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165 results for “conversational data”
Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb2Te3 Topological Insulator Chemically Grown on Silicon (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Large Spin-to-Charge Conversion at Room Temperature in Extended Epitaxial Sb<sub>2</sub>Te<sub>3</sub> Topological Insulator Chemically Grown on Silicon</em>" by <a href="https://doi.org/10.1002/adfm.202109361">E. Longo et al., <em>Adv. Funct. Mater.</em> 2021, 2109361</a></p>
Voice Conversion Challenge 2020 Listening Test Data
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances. </pre> <p>This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores. </p> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15. </pre>
Supp. Data for the article From raw microalgae to bioplastics: conversion of Chlorella vulgaris starch granules into thermoplastic starch
<p>Supplementaty Data (Videos) for the article From raw microalgae to bioplastics: conversion of Chlorella vulgaris starch granules into thermoplastic starch</p>
Data set for the journal article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO2-to-CO conversion"
<p>In the article "Improving the lifetime of hybrid CoPc@MWCNT catalysts for selective electrochemical CO<sub>2</sub>-to-CO conversion" we demonstrated that Fe impurities in a hybrid CoPc@MWCNT catalyst lead to its performance deterioration during long-term CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. The data are divided into four groups:<br> (i) Current transients and gas chromatography data for short-term electrolysis at different potentials (in an excel file we give the numbers of chromatograms for each potential; current transients are given as an origin file with datasets and plots inside)<br> (ii) Current transients and gas chromatography data for long-term electrolysis at different potentials and with different catalysts (in respective excel files we give the numbers of chromatograms; figure numbers are given in the folder names)<br> (iii) Electron microscopy images and EDX datasets (the images and datasets are collected in the folders with respective figure numbers used in the paper)<br> (iv) Calibration curves for ICP-MS</p>
Bio-oil production from biogenic wastes, the hydrothermal conversion step - Data
<p>Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project Waste2Road is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature.</p> <p>This dataset presents fully documented experiments, performed in this project, on food wastes, with different compositions, conditions and solvents. The data set is extended with data from the literature. This data set also includes bio-oil and aqueous phase analysis by gas chromatography coupled with mass spectrometry.</p>
Supplemental data for: Increased mutation and gene conversion within human segmental duplications
<p>Data used for figure generation and analysis in: <strong>Increased mutation and gene conversion within human segmental duplications</strong></p> <ol> <li>new-assemblies.zip contains all the new assemblies added in this work beyond the HPRC assemblies (Clint PTR, CHM1, HG00514, NA12878, HG03125). All other assemblies used in this analysis are available through the HPRC: <a href="https://github.com/human-pangenomics/HPP_Year1_Assemblies/blob/main/assembly_index/Year1_assemblies_v2_genbank.index">assembly_index/Year1_assemblies_v2_genbank.index</a>.</li> <li>all-sample.vcf is a vcf file with all the variant calls used in this analysis. </li> <li>alignments.zip contains all the syntenic alignments used for analysis. </li> <li>data.zip contains annotation data and other information used in analysis and figure making. </li> <li>Online tables 1-4 (Online-tables.xlsx)</li> </ol> <p>Code used in figure making and analysis is on <a href="https://github.com/mrvollger/sd-divergence-and-igc-figures">GitHub</a>.</p> <p>Snakemake pipelines used in the analysis are also on GitHub:</p> <ul> <li>Assembly alignment and IGC calling: https://github.com/mrvollger/asm-to-reference-alignment</li> <li>Variant calling from assembly alignments: https://github.com/mrvollger/sd-divergence</li> <li>Analysis of the triplet content of SNVs: https://github.com/mrvollger/mutyper_workflow</li> </ul>
Data for "Impact of Charge Conversion on NV-Center Relaxometry"
<p>Here, data sets as plotted in the preprint "Impact of Charge Conversion on NV-Center Relaxometry" (v2) are uploaded.</p> <p>The zip file "Data_v2" contains a folder for each figure in the preprint (named after the figure).</p> <p>Each folder contains the data for all graphs in each figure (e.g. (a) and (b) of figure 3) in separate csv files and, if necessary, a text document file "README", in which additional information can be found. If a fit function is plotted in a graph, the folder contains another separate csv file in which the fit data and fit function are contained.</p>
Data associated with Lark et al. 2020: U.S. cropland conversion (2008-16)
<p>Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found <strong><a href="https://zenodo.org/record/3905556#.XvLXQ21Kipo">here</a></strong>.</p> <ul> <li>Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. <em>Nature Communications </em>(In review)</li> </ul> <p>Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:</p> <ul> <li><em><strong>mtr</strong></em> = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period: <ol> <li>"<em>stable non-cropland</em>" -- areas of consistent non-cropland throughout the duration of the study period.</li> <li>"<em>stable cropland</em>" -- areas of consistent cropland throughout the duration of the study period.</li> <li>"<em>cropland expansion</em>" -- areas converted to crop production between 2008 and 2016.</li> <li>"<em>cropland abandonment</em>" -- areas converted away from crop production between 2008 and 2016.</li> <li>"<em>intermittent cropland/confusion</em>" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion. </li> </ol> </li> <li><em><strong>ytc</strong></em> = "year to cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.</li> <li><em><strong>yfc</strong></em> = "year from cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season. </li> <li><em><strong>bfc</strong></em> = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fc</strong></em> = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>bfnc</strong></em> = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fnc</strong></em> = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> </ul> <p>Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (<em>mtr</em> = 3) and those of stable croplands (<em>mtr </em>= 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).</p> <ul> <li><strong>corn_relDiff_local.tif </strong>= local yield differential (%) of corn grain.</li> <li><strong>corn_relDiff_national.tif</strong> = national yield differential (%) of corn grain.</li> <li><strong>soy_relDiff_local.tif</strong> = local yield differential (%) of soybeans.</li> <li><strong>soy_relDiff_national.tif</strong> = national yield differential (%) of soybeans.</li> <li><strong>wheat_relDiff_local.tif</strong> = local yield differential (%) of wheat.</li> <li><strong>wheat_relDiff_national.tif</strong> = national yield differential (%) of wheat.</li> </ul>
Data for "Temperature dependence of charge conversion during NV-center relaxometry in nanodiamond"
<p>In this Zenodo repository, the data as plotted in "Temperature dependence of charge conversion during NV-center relaxometry in nanodiamond" is uploaded. The file consists of folders named after the figures in the manuscript, where each folder contains csv files and a readme file in which additional information can be found. If a fit function is plotted in a figure, the fit data is also given in a csv file.</p>
Supplementary Data for "Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"
<p>This dataset contains quality assessment results for 26 vocabularies. The assessment was conducted using the <a href="https://skos-play.sparna.fr/skos-testing-tool/">qSKOS vocabulary quality assessment tool</a>.</p> <p>The 26 assessed vocabularies were converted from their original formats into the Simple Knowledge Organization System (SKOS) data model using the approach described in our paper titled <a href="https://doi.org/10.1007/978-3-031-62362-2_9">"Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"</a>, presented at the <a href="https://doi.org/10.1007/978-3-031-62362-2">24th International Conference on Web Engineering (ICWE 2024)</a>.</p> <p>The dataset contains a quality assessment for the following vocabularies:</p> <ol> <li>A Taxonomy of Evaluation Towards Standards</li> <li>Cross-Device Taxonomy</li> <li>What Makes a Data-driven Business Model? A Consolidated Taxonomy</li> <li>DDI Aggregation Method</li> <li>DDI Mode of Collection</li> <li>Building a New Taxonomy for Data Discretization Techniques</li> <li>Demopaedia</li> <li>Data Science Glossary</li> <li>A Taxonomy of Evaluation Approaches in Software Engineering</li> <li>Evaluation Thesaurus</li> <li>The Glossary of Human Computer Interaction</li> <li>Human-Factors Taxonomy</li> <li>A Taxonomy to Structure and Analyze Human–Robot Interaction</li> <li>A Taxonomy of Interaction for Instructional Multimedia</li> <li>A Taxonomy of Interrogation Methods</li> <li>Design Vocabulary for Human–IoT Systems Communication</li> <li>Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors</li> <li>Thesaurus Mass Communication</li> <li>Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey</li> <li>A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction</li> <li>A Human-Centered Taxonomy of Interaction Modalities and Devices</li> <li>A Taxonomy of Spatial Interaction Patterns and Techniques</li> <li>A Taxonomy of Social Errors in Human-Robot Interaction</li> <li>Taxonomy of Digital Research Activities in the Humanities</li> <li>Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions </li> <li>Cross-Device Interaction</li> </ol>
Data for Analysis for "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"
<p>This dataset includes, the data files for validating the statistical analysis from "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"</p> <p> </p> <p>The dataset is composed of 1500 files named following the pattern `Test-R-N-User-C-P.csv` where</p> <ul> <li>R is the n-th repetition. From 0 to 50</li> <li>N is the number of concurrent users. From 100 to 1000</li> <li>C is the treatment. chat for the framework version. chat-session for the legacy version.</li> <li>P is the problem number. 16 or 352.</li> </ul> <p>The data files corresponding to chat and problem 16 are those that in the paper are identified as Framework. The files por problem 352 are the collaborative version with students grouped.</p> <p>Each csv, is composed following the standard formate by Apache JMeter, and contains XX columns:</p> <ul> <li>timeStamp - UNIX timestamp of the request</li> <li>elapsed - Time taken to finish the request</li> <li>label - which step</li> <li>responseCode - HTTP response code</li> <li>responseMessage</li> <li>threadName</li> <li>dataType</li> <li>success - true|false</li> <li>failureMessage</li> <li>sentBytes</li> <li>grpThreads</li> <li>allThreads - Threads running</li> <li>URL - Endpoint URL</li> <li>Latency</li> <li>SampleCount</li> <li>ErrorCount - Cumulative amount of errors</li> <li>IdleTime </li> <li>Connect - Connection time</li> </ul> <p> </p>
Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity; Image data
<p>Lists of image sets included in <strong>"Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity"</strong></p> <p> </p> <p>Duplex-1 and Duplex-2 Images</p> <p>Amyloid Fibril TIRFM Images (SNCA-A53T ImagesTIRF Images)</p> <p>TEM Fibril Images</p> <p>DLS Images </p> <p>SMLM Images 1 & 2</p> <p> </p> <p>CLEM Images</p> <ul> <li>FIB SEM Images (videos)</li> <li>TEM Images </li> </ul> <p> </p> <p>Live-cell imaging</p> <ul> <li>Superoxide Images</li> <li>MitoTracker® Red Images</li> <li>Membrane Potential (TMRM) Images </li> <li>Ca 2+ Images</li> <li>NADH Autofluorescence Images</li> <li>Cell Death Images</li> <li>Amytracker Images</li> <li>Cardiolipin Images</li> <li>FRET Images</li> </ul>
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Raw data files for the manuscript entitled "Understanding your support system: the design of a stable MOF/polyazoamine support for biomass conversion"
<p>Raw data files for the manuscript entitled "Understanding your support system: the design of a stable MOF/polyazoamine support for biomass conversion" published in Chemistry of Materials. <a href="https://doi.org/10.1021/acs.chemmater.2c01731">https://doi.org/10.1021/acs.chemmater.2c01731</a></p> <p>The files are organized by manuscript figure names and are in a simple text format. The headers contain the necessary information such as column designations.</p>
Data for: Tailored frequency conversion makes infrared light visible for streak cameras
<p>Data for the publication "Tailored frequency conversion makes infrared light visible for streak cameras".</p> <p>Streak cameras are one of the most common and convenient devices to measure pulsed emission e.g. from semiconductor lightsources with picosecond time resolution. However, they are most sensitive in the visible range and possess low or negligible efficiency in the infrared and telecom regime. In this work, we present a frequency conversion based on sum-frequency generation that converts infrared to visible signals while preserving their temporal properties, making them detectable with a streak camera. We demonstrate and verify the functionality of our device by converting the emission from a quantum dot laser.</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB-Geschäftszeichen TRR142/3-2022 – Projektnummer 231447078, Project C01.</p>
Data files of the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas"
<p>Reconnection rate and energy budget files for the simulations used in for the paper "Energy conversion by magnetic reconnection in multiple ion temperature plasmas". The paper has two simulations. The first one, without cold ions, has his reconnection rate data stored in "rate_147.dat" and the energy budget data in "Ebudget_symm_nocold_Xframe.h5". The second one, with cold ions, has his reconnection rate data stored in "rate.dat" and the energy budget data in "Ebudget_symm_cold_Xframe.h5".</p> <p>On top of that is a datafile from simulation 1 at time 144.5 (corresponding to the time of the picture A in figure 1) with all the output fields from the simulation at this given time.</p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
<p><span>Tropical rainforests around the world are rapidly being converted into cash-crop agricultural systems. The associated massive losses of plant and animal species lead to changes in arthropod food webs and the energy fluxes therein. These changes are poorly understood, in particular in the extremely biodiverse canopies of tropical ecosystems. Using canopy fogging followed by stable isotope and energy flux analyses, we show that land-use conversion from rainforest to rubber and oil palm plantations not only causes a drastic reduction in energy fluxes of up to 75% but also shifts fluxes among trophic groups. While rainforests featured high levels of both herbivory and algae-microbiology, and a balanced ratio of herbivory to predation, relative fluxes were shifted towards predation in rubber and towards herbivory in oil palm plantations, indicating profound shifts in ecosystem functioning. Our results highlight that the ongoing loss of animal biodiversity and biomass in tropical canopies degrades animal-driven functions and restructures canopy food webs.</span></p>
Data for "Two-stage, low noise quantum frequency conversion of single photons from silicon-vacancy centers in diamond to the telecom C-band"
<p>The silicon-vacancy center in diamond holds great promise as a qubit for quantum communication networks. However, since the optical transitions are located within the visible red spectral region, quantum frequency conversion to low-loss telecommunication wavelengths becomes a necessity for its use in long-range, fiber-linked networks. This work presents a highly efficient, low-noise quantum frequency conversion device for photons emitted by a silicon-vacancy (SiV) center in diamond to the telecom C-band. By using a two-stage difference-frequency mixing scheme SPDC noise is circumvented and Raman noise is minimized, resulting in a very low noise rate of 10.4(7) photons per second as well as an overall device efficiency of 35.6 %. By converting single photons from SiV centers we demonstrate the preservation of photon statistics upon conversion.</p>
Data of findings in the article "Optomechanical ring resonator for efficient microwave-optical frequency conversion" by I.T. Chen et al.
<p>Data of findings in the article "Optomechanical ring resonator for efficient microwave-optical frequency conversion" by I.T. Chen et al.</p>
Data from: Rainforest conversion to plantations fundamentally alters energy fluxes and functions in canopy arthropod food webs
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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.