Posts

From Play to Rec

Jeremy Dunne is the founder of the French Open Studio Sessions, which allows artists to open up in front of a very restricted audience and show themselves from a creative and working perspective—during they are producing a recorded performance.

Adding Value to Public Data With Imputation and Forecasting

Public data sources are often plagued with missng values. Naively you may think that you can ignore them, but think twice: in most cases, missing data in a table is not missing information, but rather malformatted information which will destroy your beautiful visualization or stop your application from working. In this example we show how we increase the usable subset of a public dataset by 66.7%, rendering useful what would otherwise have been a deal-breaker in panel regressions or machine learning applications.

How Can We Add Value to Public Data With Better Curation And Documentation?

Many people ask if we can really add value to free data that can be downloaded from the Internet by anybody. Public data usually requires a lot of work to become really valuable. To start with, it is not always easy to find.

We Are Looking for Partners in France

We are looking for partners in France for our Digital Music Observatory. You can find us in Le Trianon on the Pigalle, in the JUMP Corner.

Music Creators’ Earnings in the Streaming Era

Our Digital Music Observatory contributed 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. The entire research paper is published by the UK Intellectual Property office, and we made the details of our analysis available in a joint publication.

'I lived in the mountains for six years, and I loved it because it was a place where you needed to rely on yourself to survive.'

Marie de la Montagne is a performing musician and a music publisher. She currently lives in Prague.

The Data Sisyphus

Sisyphus was punished by being forced to roll an immense boulder up a hill only for it to roll down every time it neared the top, repeating this action for eternity. When was a file downloaded from the internet? What happened with it sense? Are their updates? Did the bibliographical reference was made for quotations? Missing values imputed? Currency translated? Who knows about it – who created a dataset, who contributed to it? Which is the final, checked, approved by a senior manager?

Open Data - The New Gold Without the Rush

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.

Music Creators’ Earnings in the Streaming Era

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.

New spotifyr R Package Release

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.

Trustworthy AI: Check Where the Machine Learning Algorithm is Learning From

We do care what our children learn, but we do not care yet about what our robots learn from. One key idea behind trustworthy AI is that you verify what data sources your machine learning algorithms can learn from. As we have emphasised in our forthcoming academic paper and in our experiments, one key problem that goes wrong when you see too few small country artists, or too few womxn in the charts is that the big tech recommendation systems and other autonomous systems are learning from historically biased or patchy data.

Reprex is Contesting all Three Challenges of the EU Datathon 2021 Prize

Reprex, a Dutch start-up enterprise formed to utilize open source software and open data in open collaboration with its partners is contesting all three challenges of the EU Datathon 2021 Prizes.