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9,302 results for “DETECT”
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 2 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 2/2 of a dataset used for running and training the ML model.</p> <p>Part 1 is available at <a href="https://doi.org/10.5281/zenodo.14513933">10.5281/zenodo.14513933</a></p> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 1 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 1/2 of a dataset used for running and training the ML model.</p> <div> <div>Part 2 is available at <a href="https://doi.org/10.5281/zenodo.14524613">10.5281/zenodo.14524613</a></div> <div> </div> </div> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
Simulation of HLVK Configuration for O5 and O6 Observation Runs: Gravitational Wave Detection Rates, Skymaps, and Multimessenger Parameters (November 2024 edition)
<p>We conducted a simulation of the<strong> HLVK </strong>configuration for the upcoming O5 and O6 observing runs, estimating the expected gravitational wave (GW) detection rates based on a signal-to-noise (S/N) <strong>threshold of 10</strong>. This dataset includes skymap files and essential parameters for multimessenger studies. Additionally, a Python script is provided to facilitate the separation of BNS, BBH, and NSBH events based on mass, with a default recommendation of setting neutron star (NS) mass to <3 solar masses and black hole (BH) mass to ≥3 solar masses. Previous simulations for the O4 and O5 runs used an <strong>S/N of 8</strong> and can be found here: <a href="https://doi.org/10.5281/zenodo.7026209" target="_new" rel="noopener">https://doi.org/10.5281/zenodo.7026209</a>. </p>
ALCC: Temporally consistent annual land cover maps over China from 1985 to 2022 based on an ensemble change detection method
<p><span><span>We develope <span><span> </span>a consistent annual land cover product for China (ALCC) from 1985 to 2022. First, change areas for each year was detected baesd on an ensemble change detection method combing CCDC, BFASTm, and Chow Test. Then, stable training samples were derived from both CLCD and CLUDs. The Random Forest classifier was locally trained and used to classify change areas year by year. Finally, ALCC was generated by updating land cover classification results for change areas and remaining class label the same as the base map forthe unchanged areas. ALCC achieved a mean overall accuracy of 81.11±0.67% nationwide and exceeded 72.00% across seven geographical regions.</span></span></span></p>
YOLO-Strings: A YOLO-compatible Dataset for Detecting Stringing during 3D Printing
<p>The root data directory consists of two subdirectories, called <em>sequences</em> and <em>yolo</em>. The former contains all the individual sequences that were collected, further organized into subdirectories based on whether they have stringing or not. Each of these subdirectories contains images captured by the camera and the microscope. The latter comprises the training and validation subsets for the detection model, along with YOLO-style annotations.</p> <p> </p>
Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling
<p>The dataset contains processed T-cell receptor repertoire sequencing data from >1200 individuals of different sex and age. Note that only samples with good sequencing coverage are published (>10^5 reads per file). </p> <p>The main aim of our study is to find TCR sequence biomarkers and develop a bioinformatic pipeline that allows building an accurate and robust classifier that distinguishes COVID-19-convalescent donors from unexposed individuals. We performed immunosequencing of the rearranged TCR α and β regions for PBMCs. For the cohort described in this study (Cohort-I) we sequence both chains of the TCR heterodimer as both of these chains are required to properly predict antigen recognition26. We ran conventional T-cell repertoire data analysis and pre-processed data to remove low-coverage samples. </p> <p>Of samples in Cohort-I which passed read count threshold, 383/377 TCR α/β samples were from healthy donors (SARS-CoV-2 PCR test negative or obtained prior to pandemic) and 890/848 were from COVID-19-positive patients. The majority of samples were accompanied by information on HLA class I and II alleles. Samples were prepared and sequenced in nine batches.</p> <p>The metadata for both TCR alpha and beta repertoires contains the following information:</p> <div> <ul> <li>sequencing_date - date when seguencing was performed</li> <li>batch_name - one of the 9 unique batch identifiers</li> <li>sample_id, patient_id - information on sample identifier and donor identifier</li> <li>COVID_status, COVID_IgG, COVID_IgM, COVID_PCR - information on COVID-19 status</li> <li>HLA-A.1, HLA-A.2, HLA-B.1, HLA-B.2, HLA-C.1, HLA-C.2 - MHC class I alleles</li> <li>HLA-DPB1.1, HLA-DPB1.2, HLA-DQB1.1, HLA-DQB1.2, HLA-DRB1.1, HLA-DRB1.2 - MHC class II alleles</li> <li>file_name - name of the corresponding file in <em>fmba_clonotype_usage_tables.zip </em>archive</li> </ul> </div> <p>Each file in <em>fmba_clonotype_usage_tables.zip </em>archive stores the information on either TCR alpha or beta repertoire. Each line in a file corresponds to the unique clonotype and each clonotype is accompanied with the following information:</p> <ul> <li>count - number of reads where the clonotype was detected</li> <li>freq - count of reads with the clonotype divided by thw whole number of reads in a sample</li> <li>cdr3nt, cdr3aa - nucleotide and amino acid sequences of TCR's CDR3 sequence</li> <li>v, d, j - the V/D/J segment name which was used for the clonotype's rearrangement</li> <li>VEnd, DStart, DEnd, JStart - information on VDJ junction positions </li> </ul> <p>We proceed with selecting a set of CDR3 sequences that can serve as biomarkers and form a feature list for COVID-19 status classifier. We also validate the resulting set of clonotypes in several ways. Co-occurence of specific TCR α and β clonotypes can serve as an independent validation for biomarkers and their co-association with some specific pathogen. Additional information on donor HLAs is provided to filter the set of biomarkers based on HLA restriction: association with donor HLA serves as an additional evidence for TCR specificity to a specific set of antigens presented in a given donor and allows detecting the fingerprint of past and present infection. Furthermore, clonotypes with similar sequences can be aggregated into 'metaclonotype' biomarkers based on clonotype graph analysis.</p> <p>Finally, we train various COVID-19 status classifiers on selected batches from Cohort-I data using different algorithms and incorporating different feature sets. Verification of the robustness of our results was performed using independent batches of the Cohort-I and data from Cohort-II published previously.</p>
Supplementary Materials for LAMeD: LLM-generated Annotations for Memory leak Detection
Open the record for dataset details and reuse information.
Dataset for "Face Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for</strong></p> <p><strong>"Face Detection in Untrained Deep Neural Networks"</strong></p> <p>Seungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Paik*</p> <p>*Contact: <a href="mailto:sbpaik@kaist.ac.kr">sbpaik@kaist.ac.kr</a></p> <p> </p> <p>To run demo codes for "Face Detection in Untrained Deep Neural Networks", please download files below.</p> <p> </p> <p><strong>1. Stimulus.zip</strong></p> <p>- <strong>IMG_cntr_210521.mat</strong> : A low-level feature-controlled stimulus set was used to find units that responded selectively to face images (Stigliani, 2015). Specifically, 260 images were prepared for each class (face, hand, horn, flower, chair, and scrambled face).</p> <p>- <strong>IMG_var_pos/size/rot_210521.mat</strong> : To investigate the invariance of face-selective units to face images of various sizes, positions, and rotation angles, the image set (Stigliani, 2015) was generated after modifying the size, position, and rotation angle of the faces and other objects in the low-level feature-controlled stimulus set.</p> <p>- <strong>IMG_var_view_210106.mat</strong> : This set was used to find units that invariantly responded to face images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-90°, -45°, 0°, 45°, 90°) with ten different faces obtained from (Gourier 2004)</p> <p> </p> <p><strong>2. Data.zip</strong></p> <p>- <strong>Data_PFI_XDream/RevCorr_ClsUnit.mat</strong> : The obtained preferred feature images, using reverse-correlation method and X-Dream, of units selective to each object class.</p> <p>- <strong>Data_SVM_NeuronType.mat</strong> : Simulated face detection performance using distinct types of selective units in Conv5 and using the shuffled responses of face-selective units in untrained networks (a. all selective units, b. units selective to non-face classes (nonface-selective), c. face-selective units, d. units selective to none of these classes (non-selective)).</p> <p>- <strong>Data_SVM_invariance.mat</strong> : Face detection performance with variation of the low-level features. </p> <p>- <strong>PretrainedNet</strong> : Sample networks was untrained and trained with three types of image sets (a. face-reduced ImageNet, b. original ImageNet, c. original ImageNet with added face images (Stigliani, 2015)) (number of networks = 3). </p> <p> - Net_Untrained : untrained AlexNets<br> - Net_FD_ImageNet : AlexNets trained to face-reduced ImageNet<br> - Net_ImageNet : AlexNets trained to original ImageNet<br> - Net_ImageNetwFace : AlexNets trained to original ImageNet with added face images</p> <p>- <strong>Data_Trained.mat</strong> : Result summary of four types of networks above (number of networks = 10).</p> <p> </p>
BactPrep: A user-friendly whole-genome sequencing analysis platform for the detection of homologous recombination and horizontal gene transfer in bacteria - Sample Dataset
<p>This is the dataset used as the sample dataset for the pipeline BactPrep. This dataset consists of 218 <em>Streptococcus pneumoniae</em> PMEN1 WGS assemblies collected from the year 1984 - 2008 from 22 unique countries globally. The raw sequencing data was originally published in the work: Rapid pneumococcal evolution in response to clinical interventions (doi: 10.1371/journal.ppat.1002745) under the bioproject PRJEB2085.</p> <p>We have assembled the raw sequences records with the following steps: 1) raw reads were first quality checked using fastQC 0.11.9; 2) adapters and low quality reads were removed using Trimmomatic 0.39 with parameter “ILLUMINACLIP:TruSeq2-PE.fa:2:30:10:2:keepBothReads LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36”; 3) trimmed reads were error-corrected and assembled into WGS assemblies using SPAdes 3.15.0 with parameters "--careful --mismatch-correction”.</p>
All-Inkjet-Printed Humidity Sensors for the Detection of Relative Humidity in Air and Soil—Towards the Direct Fabrication on Plant Leaves
<p>We demonstrate the fabrication, by exclusive means of inkjet-printing, of capacitive relative humidity sensors on flexible, plastic substrate. These sensors can be successfully used for the measurement of relative-humidity in both air and common soil. We also show that the same technique may be used for the fabrication of the same type of sensors on the surface of the leaves of Elægnus Ebbingei (silverberry).Our results demonstrate the suitability of leaves as substrate for printed electronics and pave the way to the next generation of sensors to be used in fields such as agriculture and flower farming.</p>
Scintillation event list database produced for "Multiyear Detection, Classification and Hypothesis of Ionospheric Layer Causing GNSS Scintillation"
<table> <tbody> <tr> <td>Database produced for and associated with journal article https://doi.org/10.1029/2021RS007328</td> </tr> <tr> <td>This database contains scintillation events identified by the "Detect, Classify, Hypothesize" method as described in the journal article. </td> </tr> <tr> <td>Created by Datta-Barua, Seebany.</td> </tr> <tr> <td>Open Access License Creative Commons Attribution 4.0 International</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Sheets labeled "S*_yyyy_freq" contain a list of scintillation events based on data from the SAGA array.</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>For S* the abbreviations are:</td> <td>"SP": sigma phi index, i.e., phase scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"S4": S4 index, i.e., amplitude scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"SPS4": sigma phi and S4, i.e., both-phase-and-amplitude events</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>yyyy indicates the four-digit year, from 2014 through 2019</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>freq:</td> <td>"L1CA": refers to GPS L1 frequency 1575 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"L2CL": refers to GPS L2C frequency 1227 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>In each sheet "S*_yyyy_freq" the columns are:</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column A:</td> <td>Year (2014-2019)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column B:</td> <td>day of year (1-366)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column C:</td> <td>frequency (1 for L1, 2 for L2C)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column D:</td> <td>GPS satellite PRN (1-32)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column E:</td> <td>Scintillation start UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column F:</td> <td>Scintillation start UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column G:</td> <td>Scintillation end UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column H:</td> <td>Scintillation end UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column I:</td> <td>number of receivers operational (4-6)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column J:</td> <td>PFISR-based hypothesized layer (1-7, as described below)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>1:</td> <td>E layer (majority peak height < 150 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>2:</td> <td>F layer (majority peak height > 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>3:</td> <td>T layer (150 < majority peak height < 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4:</td> <td>I layer (no majority layer)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>5:</td> <td>"N"- no Poker Flat Incoherent Scatter Radar (PFISR) long pulse data and no alternating code data</td> </tr> <tr> <td> </td> <td> </td> <td>6:</td> <td>"NLP" - no PFISR long pulse</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>7:</td> <td>"NAC" - no PFISR alternating code</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>A blank sheet indicates that there were no events of that type and at that frequency found in that year.</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
A Line-level Explainable Vulnerability Detection Approach for Java
<p>Given our modern society’s level of dependency on IT technology, high quality and security are not just desirable but rather vital properties of current software systems. Empirical methods leveraging the available rich open-source data and advanced data processing techniques of ML algorithms can help software developers ensure these properties. Nonetheless, state-of-the-art bug and vulnerability prediction methods are rarely used in practice due to numerous reasons. The predictions are not actionable in most of the cases due to their level of granularity (i.e., they mark entire classes/files to be buggy or vulnerable) and because the methods seldom provide explanation why a fragment of source code is problematic. In this paper, we present a novel Java vulnerability detection method that addresses both of these issues. It is an adaptation of our previous method for JavaScript that is capable of pinpointing vulnerable source code lines of a program together with a prototype-based explanation. The method relies on the word2vec similarity of code fragments to known vulnerable source code lines. Our empirical evaluation showed promising results, we could detect 61% and 41% of the vulnerable code lines by flagging only 43% and 22% of the program code lines, respectively, using two of the best detection configurations.</p> <p>The dataset contains the extracted vulnerable code lines and word2vec models used in the experiment on 205 Java projects.</p>
Determining the efficacy of camera traps, live capture traps, and detection dogs for locating cryptic small mammal species
<p>Metal box (e.g., Elliott, Sherman) traps and remote cameras are two of the most commonly employed methods presently used to survey terrestrial mammals. However, their relative efficacy at accurately detecting cryptic small mammals has not been adequately assessed. The present study therefore compared the effectiveness of metal box (Elliott) traps and vertically oriented, close range, white flash camera traps in detecting small mammals occurring in the Scenic Rim of eastern Australia. We also conducted a preliminary survey to determine effectiveness of a conservation detection dog (CDD) for identifying presence of a threatened carnivorous marsupial, <i>Antechinus arktos,</i> in present-day and historical locations, using camera traps to corroborate detections. 200 Elliott traps and 20 white flash camera traps were set for four deployments per method, across a site where the target small mammals, including <i>A. arktos</i>, are known to occur. Camera traps produced higher detection probabilities than Elliott traps for all four species. Thus, vertically mounted white flash cameras were preferable for detecting the presence of cryptic small mammals in our survey. The CDD, which had been trained to detect <i>A. arktos</i> scat, indicated in total 31 times when deployed in the field survey area, with subsequent camera trap deployments specifically corroborating <i>A. arktos</i> presence at 100% (3) indication locations. Importantly, the dog indicated twice within Border Ranges National Park, where historical (1980s-1990s) specimen-based records indicate the species was present, but extensive Elliott and camera trapping over the last 5-10 years have resulted in zero <i>A. arktos</i> captures. Camera traps subsequently corroborated <i>A. arktos</i> presence at these sites. This demonstrates that detection dogs can be a highly effective means of locating threatened, cryptic species, especially when traditional methods are unable to detect low-density mammal populations.</p>
Detect, Fix, and Verify TensorFlow API Misuses - SANER 2022 - Experimental Data
<p>Detect, Fix, and Verify TensorFlow API Misuses - SANER 2022</p> <p>Experimental data</p>
Data from: Fusobacterium nucleatum and Bacteroides fragilis detection in colorectal tumours: optimal target site and correlation with total bacterial load
<p>These data were generated to investigate detection of <em>Fusobacterium nucleatum </em>(<em>F. nucleatum</em>) and <em>Bacteroides fragilis</em> (<em>B. fragili</em>s) across different regions of human colorectal tumours. Relative abundance of each species in DNA extracted for clinical molecular mutation testing from formalin-fixed, paraffin-embedded (FFPE) tumour samples from 42 patients was assessed using targeted real-time PCR quantitative (qPCR) (the screening cohort). DNA was then freshly extracted from specific regions of tumours testing positive for one or both species (n = 20) and from 31 additional patients, and relative abundance of each species assessed using qPCR (site investigation cohort). Total bacterial load at the tumour luminal surface (where <em>F. nucleatum</em> and <em>B. fragilis</em> were most frequently detected) was also assessed by qPCR using primers targeting amplification of 16S rRNA. 16S sequencing was performed on tumour luminal surface DNA samples from five patients as an orthogonal method to confirm the ability to detect the targeted species by qPCR.</p>
CLNews19-20: A new dataset for rumor detection in Spanish
<p>We create CLNews, a dataset for rumor detection in Spanish. Based on fact-checking agencies' data, we mapped related tweets to verify news into four categories: non-rumor, true rumor, false rumor, and unverified rumors. Mapping these news to Twitter, we collected data, including tweet timelines and conversational threads. We release this dataset to promote research in this area in Spanish.</p>
Fine-scale ecological and anthropogenic variables predict the habitat use and detectability of sloth bears in the Churia habitat of east Nepal
<p>Once widespread throughout the tropical forests of the Indian Subcontinent, the sloth bears have suffered a rapid range collapse and local extirpations in the recent decades. A significant portion of their current distribution range is situated outside of the protected areas (PAs). These unprotected sloth bear populations are under tremendous human pressures, but little is known about the patterns and determinants of their occurrence in most of these regions. The situation is more prevalent in Nepal where virtually no systematic information is available for sloth bears living outside of the PAs. We undertook a sign survey-based single-season occupancy study intending to overcome this information gap for the sloth bear populations residing in the Trijuga forest of southeast Nepal. Sloth bear sign detection histories and field-based covariates data were collected between 2nd October to 3rd December 2020 at the 74 randomly chosen 4-km^2 grid cells using a varying number of 400m long transects in each grid cell. From our results, the model-averaged estimate of site use probability (ψ ± SE) was estimated to be 0.432 ± 0.039, which is a 13% increase from the naïve estimate (0.297) not accounting for imperfect detections of sloth bear signs. The presence of termite mound and the distance to the nearest water source were the most important variables affecting the habitat use probability of sloth bears. The average site-level detectability (p ± SE) of sloth bear signs was estimated to be 0.195 ± 0.003 and was significantly determined by the index of human disturbances. We recommend considering the importance of fine-scale ecological and anthropogenic factors in predicting the sloth bear-habitat relationships across their range in the Churia habitat of Nepal, and more specifically in the unprotected areas.</p>
Synergistic use of Far- and Mid-Infrared Spectral radiances for Polar Ice Cloud Detection from Space
<p>Modtran5 was used to generate all .plt radiance files from the tp5 files provided</p> <p>All ERA5 data used in this study is provided in .mat format </p> <p>Codes for plotting the data for figures is provided in "codes" folder </p> <p> </p>
Experiment Results for the Paper: "Augur: A Step Towards Realistic Drift Detection in Production ML Systems"
<p>Contains the experiment results for the paper "Augur: A Step Towards Realistic Drift Detection in Production ML Systems". More specifically, it contains the inputs and outputs for the different experiments to run using the Predictor tool. More information about the base dataset used in the README.md file. We are going through the code marking and internal approvals process that is required to make the code open source. The process will be complete in time for camera-ready submission if the paper is accepted.</p>
Variable Message Signal annotated images for object detection
<p><strong>If you use this dataset, please cite this paper: <em>Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041</em></strong></p> <p>This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of <strong>1216</strong> instances, <strong>888</strong> positives, and <strong>328</strong> negatives, in <strong>1152</strong> jpg images with a resolution of 1280x720 pixels. These are divided into <strong>576</strong> real images and <strong>576</strong> images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.</p> <p>The folder structure of the dataset is as follows:</p> <ul> <li>vms_dataset/ <ul> <li>data.csv</li> <li>real_images/ <ul> <li>imgs/</li> <li>annotations/</li> </ul> </li> <li>data-augmentation/ <ul> <li>imgs/</li> <li>annotations/</li> </ul> </li> </ul> </li> </ul> <p>In which:</p> <ul> <li><strong>data.csv:</strong> Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.</li> <li><strong>real_images:</strong> Images extracted directly from the videos.</li> <li><strong>data-augmentation:</strong> Images created using data-augmentation</li> <li><strong>imgs:</strong> Image files in .jpg format.</li> <li><strong>annotations:</strong> Annotation files in .xml format.</li> </ul>
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.