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openCC (other)Jun 2025View details →

UCSB SONGS Mitigation Monitoring: Reef Survey - Benthic Algae and Invertebrate Abundance

These data describe annual estimates of the density of benthic algae and macroinvertebrates at three subtidal reefs collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2009 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA). In the summer of each year, divers identified and counted species of benthic algae and macroinvertebrates in quadrats of varying size that were uniformly distributed along fixed transects at each reef.

openCC (other)Jun 2025View details →

UCSB SONGS Mitigation Monitoring: Reef Survey - Benthic Algae, Invertebrate, and Substrate Cover

These data describe annual estimates of the percent cover of benthic macroalgae, sessile macroinvertebrates, and hard and soft substrates at three subtidal reefs collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2009 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA). In the summer of each year, divers identified and recorded species of sessile algae and macroinvertebrates, and substrate types under twenty uniformly placed points within five 1 m2 quadrats that were uniformly distributed along semi-permanent transects at each reef.

openCC (other)Jun 2023View details →

UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Vegetation Cover

These data describe estimates of the percent cover of marsh plants in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, species of marsh plants were identified and recorded under 98 uniformly spaced points within 4.5 m2 quadrats in each plot.

openCC (other)Jun 2023View details →

UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Plant Size

These data describe estimates of the condition and size of three species of salt marsh plants (Arthrocnemum subterminale, Frankenia salina, and Salicornia virginica) planted in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, the length of the longest axis and maximum perpendicular width of each planted individual was measured.

openCC (other)Jun 2023View details →

UCSB SONGS Mitigation Monitoring: Wetland Process Study – Soil Properties

These data describe physical and chemical properties of soil samples collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2019 at the San Dieguito Wetland in San Diego County, CA. Additional locations at Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh is Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA were added in 2021. Sampling occurred sporadically at various locations in each wetland. All soil samples were characterized for organic matter content and particle size. Additional properties were characterized for select soil samples.

opencc-by-4.0May 2020View details →

ESSENTIA analysis of audio snippets from the Million Song Dataset Taste Profile subset

This upload includes the ESSENTIA analysis output of (a subset of) song snippets from the Million Song Dataset, namely those included in the Taste Profile subset. The audio snippets were collected from 7digital.com and were subsequently analyzed with ESSENTIA 2.1-beta3. Pre-trained SVM models provided by the ESSENTIA authors on their website were applied. The file msd_song_jsons.rar contains the ESSENTIA analysis output after applying the SVM models for highlevel feature extraction. Please note that these are 204317 files. The file msd_played_songs_essentia.csv.gz contains all one-dimensional real-valued fields of the jsons merged into one csv file with 204317 rows. The full procedure and subsequent analysis is described in Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2019). Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs. Psychology of Music, 0305735619868280.

opencc-by-4.0Sep 2019View details →

Song_et_al_2020_ERSS_DATASET

This dataset contains the underlying data for the following publication: Song, L., Lieu, J., Nikas, A., Arsenopoulos, A., Vasileiou, G., & Doukas, H. (2020). Contested energy futures, conflicted rewards? Examining low-carbon transition risks and governance dynamics in China's built environment. Energy Research & Social Science, 59, 101306., https://doi.org/10.1016/j.erss.2019.101306. Full details of methods used to create the dataset and provided within this publication.

opencc-by-4.0Jul 2017View details →

MSD-A: Million Song Dataset for Artists

The MSD-A is a dataset related to the Million Song Dataset (MSD). It is a collection of artist tags and biographies gathered from Last.fm for all the artists that have songs in the MSD. In addition, the MSD Taste Profile (recommendation dataset) is adapted to artists. We provide the biographies, tags, data splits, and feature embeddings to reproduce the experiments from the paper: Oramas S., Nieto O., Sordo M., & Serra X. (2017) A Deep Multimodal Approach for Cold-start Music Recommendation. https://arxiv.org/abs/1706.09739 Source code is available at https://github.com/sergiooramas/tartarus The file dlrs-data.tar.gz in this zenodo version is corrupted. You can download the good file in this link: https://drive.google.com/open?id=0B-oq_x72w8NUbUpkMzZSc1JPd28

opencc-zeroNov 2023View details →

Male song structure predicts offspring recruitment to the breeding population in a migratory bird

Bird song is a classic example of a sexually selected trait, but much of the work relating individual song components to fitness has not accounted for song typically being composed of multiple, often-correlated components, necessitating a multivariate approach. We explored the role of sexual selection in shaping complex male song of house wrens (Troglodytes aedon) by simultaneously relating its multiple components to fitness using multivariate selection analysis, which is widely used in insect and anuran studies but not in birds. The analysis revealed significant variation in the form and strength of selection acting on song across different selection episodes, from nest-site defense to recruitment of offspring to the breeding population. Males that sang more song typically employed in close communication sired more offspring that were subsequently recruited to the breeding population than those that sang far-communication song. However, this relationship was not consistent across earlier selection episodes, as evidenced by non-linear selection acting on these song components in other contexts. Collectively, our results present a complex picture of multivariate selection on male song structure that would not be evident using univariate approaches and suggest possible trade-offs within and among song components at different points of the breeding season.

opencc-zeroDec 2023View details →

Data and R code from: Fin whale song evolution in the North Atlantic

Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species' capacity to adapt to rapidly changing acoustic environments. In this study, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades (1999–2020) across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons (2000/2001–2004/2005) in the southeast location of the Oceanic Northeast Atlantic (ONA) region, that co-occurred with hybrid songs (with both INIs). During the transition in song INIs (2002/2003) we show a clear geographic gradient in the occurrence of different song INIs in the whole ONA region. We also found gradual changes in song INIs (Figure 3A) and 20-Hz note (Figure 3B) and HF note (Figure 3C) peak frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species.

opencc-zeroDec 2023View details →

Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys

Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (Zonotrichia leucophrys) throughout the species range, including between neighboring (and genetically distinct) subspecies Z. l. nuttalli and Z. l. pugetensis. Using a machine learning approach to bioacoustic analysis, we demonstrate that variation in song is correlated with year of recording (representing cultural drift), geographic distance, and climatic differences, but the response is subspecies- and season-specific. Automated machine learning methods of bird song annotation can process large datasets more efficiently, allowing us to examine 1,913 recordings across ~60 years. We utilize a recently published artificial neural network to automatically annotate White-crowned Sparrow vocalizations. By analyzing differences in syllable usage and composition, we recapitulate the known pattern where Z. l. nuttalli and Z. l. pugetensis have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in characteristics of syllables in the White-crowned Sparrow repertoire. This supports the hypothesis that the evolution of vocalization behavior is affected by the environment, in addition to population structure.

evaluation data for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)"

Data contains sound files of mouse vocalization needed to reproduce the evaluation results for "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)" If you use any of this data, cite the original source: <a href="https://doi.org/10.1016/j.anbehav.2020.09.006">https://doi.org/10.1016/j.anbehav.2020.09.006</a>

Fig. 3 in Archaic Dialect Of Chaffinch, Fringilla Coelebs (Passeriformes, Fringillidae), Song In The Lower-Dnipro Area (South Ukraine) And Its Territorial Relations

Fig. 3. Dendrogram of territorial complexes Lower-Dnipro Area (localities are described in table 1).

Fig. 1 in Archaic Dialect Of Chaffinch, Fringilla Coelebs (Passeriformes, Fringillidae), Song In The Lower-Dnipro Area (South Ukraine) And Its Territorial Relations

Fig. 1. Components of Chaffinch song structure: 1 — phrase; 2 — inserted element; 3 — pre-flourish; 4 — flourish. a — element; b — sub-element.

Fig. 2 in Archaic Dialect Of Chaffinch, Fringilla Coelebs (Passeriformes, Fringillidae), Song In The Lower-Dnipro Area (South Ukraine) And Its Territorial Relations

Fig. 2. Study area and Lower-Dnipro dialect territory:> 50 — localities with more than 50 % specific dialectal Lower Dnipro song types in song complex; 26–50 — specific dialectal types constitute 26–50 % of song complex; 5–25 — dialectal types constitute 5–25 %; 0 — specific dialectal types were not found.

Fig. 5 in Archaic Dialect Of Chaffinch, Fringilla Coelebs (Passeriformes, Fringillidae), Song In The Lower-Dnipro Area (South Ukraine) And Its Territorial Relations

Fig. 5. Specific "southern" elements in Lower-Dnipro dialect. Elements found in song complexes of South Ukraine: LD — Lower-Dnipro dialect; SE — South-East sub-dialect of Left-bank dialect; Cr — Crimean dialect; Da — Danube dialect; Cp — Carpathian dialect.

opencc-by-4.0Jan 2016View details →

Fig. 2 in Song Repertoire And Origins Of Crimean Population Of Chiffchaff, Phylloscopus Collybita (Sylviidae)

Fig. 2. Songs of Crimean (A) and Caucasian (B) Chiffchaffs with identical specific Crimean song elements (marked).

opencc-by-4.0Sep 2015View details →

Fig. 1 in Growth Processes In The Postembryonic Development In Altricial Birds On The Example Of Song Thrush, Turdus Philomelos (Passeriformers, Turdidae): A Multivariate Approach

Fig. 1. Distribution of centroids for the age-based song thrush samples in the factor space of PC1 and PC2 (figs 1–13 denote nestling's age in days).

opencc-by-4.0Sep 2015View details →

Fig. 3 in Growth Processes In The Postembryonic Development In Altricial Birds On The Example Of Song Thrush, Turdus Philomelos (Passeriformers, Turdidae): A Multivariate Approach

Fig. 3. Results of comparison of 20 morphometric traits (1–20) in nestlings of song thrush according to their DRPI and dynamics of growth processes. Numbers of traits as given in table 1.