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33,754 results for “Enhancers”
Reproduction package to "Phonon-Trapping Enhanced Energy Resolution in Superconducting Single-Photon Detectors"
<p>This is a reproduction package to the paper "Phonon-Trapping Enhanced Energy Resolution in Superconducting Single-Photon Detectors", which is published in <a href="https://doi.org/10.1103/PhysRevApplied.16.034051">Physical Review Applied</a>. It contains all data and code to reproduce the figures in this paper.</p>
Supplementary Material for "Enhancing Fluid Animation with Fine Detail"
<p>Results for an upcoming publication. Work is based on the 3 referenced papers. </p> <p>VideoAppendix1 relates to: </p> <p>D. Morgenroth, D. Weiskopf, and B. Eberhardt, “Distributed vfx architecture for sph simulation,”Journal of WSCG, vol.21, no.3, pp.163-171, 2013, eprint:http://hdl.handle.net/11025/6986.</p> <p>VIdeoAppendix2 relates to:</p> <p>D. Morgenroth, D. Weiskopf, and B. Eberhardt, “Direct raytracing of a closed-form fluid meniscus,” The Visual Computer, vol.32, no.6-8,pp.791-800, 2016. doi:10.1007/s00371-016-1258-4.</p> <p>VideoAppendix3 relates to:</p> <p>D. Morgenroth, S. Reinhardt, D. Weiskopf, and B. Eberhardt, “Efficient 2d simulation on moving 3d surfaces,”Computer Graphics Forum, vol. 39, no.8, pp.27-38,2020.doi:10.1111/cgf.14098.</p> <p>More detailed description of the visuals can be found in the readme file.</p>
Data Materials for Fortune Telling Studies: Positive Fortune Telling Enhances Men's Financial Risk Taking
<p>We share the data materials for three studies and a meta-analysis on the effects of positive fortune telling on enhancing men's financial risk taking. A paper entitled as "<strong>Positive Fortune Telling Enhances Men’s Financial Risk Taking</strong>" was written up for publication.</p>
Dataset for "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"
<p>Dataset covering the DFT simulations performed for the journal article "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"</p>
DCNDS Project Dataset - P2P Botnet Detection Using Enhanced Peer Hunter
<p>This dataset represents the Enhanced Peer Hunter implementation that was deployed on two NREN within the AsiaConnect project in Malaysia and Indonesia. Based on a 30 days measurement from Saturday, June 19, 2021 4:00:00 PM GMT - Monday, July 19, 2021 3:59:55 PM GMT, the list of detections and all flows associated with the detected machines are listed in the dataset (2 files per country). The dataset would allow any future p2p botnet detection mechanisms to leverage this (flow) data to train and evaluate their detection mechanisms.</p> <p> Take note that all IP addresses have been anonymized using a hash function with salt to avoid leakage of information.</p> <p> </p>
Super resolution enhancement of Landsat imagery and detections of high-latitude lakes
<p>This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m<sup>2</sup> for 2017; 13 m<sup>2</sup> for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.</p> <p>All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in <strong>Table 1</strong>. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.</p> <p>Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey</p> <p>For an interactive map demo of these datasets via Google Earth Engine Apps, visit: <a href="https://ekyzivat.users.earthengine.app/view/super-resolution-demo">https://ekyzivat.users.earthengine.app/view/super-resolution-demo</a></p> <p><strong>Table 1</strong>: File naming scheme based on region, with some regions requiring two-scene mosaics.</p> <table> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Landsat ID</strong></p> </td> <td> <p><strong>Mosaic name</strong></p> </td> </tr> <tr> <td> <p><strong>Yukon Flats Basin</strong></p> </td> <td> <p>LC08_L2SP_068014_20170708_20200903_02_T1</p> </td> <td> <p>LC08_20170708_yflats_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_068013_20170708_20201015_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Old Crow Flats</strong></p> </td> <td> <p>LC08_L2SP_067012_20170903_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Mackenzie River Delta</strong></p> </td> <td> <p>LC08_L2SP_064011_20170728_20200903_02_T1</p> </td> <td> <p>LC08_20170728_inuvik_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_064012_20170728_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield Margin</strong></p> </td> <td> <p>LC08_L2SP_050015_20170811_20200903_02_T1</p> </td> <td> <p>LC08_20170811_cshield-margin_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_048016_20170829_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Baker Creek</strong></p> </td> <td> <p>LC08_L2SP_046016_20170831_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Daring Lake</strong></p> </td> <td> <p>LC08_L2SP_045015_20170723_20201015_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Peace-Athabasca Delta</strong></p> </td> <td> <p>LC08_L2SP_043019_20170810_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 1</strong></p> </td> <td> <p>LC08_L2SP_041021_20170812_20200903_02_T1</p> </td> <td> <p>LC08_20170812_potholes-north1_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_041022_20170812_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 2</strong></p> </td> <td> <p>LC08_L2SP_038023_20170823_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes South</strong></p> </td> <td> <p>LC08_L2SP_031027_20170907_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Fairbanks </strong></p> </td> <td> <p>LT05_L2SP_070014_19850831_20200918_02_T1</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><strong>References:</strong></p> <p>Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. <em>Remote Sensing</em>, <em>11</em>(18), 2163. <a href="https://doi.org/10.3390/rs11182163">https://doi.org/10.3390/rs11182163</a></p> <p>Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1707">https://doi.org/10.3334/ORNLDAAC/1707</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. <em>Environ. Res. Lett</em>, <em>16</em>, 35010. <a href="https://doi.org/10.1088/1748-9326/abc848">https://doi.org/10.1088/1748-9326/abc848</a></p> <p>Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1859">https://doi.org/10.3334/ORNLDAAC/1859</a></p>
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data
<p>In our work, we implemented the ABC-model and could show that one assay for measuring the openness of enhancers is sufficient. Further, we propose a generalised calculation of the ABC-score, which describes enhancer activity in a gene-specific manner, and which includes all TSS, without requiring any additional data. We combined our implementation of the ABC-score with an approach to quantify TF binding affinity into STARE: a framework to derive TF affinities to genes. STARE was also designed for potential application on single-cell data. You can find the code in our <a href="https://github.com/schulzlab/stare">GitHub repository</a> and more details in our <a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtad062">publication</a>.</p> <p>We provide the data for the validation of our ABC-implementation on two CRISPR-screens. We also provide the results of our analysis of single-cell data of the human heart with STARE. All data is in hg19.</p> <p>Content:</p> <ul> <li>CRISPRi_screens: One file for each CRISPRi-screen with interactions that were used to plot precision-recall curves, containing columns for different ABC scoring versions.</li> <li>Enformer: Similar to the CRISPRi_screens, but containing columns for different calculations for Enformer's predicted expression change upon in silico mutagenesis of the enhancer region.</li> <li>K562_CandidateEnhancer: K562 enhancer with the 4th column for enhancer activity, one file for each activity representation that was measured.</li> <li>K562_ABC_Predictions: Regular ABC-scores and generalised ABC-scores for each activity measurement. The files contain all scored interactions for a 10MB window, without any cut-off. We also included the results of the implementation of the ABC-score of Fulco et al. (2019).</li> <li>STARE_Hocker_*: Whole STARE output for human heart single-cell data, one for regular ABC, generalised ABC, generalised ABC with average Hi-C matrix and one based on co-accessibility analysis. All approaches were run with a 5 MB window (except for GeneralisedABC500kb), the ABC-based runs with a score cut-off of 0.02. Each folder contains two subdirectories, one for the ABC-scoring and one for the Gene-TF affinity matrices. The 'ABC_output' also contains a GeneInfo file for each cell type, summarising different attributes per gene.</li> <li>INVOKE_Hocker_*: Folder with the input and output of INVOKE (see https://github.com/schulzlab/tepic), based on the STARE runs. CS genes stands for cell type-specific genes, defined as genes with a z-score across cell types of ≥ 2 and TPM ≥ 0.5. The INVOKE commands were as follows: <ul> <li>Rscript INVOKE.R --dataDir=<TF-Gene matrix> --outDir=<out_path> --response=Expression --regularization=E --performance=TRUE --outerCV=10 --seed=1234</li> </ul> </li> </ul> <p>Importantly, the results are based on data from the following publications:</p> <ul> <li>CRISPRi-screens: <ul> <li>Gasperini, Molly, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Seungsoo Kim, Melissa D. Zhang, Dana Jackson, et al. “A Genome-Wide Framework for Mapping Gene Regulation via Cellular Genetic Screens.” <em>Cell</em> 176, no. 1–2 (January 2019): 377-390.e19. https://doi.org/10.1016/j.cell.2018.11.029.</li> <li> <p>Schraivogel, Daniel, Andreas R. Gschwind, Jennifer H. Milbank, Daniel R. Leonce, Petra Jakob, Lukas Mathur, Jan O. Korbel, Christoph A. Merten, Lars Velten, and Lars M. Steinmetz. “Targeted Perturb-Seq Enables Genome-Scale Genetic Screens in Single Cells.” <em>Nature Methods</em> 17, no. 6 (June 2020): 629–35. https://doi.org/10.1038/s41592-020-0837-5.</p> </li> <li> <p>Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</p> </li> </ul> </li> <li>Enformer model: Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. “Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions.” <em>Nature Methods</em> 18, no. 10 (October 2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.</li> <li>K562 predictions and average Hi-C matrix: Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</li> <li>Hi-C matrix for K562 predictions: Rao, S. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell, 159(7), 1665–1680</li> <li>STARE and INVOKE runs: Hocker, J. D. et al. (2021). Cardiac cell type–specific gene regulatory programs and disease risk association. Science Advances, 7(20), eabf1444</li> <li>H3K27ac HiChIP for STARE runs: Anene-Nzelu, C. G. et al. (2020). Assigning Distal Genomic Enhancers to Cardiac Disease–Causing Genes. Circulation, 142(9), 910–912</li> <li>INVOKE software: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction Schmidt et al., Nucleic Acids Research 2016; doi: 10.1093/nar/gkw1061</li> </ul> <p> </p>
Simultaneous dynamic glucose-enhanced (DGE) MRI and fiber photometry measurements of glucose in the healthy mouse brain
<p>This dataset was acquired for the DGE and fiber photometry study published in NeuroImage ( <a href="https://doi.org/10.1016/j.neuroimage.2022.119762">https://doi.org/10.1016/j.neuroimage.2022.119762</a>).<br> Comprises of three datasets: DGE MRI, fiber photometry and two-photon microscopy.</p>
Dataset for the paper "Website Fingerprinting: Attacking Popular Privacy Enhancing Technologies with the Multinomial Naïve-Bayes Classifier"
<p>This dataset contains website fingerprints of 775 websites analyzed in the paper "Website Fingerprinting: Attacking Popular Privacy Enhancing Technologies with the Multinomial Naïve-Bayes Classifier" published in the Proceedings of the 2009 ACM workshop on Cloud computing security (CCSW 2009, DOI: 10.1145/1655008.1655013).</p>
Strong enhancement of electromagnetic shower development induced by high-energy photons in a thick oriented tungsten crystal
<p>We have observed a significant enhancement in the energy deposition by 25-100 GeV photons in a 1 cm thick tungsten crystal oriented along its <111> lattice axes (data of Fig. 2). At 100 GeV, this enhancement, with respect to the value observed without axial alignment, is more than twofold. This effect, together with the measured huge increase in secondary particle generation (data of Fig. 3) is ascribed to the acceleration of the electromagnetic shower development by the strong axial electric field. The experimental results have been critically compared with a newly developed Monte Carlo adapted for use with crystals of multi-X_0 thickness (data of Fig.2, 3 and 4). These results may prove to be of significant interest for the development of high-performance photon absorbers and highly compact electromagnetic calorimeters and beam dumps for use at the energy and intensity frontiers.</p>
Data Set: Single-well pore pressure preconditioning for Enhanced Geothermal System stimulation
<p>This is the Python code and plotted figure data used to create the figures for the submitted manuscript, "Single-well pore pressure preconditioning for Enhanced Geothermal System stimulation" submitted to JGR: Solid Earth in 2022.</p> <p>The manuscript concerns a novel technique developed for EGS stimulation, called pore pressure or effective normal stress preconditioning, which preemptively alters the stress field along a fault prior to injection, such that the risk of induced seismicity is reduced. Using a slightly altered version of a preexisting model (a combination of an analytical pore pressure model and a linear slip weakening seismicity model) the effect of this kind of treatment is evaluated.</p>
CS6 Sea urchin enhancement Norway - Socio-economic analysis
<p>The dataset builds on dataset for this case study for AquaVitae deliverable D7.3 Profitability analysis, containing cost and profitability data for harvest of sea urchins and for urchin roe enhancement. For Deliverable D7.4 it is enhanced with data on economic multipliers, kelp reforestation data, and socioeconomics, focusing on value added, employment, wages, and economic ripple effects.</p>
Can diet niche partitioning enhance sexual dimorphism?
<ol> <li class="MsoNormal"> <span>Classic evolutionary theory suggests that sexual dimorphism evolves primarily via sexual and fecundity selection. However, theory and evidence is beginning to accumulate suggesting that resource competition can drive the evolution of sexual dimorphism, </span>via<span> ecological character displacement between sexes. A key prediction of </span>this<span> hypothesis is that the extent of ecological divergence between sexes will be associated with the extent of sexual dimorphism. </span> </li> <li class="MsoNormal"> <span>As the stable isotope ratios of animal tissues provide a quantitative measure of various aspects of ecology, we carried out a meta-analysis examining associations between the extent of isotopic divergence between sexes and the extent of body size dimorphism. </span>Our <span>models demonstrat</span>e<span> that large amounts of between-study variation in isotopic (ecological) divergence between sexes is non-random and may be associated with the traits of study subjects. We therefore completed meta-regressions to examine whether the extent of isotopic divergence between sexes is associated with the extent of sexual size dimorphism. </span> </li> <li class="MsoNormal"><span>We found modest but significantly positive associations across species between size dimorphism and ecological differences between sexes, that increased in strength when the ecological opportunity for dietary divergence between sexes was greatest.</span></li> <li class="MsoNormal"><span>Our results therefore provide further evidence that ecologically mediated selection, not directly related to reproduction, can contribute to the evolution of sexual dimorphism.</span></li> </ol>
Supporting Information for the Journal Article "Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning"
<p>This data set contains all data produced when exploring the Williamson ether synthesis starting from iodoethane and phenol.</p> <p><br> The set is structures as follows:</p> <ul> <li>analysis: Contains the script used to analyze the exploration and the output of said script</li> <li>check_barrier: Contains the output of the manual calculations done to check the barrier of the reaction</li> <li>exploration: Contains the scripts used to initialize and carry out the exploration as well as the two starting structures as XYZ files</li> <li>raw_data: a dump of the MongoDB database with all the data produced during the exploration</li> </ul>
Data of "Aerosol-cloud interaction enhanced the Tibetan Plateau warming " - version1
<p>The data (version 1) of "Aerosol-cloud interaction enhanced the Tibetan Plateau warming ".</p> <p>This is source observation data of figures in the manuscript "Aerosol-cloud interaction enhanced the Tibetan Plateau warming". The manuscript is currently in the status of initial submission. </p> <p> </p>
Speciation and Structures in Pt Surface Sites Stabilized by N-Heterocyclic Carbene Ligands Revealed by DNP Enhanced Indirect-ly Detected 195Pt NMR Spectroscopic Signatures and Fingerprint Analysis
<p>Raw NMR data for the paper published under DOI: 10.1021/jacs.2c08300</p>
Data from: Enhanced climate tolerance for trees derived from microbial communities
<p>Changing climates are pushing species outside of their evolved tolerances; populations must acclimate or adapt to the new conditions or migrate to avoid extinction. However, because plants associate with diverse microbial communities that shape their phenotype, shifts in microbial associations may provide an alternative source of novel climate tolerance. Here we show that tree seedlings inoculated with microbial communities sourced from drier, warmer, or colder sites displayed higher survival when facing drought, heat, or cold stress, respectively. Microbially mediated drought tolerance was associated with increased diversity of arbuscular mycorrhizal fungi, while cold tolerance was related to reduced diversity of non-adapted taxa. Understanding microbially mediated climate tolerance may enhance our ability to predict and manage the adaptability of forest ecosystems to changing climates.</p>
CS6 Sea urchin enhancement Norway – Ecosystem service analysis
<p>The dataset builds on dataset for this case study for AquaVitae deliverables D6.2 quantification of ES and D7.4 Socio-economic impact assessment of CSs of new/emerging LTS aquaculture, which in turn built on dataset for D7.3 Profitability analyses. For Deliverable D7.5 it is enhanced with data on ecosystem services related to kelp reforestation and their valuation.</p>
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
Social plasticity enhances signal-preference co-divergence
<p>Data associated to the manuscript titled:</p> <p>Social plasticity enhances signal-preference co-divergence</p> <p>by</p> <p>Camille Desjonquères, Bretta Speck, Sara Seidita, Lauren A. Cirino, Ignacio Escalante, Clinton Sergi, Jak Maliszewski, Gerlinde Hoebel, Nathan W. Bailey, Rafael L. Rodríguez</p>
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.