About
We need to understand more and rank less
Rankless is an experimental data visualization project that allows users to explore the impact of thousands of universities. It is built on the idea that universities generate impact that is specific to a geography and to certain topics, and that rankings obscure that impact by reducing it to a single dimension. By transcending rankings, we highlight a university’s multidimensional impact by showing you who they work with and who cites them. To understand more, sometimes, we need to rank less.
Our Team
Rankless was developed at the Center for Collective Learning (CCL) at Corvinus University of Budapest by a team of four people. The main person behind the project is Endre Borza, an economist working as a data engineer who constructed Rankless from the ground up. The graphic and interaction design of Rankless is the work of Máté Barkóczi, a designer working on his Master’s at MOME and as an intern at CCL. Vera Hamar, Executive Director of CCL, supported Rankless as a project manager and coordinator. César A. Hidalgo, Director of CCL, supervised the project.
The Center for Collective Learning (CCL) is an interdisciplinary research laboratory with offices in Toulouse, France and Budapest, Hungary. For more than a decade, CCL has advanced the state of the art in economic development, data visualization, and applications of artificial intelligence. Previous data visaualization projects by members of CCL include The Observatory of Economic Complexity, Pantheon, and Data USA, among dozens of others.
Rankless was created at the Center for Collective Learning at the Corvinus Institute for Advanced Studies (CIAS) at Corvinus University in Budapest. This project was supported by the Learn Data ERA Chair from the European Research Executive Agency.
Contact
You can reach us by contacting Veronika and Endre directly @ rankless@centerforcollectivelearning.org
Frequently Asked Questions
Questions and answers about data sources used, update frequency, filtering, impact measurement, extensions and visualization.
We use open, up-to-date data, process it with transparent methods and visualize the results in an intuitive, unique way.
What are your data-sources?
What are indexed citations?
Can I download/export Rankless data?
How often do you update your data?
How accurate is your data?
Do you filter the data?
How do you assign papers and journals to subject categories?
How do you measure impact?
What kind of visualization engine do you use?
How do you measure specialization?
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