Reprex Website

Visit our new website at

We want to help small organizations with big data. Big data creates inequalities. Only the largest corporations, government bureaucracies, and scientific organizations have the human financial resources to create large, consistent datasets. We help our clients to access datasets of sufficient size, and to build trustworthy scientific, policy, business, or AI applications with the data.

Our new website, leaves the two flagship project — Listen Local and Demo Music Observatory pages intact, and connects through our service offering. The Data&Lyrics blog will remain open for many interesting topics that we find interesting for our partners, friends, readers.

Data Access

Reprex grew out of CEEMID, a project that connected data and pooled surveys across twelve countries and over fifty music industry stakeholders to create thousands of relevant policy, statistical, and scientific indicators for the music and creative industries. (See the Central European Music Industry Report 2020 at Reprex offers services to help small organizations access data and use data in a trustworthy way.

  • Data Curation: Find the best value data solution for your research or evaluation project.
  • Open Data: Access thousands of statistical, business, and policy indicators in the cultural and creative industries and sustainability.
  • Survey harmonization: Reuse already existing surveys and harmonized question banks; create longitudinal and cross-sectional datasets.

We encourage our clients to become partners in our permanent data collection and sharing platforms: the data observatories.

Data Use: Research Automation & Trustworthy AI

AI requires large datasets to be effective, small organizations face challenges in harnessing the benefits of algorithms — or, worse, biased algorithms created for different purposes will work against their strategic goals. We want to help our clients with pooling, sharing data, and validating or creating trustworthy applications.

  • Reproducible Research: Automate data collection, processing, validation, correction, and documentation to support error-prone human work.
  • Trustworthy AI: Use validated, trustworthy AI applications or identify the biases of algorithms working against your goals.

Our Listen Local project is our flagship trustworthy, transparent AI project.