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120 results for “data augmentation”
scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data
<p>This repository contains the training data and source code to reproduce the results of our paper:<br>scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data</p> <p>More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).</p>
Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition
<p>This repository contains the <strong>Wallhack1.8k dataset</strong> for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the <strong>CAD models</strong> (of 3D-printable parts) of the WiFi systems proposed in [2].</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a></p> <p><strong>Dataset Description</strong></p> <p>The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).</p> <p>To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:</p> <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> <p>These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector <em>H</em> being stored in the "data" field and the class label being stored in the "class" field. <em>H </em>is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of <em>H</em> (e.g., via <em>numpy.abs(H)</em>) yields the subcarrier amplitudes <em>A</em>.</p> <p>To extract the 52 L-LTF subcarriers used in [1], the following indices of <em>A</em> are to be selected:</p> <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> <p>Additional 56 HT-LTF subcarriers can be selected via:</p> <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> <p>For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>.</p> <p>Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the <em>spectrograms/</em> directory.</p> <p>The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]</p> <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> <p><strong>Dataset Overview:</strong></p> <p>Table 1: Raw WiFi packet sequences.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td><em>"no presence" / label 0</em></td> <td><em>"walking" / label 1</em></td> <td><em>"walking + arm-waving" / label 2</em></td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td><strong>44</strong></td> </tr> </tbody> </table> <p>Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td> <p><em>"no presence" / </em> label 0</p> </td> <td> <p><em>"walking"</em> / label 1</p> </td> <td><em>"walking + arm-waving" / </em>label 2</td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td><strong>1,806</strong></td> </tr> </tbody> </table> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, <em>In IFIP International Conference on Artificial Intelligence Applications and Innovations</em> (pp. 42-56). Cham: Springer Nature Switzerland<em>,</em> doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>.</p> <p>[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>.</p> <p>BibTeX citations:</p> <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}<br><br>@INPROCEEDINGS{10647666,<br> author={Strohmayer, Julian and Kampel, Martin},<br> booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, <br> title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, <br> year={2024},<br> volume={},<br> number={},<br> pages={3594-3599},<br> keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},<br> doi={10.1109/ICIP51287.2024.10647666}}<br><br><br></pre>
SEMFIRE forest dataset for semantic segmentation and data augmentation
<p><strong>SEMFIRE Datasets (Forest environment dataset)</strong></p> <p>These datasets are used for semantic segmentation and data augmentation and contain various forestry scenes. They were collected as part of the research work conducted by the Institute of Systems and Robotics, University of Coimbra <a href="https://isr.uc.pt/index.php/people?task=showprojects.show&idProject=203">team</a> within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. <a href="http://semfire.ingeniarius.pt/">CENTRO-01-0247-FEDER-032691</a>) research project coordinated by <a href="https://ingeniarius.pt/">Ingeniarius Ltd.</a></p> <p>The semantic segmentation algorithms attempt to identify various semantic classes (e.g. background, live flammable materials, trunks, canopies etc.) in the images of the datasets.</p> <p>The datasets include diverse image types, e.g. original camera images and their labeled images. In total the SEMFIRE datasets include about 1700 image pairs. Each dataset includes corresponding .bag files.</p> <p>To launch those .bag files on your ROS environment, use the instructions on the following Github <a href="https://github.com/Forestry-Robotics-UC/fruc_rosbags">repository</a></p> <p>Description of<strong> </strong>each <strong>dataset:</strong></p> <ol> <li><strong>2019_2020_quinta_do_bolao_coimbra:</strong> Robot moving on a path through a forest environment</li> <li><strong>2020_ctcv_parking_lot_coimbra:</strong> Robot moving in a circle in a parking lot for testings</li> <li><strong>2020_sete_fontes_forest: </strong>A set of forest images acquired by hand-held apparatus</li> </ol> <p>Each <strong>dataset</strong> consists of following <strong>directories:</strong></p> <ol> <li><strong>images directory: </strong>diverse image types, e.g. original camera images and their labeled images</li> <li><strong>rosbags directory: </strong>.bag files, which correspond to the image directory</li> </ol> <p>Each <strong>images directory </strong>consists of following <strong>directories:</strong></p> <ul> <li><strong>img:</strong> original camera images</li> <li><strong>lbl:</strong> single channel images (ground truth) with corresponding labels for each image in<strong> img</strong></li> <li><strong>lbl_colored: </strong>camera <strong> </strong>images in <strong>lbl</strong> colorized according to different semantic classes (for more details see the datasets descriptions)</li> <li><strong>lbl_overlaid: </strong>camera images in <strong>img </strong>overlaid with corresponding labels (colored)</li> </ul> <p>Each <strong>rosbags directory </strong>contains .bag files with the following <strong>topics:</strong></p> <ul> <li><strong>2019_2020_quinta_do_bolao_coimbra_rosbags: </strong> <ul> <li>/back_lslidar_packet</li> <li>/dalsa_camera_720p/compressed</li> <li>/flir_ax8/compressed</li> <li>/front_lslidar_packet</li> <li>/gps_fix</li> <li>/gps_time</li> <li>/gps_vel</li> <li>/imu/data</li> <li>/realsense/aligned_depth_to_color/image_raw</li> <li>/realsense/color/camera_info</li> <li>/realsense/color/image_raw/compressed</li> <li>/realsense/depth/camera_info</li> <li>/realsense/depth/image_rect_raw/compressed</li> <li>/realsense/extrinsics/depth_to_color</li> </ul> </li> <li><strong>2020_ctcv_parking_lot_coimbra_rosbags:</strong> <ul> <li>/dalsa_camera_720p/compressed</li> <li>/gps_fix</li> <li>/gps_ime</li> <li>/fused_point_cloud</li> <li>/imu/data</li> <li>/imu/mag</li> <li>/imu/rpy</li> </ul> </li> <li><strong>2020_sete_fontes_forest_rosbags: </strong> <ul> <li>/realsense/camera_info</li> <li>/realsense/depth_compressed/compressedDepth</li> <li>/realsense/nir/left/compressed</li> <li>/realsense/nir/right/compressed</li> <li>/realsense/rgb/compressed</li> </ul> </li> </ul> <p>All datasets include a detailed description as a text file. In addition, they include a rosbag_info.txt file with a description for each ROS inside the .bag files as well as a description for each ROS topic.</p> <p> </p> <p>The following table shows the statistical description of typical portuguese woodland configurations with structured plantations of <em>Pinus pinaster </em>(<em>Pp, </em>pine trees) and <em>Eucalyptus globulus </em>(<em>Eg, </em>eucalyptus).</p> <table> <tbody> <tr> <td> </td> <td><strong>"Low density" structured plantation</strong></td> <td><strong>"High density" structured plantation</strong></td> </tr> <tr> <td><strong>Tree density (assuming plantation in rows spaced 3m apart in all cases)</strong></td> <td> <p><em>Eg</em>: 900 trees/ha</p> <p><em>Pp</em>: 450 trees/ha</p> </td> <td> <p><em>Eg</em>: 1400 trees/ha</p> <p><em>Pp</em>: 1250 trees/ha</p> </td> </tr> <tr> <td> <p><strong>Average heights and corresponding ages of plantation trees</strong></p> </td> <td> <p><em>Eg</em>: 12m (6 years old)</p> <p><em>Pp</em>: 10m (15 years old)</p> </td> <td> <p><em>Eg</em>: 12m (6 years old)</p> <p><em>Pp</em>: 10m (15 years old)</p> </td> </tr> <tr> <td> <p><strong>Maximum heights and corresponding fully-matured ages of plantation trees</strong></p> </td> <td> <p><em>Eg</em>: 20m (11 years old)</p> <p><em>Pp</em>: 30m (40 years old)</p> </td> <td> <p><em>Eg</em>: 20m (11 years old)</p> <p><em>Pp</em>: 30m (40 years old)</p> </td> </tr> <tr> <td> <p><strong>Diameter at chest level (DCL – 1,3m) of plantation trees (average/maximum)</strong></p> </td> <td> <p><em>Eg</em>: 15cm/25cm</p> <p><em>Pp</em>: 20cm/50cm</p> </td> <td> <p><em>Eg</em>: 15cm/25cm</p> <p><em>Pp</em>: 20cm/50cm</p> </td> </tr> <tr> <td> <p><strong>Natural density of herbaceous plants</strong></p> </td> <td> <p>30% of woodland area</p> </td> <td> <p>30% of woodland area</p> </td> </tr> <tr> <td> <p><strong>Natural density of bush and shrubbery</strong></p> </td> <td> <p>30% of woodland area</p> </td> <td> <p>30% of woodland area</p> </td> </tr> <tr> <td> <p><strong>Natural density of arboreal plants (not part of plantation)</strong></p> </td> <td> <p>5% of woodland area</p> </td> <td> <p>5% of woodland area</p> </td> </tr> </tbody> </table> <ul> </ul>
augmentation_data_for_DAISM
<p>The purified dataset for data augmentation for DAISM-DNN<sup>XMBD</sup> can be downloaded from this repository.</p> <p>The pbmc8k dataset downloaded from 10X Genomics were processed and uesd for data augmentation to create training datasets for training DAISM-DNN models. pbmc8k.h5ad contains 5 cell types (B.cells, CD4.T.cells, CD8.T.cells, monocytic.lineage, NK.cells), and pbmc8k_fine.h5ad cantains 7 cell types (naive.B.cells, memory.B.cells, naive.CD4.T.cells, memory.CD4.T.cells,naive.CD8.T.cells, memory.CD8.T.cells, regulatory.T.cells, monocytes, macrophages, myeloid.dendritic.cells, NK.cells).</p> <p>For RNA-seq dataset, it contains 5 cell types (B.cells, CD4.T.cells, CD8.T.cells, monocytic.lineage, NK.cells). Raw FASTQ reads were downloaded from the NCBI website, and transcription and gene-level expression quantification were performed using Salmon (version 0.11.3) with Gencode v29 after quality control of FASTQ reads using fastp. All tools were used with default parameters.</p>
data-augmentation-ner-results
<p>Model evaluation results produced in the context of evaluating data augmentation for Named Entity Recognition over the German legal domain.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/data-augmentation-ner-legal">Github page</a>.</p>
data-augmentation-ner-datasets
<p>Dataset preparation and augmentation results produced in the context of evaluating data augmentation for Named Entity Recognition over the German legal domain.</p> <p>Detailed information can be found on the <a href="https://github.com/fusion-jena/data-augmentation-ner-legal">Github page</a>.</p> <p>This dataset is based on the <a href="http://github.com/elenanereiss/Legal-Entity-Recognition">German LER dataset</a> that is freely available under the <a href="https://creativecommons.org/licenses/by/4.0/deed.en">CC-BY 4.0 license</a></p>
Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information
<p>The research data for the paper "Applying Sensor Fusion to Augment Hyperspectral Data with Depth Information"<br> <br> Data in the archive "hyperdepth.tar.gz" includes:</p> <p><br> <strong>calibration_images/</strong><br> includes preprocessed images for calibrating both cameras</p> <p><strong>pointclouds/</strong><br> Includes individual hyperspectral point clouds for each view (front, rightmost, right, leftmost, left with postfixes correspondingly: edesta, oikea, oikea2, vasen, vasen2)<br> <br> <strong>raw_images/</strong><br> Two directories "day5" and "day6" which include the raw hyperspectral images and kinect images<br> <br> Some extra images are included which were not used in the research paper.</p> <p> </p> <p><strong>2022-03-11_112336_stereocalibration.json</strong> includes calibration results (mainly the intrinsic camera matrix and extrinsic parameters) for the setup.</p>
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
Station data of passive and active fluorescence measurements of chlorophyll-a (Chl), phycoerythrin (PE), chromophoric dissolved organic matter (CDOM), and variable fluorescence (Fv/Fm) from CCE-CalCOFI Augmented cruises in the California Current System, 2012 - October 2020.
Active and passive fluorescence measurements are made using the ALFA5 system (Chekalyuk and Hafez, 2013) on water from the ship’s underway system (these are not samples from bottles!) during CalCOFI cruises while on station. The instrument uses excitation at 405 and 510 nm to measures passively the fluorescence of chlorophyll-a (Chl), three different phycoerythrins (PE1, PE2 and PE3) and chromophoric dissolved organic matter (CDOM). Variable fluorescence (Fv/Fm) is measured actively using pump-during-probe (PDP) measurements of Chl a fluorescence induction. Fluorescence measurements are normalized to the water’s Raman fluorescence. ALF data are merged with CTD and bottle data that were collected by the CalCOFI group.
Data for Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
<p><strong>Description of the datasets</strong></p> <p>Data are organized as folders and compressed with tar.gz.</p> <p>There are two compressed data folder: <strong>data </strong>which used for cattle genome graphs experiment and <strong>data_human</strong> which we used for human genome graphs experiment. </p> <p><strong>Cattle genome graphs experiments</strong></p> <p>First you need to unzip the file using command <em>tar -xvzf data.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>Utilities: contain bovine ARS-UCD 1.2 fasta reference with the accompanying index.</li> <li>Bin: contain the softwares used in the paper (vg, liftover, vcf2diploid)</li> <li>Part1: data for analysis in variant prioritization section, further subdivided into: <ul> <li>vcf_sim: variant files from four animal in each breed used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul> </li> <li>Part2: data used for analysis in the section of graph mapping with breeds-filtered variants, further subdivided into: <ul> <li>vcf_breed: variant files used to graphs construction.</li> </ul> </li> <li>Part3: data used for analysis in the section of consensus genome, further subdivided into: <ul> <li>read_sims: simulated reads as in the part1, but the coordinates are liftovered to the new consensus genomes.</li> <li>reference: contain the original reference and consensus references.</li> <li>vcf_consensus: contain major allele variants to construct consensus genomes.</li> </ul> </li> <li>Part4: data analysis in the section of whole genome graph construction and variant genotyping. <ul> <li>vcf_construct: variants from chromosome 1-29 from 82 Brown Swiss used to construct BSW whole genome graph.</li> <li>BSW_graph: whole genome Brown Swiss graph with the three accompanying indexes (xg,gcsa, and gbwt).</li> </ul> </li> </ul> <p><strong>Human genome graphs experiments</strong></p> <p>First you need to unzip the <em>data_human</em> file using command <em>tar -xvzf data</em><em>_hum.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>reference: the g1k_v37 reference used as a graph backbone</li> <li>vcf_sim: variant files from four individuals in each population used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul>
Solar flare forecasting based on magnetogram sequences learning with MViT and data augmentation
<p><strong>Source codes and dataset of the research "Solar flare forecasting based on magnetogram sequences learning with MViT and data augmentation".</strong></p><p>Our work employed PyTorch, a framework for training Deep Learning models with GPU support and automatic back-propagation, to load the MViTv2 s models with Kinetics-400 weights. To simplify the code implementation, eliminating the need for an explicit loop to train and the automation of some hyperparameters, we use the PyTorch Lightning module. The inputs were batches of 10 samples with 16 sequenced images in 3-channel resized to 224 × 224 pixels and normalized from 0 to 1.</p><p>Most of the papers in our literature survey split the original dataset chronologically. Some authors also apply k-fold cross-validation to emphasize the evaluation of the model stability. However, we adopt a hybrid split taking the first 50,000 to apply the 5-fold cross-validation between the training and validation sets (known data), with 40,000 samples for training and 10,000 for validation. Thus, we can evaluate performance and stability by analyzing the mean and standard deviation of all trained models in the test set, composed of the last 9,834 samples, preserving the chronological order (simulating unknown data).</p><p>We develop three distinct models to evaluate the impact of oversampling magnetogram sequences through the dataset. The first model, Solar Flare MViT (SF MViT), has trained only with the original data from our base dataset without using oversampling. In the second model, Solar Flare MViT over Train (SF MViT oT), we only apply oversampling on training data, maintaining the original validation dataset. In the third model, Solar Flare MViT over Train and Validation (SF MViT oTV), we apply oversampling in both training and validation sets.</p><p>We also trained a model oversampling the entire dataset. We called it the "SF_MViT_oTV Test" to verify how resampling or adopting a test set with unreal data may bias the results positively.</p><p><strong>GitHub version</strong></p><p>The .zip hosted here contains all files from the project, including the checkpoint and the output files generated by the codes. We have a clean version hosted on GitHub (<a href="https://github.com/lfgrim/SFF_MagSeq_MViTs">https://github.com/lfgrim/SFF_MagSeq_MViTs</a>), without the magnetogram_jpg folder (which can be downloaded directly on <a href="https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip">https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip)</a> and the output and checkpoint files. Most code files hosted here also contain comments on the Portuguese language, which are being updated to English in the GitHub version.</p><p><strong>Folders Structure</strong></p><p>In the Root directory of the project, we have two folders: </p><ul><li>magnetogram_jpg: holds the source images provided by Space Environment Artificial Intelligence Early Warning Innovation Workshop through the link <a href="https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip">https://tianchi-competition.oss-cn-hangzhou.aliyuncs.com/531804/dataset_ss2sff.zip. </a>It comprises 73,810 samples of high-quality magnetograms captured by HMI/SDO from 2010 May 4 to 2019 January 26. The HMI instrument provides these data (stored in hmi.sharp_720s dataset), making new samples available every 12 minutes. However, the images from this dataset were collected every 96 minutes. Each image has an associated magnetogram comprising a ready-made snippet of one or most solar ARs. It is essential to notice that the magnetograms cropped by SHARP can contain one or more solar ARs classified by the National Oceanic and Atmospheric Administration (NOAA).</li><li>Seq_Magnetogram: contains the references for source images with the corresponding labels in the next 24 h. and 48 h. in the respectively M24 and M48 sub-folders.<ul><li>M24/M48: both present the following sub-folders structure:<ul><li>Seqs16;</li><li>SF_MViT;</li><li>SF_MViT_oT;</li><li>SF_MViT_oTV;</li><li>SF_MViT_oTV_Test.</li></ul></li></ul></li></ul><p>There are also two files in root:</p><ul><li>inst_packages.sh: install the packages and dependencies to run the models.</li><li>download_MViTS.py: download the pre-trained MViTv2_S from PyTorch and store it in the cache.</li></ul><p>M24 and M48 folders hold reference text files (flare_Mclass...) linking the images in the magnetogram_jpg folders or the sequences (Seq16_flare_Mclass...) in the Seqs16 folders with their respective labels. They also hold "cria_seqs.py" which was responsible for creating the sequences and "test_pandas.py" to verify head info and check the number of samples categorized by the label of the text files. All the text files with the prefix "Seq16" and inside the Seqs16 folder were created by "criaseqs.py" code based on the correspondent "flare_Mclass" prefixed text files.</p><p>Seqs16 folder holds reference text files, in which each file contains a sequence of images that was pointed to the magnetogram_jpg folders.</p><p>All SF_MViT... folders hold the model training codes itself (SF_MViT...py) and the corresponding job submission (jobMViT...), temporary input (Seq16_flare...), output (saida_MVIT... and MViT_S...), error (err_MViT...) and checkpoint files (sample-FLARE...ckpt). Executed model training codes generate output, error, and checkpoint files. There is also a folder called "lightning_logs" that stores logs of trained models.</p><p><strong>Naming pattern for the files:</strong></p><ul><li>magnetogram_jpg: follows the format<i> </i>"hmi.sharp_720s.<SHARP-ID>.<date>.magnetogram.fits.jpg" and</li><li>Seqs16: follows the format "hmi.sharp_720s.<i><</i>SHARP-ID<i>></i>.<init-date>.to.<end-date>", where:<ul><li>hmi: is the instrument that captured the image</li><li>sharp_720s: is the database source of SDO/HMI.</li><li><SHARP-ID>: is the identification of SHARP region, and can contain one or more solar ARs classified by the (NOAA).</li><li><date>: is the date-time the instrument captured the image in the format yyyymmdd_hhnnss_TAI (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li><li><init-date>: is the date-time when the sequence starts, and follow the same format of <date>.</li><li><end-date>: is the date-time when the sequence ends, and follow the same format of <date>.</li></ul></li><li>Reference text files in M24 and M48 or inside SF_MViT... folders follows the format "<prefix>flare_Mclass_<forecasting-horizon>_<dataset>.txt<over>", where:<ul><li><prefix>: is Seq16 if refers to a sequence, or void if refers direct to images.</li><li><forecasting-horizon>: "24h" or "48h".</li><li><dataset>: is "TrainVal<n>" or "Test". The <n> refers to the split of Train/Val.</li><li><over>: void or "_over" after the extension (...txt_over): means temporary input reference that was over-sampled by a training model.</li></ul></li><li>All SF_MViT...folders:<ul><li>Model training codes: "SF_MViT_<oversampling-type>_M+_<forecasting-horizon>_<split-type><gpu-type>", where:<ul><li><oversampling -type>: void or "oT" (over Train) or "oTV" (over Train and Val) or "oTV_Test" (over Train, Val and Test);</li><li><forecasting-horizon>: "24h" or "48h";</li><li><split-type>: "oneSplit" for a specific split or "allSplits" if run all splits.</li><li><gpu-type>: void is default to run 1 GPU or "2gpu" to run into 2 gpus systems;</li></ul></li><li>Job submission files: "jobMViT_<queue>", where:<ul><li><queue>: point the queue in Lovelace environment hosted on CENAPAD-SP (<a href="https://www.cenapad.unicamp.br/parque/jobsLovelace">https://www.cenapad.unicamp.br/parque/jobsLovelace</a>)</li></ul></li><li>Temporary inputs: "Seq16_flare_Mclass_<forecasting-horizon>_<dataset>.txt<over>:<ul><li><dataset>: train or val;</li><li><over>: void or "_over" after the extension (...txt_over): means temporary input reference that was over-sampled by a training model.</li></ul></li><li>Outputs: "saida_MViT_Adam_10-7<split>", where:<ul><li><split>: k0 to k4, means the correlated split of the output, or void if the output is from all splits.</li></ul></li><li>Error files: "err_MViT_Adam_10-7<split>", where:<ul><li><split>: k0 to k4, means the correlated split of the error log file, or void if the error file is from all splits.</li></ul></li><li>Checkpoint files: "sample-FLARE_MViT_S_10-7-epoch=<n-epoch>-valid_loss=<loss-value>-Wloss_k=<n-split>.ckpt", where:<ul><li><n-opoch>: epoch number of the checkpoint;</li><li><loss-value>: corresponding valid loss;</li><li><n-split>: 0 to 4.</li></ul></li></ul></li></ul>
Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset
<p>There are four datasets:</p> <p>1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.</p> <p>2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.</p> <p>3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.</p> <p>The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.</p>
Phenotype Driven Data Augmentation Methods for Transcriptomic Data
<p>This repository contains the data and associated results of all experiments conducted in our work "<em>Phenotype Driven Data Augmentation Methods for Transcriptomic Data</em>". In this work, we introduce two classes of phenotype driven data augmentation approaches – signature-dependent and signature-independent. The signature-dependent methods assume the existence of distinct gene signatures describing some phenotype and are simple, non-parametric, and novel data augmentation methods. The signature-independent methods are a modification of the established Gamma-Poisson and Poisson sampling methods for gene expression data. We benchmark our proposed methods against random oversampling, SMOTE, unmodified versions of Gamma-Poisson and Poisson sampling, and unaugmented data. <br> </p> <p>This repository contains data used for all our experiments. This includes the original data based off which augmentation was performed, the cross validation split indices as a json file, the training and validation data augmented by the various augmentation methods mentioned in our study, a test set (containing only real samples) and an external test set standardised accordingly with respect to each augmentation method and training data per CV split. </p> <p>The compressed files <code>5x5stratified_{x}percent.zip</code> contains data that were augmented on <code>x%</code> of the available real data. <code>brca_public.zip</code> contains data used for the breast cancer experiments. <code>distribution_size_effect.zip</code> contains data used for hyperparameter tuning the reference set size for the modified Poisson and Gamma-Poisson methods. </p> <p>The compressed file <code>results.zip</code> contains all the results from all the experiments. This includes the parameter files used to train the various models, the metrics (balanced accuracy and auc-roc) computed including p-values, as well as the latent space of train, validation and test (for the (N)VAE) for all 25 (5x5) CV splits.</p> <p><strong>PLEASE NOTE:</strong> If any part of this repository is used in any form for your work, please <strong>attribute</strong> the following, in addition to attributing the original data source - TCGA, CPTAC, GSE20713 and METABRIC, accordingly:</p> <pre>@article{janakarajan2025phenotype,<br> title={Phenotype driven data augmentation methods for transcriptomic data},<br> author={Janakarajan, Nikita and Graziani, Mara and Rodr{\'\i}guez Mart{\'\i}nez, Mar{\'\i}a},<br> journal={Bioinformatics Advances},<br> volume={5},<br> number={1},<br> pages={vbaf124},<br> year={2025},<br> publisher={Oxford University Press}<br>}</pre> <p> </p>
Variable Misuse tool: Dataset for data augmentation (3)
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
Variable Misuse tool: Dataset for data augmentation (7)
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
Variable Misuse tool: Dataset for data augmentation (2)
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
Location-based augmented reality (LBAR) spatial data test
<p>This repository gathers video data (screen capture) collected on a field test conducted on the 11th of May 2022, at the HEIG-VD in Yverdon-les-Bains, Switzerland.<br> <br> The goal of the test was to submit LBAR interfaces to different sources of spatial data. The 5 conditions compared were:<br> <br> 1) ARCore interface (visual odometry) fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 2) ARCore interface (visual odometry) fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 3) A-Frame + LBAR.js interface fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 4) A-Frame + LBAR.js interface fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 5) A-Frame + LBAR.js interface fed with position and orientation data provided by an external Inertial Navigation Station Xsens MTi-680g (IMU + GNSS/RTK).</p>
Auxiliary data files for replication of "Augmenting the availability of historical GDP per capita estimates through machine learning"
<p>This repository holds auxiliary data files needed for the replication "Augmenting the availability of historical GDP per capita estimates through machine learning". All further information and data is provided in the <a href="https://github.com/philmkoch/historicalGDPpc" target="_blank" rel="noopener">GitHub repository</a>.</p> <p>The data included in this auxiliary folder is based on the work by Laouenan et al. (https://www.nature.com/articles/s41597-022-01369-4). </p>
Data study "The Impact of Augmented Reality on Biodiversity Learning in a Pedagogical Scenario Based on Analogical Reasoning: An Experimental Study"
<p>This data was collected in 2023 as part of a study on the impact of location-based AR on biodiversity education. </p>
Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper
<p>Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper</p>
ScienceDex guides
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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.