TTO Performance Dashboard

Technology transfer offices are asked every year to show what they have achieved, usually by a provost, a board or a state legislature. The raw numbers are easy to pull from a licensing database, but they are hard to read on their own: a rise in disclosures may just reflect a bigger research budget, and a jump in revenue may come from one royalty stream. This dashboard puts several years side by side and turns the counts into ratios, so you can see whether the office is actually getting more productive.

1. Add your fiscal years. Start with the oldest year you want to show and use Add year for each later one, up to five. Give each a label such as FY2023.
2. Enter the seven figures for each year. Counts are whole numbers; licensing revenue and research expenditure are in millions of US dollars, so $2.5M is entered as 2.5. Leave a cell blank if you don't have it.
3. Read the ratio table and the charts. The table shows each ratio by year with its formula and the change from the first to the last year. The charts show each figure and ratio as bars, one per year.
4. Optionally pick a peer. Choose a university from the directory to see its published figures beside your latest year, with the period and source of each.
5. Download the CSV. The file contains your inputs, every calculated ratio and the formulas, ready for a spreadsheet or an annual report.

Your entries are saved in your browser, so you can close the page and pick up where you left off. Nothing is uploaded. If you want to see the dashboard working before typing in your own numbers, press Load example; the example figures belong to a made-up university and are labelled as such.

Each ratio answers a specific question about the office. None is good or bad on its own; what matters is the direction over several years and the reasons behind it.

• Disclosures per $10M research = disclosures ÷ (research expenditure ÷ $10M). How well the office draws inventions out of the research base. A falling value while research grows can point to weak outreach to new faculty or departments.
• Patent applications per disclosure = patent applications ÷ disclosures. How selective the office is about protection. A high value means most disclosures are filed on, which is expensive; a low value means the office is filtering hard, or that the patent budget is constrained.
• Licenses and options per disclosure = licenses and options ÷ disclosures. A rough conversion rate from invention to deal. Deals in a year come from disclosures received in earlier years, so read it over several years.
• Startups per $100M research = startups ÷ (research expenditure ÷ $100M). Startup formation scaled to the size of the research enterprise. With small counts, one or two companies make a large difference.
• Revenue per license executed = licensing revenue ÷ licenses and options executed that year. A yield figure, not an average deal size, because most revenue comes from older licenses.
• Licensing revenue as % of research spend = licensing revenue ÷ research expenditure × 100. How large licensing income is relative to the research that produced it. It helps set realistic expectations when licensing income is discussed as a way to fund research.

The dashboard deliberately does not compare you with an industry average or give an overall score. Offices count options, revenue and startups in different ways, and a single number would hide those differences. The peer comparison shows one university's published figures with their sources so you can judge how comparable they are.

The built-in example is a hypothetical university. All figures are invented to show the arithmetic. In FY2025 it spent $296M on research, received 121 invention disclosures, filed 80 patent applications, was issued 27 US patents, executed 23 licenses and options, formed 6 startups and received $3.1M in licensing revenue.

• Disclosures per $10M research: 121 ÷ 29.6 = 4.09
• Patent applications per disclosure: 80 ÷ 121 = 0.66
• Licenses and options per disclosure: 23 ÷ 121 = 0.19
• Startups per $100M research: 6 ÷ 2.96 = 2.03
• Revenue per license executed: $3.1M ÷ 23 = about $135K
• Licensing revenue as % of research spend: 3.1 ÷ 296 × 100 = 1.05%

Five years earlier, in FY2021, the same university had 92 disclosures on $240M of research, or 3.83 per $10M. Disclosures rose 32% over the period while research spending rose 23%, so the office drew in more inventions per research dollar, not just more inventions. That is the kind of statement the ratio view is meant to support: specific, traceable to the inputs, and honest about scale.

The same example also shows why one year can mislead. Startups fell from 4 to 2 between FY2022 and FY2023 before rising to 6. With counts this small, a three-year total says more than any single year.

The peer list comes from the Commercify university directory, which records only published figures. Research expenditure is from the NSF HERD survey for FY2024. US patents issued come from the National Academy of Inventors Top 100 list for 2025 or from the university's own report. Disclosures, licenses, startups and revenue come from each tech transfer office's latest published annual figures, where the office publishes them.

Three cautions apply. First, the periods differ: research is FY2024, patents are usually calendar 2025, and office figures are whichever fiscal year the office last reported, so a ratio built from them mixes years. The comparison table says so when it happens. Second, definitions differ: one office may count options with licenses and another may not. Third, many universities publish only some figures, and the table shows a dash for anything not published rather than filling the gap with an estimate.

Pick a peer of similar research size and mission where you can. Comparing a mid-sized public university with the largest private research institutions mostly measures the difference in scale.

• Changing definitions between years. If FY2023 counts options and FY2024 does not, the trend is an artifact. Write the definitions down and keep them.
• Reading one year in isolation. Startups, revenue and patents issued are lumpy. Use the five-year view and multi-year totals.
• Treating revenue as the only outcome. Licensing income can be concentrated in a few licenses. Disclosures, licenses and startups say more about whether the process is working.
• Ignoring the lag. A patent issued this year was filed several years ago, and a license signed this year may not produce royalties for years. Expect ratios that link stages to move slowly.
• Comparing raw counts across institutions. Normalize by research spending first, and check that the peer's period and definitions are close to yours.
• Presenting estimates as results. If a figure is provisional, say so. Leave it blank in the dashboard rather than guessing.

Annual activity metrics show how busy the office is and how well it converts research into deals. They don't show which technologies in the pipeline are closest to a deal, or which deserve more of a limited patent budget. For that, track individual inventions in the innovation pipeline manager and rank them with the innovation prioritization matrix. To gauge how mature a single technology is, use the TRL assessment.