This is a mirror to the example dataset "The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation" paper by Manco et al. Project page on Github: https://github.com/mulab-mir/song-describer-dataset Dataset on Zenodoo: https://zenodo.org/records/10072001 Explore the dataset on your local machine: import datasets from renumics import spotlight

ds = datasets.load_dataset('renumics/song-describer-dataset') spotlight.show(ds)

Song Describer is a data collection platform for annotating music with textual descriptions.

Dataset Summary MuChoMusic is a benchmark designed to evaluate music understanding in multimodal audio-language models (Audio LLMs). The dataset comprises 1,187 multiple-choice questions created from 644 music tracks, sourced from two publicly available music datasets: MusicCaps and the Song Describer Dataset (SDD). The questions test knowledge and reasoning abilities across dimensions such as music theory, cultural context, and functional applications. All questions and answers have been… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/muchomusic.

Dataset Description

This dataset contains a collection of Fiona Apple's songs along with audio feature data sourced from Spotify. The dataset provides insights into musical attributes across multiple albums, allowing for analysis of tempo, mood, energy, and more. It can be used to study trends and characteristics in Fiona Apple’s music, explore the evolution of her musical style, or perform mood and genre-related analyses.

Data Overview

• Total Tracks: 50+ songs
• Albums: Includes songs from albums like Fetch the Bolt Cutters, The Idler Wheel…, Extraordinary Machine, When The Pawn…, Tidal, and singles.
• Date Range: Covers songs released from 1996 to 2021.

Column Descriptions

Each column in the dataset represents a unique characteristic of the song, as described below:

1. track_name:

   • Description: The title of the song.
   • Type: Text
   • Example: "I Want You To Love Me"

2. album_name:

   • Description: The name of the album the song appears on.
   • Type: Text
   • Example: "Fetch The Bolt Cutters"

3. release_date:

   • Description: The release date of the song (format: YYYY-MM-DD).
   • Type: Date
   • Example: "2020-04-17"

4. duration_ms:

   • Description: The duration of the song in milliseconds.
   • Type: Integer
   • Example: 237713 (represents ~3 minutes and 57 seconds)

5. key:

   • Description: The musical key of the song (0-11, where 0 = C, 1 = C#/Db, …, 11 = B).
   • Type: Integer
   • Example: 9 (represents A key)

6. mode:

   • Description: The modality of the song (1 = major, 0 = minor).
   • Type: Integer (binary)
   • Example: 1 (major mode)

7. danceability:

   • Description: Describes how suitable a track is for dancing. Ranges from 0.0 (least danceable) to 1.0 (most danceable).
   • Type: Float
   • Example: 0.528

8. energy:

   • Description: Measure of intensity and activity in the track. Ranges from 0.0 to 1.0.
   • Type: Float
   • Example: 0.504

9. loudness:

   • Description: Average loudness of the track in decibels (dB), where values typically range between -60 and 0.
   • Type: Float
   • Example: -8.821

10. speechiness:

    • Description: Estimates the presence of spoken words in a track. Higher values indicate more speech-like qualities.
    • Type: Float
    • Example: 0.09

11. acousticness:

    • Description: Likelihood of a track being acoustic. Ranges from 0.0 (not acoustic) to 1.0 (highly acoustic).
    • Type: Float
    • Example: 0.559

12. instrumentalness:

    • Description: Predicts whether a track has no vocals. The closer to 1.0, the more instrumental the track is.
    • Type: Float
    • Example: 0.0414

13. liveness:

    • Description: Detects the likelihood of a live performance. Values above 0.8 indicate a high probability of being live.
    • Type: Float
    • Example: 0.0936

14. valence:

    • Description: Indicates the musical positivity conveyed by a track, ranging from 0.0 (sad) to 1.0 (happy).
    • Type: Float
    • Example: 0.712

15. tempo:

    • Description: The speed of the track in beats per minute (BPM).
    • Type: Float
    • Example: 141.369

The Dataset's Purpose: This dataset's goal is to give a complete collection of music facts and lyrics for study and development. It aspires to be a useful resource for a variety of applications such as music analysis, natural language processing, sentiment analysis, recommendation systems, and others. This dataset, which combines song information and lyrics, can help academics, developers, and music fans examine and analyse the link between listeners' preferences and lyrical content.

Dataset Description:

The music dataset contains around 660 songs, each with its own set of characteristics. The following characteristics are included in the dataset:

Name: The title of the song. Lyrics: The lyrics of the song. Singer: The name of the singer or artist who performed the song. Movie: The movie or album associated with the song (if applicable). Genre: The genre or genres to which the song belongs. Rating: The rating or popularity score of the song from Spotify.

The dataset is intended to give a wide variety of songs from various genres, performers, and films. It includes popular songs from numerous ages and places, as well as a wide spectrum of musical styles. The lyrics were obtained from publically accessible services such as Spotify and Soundcloud, and were converted from audio to text using speech recognition algorithms. While every attempt has been taken to assure correctness, please keep in mind that owing to the limits of the data sources and voice recognition algorithms, there may be inaccuracies or missing lyrics encountered upon transcribing.

Use Cases in Research and Development:

This music dataset has several research and development applications. Among the possible applications are:

1. Music Analysis: By analysing the links between song elements such as genre, vocalist, and rating, researchers can acquire insights into the features and patterns of various music genres.
2. Natural Language Processing (NLP): NLP researchers may use the lyrics to create language models, sentiment analysis algorithms, topic modelling approaches, and other text-based music studies.
3. Recommendation Systems: Using the information, developers may create recommendation systems that offer music based on user preferences, lyrics sentiment, or genre similarities.
4. Music Generating Machine Learning Models: The dataset may be used to train machine learning models for generating new lyrics or making music compositions.
5. Music Sentiment Analysis: To get insights into the emotional components of music and its influence on listeners, researchers might analyse the feelings conveyed in song lyrics.
6. Movie Soundtracks Analysis: Researchers can explore the association between song attributes and their use in movie soundtracks by investigating the movie attribute.

Overall, the goal of this music dataset is to provide a rich resource for academics, developers, and music fans to investigate the complicated relationships between song features, lyrics, and numerous research and development applications in the music domain.

This is the statistics for the Top 10 songs of various spotify artists and their YouTube videos. The Creators above generated the data and uploaded it to Kaggle on February 6-7 2023. The license to use this data is "CC0: Public Domain", allowing the data to be copied, modified, distributed, and worked on without having to ask permission. The data is in numerical and textual CSV format as attached. This dataset contains the statistics and attributes of the top 10 songs of various artists in the world. As described by the creators above, it includes 26 variables for each of the songs collected from spotify. These variables are briefly described next:

Track: name of the song, as visible on the Spotify platform. Artist: name of the artist. Url_spotify: the Url of the artist. Album: the album in wich the song is contained on Spotify. Album_type: indicates if the song is relesead on Spotify as a single or contained in an album. Uri: a spotify link used to find the song through the API. Danceability: describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. Energy: is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. Key: the key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. Loudness: the overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. Speechiness: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. Acousticness: a confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. Instrumentalness: predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. Liveness: detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. Valence: a measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). Tempo: the overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. Duration_ms: the duration of the track in milliseconds. Stream: number of streams of the song on Spotify. Url_youtube: url of the video linked to the song on Youtube, if it have any. Title: title of the videoclip on youtube. Channel: name of the channel that have published the video. Views: number of views. Likes: number of likes. Comments: number of comments. Description: description of the video on Youtube. Licensed: Indicates whether the video represents licensed content, which means that the content was uploaded to a channel linked to a YouTube content partner and then claimed by that partner. official_video: boolean value that indicates if the video found is the official video of the song. The data was last updated on February 7, 2023.

Rank of models that describe song minimum frequency across all sites.

Slovenian Folk Song Ballads 404 Slovenian folk song ballads with family themes, collected between the years 1819 and 1995. This dataset is an archive of the “Slovenian Folk Song Ballads” corpus (scores, measure maps, analyses, recordings, synchronizations, metadata). It provides both raw data and data for integration with the Dezrann music web platform: https://www.dezrann.net/explore/slovenian-folk-song-ballads. Family songs are folk song ballads, i.e., songs with relatively short but repetitive melodic sections through which the singer narrates a longer story through the lyrics. The melody and lyrics are known not to be fully dependent on each other, meaning, the same melody could be adapted for another lyric and vice versa. Thematically, they fall into the category of those that sing about family destinies, including several motifs from Slovenian, Slavic, and broader European themes. The content often focuses on describing the natural course of family life, from courtship to childbirth. The themes and motifs of family ballads (feudal, rural environment, the time of Turkish invasions, pilgrimages, etc.) revolve around socio-legal relations and both immediate and broader family matters in historical periods from which individual ballads originate. The collection of Slovenian folk song ballads contains transcribed field material collected by Slovenian ethnologists, folklorists, ethnomusicologists and various colleagues of Glasbenonarodopisni inštitut ZRC SAZU spanning from the years 1819 to 1995. Categorized thematically as family ballads, this collection features 404 folk songs, and includes the initial verse of the lyrics, extensive metadata, and musical analysis, encompassing contours, harmony, and song structure (melody and lyric) (see (Borsan et al., 2023) and (Borsan et al., under submission)). 23 of these songs have historical recordings. License: CC-BY-NC-SA-4.0 Maintainers: Vanessa Nina Borsan vanessa@algomus.fr, Mathieu Giraud mathieu@algomus.fr References (Borsan et al., submitted) https://dx.doi.org/10.57745/SINZFK https://www.algomus.fr/data Dataset content slovenian-folk-song-ballads.json. Main archive catalog, with metadata, as described on https://doc.dezrann.net/metadata score/: Scores measure-map/: Measure maps as defined by https://doi.org/10.1145/3625135.3625136, analysis/: Analyses, in .dez format, as described on https://doc.dezrann.net/dez-format audio/: Audio recordings synchro/: Sychronizations between musical time and audio time, as described on https://doc.dezrann.net/synchro

Dataset Description

This dataset dives deep into the mesmerizing world of Björk Guðmundsdóttir, an artist known for breaking boundaries across music, art, and technology. Spanning decades of Björk’s career, this dataset offers comprehensive data on her songs, including Spotify audio features and lyrical content, providing an unparalleled opportunity to explore her artistry through data.

Dataset Overview

• Total Tracks: Over 200 songs from Björk’s extensive discography.
• Albums Included: From iconic albums like Debut and Homogenic to Vulnicura and Utopia, including singles and collaborations.
• Time Span: Covers Björk’s music from 1993 to 2023, offering a longitudinal perspective on her artistic journey.

Column Descriptions

Each column captures a unique characteristic of Björk’s music:

Context

he music industry has a well-developed market with a global annual revenue around $15 billion. The recording industry is highly competitive and is dominated by three big production companies which make up nearly 82% of the total annual album sales.

Artists are at the core of the music industry and record labels provide them with the necessary resources to sell their music on a large scale. A record label incurs numerous costs (studio recording, marketing, distribution, and touring) in exchange for a percentage of the profits from album sales, singles and concert tickets.

Unfortunately, the success of an artist's release is highly uncertain: a single may be extremely popular, resulting in widespread radio play and digital downloads, while another single may turn out quite unpopular, and therefore unprofitable.

Knowing the competitive nature of the recording industry, record labels face the fundamental decision problem of which musical releases to support to maximize their financial success.

How can we use analytics to predict the popularity of a song? In this assignment, we challenge ourselves to predict whether a song will reach a spot in the Top 10 of the Billboard Hot 100 Chart.

Taking an analytics approach, we aim to use information about a song's properties to predict its popularity. The dataset songs.csv consists of all songs which made it to the Top 10 of the Billboard Hot 100 Chart from 1990-2010 plus a sample of additional songs that didn't make the Top 10. This data comes from three sources: Wikipedia, Billboard.com, and EchoNest.

The variables included in the dataset either describe the artist or the song, or they are associated with the following song attributes: time signature, loudness, key, pitch, tempo, and timbre.

Content

Here's a detailed description of the variables:

year = the year the song was released songtitle = the title of the song artistname = the name of the artist of the song songID and artistID = identifying variables for the song and artist timesignature and timesignature_confidence = a variable estimating the time signature of the song, and the confidence in the estimate loudness = a continuous variable indicating the average amplitude of the audio in decibels tempo and tempo_confidence = a variable indicating the estimated beats per minute of the song, and the confidence in the estimate key and key_confidence = a variable with twelve levels indicating the estimated key of the song (C, C#, . . ., B), and the confidence in the estimate energy = a variable that represents the overall acoustic energy of the song, using a mix of features such as loudness pitch = a continuous variable that indicates the pitch of the song timbre_0_min, timbre_0_max, timbre_1_min, timbre_1_max, . . . , timbre_11_min, and timbre_11_max = variables that indicate the minimum/maximum values over all segments for each of the twelve values in the timbre vector (resulting in 24 continuous variables) Top10 = a binary variable indicating whether or not the song made it to the Top 10 of the Billboard Hot 100 Chart (1 if it was in the top 10, and 0 if it was not)

Acknowledgements

MITx ANALYTIX

This upload contains the supplementary material for our paper presented at the MMM2024 conference.

Dataset

The dataset contains rich text descriptions for music audio files collected from Wikipedia articles.

The audio files are freely accessible and available for download through the URLs provided in the dataset.

Example

A few hand-picked, simplified examples of the dataset.

Dataset structure

We provide three variants of the dataset in the data folder.

All are described in the paper.

1. all.csv contains all the data we collected, without any filtering.
2. filtered_sf.csv contains the data obtained using the self-filtering method.
3. filtered_mc.csv contains the data obtained using the MusicCaps dataset method.

File structure

Each CSV file contains the following columns:

• file: the name of the audio file
• pageid: the ID of the Wikipedia article where the text was collected from
• aspects: the short-form (tag) description texts collected from the Wikipedia articles
• sentences: the long-form (caption) description texts collected from the Wikipedia articles
• audio_url: the URL of the audio file
• url: the URL of the Wikipedia article where the text was collected from

Citation

@inproceedings{wikimute,
  title = {WikiMuTe: {A} Web-Sourced Dataset of Semantic Descriptions for Music Audio},
  author = {Weck, Benno and Kirchhoff, Holger and Grosche, Peter and Serra, Xavier},
  booktitle = "MultiMedia Modeling",
  year = "2024",
  publisher = "Springer Nature Switzerland",
  address = "Cham",
  pages = "42--56",
  doi = {10.1007/978-3-031-56435-2_4},
  url = {https://doi.org/10.1007/978-3-031-56435-2_4},
}

License

The data is available under the Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license.

Each entry in the dataset contains a URL linking to the article, where the text data was collected from.

YouTube Music Hits Dataset

A collection of YouTube music video data sourced from Wikidata, focusing on videos with significant viewership metrics.

  Dataset Description





  Overview

24,329 music videos View range: 1M to 5.5B views Temporal range: 1977-2024

  Features

youtubeId: YouTube video identifier itemLabel: Video/song title performerLabel: Artist/band name youtubeViews: View count year: Release year genreLabel: Musical genre(s)

  View… See the full description on the dataset page: https://huggingface.co/datasets/akbargherbal/youtube-music-hits.

Context Songs, like any other audio signal, feature distinctive fundamental frequencies, timbre components, and other properties. Each song is unique in these respects, which is why they can be patterned.

Objective Your task is to use machine learning models to predict the release year (between 1922 and 2011) of a song that is described by 90 attributes of average timbre and covariance.

Data Description TA01 to TA12 – Timbre avarages TC01 to TC78 – Timbre covariances Year – Release year

The song of the northern mockingbird, Mimus polyglottos, is notable for its extensive length and inclusion of numerous imitations of several common North American bird species. Because of its complexity, it is not widely studied by birdsong scientists. When they do study it, the specific imitations are often noted, and the total number of varying phrases. What is rarely noted is the systematic way the bird changes from one syllable to the next, often with a subtle transition where one sound is gradually transformed into a related sound, revealing an audible and specific compositional mode. It resembles a common strategy in human composing, which can be described as variation of a theme. In this paper, we present our initial attempts to describe the specific compositional rules behind the mockingbird song, focusing on the way the bird transitions from one syllable type to the next. We find that more often than chance, syllables before and after the transition are spectrally related, i.e., transitions are gradual, which we describe as morphing. In our paper, we categorize four common modes of morphing: timbre change, pitch change, squeeze (shortening in time), and stretch (lengthening in time). This is the first time such transition rules in any complex birdsong have been specifically articulated.

The dataset contains 5 columns and 410 rows. Here's a summary of the columns and their potential usability for various stakeholders:

Song: Description: The name of the song. Usability: Useful for music analysts, fans, and stakeholders in marketing or event planning to identify or discuss specific songs. Artist(s): Description: The artist or group that performed the song. Usability: Beneficial for collaborations and promotional activities. Also helps listeners or fans identify their favorite artists. Writer(s): Description: The individual(s) or group(s) who wrote the song. Usability: Relevant for copyright discussions, royalties, and insights into songwriting trends for music producers and legal teams. Album(s): Description: The album to which the song belongs. Usability: Important for categorization and inventory management in streaming services or physical media retailers. Year: Description: The release year of the song or album. Usability: Helps track musical trends, analyze time-based popularity, and create chronological playlists. Observations: The columns Song, Artist(s), Album(s), and Year have significant missing values. This might limit their usability until cleaned. The Writer(s) column is nearly complete, making it the most reliable source of data in this dataset.

Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ".txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ".txt" extension. downloads_fs_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n. Two different datasets with users, items, implicit feedback interactions between users and items, item tags, and item text descriptions are provided, one for Music Recommendation (KGRec-music), and other for Sound Recommendation (KGRec-sound).

Setophaga townsendi is a species of wood-warbler (family Parulidae) in northwestern North America that has a geographic structure in the mitochondrial and nuclear genomes: while interior populations have differentiated mitonuclear ancestry from the sister species S. occidentalis, coastal populations have a mix of inland and S. occidentalis mitonuclear ancestries. This coastal-to-inland transition in genomic ancestry raises the possibility of similar geographic structure in phenotypic traits, especially those involved in mate choice. Using qualitative and multivariate approaches, we investigated whether there is a sharp transition between coastal and inland populations in both songs and nuclear DNA. We find there is a shallow geographic cline in the Type I song but not in the Type II song. Nuclear DNA shows a gradient between the coast and inland. There is little correlation between variation in song and the isolation-by-distance pattern in the nuclear DNA. The learned songbird song is s..., Data collection Song recordings were collected at 30 locations across British Columbia from May to July of 2017, using a Marantz PMD660 digital recorder and an Audio-Technica 815a Shotgun microphone. Recordings were typically eight to ten minutes long and consisted of ten to forty songs. For songs recorded after June 25th, a playback of song recordings was used to encourage birds to sing. We designed playbacks to consist of three song variants from different regions of the S. townsendi range, to avoid playback matching. This dataset consists of songs of 249 birds (180 from field recordings, 39 from Xeno-Canto, and 30 from Macaulay Library). For each bird, songs were characterized into types based on visual similarity and the results of Janes and Ryker (2016) and Janes (2017). We classified the clear song as the Type I song (i.e., used more in female attraction), and the buzzy song as the Type II song (i.e., used more in territorial defense). We randomly selected three numbers from the ..., , # Gradual transitions in genetics and songs between coastal and inland populations of Setophaga townsendi

This dataset consists of song recordings that were collected at 30 locations across British Columbia from May to July of 2017, using a Marantz PMD660 digital recorder and an Audio-Technica 815a Shotgun microphone.

Three songs of each song type were analyzed using Raven Pro 1.4 for an analysis of song variation across the range of Setophaga townsendi.

These song variables were used in a PCA to quantify song variation.

Description of the Data and file structure

This dataset consists of songs of 249 birds (180 from field recordings, 39 from Xeno-Canto, and 30 from Macaulay Library). For each bird, songs were characterized into types based on visual similarity and the results of Janes and Ryker (2016) and Janes (2017). We classified the clear song as the Type I song (i.e., used more in female attraction), and the buzzy song as the Type II song (i.e., used more in territ...

Dataset Card for Song A Day

I've been writing a song a day since 1/1/09. This dataset includes every song up through October 28th, 2024.

  Dataset Description

LYRICS: The lyrics part of the dataset has been cobbled together from several sources. While every song that was "written" has lyrics for it somewhere, I have not been the best at maintaining good data hygiene with them. Plus, a lot of the songs were improvised. For the improvised songs, I used AssemblyAI to… See the full description on the dataset page: https://huggingface.co/datasets/Jonathanmann/songaday.

This is a dataset of 1309 songbird (passeri) species for phylogenetic comparative analysis of female song incidence, elaboration, and length, and associated code and phylogenetic trees to recreate the analyses in the associated publication. Ordinal scales are based on published species accounts, primarily Birds of the World (see publication for full list and description of all song variables). Song variables are provided in both nominal and numerical ordinal formats. We scored three aspects of female song: (1) song incidence, (2) song quality or elaboration, and (3) song length. For each of these song variables, headers are listed in parentheses, with nomial headings listed first and numerical ordial headings listed second. Song incidence (DimorphSongOccurrence; DimorphSongOcc_ord; "occ") – How often or to what extent do females sing compared to males? 0 = female song absent, 1 = female song rare (most individuals do not sing; female song has only been observed in a few individuals or certain populations some years), 2 = female song occurs occasionally (it is observed periodically in some individuals but occurs noticeably less than male song or only during truncated parts of the year), 3 = female song occurs regularly (it can be reliably observed in many or most individuals but is somewhat less obvious than male song), 4 = female song occurs to the same extent as male song, 5 = females sing more than males. Elaboration (DimorphSongElab; DimorphSongElb_ord; "elb") – To what extent are female songs described as ‘elaborate’ compared to male songs? This often included qualitative descriptions of song complexity, amplitude, or strength (e.g., female songs were often described as softer or weaker than male song). 0 = female song absent, 1 = female song is substantially less elaborate than male song, 2 = female song is somewhat less elaborate than male song, 3 = female song is similarly elaborate to male song, 4 = female song is more elaborate than male song. Because length was scored independently of elaboration, we did not include information on song length in this elaboration score. Length (DimorphSongLength; DimorphSongLen_ord; "len") – How does the duration of female songs compare to male song? 0 = female song absent, 1 = female song is substantially shorter than male song, 2 = female song is somewhat shorter than male song, 3 = female song is similar in length to male song, or 4 = female song is longer than male song. Female song present vs absent (FemSongFinal_PrsAbs; prs_abs) - For some analyses, we also collapsed song incidence into a binary variable representing female song absent (absent=0) vs present (present=1)

The predictor variables used to test hypotheses associated with female song were compiled from several sources including: (1) daily nest predation rates from Unzeta et al. 2020: Unzeta, M., Martin, T. E., & Sol, D. (2020). Daily nest predation rates decrease with body size in passerine birds. The American Naturalist, 196, 743-754. (2) life- and natural history traits from Dale et al. 2015: Dale, J., Dey, C. J., Delhey, K., Kempenaers, B., & Valcu, M. (2015). The effects of life history and sexual selection on male and female plumage colouration. Nature, 527, 367-370. (3) territoriality and duet data from Tobias et al. 2016: Tobias, J. A., Sheard, C., Seddon, N., Meade, A., Cotton, A. J., & Nakagawa, S. (2016). Territoriality, social bonds, and the evolution of communal signaling in birds. Frontiers in Ecology and Evolution, 4, 74. A brief description of each predictor variable is below. For more details, see the supplementary methods associated with the publication. Daily nest predation rates (DPR) – Calculations from both personal field data (as exposure days calculated by the Mayfield method; Mayfield 1961) and nest success reported from the literature. Cavity nesters were omitted from Unzeta et al. 2020, but added to the current study when available from field studies (T. Martin unpublished data) and the literature (Martin 1995, Martin and Clobert 1996, Remes et al. 2012). Breeding latitude (degrees_from_equator) – Each species’ geographical location was computed as the latitude (degrees from equator) of the breeding range centroid. Body size (log mass; log_CRC_species_mass) – Body mass data was collated from Dunning 2008. These data were log-transformed prior to statistical analysis. Sexual size dimorphism (SSD_wing) – Sexual size dimorphism was calculated as the log (male wing length) − log(female wing length) to provide a proportional index of relative sizes of the sexes. See Dale et al. 2007 for a detailed explanation of this metric. Biparental care (paternal_care) – Biparental care was scored as 0 = absent or 1 = present primarily based on data provided in Cockburn 2006. Cooperative breeding (cooperation) – Cooperative breeding was scored as 0 = absent, 0.5 = suspected, or 1 = present also primarily based on data from Cockburn 2006. For species in our data set not present in Cockburn 2006, additional parental care scores were obtained from del Hoyo et al. 2003-2011. Social mating system (mating_system) – Social polygyny was scored on a four-point scale following Owens and Hartley 1998, with 0 = strict social monogamy, 1 = monogamy with infrequent instances of polygyny (20% of males; e.g., red-winged blackbird, Agelaius phoeniceus) or lek polygyny (e.g., lance-tailed manakin, Chiroxiphia lanceolata). Migratory behaviour (Migratory) – Migration was scored on a scale from 0 to 2, with 0 = resident (breeding and non-breeding ranges identical), 1 = partial migration (some overlap between breeding and non-breeding ranges), 2 = complete migration (no overlap between breeding and non-breeding ranges). Assignments were made based on the range maps within del Hoyo et al. 2003-2011. Territoriality (Territory) – Species were classified as 0 = non-territorial, 1 = seasonally or weakly territorial, or 2 = year-round territorial. We defined year-round territoriality as territory defence lasting throughout the year rather than residency within a restricted area, including migrants that are territorial on both the breeding and non-breeding grounds. Species that are vocal and aggressive (responsive to playbacks) for part of the year but remain in the same area silently for the rest of the year were classified as seasonal rather than year-round territorial. Seasonal or weak territoriality primarily included species with broadly overlapping home ranges or that joined mixed species flocks. Non-territorial species never defend territories and included species that defend a very small area around a nest site. Duetting (Duet) – Duets were scored as 0 = absent or 1 = present for each species and were defined as acoustic signals involving two individuals. In line with previous work, duets had to be composed of long-range acoustic signals that are coordinated or stereotyped in some way, whether they be loosely synchronous, regularly alternating, or precisely interwoven. While duets of songbirds are often comprised of songs, duets could include other long-range vocalizations with song-like functions. Duet presence/absence was scored based on a variety of sources, including published literature, field observations, regional experts, and sound archives. Female Song Present, Absent, or duetting species (prs_abs_duet) - a 3 category ordinal scale of In addition to the above predictor variables, we measured seasonality and wing length (see Dale et al. 2015 for a description of these variables). These two variables were highly correlated (r > 0.85) with latitude and Log mass, respectively, and we therefore left them out of final analyses. Additional variables are included from the original datasets of Unzeta et al. 2020, Dale et al. 2015, Tobias et al. 2016.

In the avian sub-order Passeri (the songbirds), song develops according to both a flexible neural template and auditory input from conspecifics, making innately-constrained characters of song difficult to isolate. In a hybridizing population of Black-capped Chickadees (Poecile atricapillus) and Carolina Chickadees (Poecile carolinensis), we found that genetic ancestry was weakly predictive of a multidimensional measure of song variety (a continuously distributed quantitative alternative to categorical song repertoire size) but did not successfully predict one-dimensional song variety. We used species-diagnostic autosomal markers to genotype 55 individuals inside and outside of the Black-capped Chickadee/Carolina Chickadee hybrid zone in Missouri and Kansas. Using active recording methods, we then obtained high-volume, high-quality song recordings of 10 genotyped chickadees from a single hybrid zone population on a small, lake-bounded peninsula in west-central Missouri. We extracted acou..., , , # Data from: Genotype predicts quantitative song variety in a chickadee hybrid zone despite limited sampling

Dataset DOI: 10.5061/dryad.v6wwpzh6g

Description of the data and file structure

Files and variables

Folders

Sparrowfoot_Park_original_recordings.zip

Description:Â This folder contains the recordings made at Sparrowfoot Park from which data were extracted in their original state. Recordings are split into folders by individual bird, and each folder is named with the bird's individual ID code used in publication and, in parentheses, an informal name with which it was referred to in the field. Metadata is included verbally at the end of each recording and embedded within the file names in the following format: Poecile.sp_LeftColorBands.RightColorBands_Date_Locality.County.State_RecordistInitials_RecordingSequence. All files are in .wav format.

Chickadee-MS-directory.zip

Description:Â This folder is the working director...,