, data warehouses, reviewer queues, or research intelligence workflows.

❓ FAQ

Can I search ORCID by name, institution, topic, or keyword?

Yes. Use Search queries for names, topics, affiliations, keywords, or ORCID Lucene syntax. Use Institutions when your target is an institution map or comparison.

Can I fetch exact profiles by ORCID iD?

Yes. Add exact ORCID iDs in the ORCID iDs section. The Actor validates the ORCID iD format and saves one row for each public profile it can extract.

Does this include publications or works?

Yes, when public ORCID data provides them. Choose counts only, recent works, or full works up to your configured per-profile limit. Work items can include title, type, year, journal, DOI, URL, and ORCID put code.

Does this find private emails or member-only ORCID data?

No. The Actor exports public ORCID profile data only. It does not guess private contact details, use ORCID member-only data, or require user-supplied ORCID credentials.

Can I use this for reviewer or expert discovery?

Yes. Add a discovery query and choose expert shortlist, reviewer shortlist, collaborator matches, or field radar. Ranking and conflict signals are deterministic and based on the returned public profile facts.

Why not use the ORCID API directly?

You can use the ORCID public API directly if you want to build and maintain your own query handling, pagination, normalization, exports, and charging. This Actor packages those steps into an Apify run with dataset exports, API access, schedules, webhooks, and a stable output schema.

What are ORCID alternatives?

For broader academic metadata, users often combine ORCID with sources such as OpenAlex, Crossref, Zenodo, Semantic Scholar, institutional directories, or grant databases. ORCID is strongest when you need researcher-controlled public profile facts, ORCID iDs, affiliations, works, and external identifiers.

📝 Changelog

• 0.1: Initial release.

🆘 Support

For issues, questions, or feature requests, file a ticket and I'll fix or implement it in less than 24h 🫡

🔗 Other actors

• OpenAlex Scraper ↗ - Export OpenAlex scholarly works, authors, institutions, concepts, and journals for research datasets.
• Semantic Scholar Author Profiles Scraper ↗ - Collect author profiles, citations, h-index, publication history, affiliations, and external IDs from Semantic Scholar.
• Zenodo Scraper ↗ - Export Zenodo research records, datasets, software, authors, DOIs, files, and repository metadata.
• Crossref DOI Metadata Scraper ↗ - Collect citation metadata for DOI-backed papers, books, proceedings, and datasets from Crossref.
• EU Funding & Tenders Scraper ↗ - Export EU grants and tenders with deadlines, budgets, programme details, and official source URLs.

Made with ❤️ by Maxime Dupré