Prior to 2020, only the results of the Top 200 ranked universities were published in ARTU. This was increased to the Top 400 in 2020 based on feedback.

The arithmetic average is not suited to describe the overall performance of an institution across THE, QS, and ARWU where the institution is compared to a different group of peers from one ranking to another. The arithmetic average rank of an institution across these three rankings does not follow a normal (parametric) distribution and is generally worse than the corresponding aggregate rank adopted by ARTU. ARTU treats the data using a non-parametric approach through ordinal ranking, ordering universities by their aggregate score. Consequently, the aggregate rank of a university is less sensitive to anomalies in the performance of other universities, thus providing a more realistic position of a university in comparison to its peers.

For universities whose rank is published as a range by THE and ARWU, the overall score is recalculated from the component scores according to the corresponding component weighting. The overall score is then ranked in descending order. Universities with an existing distinct rank are not affected by this process. For ARWU, the adjusted component weighting applied to institutions specialising in humanities and social sciences, such as the London School of Economics, has been accounted for in this process. Recalculation of distinct ranks is not possible for QS due to the limited availability of data on component scores released by the ranking agency. Universities with a QS rank beyond 400, 500 or 600 (depending on the year) are therefore not considered by ARTU. Visit the ARTU methodology page for more information: https://research.unsw.edu.au/artu/methodology.

Universities formed through a merger are considered as new entities and are ranked separate from the founding institutions. For example, Sorbonne University was established in 2018 by a merger of Paris-Sorbonne University, Pierre et Marie Curie University, along with other institutions. As a result of the merger, Sorbonne University is only ranked from ARTU 2018 and onwards, while its component entities Pierre et Marie Curie University and Paris-Sorbonne university no longer exist as standalone universities and are thus only ranked in ARTU 2012-2017.

A university system is a set of multiple affiliated universities and colleges. For example, the University of California (UC) system is composed of several campuses: UC Berkeley, UC Los Angeles etc. All university systems are individually reviewed by the ARTU data team. Where a university system/campus is featured as the same entity across THE, QS and ARWU, then the university system/campus is ranked by ARTU. However, if the university system is featured in one ranking, while its campuses appear in other rankings, then neither the university system nor their campuses are ranked by ARTU. In the above example, UC Berkeley is the same entity across THE, QS and ARWU, and thus it is eligible for ARTU. However, the University of Massachusetts is not eligible for ARTU as THE only ranks the University of Massachusetts system as a whole, while ARWU and QS ranks individual campuses within the system (e.g. Amherst, Boston, Lowell, and Worcester).

The arithmetic sum for all universities ranked in THE & ARWU, and in the Top 600 in QS is calculated prior to determining the ARTU rank. This means a university does not need to be ranked in the Top 400 of any of the individual rankings to be ranked in the Top 400 in ARTU. Examples of this in the 2023 ARTU release are: Universiti Teknologi Malaysia (UTM), Politecnico di Torino, and University of Waikato (UoW). Visit the ARTU methodology page for more information: https://research.unsw.edu.au/artu/methodology.

Population data is available for every year and country from UN Population data. The dataset, downloaded in October 2023, was last updated by the UN on 7^th October 2022. It includes population estimates from 2012 to 2021 and projections from 2022 to 2023 based on the medium-variant scenario. The medium-variant is a widely used scenario in demographic forecasting, assuming moderate levels—neither exceptionally high nor exceptionally low—of fertility, mortality, and migration. This estimate/projection provides a balanced perspective on future population trends, representing a middle-of-the-road scenario. There are no missing values for the population data from the source.

UN GDP and OECD R&D data were used to calculate the ARTU Top 200 by GDP and ARTU Top 200 by R&D indicators. GDP and R&D data was only available up to 2021 as of October 2023 with the exception of Canada that had 2022 R&D data. Countries with missing data from 2012 to 2021 were estimated using linear interpolation (i.e., using the closest known values to estimate the missing middle value). Data for 2022 and 2023 use the most recent available data. The year data availability can be vary depending on the country, please see example below

Example of estimation using Australia OECD R&D dataset (decimal points removed, $billion USD):
Year 2011, R&D value = 21522
Year 2013, R&D value = 22441
Year 2015, R&D value = 21157
Year 2017, R&D value = 21236
Year 2019, R&D value = 21738

To get the missing value for 2012, linear interpolation is applied using 2011 and 2013 data as per formula below.
Year 2012 = (Year 2011 (known) + Year 2013 (known))/2 = (21522 + 22441)/2 =  21981

Future years use the most recent available data:
Year 2020 = 21738
Year 2021 = 21738
Year 2022 = 21738
Year 2023 = 21738

ARTU utilises UN data for population & GDP and OECD data for R&D expenditure as they represent the most complete and robust datasets available to the public. Other data sources such as World Bank/IMF were considered but did not have the same coverage or indicators as UN or OECD data and were therefore not used for ARTU.

Population: https://data.un.org/Data.aspx?q=population&d=PopDiv&f=variableID%3a12
GDP: http://data.un.org/Data.aspx?d=SNAAMA&f=grID%3a101%3bcurrID%3aUSD%3bpcFlag%3a0
R&D: https://data.oecd.org/rd/gross-domestic-spending-on-r-d.htm

Note both GDP and R&D data is represented by $billions USD.

UN GDP and OECD R&D data is not available for all countries, so therefore it is not possible to calculate a World entity.

Please contact UNSW Sydney’s Division of Research and Enterprise (DVCR.RADAR@unsw.edu.au).