Ukraine: singer identity table

The aim of the following table is to help a human verification of the identity of Ukrainain singers.

The aim of this table is to get a first experience in human verification, and you do not necessarily have to fill out all information, and we do not yet go in depths with language, genre, scene, that will come up later.

1. Please check that the Wikipedia, Spotify and Musicbrainz links link to the same person.
2. Please check if the offical website of the artist can be found (no Facebook pages at this stage yet.)
3. If the official website is missing, fill it in as https://example_band.com/url/.
4. If the date of birth is missing, try to look it up in a reliable source.
5. If the place of birth is odd, just try to look up the real place of birth in a reliable source.
6. You can add manually artists to the table.

To help an easy workflow, we created two versions of this table: an easy-to-click through HTML version, and an (almost) identitcal, but easier to edit Google Spreadsheet version. This table contains all female and male singers known to Wikipedia.

• For instructions check the Lithuanian version here. Beware that the table is not clickable in PDF and Word outputs.

• The editable version for this table can be found on the Google Drive (you can click here if you have access to the Ukrainian files.)

You must edit at least one column.

curator_verified: It is set by default to 0. Change it to 1 if you did not need to modify anything. Change it to 2 if you did make a change. curator_comment: Explain in the comment field with a few words what did you change.

Do not change the ordering of the table and the current columns. You can add further columns for yourself, but do not change the table structure.

If you do so, make sure that the artist has a properly spelled name, and at least one of the possible URLs,i.e. at least Wikipedia, Spotify, Musicbrainz, or official website. For artists only on Bandcamp, etc… we will get there next time.

musician_identity_table(UA_singers, "excel") %>%
  writexl::write_xlsx( path=file.path(here::here(), "to_verify", "UA_singers.xlsx"))

What will we do with this data?

• The Listen Local System uses the semantic web to find reliable information about the biographies and works of these artists. Once we clarified the identity of the artist, we will automatically connect plenty of information, and curators will receive similar tables to review critical information.
• We created this information from Wikipedia’s database, Wikidata, which contains all the data of all well formatted Wikipedia pages. Some Wikipedia pages are not well formatted, we will get back to those later. We will create similar discovery tables from Bandcamp, Spotify, MusicBrainz. This is a fast way of creating the Czech Demo Music Database, and pre-populate it with a large amount of data to start developing new apps and services.
• We will always flag missing information and wrong information, and we will show with data comparisons why it is damaging to the artist (in this case, Czech singers) to have wrong information, for example, erroneous or not professionally edited Wikipedia pages.
• We will ask artists to opt-in and self-correct that information and offer them services to correct their biographical information on websites that Spotify, YouTube, journalist, etc. use to learn about their music.

This is just the beginning.

Brainstorming

• Our application will help our curators to develop new playlist, for example, just from this information, the Listen Local App will give you a first version of Czech singer-songwriters playlist, a Czech Acoustic Singer-Songwriter Women list on Spotify (and later on YouTube.) The curator can add /change songs on that list and find new artists unknown to us. [First microservice prototypes for music fans and supporters of our ideas]((https://listen-local-collaboration.dataobservatory.eu/listenlocal.html#prototype-fans-supporters)

• We can offer a service to band managers/lables to make verify that all their artists information is properly presented in all major web services, be it information services like All Music, MusicBrainz, FreeDB or Wikipedia (all langauge versions) or streaming platforms such as Spotify or YouTUbe. First microservice prototypes for artists and managers

• First microservice prototypes for venues and programmers

We will create a regularly updated, automatically created list of all Czech singers in this table, with their single most popular song on Spotify. When the popularity of their songs change, the playlist will be updated (every week once.)

• We will measure the relative performance of artists with correct and incorrect data, and before/after scenarios. We will create write-in databases for countries where we have no curators now (for example, Latvia), to measure week by week the performance difference. For example, how do 10 Czech aritsts of Spotify poularity of 5 compare to 10 Latvian artits of popularity 5 one week, one month, two months after the Czech data was corrected but the Latvian not? Etc.