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115 results for “trait dataset”
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Genotyping-by-sequencing (GBS) dataset for genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))
<p>These vcf-files constitute underlying raw data material for the manuscript "Genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))". For more detailed information please consult the README file in the repository.</p>
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Predicting Phenotypic Traits Using a Massive RNA-seq Dataset
<h2><strong>Abstract</strong></h2><p>The included datasets are a conglomerate of all available <i>Arabidopsis thaliana</i> RNA-seq data available from NCBI as of November 2022 processed to count data. In addition, the associated annotation files from NCBI BioProject database and processed versions of this data is included. Data has been processed according to the "Data Description Methods" in the manuscript titled "Predicting Phenotypic Traits Using a Massive RNA-seq Dataset" (in publication). The associated Methods can be found at this repository:<a href="https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics"> https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics</a>. These datasets can be used for exploring machine learning methods for predicting both continuous (Age) and categorical (Tissue) phenotypic traits using gene expression. Additionally, the gene expression data can be used on its own for the investigation of gene expression in <i>Arabidopsis thaliana.</i></p><h3><strong>Note to Researchers</strong></h3><p>This repository contains all of the datasets and information necessary to recreate the experiments in our paper. However, if may be that you are interested in our dataset for testing your own hypotheses/programs. If this is the case, we predict that you are looking for one or more of the the following 5 datasets<br> </p><h3><strong>Note on File Compression</strong></h3><p>All files in this repository are compressed using bzip2 to conserve space and allow for easier file transfer. The unzip command on linux systems is `bzip2 -d FILE_NAME`. For other computer systems (Windows and Apple) please consult your user manual.</p><h3><strong>Description All Datasets:</strong></h3><p><strong>Title:</strong> Gene Expression Count Data of all <i>Arabidopsis thaliana</i> data available from NCBI SRA as of November 2022<br><strong>Abstract: </strong>Gene Expression Count data was created using the workflow GEMmaker. The resulting Gene Expression Matrix (GEM) was then normalized and thresholded. The following 4 files are normalizations of the same data for Trimmed Mean of M values (TMM), Median Ratios Normalization (MRN), Transcripts Per kilobase Million (TPM), and No Normalization (NoNo) respectively. Additionally, Each file is included as a tsv and a python pickle. The tsv file is human readable, whereas the pickle file can be read into memory substantially faster. Format for tsv is each row represents a sample and each column represents a gene. <strong>NCBI_Nov2022_SRR_runinfo.csv</strong> is the starting file from NCBI which reports SRR information for each sample. <strong>Note 1 to Researchers: </strong>MRN normalization performed the best in our experiments and is likely what you want to use if you are doing additional expermentation with this dataset. Otherwise start with NoNo and perform your own normalizations. <strong>Note 2 to Researchers:</strong> the 54547 dataset will need to be thresholded prior to use. We include it in addition to the 32432 datasets in case you wish to try a different thresholding to the one outlined in our manuscript. <br><strong>Author:</strong> John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Count Data<br><strong>Organism:</strong> <i>Arabidopsis thaliana</i><br><strong>Files:</strong></p><p><strong>NCBI_Nov2022_SRR_runinfo.csv - </strong>Arabidopsis RNA-seq SRA RunInfo Retrieved from NCBI November 2022. This is the unprocessed data.<br><strong>Dataset_54547_NoFilter_raw.pkl </strong>- Raw File Before thresholding (".pkl" format). Same as NoNo normalization without thresholding.<br><strong>Dataset_54547_NoFilter_raw.tsv </strong>- Raw File Before thresholding (".tsv" format). Same as NoNo normalization without thresholding.<br><strong>Dataset_32432_MRN.pkl</strong> - MRN normalized (".pkl" format)<br><strong>Dataset_32432_MRN.tsv - </strong>MRN normalized (".tsv" format)<br><strong>Dataset_32432_NoNo.pkl - </strong>NoNo normalized (".pkl" format)<br><strong>Dataset_32432_NoNo.tsv - </strong>NoNo normalized (".tsv" format)<br><strong>Dataset_32432_TMM.pkl - </strong>TMM normalized (".pkl" format)<br><strong>Dataset_32432_TMM.tsv - </strong>TMM normalized (".tsv" format)<br><strong>Dataset_32432_TPM.pkl - </strong>TPM normalized (".pkl" format)<br><strong>Dataset_32432_TPM.tsv - </strong>TPM normalized (".tsv" format)<br><br><br><strong>Title: </strong>Meta Data Arabidopsis Age and Tissue<br><strong>Abstract: </strong>Meta Data for Age and Tissue after processing. In our experiment this was used as response variable to gene expression. Shared columns are "bio_sample", "bioproject_name", "experiment". In addition to these processed datasets, <strong>NCBI_Nov2022_BioSample_data.tsv </strong>is the unprocessed starting material for these two data frames.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Metadata on phenotypes. ".tsv" format <br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files: </strong><br><strong>NCBI_Nov2022_BioSample_data.tsv - </strong>Arabidopsis BioSample data retrieved from NCBI November 2022. This is the unprocessed data.<br><strong>df_metadata_tissue.tsv</strong> - Tissue Annotations for 24876 samples<br><strong>df_metadata_age.tsv</strong><i><strong> - </strong></i>Age Annotations for 16078 samples. In addition to shared columns includes<i> "</i>days<i>_</i>age"(how many days old the sample is converted to days) and "annotation_age" (how the annotation was reported for this sample in the raw data file-- i.e. "days", "weeks" etc.)<br><br><br><strong>Title: </strong>Machine Learning Dataset for <i>Arabidopsis thaliana</i> <strong>Age</strong><br><strong>Abstract: </strong>The dataset used for Machine learning on the phenotype Age that is a combination of the Gene Expression Matrix and the Annotation Matrix. Consists of a list of 4 for the train and test splits.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Matrix and Annotations Combined, split into train and test <br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Age_TrainTestSplits_mrn.pkl</strong> - MRN normalized<br><strong>Dataset_Age_TrainTestSplits_NoNo.pkl </strong>- NoNo normalized<br><strong>Dataset_Age_TrainTestSplits_tmm.pkl </strong>- TMM normalized<br><strong>Dataset_Age_TrainTestSplits_tpm.pkl - </strong>TPM normalized<br><br><br><strong>Title: </strong>Machine Learning Dataset for <i>Arabidopsis thaliana</i> <strong>Tissue</strong><br><strong>Abstract: </strong>The dataset used for Machine learning on the phenotype Tissue that is a combination of the Gene Expression Matrix and the Annotation Matrix. Consists of a list of 4 for the train and test splits. Saved as python ".pkl" files.<br><strong>Author:</strong> John Anthony Hadish<br><strong>Data Type: </strong>Gene Expression Matrix and Annotations Combined, split into train and test. Saved as python ".pkl" files.<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Tissue_TrainTestSplits_mrn.pkl </strong>- MRN normalized<br><strong>Dataset_Tissue_TrainTestSplits_NoNo.pkl </strong>- NoNo normalized<br><strong>Dataset_Tissue_TrainTestSplits_tmm.pkl </strong>- TMM normalized<br><strong>Dataset_Tissue_TrainTestSplits_tpm.pkl </strong>- TPM normalized<br><strong>Dataset_Tissue_TrainTestSplits_mrn_4category.pkl </strong>- MRN for the tissue-4 dataset<br><br><br><strong>Title: </strong>BioProject Names<br><strong>Abstract:</strong> Three Column File With BioProject Name, BioSample Name, and Experiment Name<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>BioProject_Names_All.tsv</strong><br><br><br><strong>Title:</strong> Manuscript Supplemental Material<br><strong>Abstract:</strong> Supplemental tables and figures described in the manuscript (included with manuscript and here for convenience). Please see manuscript for additional information.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv", ".png".pdf"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Supplemental_Figures.zip </strong>- Supplemental figures from the manuscript. Includes description of each figure.<br><strong>Supplemental_Tables.zip </strong>- Supplemental tables from the manuscript. Includes description of each table.<br><br> </p><p><strong>Title:</strong> Splits of data for 3 experiments<br><strong>Abstract:</strong> 2 column tsv files. The first column is the experiment (sample) name, and the second column is if it is included in the train or test data. <strong>Included here to make sure pkl files are reproducible in case the pkl package breaks in the future.</strong> Not used by scripts, included to prevent future potential loss of data.<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>".tsv"<br><strong>Organism: </strong><i>Arabidopsis thaliana</i><br><strong>Files:</strong><br><strong>Dataset_Tissue_TrainTestSplits_4category_namesOnly.tsv</strong><br><strong>Dataset_Tissue_TrainTestSplits_namesOnly.tsv</strong><br><strong>Dataset_Age_TrainTestSplits_namesOnly.tsv</strong></p><p> </p><p><strong>Title:</strong> Git Code Repository<br><strong>Abstract:</strong> A tar bz2 compression of the git repository containing all of the code created for this manuscript. The same code found in this file is also avalible on GitLab at the link: https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics<br><strong>Author: </strong>John Anthony Hadish<br><strong>Data Type: </strong>Git Repository, python code<br><strong>Files:</strong><br><strong>modeling-with-transcriptomics-main.tar.bz2</strong> - Compressed Git repository of all code used in paper.</p>
Dataset and R code: Above and belowground functional trait coordination in the Neotropical understory genus Costus
<p>Dataset and R code accompanying the paper "Above and belowground functional trait coordination in the Neotropical understory genus <em>Costus</em>" published by AoB Plants. </p>
Dataset for: Climate and shared evolutionary history drive trait variation among species of Neotropical understory monocots
<p>Dataset and p-values form phylogenetically generalized least square models accompanying the manuscript "Climate and shared evolutionary history drive trait variation among species of Neotropical understory monocots".</p>
Chinese Calligraphy Reproduction Dataset with Writes' Trait EI
<p>This is a dataset of Chinese calligraphy reproductions, with 605 reference characters and 48,826 reproduced characters. The excel file contains the correspondence between reference characters and reproducedcharacters. In addition, it contains information about the writer's emotional intelligence. The dataset can be used to predict the writer's trait emotional intelligence from calligraphy reproduction.</p>
Dataset: Environmental conditions and male quality traits simultaneously explain variation of multiple colour signals in male lizards
<p>Dataset and R code associated with the following publication:</p> <p>Badiane et al. (2022), Environmental conditions and male quality traits simultaneously explain variation of multiple colour signals in male lizards. Journal of Animal Ecology, in press</p> <p>This dataset includes the following files:</p> <p>- An excel file containing the reflectance spectra of all individuals from all the study populations</p> <p>- An excel file containing the variables collected at the individual and population levels</p> <p>- Two R scripts corresponding to the analyses performed in the publication</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Ecological and Forest Inventory of Catalonia: Complete Plant Trait Dataset for Imputation Assessment
<p>Plant trait and forest data were retrieved from the Ecological and Forest Inventory of Catalonia (IEFC), carried out between 1988 and 1998 (Gracia et al. 2000‒2004). The subset of the IEFC was limited to 13 study species. Forest structure, lithology and sampling information for each plot were retrieved from the IEFC database. Climate data were obtained from the Climatic Digital Atlas of Catalonia, with a spatial resolution of 180 m (Ninyerola et al. 2000).</p> <p>We selected five plant traits (leaf mass per area, LMA, mg cm<sup>-2</sup>; leaf nitrogen per unit mass, <em>N</em><sub><em>mass</em></sub>, %mass; maximum tree height, <em>H</em><sub><em>max</em></sub><em>,</em><sub><em> </em></sub>m; wood density, WD, gm cm<sup>-3</sup>; leaf biomass to sapwood area ratio, <em>B</em><sub><em>L</em></sub><em>:A</em><sub><em>S</em></sub>, t m<sup>-2</sup>) that are used to describe major plant functional strategies. The auxiliary variables we considered were species identity, a set of climatic variables (mean annual temperature, annual thermal amplitude, both in °C), a set of forest structure variables (total aboveground biomass [T ha<sup>-1</sup>] and stem density [stems ha<sup>-1</sup>]), a set of topographical variables (county, elevation [m.a.s.l.], slope [°] and aspect), lithology (calcareous, non-calcareous or undetermined) and sampling month.</p>
Dataset for agronomic, quality and resistance traits in tomato BRESOV materials
<p>Different dataset for agronomic data, qualitative data and resistances to pathogens related to tomato BRESOV material</p>
CanFlyet: Habitat zone and diet trait dataset for Diptera species of Canada and Greenland
<p>True flies (Diptera) are an ecologically important group that play a role in agriculture, public health, and ecosystem functioning. As researchers continue to investigate this order, it is beneficial to link the growing occurrence data to biological traits. However, large-scale ecological trait data are not readily available for fly species. While some databases and datasets include fly data, many ecologically relevant traits for taxa of interest are not included. In this dataset we provide ecological traits (habitat and diet) for fly species of Canada and Greenland having occurrence records on the Barcode of Life Data Systems (BOLD). Trait data were compiled based on literature searches conducted from April 2021 - January 2023 and assigned at the lowest taxonomic level possible. The dataset contains traits for 983 species across 380 genera, 25 subfamilies, and 61 families. This dataset allows for assignment of traits to occurrence data for Diptera species and can be used for further research into the ecology, evolution, and conservation of this order. </p>
Global dataset for "Global leaf-trait mapping based on optimality theory "
<p>This repository contains Global data used for “<em>Global leaf-trait mapping based on optimality theory</em><strong>” </strong>published in GEB.</p> <ol> <li>Global_Maps_SLA represents climatology of published Global SLA used for comparison (details products see table 1 and figure 4).</li> <li>Global_Maps_Na represents climatology of published Global Narea used for comparison (details see table 1 and figure 4).</li> <li>Global_Maps_Nmass represents climatology of published Global Nmass used for comparison (details see table 1 and figure 4).</li> <li>TS_SLA is simulated time-series of <em>SLA</em> based on optimality theories from 1992 to 2015</li> <li>TS_Na is simulated time-series of <em>Narea </em>based on optimality theories from 1992 to 2015</li> <li>TS_Nmass is simulated time-series of <em>Nmass </em>based on optimality theories<em> </em> from 1992 to 2015</li> <li>TS_LMA_decidudous is simulated time-series of deciduous <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_LMA_evergreen is simulated time-series of evergreen <em>LMA</em> based on optimality theories from 1982 to 2016</li> <li>TS_Vcmax25 is simulated time-series of Vcmax25 based on optimality theories from 1982 to 2016</li> </ol>
R code and datasets for flower-visitor interactions (pollinators, robbers, thieves) and plant traits from Mount Cameroon
<p>Raw data and R code for: <strong>Cheaters among pollinators: Nectar robbing and thieving vary spatiotemporally with floral traits in Afrotropical forests. </strong><i>Ecosphere, 2023</i>. doi: 10.1002/ecs2.4696<br> </p><p>When using the dataset for anything, cite the Sakhalkar et al. <i>Ecosphere </i>paper.</p><p><br>All related information can be found in the cited paper. For additional information, refer to the paper or write to either robert.tropek@gmail.com or sailee.sakha@gmail.com.</p>
Code and datasets associated with: A sex-linked supergene with large effects on sperm traits has little impact on reproductive traits in female zebra finches
Open the record for dataset details and reuse information.
CanFlyet: Habitat zone and diet trait dataset for Diptera species of Canada and Greenland
Open the record for dataset details and reuse information.
Dataset for Ongole at al. - Functional traits predict tree-level phenological strategies in a mesic Indian savanna
<p>Leaf phenology influences terrestrial primary production by determining the period of carbon uptake. While dominant leaf habit (evergreen/deciduous) is predictable at large scales based on environmental factors, there is substantial variability in the timing of key events such as leaf flush and senescence at smaller site-level scales. How much of this variability is explained by species functional traits related to a plant's carbon economy? We monitored leaf phenology for 113 individual trees of eleven dominant species in an Indian mesic savanna. Specifically, we related leaf functional traits and wood density to the (i) timing of leaf flush, leaf maturity, peak canopy and senescence, (ii) duration of leaf deployment, (iii) duration from start of senescence to leaflessness, and (iv) time to attainment of peak mature canopy following leaf flush. We expected species that use resources conservatively (low specific leaf area (SLA), high leaf dry matter content, low leaf nitrogen and high wood density) to senesce later and retain their leaves longer. For all species, leaflessness was most pronounced in early dry season and leaf flushing occurred in the late dry season. Species with high leaf carbon and dry matter content showed earlier leaf maturation, attained peak mature canopies sooner and deployed leaves longer. Species with high SLA, leaf nitrogen and low wood density showed earlier senescence but relationships were weak. In this savanna, phenology at fine scales was indeed associated with species functional traits relating to carbon investment, but these relationships were strong only when intra-specific variation in phenology was low.</p>
Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included
<p>Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas, 330 markers, sequences included.</p>
Dataset supplementing Lichtenberg et al. (2017) Foraging traits modulate stingless bee community disassembly under forest loss. Journal of Animal Ecology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) Foraging traits modulate stingless bee community disassembly under forest loss. Journal of Animal Ecology. DOI 10.1111/1365-2656.12747.</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
STraDa: A Singer Traits Dataset
<p><strong>What is STraDa?</strong></p> <p>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.</p> <p>STraDa is <strong>large-scale</strong> music audio dataset that contains <strong>singers' metadata</strong>, <strong>tracks' metadata</strong>, IDs for <strong>downloading audios</strong> of 30s (preview parts) by using <a href="https://developers.deezer.com/api">Deezer API</a>. This dataset could be used for various MIR tasks, such as singer identification, singer recognition, singer gender/age detection, genre classification, language classification. The <strong>training set</strong> contains <strong>25194</strong> excerpts of 30s, and <strong>5264</strong> singers. The <strong>testing set</strong> 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.</p> <p> </p> <p><strong>What does STraDa contain?</strong></p> <p>An important feature of STraDa is that <strong>each track only has a single lead singer</strong>, which improves the accuracy of annotations.</p> <p>The annotations in the training set are gathered and <strong>cross-validated</strong> from 4 different data sources: <a href="https://www.deezer.com/en/">Deezer</a>, <a href="https://www.wikidata.org/wiki/Wikidata:Main_Page">Wikidata</a>, <a href="https://musicbrainz.org">musicbrainz</a>, <a href="https://www.discogs.com">discogs</a>. </p> <p>The testing set is curated and annotated manually to ensure <strong>perfect accuracy</strong>.</p> <p><strong>Singers' metadata</strong> contains gender, birth year and active country. <strong>Tracks' metadata</strong> contains genre, language and release date.</p> <p> </p> <p><strong>What could STraDa be used for?</strong></p> <p><strong>STraDa </strong>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.</p> <p> </p> <p><strong>Dataset use</strong></p> <p>This dataset is only available for conducting non-commercial research related to audio analysis under license <strong>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. <strong>We do NOT grant permission for any modification, generation or manipulation using these audios.</strong></strong></p> <p>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.</p> <p><strong>Citation</strong></p> <p><strong>If you use STraDa, please cite following paper:</strong></p> <pre>@inproceedings{kong2024stradasingertraitsdataset, title={STraDa: A Singer Traits Dataset}, author={Yuexuan Kong and Viet-Anh Tran and Romain Hennequin}, booktitle={Interspeech 2024}, year={2024} }</pre>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.