{
  "slug": "dataport-ar",
  "name": "Dataport Ar",
  "description": "IEEE DataPort is a research data platform enabling researchers, engineers, and scientists to upload, store, and share various types of standard and Open Access datasets up to 2TB (or 10 TB for Institutional Subscribers). It supports a wide range of data formats, provides persistent identifiers (DOIs) for citation, and offers cloud-based data analysis capabilities.",
  "url": "https://optimly.ai/brand/dataport-ar",
  "websiteUrl": null,
  "logoUrl": "https://logo.clearbit.com/datamanagement.hms.harvard.edu",
  "baiScore": 29,
  "bai_tier_status": "active",
  "bai_score_status": "active",
  "archetype": "Incumbent",
  "archetype_status": "active",
  "category": "Data Management",
  "categorySlug": null,
  "keyFacts": [],
  "aiReadiness": [],
  "competitors": [],
  "competitorsProse": null,
  "inboundCompetitors": [],
  "aiAlternatives": [],
  "parentBrand": null,
  "subBrands": [],
  "updatedAt": "2026-08-09T00:03:49.386Z",
  "verifiedVitals": {
    "website": "https://datamanagement.hms.harvard.edu",
    "founded": "Not explicitly stated for the platform itself, but supported by the long-standing IEEE organization.",
    "headquarters": "Not explicitly stated (Operated by IEEE)",
    "pricing_model": "Freemium (free standard dataset submission, free individual subscription for IEEE members), Subscription-based (individual and institutional paid tiers), Fee-based (upfront fee for Open Access dataset submission).",
    "core_products": "Research data repository, dataset hosting, data sharing platform, cloud-based data analysis access, dataset citation and DOI generation.",
    "key_differentiator": "Leverages the reputation and network of IEEE, offers significant data storage capacity (up to 10TB), extensive support for diverse data formats, seamless integration with ORCID and IEEE Xplore, and provides cloud access for data analysis.",
    "target_markets": "Researchers, engineers, scientists, academic institutions, universities, research organizations, IEEE Society Members.",
    "subcategory": "Research Data Repository"
  },
  "intentTags": {
    "problemIntents": [
      "Local Storage & Institutional Repositories: Researchers manually store data on personal devices or departmental servers, with limited public access or standardized metadata, leading to poor discoverab",
      "Journal Supplementary Materials: Publishing datasets as supplementary files alongside journal articles, which may lack dedicated data management features, unique identifiers (DOIs), or structured meta",
      "Data Siloing: Choosing not to share or publicly archive research data, resulting in data inaccessibility, hindering research reproducibility, limiting collaborative opportunities, and reducing the bro"
    ],
    "solutionIntents": [
      "IEEE DataPort research data upload",
      "Upload dataset IEEE",
      "IEEE Xplore data sharing",
      "General Cloud Storage Services: Using services like Google Drive, Dropbox, or institutional cloud storage solutions for data sharing. These typically lack research-specific functionalities such as per"
    ],
    "evaluationIntents": []
  },
  "timestamp": 1786289275234
}