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If open data is the new gold, why even those who release fail to reuse it? We created an open collaboration of data curators and open-source developers to dig into novel open data sources and/or increase the usability of existing ones. We transform reproducible research software into research- as-service.

Our Digital Music Observatory contributes to the Music Creators’ Earnings in the Streaming Era project with understanding the level of justified and unjustified differences in rightsholder earnings, and putting them into a broader music economy context.

After a very thorough modernization of the package’s exception handling, documentation, and code dependencies that I did in the last week, the spotifyr package has passed again the peer-review standards and it is back on CRAN. The package is an excellent starting to point for R newbies to try their hands on musicology analysis with a few keystrokes. And of course, it is an essential part of the research infrastructure of musicology worldwide in far more advanced applications.

Live music is the breadwinner of most music professionals, artists, technicians, and managers alike, and it is a very local business. To analyze the live music economy, and its connection with the recorded music business, we need to create indicators on regional, provincial, or metropolitan area level. We just made another step to localize our Digital Music Observatory.

Open data is like gold in the mud below the chilly waves of mountain rivers. Panning it out requires a lot of patience, or a good machine. I think we will come to as surprising and strong findings as Bellingcat, but we are not focusing on individual events and stories, but on social and environmental processes and changes.

101 Dalmatians was released in 1985 and 1991 which made thousands of families (in the U.S.) want to adopt one. The American Kennel Club reported that the annual number of Dalmatian puppies registered skyrocketed from 8,170 animals to 42,816.

The idea behind Listen Local is simple: we want machine learning algorithms of Spotify, YouTube, or other services to learn more about Slovak music. In order to make machines learn about Slovak music, we have to make machine-readable tables of Slovak music for AI learners

Facilitating private-public partnerships is one step to encourage the data community to work with valuable open data. However, transparency and a high level quality assurance step must be given. In a joint collaboration with data curators, developers, technical specialists and academics, the datasets should be retrieved, cleaned and assessed in order to deliver efficient, relevant and credible information. The constant monitoring and regulation as well as compliance with data security guidelines are indispensable.

Many interesting phenomena are difficult to quantify in a meaningful way and writing a catchy song with international appeal is probably more an art than a science. Nevertheless that should not deter us from trying as music, too, is bound by certain rules and regularities that can be researched.

Although there are a variety of open data sources available (and the numbers continue to increase), the availability of open algorithmic tools to interpret and communicate open data efficiently is lagging behind. One of the greatest challenges for open data in 2021 is to demonstrate how we can maximize the potential of open data by designing smart tools for open data analytics.