The Discogs Data Dumps (August 2025) provide a comprehensive archive of the Discogs music database, offering detailed metadata on releases, artists, labels, and marketplace data. This dataset includes structured information on millions of vinyl records, CDs, digital releases, and more, making it an invaluable resource for music researchers, collectors, and developers.

The archive is available in JSON format and contains various data files, including release details, artist discographies, label catalogs, and user-generated contributions. It is regularly updated and serves as a foundation for building applications, analyzing music trends, and exploring Discogs’ extensive music catalog.

About

From website:

Welcome to Discogs, a community-built database of music information. Imagine a site with discographies of all labels, all artists, all cross-referenced. It's getting closer every day.

Openness/re-use

All material is in the public domain:

This data is released under the Public Domain license: http://creativecommons.org/licenses/publicdomain/

Data dumps are available at:

• http://www.discogs.com/data/

Other formats

An RDF version is available as package:data-incubator-discogs.

This dataset contains information about audio recordings, including commercial releases, promotional releases and bootleg or off-label releases. Discogs is one of the largest online databases of electronic music releases and of releases on vinyl media. The source data comes from submissions contributed by users who have registered accounts on discogs.com. This version is based on the regularly monthly data releases from the website which have been placed into the Public Domain.

Note this RDF version of Discogs is no longer updated, it was taken off-line during the shutdown of Kasabi. A dump of the dataset has been uploaded to the Internet Archive

Traffic analytics, rankings, and competitive metrics for discogs.com as of June 2025

Discogs is an online platform and database focusing on audio recording. Its domain, discogs.com, was registered on the 30th of August 2000 and it is currently owned by Zink Media Inc., a company based in Portland (Oregon), U.S. The mobile app of the website is currently available for download on both the Apple App Store and Google Play Store. According to a study conducted by Airnow from June 2019 to June 2020, the Discogs app recorded the highest number of downloads on the Apple App Store in January 2020. On this month, the app registered roughly ************ downloads. Furthermore, the app recorded its highest number of downloads on the Google Play Store in May 2020, reaching about *** thousand downloads.

The statistic above presents the most expensive items sold on Discogs as of March 2019. The most expensive item sold on the online music database in the measured period was 'The Black Album' by Prince, which sold for ****** U.S. dollars. Also in the list was 'God Save The Queen' by the Sex Pistols and 'Love Me Do' by The Beatles.

The statistic above presents the most expensive items listed on Discogs between January and March 2018. The most expensive item listed on the online music database was a promotional 7-inch single of 'Love Me Do' by The Beatles, priced at over ****** U.S. dollars. Pink Floyd's sixth studio album 'Meddle' also featured in the top five, and was available in early 2018 on Discogs on blue transparent vinyl for ***** U.S. dollars.

This dataset contains AllMusic ground-truth genre annotations and is complementary to the rest of the AcousticBrainz Genre datasets distributed at https://zenodo.org/record/2553414.

The MediaEval AcousticBrainz Genre datasets are datasets of genre annotations and music features extracted from audio suited for evaluation of hierarchical multi-label genre classification systems.

The datasets are used within the MediaEval AcousticBrainz Genre Task. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems.

We provide four datasets containing genre and subgenre annotations extracted from four different online metadata sources:

AllMusic and Discogs are based on editorial metadata databases maintained by music experts and enthusiasts. These sources contain explicit genre/subgenre annotations of music releases (albums) following a predefined genre namespace and taxonomy. We propagated release-level annotations to recordings (tracks) in AcousticBrainz to build the datasets.

Lastfm and Tagtraum are based on collaborative music tagging platforms with large amounts of genre labels provided by their users for music recordings (tracks). We have automatically inferred a genre/subgenre taxonomy and annotations from these labels.

For details on format and contents, please refer to the data webpage.

Citation

If you use the MediaEval AcousticBrainz Genre dataset or part of it, please cite our ISMIR 2019 overview paper:

Bogdanov, D., Porter A., Schreiber H., Urbano J., & Oramas S. (2019). The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale. 20th International Society for Music Information Retrieval Conference (ISMIR 2019).

Acknowledgements

This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 AudioCommons.

The AcousticBrainz Genre Dataset consists of four datasets of genre annotations and music features extracted from audio suited for evaluation of hierarchical multi-label genre classification systems.

The datasets are used within the MediaEval AcousticBrainz Genre Task. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems.

We provide four datasets containing genre and subgenre annotations extracted from four different online metadata sources:

AllMusic and Discogs are based on editorial metadata databases maintained by music experts and enthusiasts. These sources contain explicit genre/subgenre annotations of music releases (albums) following a predefined genre namespace and taxonomy. We propagated release-level annotations to recordings (tracks) in AcousticBrainz to build the datasets.

Lastfm and Tagtraum are based on collaborative music tagging platforms with large amounts of genre labels provided by their users for music recordings (tracks). We have automatically inferred a genre/subgenre taxonomy and annotations from these labels.

For details on format and contents, please refer to the data webpage.

Note, that the AllMusic ground-truth annotations are distributed separately at https://zenodo.org/record/2554044.

Citation

If you use the MediaEval AcousticBrainz Genre dataset or part of it, please cite our ISMIR 2019 overview paper:

Bogdanov, D., Porter A., Schreiber H., Urbano J., & Oramas S. (2019). The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale. 20th International Society for Music Information Retrieval Conference (ISMIR 2019).

Acknowledgements

This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 AudioCommons.

In the first quarter of 2019, the self-titled album by Bathory, a heavy metal band from Sweden, was the most sold item on Discogs in Italy. The LP was sold for 1,902.03 U.S. dollars. Discogs is an online platform and database focusing on audio recording. Its domain, discogs.com, was registered on the 30th of August 2000 and it is currently owned by Zink Media Inc., a company based in Portland (Oregon), U.S.

What is STraDa?

STraDa is a dataset that was presented at the late breaking demo session of ISMIR 2023. The detailed description of the dataset is in README.md.

STraDa is large-scale music audio dataset that contains singers' metadata, tracks' metadata, IDs for downloading audios of 30s (preview parts) by using Deezer API. This dataset could be used for various MIR tasks, such as singer identification, singer recognition, singer gender/age detection, genre classification, language classification. The training set contains 25194 excerpts of 30s, and 5264 singers. The testing set contains 200 songs from 200 singers that are balanced across two genders, 5 languages and 4 age groups (5 song/gender/language/age group), that could be used for bias analysis.

What does STraDa contain?

An important feature of STraDa is that each track only has a single lead singer, which improves the accuracy of annotations.

The annotations in the training set are gathered and cross-validated from 4 different data sources: Deezer, Wikidata, musicbrainz, discogs.

The testing set is curated and annotated manually to ensure perfect accuracy.

Singers' metadata contains gender, birth year and active country. Tracks' metadata contains genre, language and release date.

What could STraDa be used for?

STraDa could be used for singer identification, singer recognition, singer gender/age detection, song genre/language identification. The balance in the testing set could enable bias analysis.

Dataset use

This dataset is only available for conducting non-commercial research related to audio analysis under license Creative Commons Attribution Non Commercial 2.5 Generic. It's important to note that data under this license are data contained in STraDa, not applicable to audios. We do NOT grant permission for any modification, generation or manipulation using these audios.

We wholeheartedly welcome researchers to use STraDa for their own research purpose. Please send an email to ykong@deezer.com if you have any questions about the data.

Citation

If you use STraDa, please cite following paper:

@inproceedings{kong2024stradasingertraitsdataset, title={STraDa: A Singer Traits Dataset}, author={Yuexuan Kong and Viet-Anh Tran and Romain Hennequin}, booktitle={Interspeech 2024}, year={2024} }