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Data for manuscript "Automatic detection of orientation entropy within scenes"
<p>Raw Neuropscan .cnt data for 15 participants with conditions and stimulus order described in the logbook for each participant.</p>
Fluorescence microscopy of Chlamydomonas reinhardtii for mCherry detection: secretion peptides strains.
<p><strong>Overview</strong></p> <p>Life-cell imaging was performed with a confocal fluorescence microscopy to observe mCherry in the secretion vacuoles. mCherry fluorescence compartmentalization was observed by a Confocal Zeiss LSM 780-NLO, using an argon laser 543 nm to excite mCherry and a spectral detector set approximately to 610-650 nm range. For chlorophyll, we used a laser at 405 nm for excitation, and spectral detector set to 680 nm region. All pictures were taken with the same system configuration and analyzed by Fiji, an ImageJ distribution software. Cells images were acquired in bundles of 0.4 μm afar photos per channel in the z-axis. </p> <p> </p> <p><strong>Files info:</strong></p> <p>Each file is the raw image obtained from fluorescent microscopy.</p> <p> </p> <p><strong>Organization</strong></p> <p>Construct_name.czi - Ex: "pAH04mCherry.czi"</p> <p>pAH04mCherry -> construct without signal peptide</p> <p>pJP22mCherry -> construct with signal peptide from arylsulfatase 1 (<em>Chlamydomonas </em><em>reinhardtii</em>)</p> <p>pJP26mCherry -> construct with signal peptide from binding protein 1 (<em>C. </em><em>reinhardtii</em>)</p> <p>pJP28mCherry -> construct with signal peptide from carbonic anhydrase 1 (<em>C. </em><em>reinhardtii</em>)</p> <p>pJP29 mCherry -> construct with signal peptide from ice-binding protein 1 (Artic <em>Chlamydomonas sp</em>)</p> <p>pJP30-35mCherry -> construct with signal peptide from in silico identified list (DOI 10.5281/zenodo.556792).</p> <p>Wildtype cc1690 -> parental strain used for transformation.</p> <p> </p> <p>For more information on the constructs, check our paper. </p> <p> </p> <p><strong>Consider citing our work. </strong></p> <p>Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433</p> <p> </p>
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Machine-learning-based seismic detection and location around the Tanlu fault zone in eastern China
<p>REAL, HypoInverse, and HypoDD catalog around the Tanlu fault zone in eastern China.</p>
LIONHEART Cancer Detection Resources
<p>Resources required to run LIONHEART ( <a href="https://github.com/BesenbacherLab/lionheart">https://github.com/BesenbacherLab/lionheart</a> ) cancer detection.</p> <p>Note: The shared LIONHEART scores and models are derived from data with a non-commercial, research-purpose only license.</p>
Dataset for "NanoVar: a Comprehensive Workflow for Structural Variant Detection to uncover the Genome's Hidden Patterns"
<h2><strong>Output Files for Long-Read Structural Variant and Repeat Analysis in Colorectal Cancer Samples (HRR698464, HRR698460, C586, C588)</strong></h2> <h3>Description:</h3> <p>This Zenodo dataset includes comprehensive output files generated during the application of a long-read sequencing analysis protocol for structural variant (SV) detection and repeat element characterization in colorectal cancer samples. The dataset is organized into two main directories:</p> <p><strong>1. HRR698464_MSI-H_Tumor</strong><br>This directory contains all primary output files generated from the analysis pipeline applied to the MSI-H tumor sample HRR698464 (also referred to as patient C586.T). Each subdirectory corresponds to a specific stage in the protocol:</p> <ul> <li>NanoPlot_output<br>Output from Stage 1 – Quality assessment of raw reads using NanoPlot.</li> <li>SAMtools_output<br>BAM file processing outputs from Stage 2 – Alignment of long reads to the reference genome using SAMtools.</li> <li>NanoVar_output<br>Output from Stage 3 – Structural variant calling using NanoVar.</li> <li>VCF_filtering_output<br>Output from Stage 4 – Filtering of structural variants using SURVIVOR and BCFtools; includes the filtered VCF files.</li> <li>NanoINSight_output<br>Output from Stage 5 – Characterization of repeat elements using NanoINSight.</li> <li>VEP_output<br>Output from Stage 6 – Annotation of structural variants using Ensembl Variant Effect Predictor (VEP).</li> </ul> <p> </p> <p><strong>2. Additional_output_files</strong><br>This directory contains supplementary output files used for comparison and visualization in Figures 4–7 of the associated publication. These include:</p> <ul> <li>HRR698460.NanoPlot.report.html<br>NanoPlot quality summary of a lower-quality tumor sample (HRR698460), used in Figure 4 for comparison with HRR698464.</li> <li>C586.N.nanovar.pass.vcf<br>NanoVar VCF output for the matched normal sample of patient C586, used to filter somatic calls in Stage 4.</li> <li>C586.N.nanovar.pass.report.html<br>NanoVar summary report of the normal sample of C586; used in Figure 5a.</li> <li>C588.N.nanovar.pass.vcf<br>NanoVar VCF output of the MSS normal sample (C588) for comparison with the MSI-H patient (C586).</li> <li>C588.N.nanovar.pass.report.html<br>NanoVar summary report of the MSS normal sample; used in Figure 5b.</li> <li>C588.T.nanovar.pass.vcf<br>NanoVar VCF output of the MSS tumor sample (C588); used in comparative analyses with the MSI-H sample.</li> <li>C588.T.nanovar.pass.report.html<br>NanoVar summary report of the MSS tumor sample; used in Figure 5b.</li> <li>MSS.tumor.unique.vcf<br>VCF file of somatic SVs in the MSS sample, generated by comparing matched tumor and normal pairs.</li> <li>MSS.tumor.unique.RepeatMasker.tbl<br>RepeatMasker output annotating somatic insertions in the MSS tumor sample; used in Figure 6.</li> <li>MSS.tumor.unique.vep.html<br>Ensembl VEP annotation report of somatic SVs in the MSS patient; used in Figures 7a and 7b.</li> <li>This dataset supports reproducibility and transparency of the protocol and offers a valuable resource for researchers interested in long-read-based SV detection, repeat annotation, and comparative cancer genomics.</li> </ul>
Oxford Nanopore Direct RNA Sequencing datasets for detecting rRNA modifications in the Brassica oleracea mitoribosome
<p>Oxford Nanopore Direct RNA Sequencing (DRS) was applied for the detection of rRNA modifications in the <em>Brassica oleracea</em> mitoribosome. A comparison between native rRNA transcripts and in vitro transcribed (IVT) rRNA transcripts that were devoid of any modification indicated systematic base-calling errors and/or variations in current intensities that led to the prediction of the modified nucleotides (Begik et al., 2018).</p> <p>For Nanopore (DRS) library preparation, custom reverse transcription adapters (RTAs) containing Deeplexicon multiplexing barcodes (BC1, BC2 or BC3) were designed for sequence-specific ligation to the 3’-ends of <em>B. oleracea</em> mitochondrial rRNAs.</p> <p>Basecalling and demultiplexing of ONT direct RNA sequencing data were performed by Guppy and Deeplexicon, respectively.</p> <p>For the analysis and visualization of current intensities, a full description is available in the associated publication.</p> <p>The <strong>Eventalign_18S.R</strong> Rscript was used to make the nanopore <strong>18S </strong>signal analysis</p> <p>The <strong>Eventalign_26S.R</strong> Rscript was used to make the nanopore <strong>26S </strong>signal analysis</p> <p>The raw data for the scripts are in the following folders:</p> <p>- For 26S: 26S_Native.barcode01 and 26S_IVT.barcode03<br>- For 18S: 18S_Native.barcode01 and 18S_IVT.barcode02</p> <p>These folders contain the read alignment files output from minimap2 (in .bam format) and the eventalign files generated by the f5c software (in tsv format).</p> <p>The FASTA folder contains the mitochondrial 18S and 26S rRNA gene reference sequence in fasta format</p>
Dynamics of island mass effect: detection input & output data
<p>"dynamic_IMEdetection.zip" contains a dynamic island mass effect detection software package.</p> <p>To run:</p> <ul> <li>Unzip "dynamic_IMEdetection.zip"</li> <li>Download input data</li> <li>change path, region to process, and options in "main_dynamicIME_detection.m" to work with the location of the unzipped "dynamic_IME_input_output_data" directory.</li> <li>Run "main_dynamicIME_detection.m"</li> </ul> <p>"dynamic_IME_input_output_data.zip" contains the input data and ouput IME and BO masks of the "dynamic_IMEdetection" software for four case studies:</p> <ul> <li>I04 = Rapa Nui</li> <li>I07 = Society Islands</li> <li>I09 = Samoa</li> <li>I18 = Fiji-Tonga</li> </ul> <p>Input data are custom multi-satellite products computed using the custom-made satellite binning software found here: <a href="../doi/10.5281/zenodo.13376824">10.5281/zenodo.13376824</a>.</p> <p>Output IME and BO masks are computed using the dynamic IME detection software (i.e. "dynamic_IMEdetection") which works with the original naming (i.e. "I04", "I07", "I09", and "I18") and subdirectory path of of the share data "dynamic_IME_input_output_data".</p> <p>Output IME/BO mask, chlorophyll concentration, and modelled surface current timeseries in *.gif format:</p> <ul> <li>RapaNui_8Dbin_20160520-0712_20161205-2230_map-time-series.gif = Rapa Nui between 2016/05/20 and 2016/12/05</li> <li>SocietyIsl_8Dbin_20160622-1012_20170209-0945_map-time-series.gif = Society Islands between 2016/06/22 and 2017/02/09</li> <li>Samoa_8Dbin_20160810-1115_20170226-1030_map-time-series.gif = Samoa between 2016/08/10 and 2017/02/26</li> <li>FijiTonga_8Dbin_20170218-1240_20170906-1205_map-time-series.gif = Fiji - Tonga between 2017/02/18 and 2017/09/06</li> </ul>
Parallel Path Detection Test Dataset
Open the record for dataset details and reuse information.
Supplementary Data for 'Machine learning detection of SARS-CoV-2 high-risk variants'
Open the record for dataset details and reuse information.
FIGURE 2 in Detection, extraction, and collection of eriophyoid mites
FIGURE 2. Schematic drawings illustrating the extraction procedure: a) materials; b) washing phase; c) sieving phase and sieve rinsing; d) sediment collection on a Petri dish; e) microscope observation phase; f) eriophyoid concentration on a filter; g) eriophyoids on a filter; h) sediment collection on a centrifuge tube; i) first centrifugation; j) sieving phase of the tube contents; k) sediment collection on a Petri dish; l) salt solution adding to the sediment of the first centrifugation; m) second centrifugation. Black arrows refer to mite extraction from plant samples and counting; gray arrows refer to mite concentration; white arrows refer to egg concentration.
FIGURE 1 in Detection, extraction, and collection of eriophyoid mites
FIGURE 1. Extraction procedure: A) plant sample manipulation and placed in a container; B) washing of the plant sample; C) sieving of the suspension; D) sieve rinsing; E) sediment collection on a Petri dish; F) mite view at a dissecting microscope; G) vacuum pump apparatus; H) filter mounted on the vacuum pump apparatus.
Event-based vision datasets for pedestrian detection in urban scenarios
<p>Datasets supporting the Spiking Perception and processing for Intelligent Detection of pEdestrians on urban Roads (SPIDER) Project. The codebase is available on Gihub at <a href="https://github.com/th-nuernberg/spider">https://github.com/th-nuernberg/spider</a>.<br><br>In Project SPIDER, we propose a novel solution for urban road surveillance using event-based neuromorphic cameras (i.e. Dynamic Vision Sensors – DVS), neural algorithms, and embedded neuromorphic computing platforms (i.e. Brainchip Akida). The solution is described by rapid detection and identification of abnormal activities, typically describing roadside pedestrian and bicyclist dynamics.<br><br>In order to train the detection system, we decided to use DVS camera events. To also obtain ground truth information we added a traditional CMOS camera on the mount with a preset offset capturing the same field of view. The CMOS frames were only used as support in the labeling process and were not included in the training of the system, the testing, or the evaluation, respectively. We have chosen two locations with different properties of the road, the number of pedestrians and bicyclists, and overall different traffic properties. </p><p><strong>The SPIDER project is an award-winning edge solution for the </strong><a href="https://www.tinyml.org/event/tinyml-hackathon-2023-pedestrian-detection/"><strong>TinyML Vision Zero San Jose Hackathon</strong></a><strong>. The project placed 2nd among 29 teams in the world in the final. The final pitch is available at </strong><a href="https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s">https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s</a></p><p> </p><p><i>Dataset archive content:</i></p><p> </p><p><strong>Dataset location 1</strong></p><p>• 4 lanes (4 per direction) wide street</p><p>• Location: https://goo.gl/maps/JaYGwaTaBHj5H6SL9</p><p>• 50 kmh (urban) speed limit</p><p>• Near the university campus with Pedestrians (people walking) and bicyclists (people biking, scooting, rolling, etc.)</p><p>• Ideal Operating Environment<br> </p><p><br><strong>Dataset location 2</strong></p><p>• 8 lanes (4 per direction) wide street</p><p>• Location: https://goo.gl/maps/jar6AjysZiM2LP5S7</p><p>• 50 kmh (urban) speed limit</p><p>• Near the main train stations of the city and a location with Pedestrians (people walking, running, or jogging), and cyclists (people biking, scooting, rolling, etc.)<br><br> </p><p><strong>Dataset location 3</strong></p><p>• 6 lanes (3 per direction) wide street on the bridge</p><p>• Location: https://goo.gl/maps/SEEsmpgmLPcD8fG7A</p><p>• 50 kmh (urban) speed limit</p><p>• Near ring street of Munich and a location with Pedestrians (people walking, running, or jogging) and bicyclists (people biking, scooting, rolling, etc.)</p><p>• Night-time data acquisition <br><br> </p>
Fig. 2. Othius punctulatus, aedeagus. A in Othius punctulatus(Goeze, 1777) (Coleoptera: Staphylinidae) Detected in the Pacific Northwest: A Palearctic Genus New to the USA
Fig. 2. Othius punctulatus, aedeagus. A) Lateral view, B) Dorsal (parameral) view. Scale = 0.5 mm. Modified from Assing (1997).
Data associated with the study, "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model".
<p>Raw data associated with the study entitled "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model". There were 16 subjects with 1. electrocardiogram (ECG), 2. mean arterial pressure (MAP), 3) photoplethysmography (pleth), 4) Bioimpedance Cardiography measured via a Cheetah (Startling/Medtronic) system referred to as cheetah, 5) electrical impedance from a Sentec electrical impedance tomography system, referred to as ST, and electrical impedance from a sciospec impedance analyzer, referred to as SS. Each subject underwent a lower body negative pressure (LBNP) procedure, where the LBNP was increased modeling a small hemorrhage by drawing blood to their lower extremeties. The dataset contains 5 mat (MATLAB data files), with time reported in minutes on the day of the study, i.e.10 am = 600 minutes. The file details are as follows:</p><ul><li><strong>LBNP data</strong>: LBNP_level_times.mat. The data contains 1 structure (LBNPinf) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>lbnp: LBNP value at each noted time</li></ul></li><li><strong>Vital Sign data</strong>: raw_labchart_data.mat. The data contains 1 structure array (Labchart) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>ecgs: ECG data</li><li>MAP: MAP data</li><li>pleth: pleth data recorded from a single channel (V)</li></ul></li><li><strong>Sentec EIT data</strong>: raw_av_ST_impedance_data.mat. The data contains 1 structure array (STout) of length 16 (corresponding to each subject) with the following fields<ul><li>t_thx: time vector in minutes corresponding to thorax data</li><li>Z_thx: average impedance data at each time over the thorax</li><li>t_spl: time vector in minutes corresponding to abdomen data</li><li>Z_spl: average impedance data at each time over the abdomen</li></ul></li><li><strong>Sciospec EIS data</strong>: raw_sciospec_dat.mat. The data contains 1 cell array & 1 structure array (sciodat) of length 16 (corresponding to each subject).<ul><li>Cell array: Locations of the Sciospec measurements: 'Thoracic', 'Abdominal', 'Arm'</li><li>Sciodat fields:<ul><li>sciodat structure array of length 3 corresponding to the 'Thoracic', 'Abdominal', 'Arm' locations, respectively. Each component has the following fields<ul><li>tvec: time vector in minutes</li><li>fs: frequencies that the impedance is recorded over (Hz)</li><li>Zmat: matrix of impedance data size time versus frequency</li><li>erflg: not used</li></ul></li></ul></li></ul></li><li><strong>Bioimpedance cardiography data</strong>: raw_cheetah_bioimpedance.mat. The data contains a cell array of column labels (col_labs, 1x11) and a matrix (cheetah_db) of the BC data.</li></ul><p> </p><p> </p><p> </p>
Detecting Usage Protocol Violations in Deep Learning Applications
<p>This repo contains a replication package of the "Detecting Usage Protocol Violations in Deep Learning Applications" paper.</p><p>In the paper, we use two benchmarks: </p><ul><li>The first benchmark we used from NeuraLint can be found under the director name Benchmark1/SOSamples</li><li>The second benchmark we used from Humbatova et al. can be found under the director name Benchmark2/SOSamples</li></ul><p>To reproduce the results in the paper: </p><ul><li>Download the NeuralStateAnalysis Zip file.</li><li>Extract the file and go to the NeuralStateAnlaysis directory.</li><li>( Optional ) Install the requirements by running 'Pip install requirements.txt.' N.B: The requirements.txt file is already in this repo.<ul><li>To run NeuralState on Benchmark1:<ul><li>Go to Benchmark1/SOSamples directory,</li><li>Open any of the programs you want to run,</li><li>Set the path: Path-to-folder/NeuralStateAnalysis/</li><li>Then, do 'python program_id.' Since the 'NeuralStateAnalysis(model).debug()' call is already present in all programs, you'll be able to reproduce results.</li></ul></li><li>To run NeuralState on Benchmark2:<ul><li>Go to Benchmark2/SOSamples directory,</li><li>Open any of the programs you want to run,</li><li>Set the path: Path-to-folder/NeuralStateAnalysis/</li><li>Then, do 'python program_id.' Since the 'NeuralStateAnalysis(model).debug()' call is already present in all programs, you'll be able to reproduce results.</li></ul></li></ul></li></ul>
Underlying dataset of Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies
<p>YouTube data collection for cyberbullying studies. </p><p>Citation:</p><p>M. Dadvar, R.B. Trieschnigg and F.M.G. de Jong, Experts and Machines Against Bullies: A Hybrid Approach to Detect Cyberbullies. In 27th Canadian Conference on Artificial Intelligence, University of Waterloo, Montréal, Canada, 2014</p>
Dataset for the paper "Test Polarity: Detecting Positive and Negative Tests".
<p><strong>Dataset for the paper "Test Polarity: Detecting Positive and Negative Tests".</strong></p><p>Test polarity results for 12 Python Standard Libraries: gzip, email, calendar, ftplib, collections, os, tarfile, pathlib, logging, smtplib, argparse, and configparser.</p>
CommRad RF: A dataset of communication radio signals for detection, identification and classification
<p>In this age of information, data is the most valuable commodity. In today's world it is easier to acquire millions of standard/known and even non- standard/unknown RF signals. Perhaps, having such huge amount of data can help people to develop novel deep learning techniques. This dataset focuses on communication radios baseband signals. It includes RF signals from multiple radios using different frequencies, in different environments. Over 2700 RF signals from 27 different radios are included in the dataset. An RF receiver was used to capture the dataset, which automatically detects RF signals and records them until they disappear. In order to create a signal library, the receiver is connected to a laptop that pre-processes and stores RF signatures from different emitters.</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.