Big data creates injustice. We want to make sure that small enterprises and organizations, independent creators and great individual artists and researchers can join much small data to create an even playing field when it comes to recommendation engines, pricing, forecasting or any evidence-based business or policy goal.
Our Listen Local project is aiming to build local (national, regional or city-level) music recommendation engines. We believe that there is a huge need for locally relevant algorithmic curation and recommendation to create an even playing field, otherwise content supported by algorithms on large markets and large consumer bases will undermine local cultural ecosystems.
Our Demo Music Observatory which aims to partner with the representative music organizations, business users and universities to be recognized as the European Music Observatory.
Our CCS Demo Observatory, which has similar ambitions in the creative and cultural sectors, for example, in films, books, video games and digital heritage.
Reprex B.V. is a reproducible research company. We believe that whenever a business or policy consulting team, a research institute, or data journalism team has already used, formatted, and analyzed data from an external source at least twice, this procedure should be automated. This makes it error-free, well documented, cheap and re-useable. Furthermore, making data collection ongoing instead being ad hoc saves data acquisition, validation and supervision costs. We would like to help medium-sized business, policy, NGO, scientific and data journalism organizations in this, who do not have the institutional capacity to hire data scientists and engineers.
Our solutions cover automating data acquisition, processing, validation, auditing, documentation, visualization and presentation workflows, so that they can focus on what humans are best: making sense of the data.
reproducible example in data science. When you are stuck with a problem, creating a reproducible example allows other computer scientists, statisticians, programmers or data users to solve it. In 80% of the cases, you usually find the solution while creating a generalized example – which also means that you never have to repeat this task again. If you have ever downloaded, formatted, visualized, cited a data source twice in your work, there is a high chance that you will need it again. We would like to make this process automatic, with daily updating the data, the formatting, the visualizations and the citations.