Updated October 2026 · Prepared by Edhitch’s rankings advisory team
What data do THE and QS need from an Indian university?
Both rankings combine their own sources — reputation surveys and Scopus bibliometrics — with data the institution submits itself. For THE, institutions provide and sign off figures such as academic staff, students, doctorates awarded and income. For QS, institutions submit data through QS HUB, including the faculty and student counts behind several indicators, and nominate contacts for its academic and employer surveys. If those numbers disagree with what you report to NIRF and AISHE, your ranking rests on figures you cannot defend.
Why clean data matters more than it looks: your faculty count is not one indicator — it is the denominator for several. In QS it drives Faculty Student Ratio and Citations per Faculty. In THE it drives the staff-to-student ratio, research productivity and the doctorates-to-staff ratio. One wrong faculty figure can move several indicators at once, in the same direction.
THE World University Rankings 2027: what is measured
THE’s 2027 methodology groups its indicators into five pillars. Key institutional statistics generally refer to the 2024 academic year; bibliometrics come from Elsevier’s Scopus (2021–2025 publications).
| Pillar (weight) | Indicators and weights | Depends on data you supply |
|---|---|---|
| Teaching (29.5%) | Teaching reputation 15% · staff-to-student ratio 4.5% · doctorate-to-bachelor’s ratio 2% · doctorates-awarded-to-academic-staff ratio 5.5% · institutional income 2.5% | Staff, students, doctorates, income |
| Research environment (29%) | Research reputation 18% · research income 5.5% · research productivity 5.5% | Research income; staff count (productivity is scaled by staff) |
| Research quality (30%) | Citation impact 15% · research strength 5% · research excellence 5% · research influence 5% | Mainly Scopus — affected by how your publications are attributed to you |
| International outlook (7.5%) | International students 2.5% · international staff 2.5% · international collaboration 2.5% | International student and staff counts |
| Industry (4%) | Industry income 2% · patents 2% | Industry income |
THE excludes institutions that do not teach undergraduates, have fewer than 1,000 relevant publications over 2021–2025 (minimum 100 a year), or have 80% or more of their output in one subject area. Institutions that submit data but miss these criteria can appear as unranked “reporter” institutions. Source: THE World University Rankings 2027 methodology.
QS World University Rankings: what is measured
QS groups nine indicators into five lenses. Institutions submit data through QS HUB, and can nominate up to 400 contacts each for the Academic and Employer reputation surveys.
| Lens (weight) | Indicators and weights | Depends on data you supply |
|---|---|---|
| Research and Discovery (50%) | Academic Reputation 30% · Citations per Faculty 20% | Survey contact list; faculty count (denominator for citations) |
| Employability and Outcomes (20%) | Employer Reputation 15% · Employment Outcomes 5% | Survey contact list; graduate outcome data |
| Global Engagement (15%) | International Faculty Ratio 5% · International Student Ratio 5% · International Research Network 5% | International faculty and student counts |
| Learning Experience (10%) | Faculty Student Ratio 10% | Faculty and student counts |
| Sustainability (5%) | Sustainability 5% | Sustainability evidence |
To be ranked, QS requires an institution to be in the global top 20% for Academic Reputation, have at least 100 Scopus-indexed papers over five years, and be ranked in its regional ranking where one applies (India falls under QS Asia). Source: QS World University Rankings methodology guide.
Where your NIRF data and THE/QS data overlap
Most of the institutional data THE and QS ask for already exists in your NIRF submission — but under different definitions. The overlap is the opportunity; the definitions are the trap.
| Data item | NIRF | THE | QS |
|---|---|---|---|
| Faculty / academic staff | TLR (faculty-student ratio, faculty qualifications) | Staff-to-student ratio; research productivity; doctorates-to-staff | Faculty Student Ratio; Citations per Faculty |
| Student enrolment | TLR (student strength incl. PhD) | Staff-to-student ratio; doctorate-to-bachelor’s | Faculty Student Ratio; International Student Ratio |
| PhD students and doctorates awarded | TLR, RP | Doctorate ratios | — |
| International students and staff | OI (region diversity) | International outlook | Global Engagement |
| Research and industry income | RP (sponsored research, consultancy) | Research income; industry income | — |
| Publications and citations | RP (indexed publications) | Research quality (Scopus) | Citations per Faculty (Scopus) |
| Graduate outcomes | GO (placement, higher studies) | — | Employment Outcomes |
Where THE and QS submissions usually go wrong
- Different numbers for the same fact. The faculty count in your QS submission does not match the one in NIRF or AISHE, and nobody can explain why.
- Definitions applied loosely. Headcount vs full-time equivalent, who counts as academic staff, which students count as international — each ranking defines these its own way.
- Publications attributed to the wrong name. Variants of your institution’s name in Scopus split your output across profiles, lowering citation-based indicators.
- Income figures that do not reconcile. Research and industry income that cannot be traced back to audited accounts.
- Last-minute collection. Data assembled from departments in the final weeks, with no record of where each number came from.
The clean-data checklist before you submit to THE or QS
In short
Fix one census date, reconcile every count with your NIRF and AISHE figures, apply each ranking’s own definitions on top of one master record, clean your Scopus affiliation profile, tie income to audited accounts, and keep an evidence trail for every number you sign off.
- Fix one census date. Decide the reference date for staff and student counts, and use it everywhere.
- Reconcile with NIRF and AISHE. Start from the same faculty, student and PhD figures you reported to NIRF and AISHE, and document every difference.
- Apply each ranking’s definitions. Map your master record to THE’s and QS’s definitions (headcount, FTE, international status) instead of keeping separate spreadsheets.
- Clean your Scopus affiliation profile. Merge name variants and claim missing publications before the bibliometric window closes.
- Tie income to audited accounts. Research and industry income should trace back to your financial statements.
- Build your survey contact lists early. QS lets you nominate up to 400 contacts each for its academic and employer surveys.
- Keep the evidence trail. Record the source of every submitted number, so this year’s cycle becomes next year’s starting point.
- Sign off once. One owner approves the THE and QS figures after checking them against NIRF.
How Master Data Map prepares your THE and QS data
Rankers reward consistent, defensible data — and that is what Master Data Map produces. Master Data Map (MDM), Edhitch’s accreditation and ranking software, prepares your THE and QS rankings data the same way it prepares your NIRF submission — from one institutional record. Faculty, students, doctorates, international counts, income and outcomes are entered once, with evidence attached, and mapped to each ranking’s definitions.
| Step | What Master Data Map does |
|---|---|
| One record | The same record produces your NAAC SSR and AQAR, NBA SAR, NIRF data, and THE and QS rankings data. |
| Consistency checks | Flags where the same fact would be reported differently across NIRF, THE and QS, before you submit. |
| Evidence vault | Keeps the source behind each number, so every figure you sign off can be defended. |
| Next cycle | Each cycle starts from last year’s record, not a blank sheet. |
Indian universities in THE and QS
In the QS World University Rankings 2027, released in June 2026, IIT Delhi was India’s highest-ranked institution at 118, with 52 Indian universities ranked. In the THE World University Rankings 2027, the Indian Institute of Science, Bengaluru was India’s highest-ranked institution, at joint 254. For most Indian institutions, the gap is not only research output — it is whether the data they submit is complete, consistent and defensible.
Frequently asked questions
What data do universities submit to THE?
Institutions provide and sign off their own institutional data for the THE World University Rankings, including academic staff, students, doctorates awarded and income figures (institutional, research and industry income), plus international student and staff counts. THE combines this with Scopus bibliometrics and its reputation survey.
How do universities submit data to QS?
Institutions submit data through QS HUB. They also nominate up to 400 contacts each for QS’s Academic and Employer reputation surveys. Faculty and student counts submitted to QS underpin the Faculty Student Ratio, the international ratios and the Citations per Faculty denominator.
What are the THE World University Rankings 2027 pillars?
Teaching (29.5%), Research environment (29%), Research quality (30%), International outlook (7.5%) and Industry (4%).
What are the QS World University Rankings indicators?
Academic Reputation (30%), Citations per Faculty (20%), Employer Reputation (15%), Faculty Student Ratio (10%), Employment Outcomes (5%), International Faculty Ratio (5%), International Student Ratio (5%), International Research Network (5%) and Sustainability (5%).
Can NIRF data be reused for THE and QS?
Much of it can — faculty, students, PhDs, international counts, research income and publications appear in all three. But THE and QS apply their own definitions, so NIRF figures need to be mapped, not copied. Keeping one master record and mapping it to each ranking avoids three different numbers for the same fact.
Why do THE and QS submissions go wrong?
The most common causes are counts that disagree with NIRF or AISHE, definitions applied loosely (headcount vs FTE, who counts as international), publications split across Scopus name variants, income that does not reconcile with audited accounts, and data collected at the last minute without a source trail.
How does Master Data Map help with THE and QS?
Master Data Map (MDM) prepares your THE and QS rankings data from the same institutional record as your NIRF, NAAC and NBA data, flags where the same fact would be reported differently, and keeps the evidence behind each submitted number.
THE & QS Data Review
30-minute session with our rankings team. We’ll look at what you submit to THE and QS, compare it with your NIRF data, and show where the numbers disagree.
Enquiry Received!
Our rankings team will respond within 24 hours.
For immediate help: WhatsApp +91-92051 19385