Universities spend $90B on research but rely on databases that disagree. LumericIQ reconciles Academic Analytics, Web of Science, and Scopus into a single, accurate view of faculty performance.
Academic Analytics, Web of Science, and Scopus each tell a different story about the same faculty. Duplicates, mismapped departments, missing records, no per-capita normalization.
We found hundreds of duplicate entries and 12 mismapped faculty in a single school’s data. WoS severely underreported output that AA captured, and vice versa.
Fixing this takes 30+ hours of analyst time per school per cycle: downloading, deduplicating, cross-referencing by hand. For just one school within one university.
No commercial tool offers per-capita normalization. A 40-person department producing 200 papers looks identical to a 10-person department producing 200.
LumericIQ sits on top of your existing subscriptions. Universities keep Academic Analytics, Web of Science, and Scopus. We reconcile the conflicts and deliver clean, decision-ready dashboards. Zero switching cost.
Automated deduplication, department correction, and author disambiguation across AA, WoS, and Scopus. Continuously updated, not a one-time snapshot.
The metric every dean needs but no tool provides. Normalize output per faculty member, revealing which departments are truly productive, not just large.
Track research trajectory over 1–10 year windows. Compare against peer institutions with normalized metrics for accreditation, rankings, and fundraising.
Academic Analytics would have to reconcile against competitors’ databases, implicitly admitting its own data is incomplete. Clarivate and Elsevier would each need to license the other’s data, something neither has commercial incentive to do. LumericIQ exists in the gap between these siloed competitors, and their structural incentives keep that gap open.
Algorithms trained on real institutional data with known ground-truth, improving with each university onboarded. A competitor would need equivalent access and years of iteration.
Once configured to departmental structure and benchmarked against historical baselines, the cost of switching resets years of data. Retention compounds over time.
Benchmarking improves as more institutions join. Each new customer enriches the comparison dataset for all existing customers, creating a flywheel incumbents cannot replicate.