Find research datasets worth reusing

An isolated mass gap black hole or neutron star detected with astrometric microlensing

This repository contains data, models, and simulations associated with the ApJ Letter "An isolated mass gap black hole or neutron star detected with astrometric microlensing" and its corresponding Supplement.

Detection of early seeding of Richter transformation in chronic lymphocytic leukemia: scRNA-seq data

Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)high–B cell receptor (BCR)low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seed- ing of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT. This repository contains the processed scRNA-seq data (expression matrices, Seurat objects, metadata) related with this publication.

Fireball events detected in Romania by the FRIPON-MOROI network

In this table we present the fireball events detected by the Fireball Recovery and Inter Planetary Observation Network (FRIPON) (Colas et al., 2020) with the use of the Meteorite Orbits Reconstruction by Optical Imaging (MOROI) network (Nedelcu et al. 2018). The data include events recorded by at least two different fireball cameras installed over the Romanian territory. For each event we provide: the name, the stations that detected it, whether it has noticeable deceleration (Vf/V0 < 0.8, where Vf is the velocity of the meteoroid at the end of the luminous trajectory and V0 is the initial velocity of the meteoroid). The events are named by the time when they were captured as follows: year, month, day followed by hour, minute, second in UT. The events with noticeable deceleration are analysed in (Boaca et al. 2022), https://iopscience.iop.org/article/10.3847/1538-4357/ac8542. If using this data, please reference the above publication.  The online presentation of the paper can be found on the AAS youtube channel: https://www.youtube.com/watch?v=Wx186QB5ZVE

CoFly-WeedDB: A UAV image dataset for weed detection and species identification

The CoFly-WeedDB contains 201 RGB images (~436MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5m and 3m/s, respectively, aiming to provide a close and clear view of the weed instances.  All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.

openmit-licenseJun 2022View details →

The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations: Data

<pre>This repository accompanies the paper ``` The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations by Martina Monaco, Juliane Dannberg, Rene Gassmoeller, Stephen Pugh ``` The global models presented in the manuscript were run using the following dependencies: ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.3.0-pre (master, 74e48be) -- . using deal.II 9.3.0 -- . with 32 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.10.1 -- . using p4est 2.2.0 ----------------------------------------------------------------------------- ``` This repository contains: - The 'all_model_series' folder with the files used to analyze the depth averages. Each series (100, Aoki, Hefesto) has its own subfolder; - The 'plugins' folder, with the required plugin to run the models. To compile the plugin, navigate into this directory and follow the steps: 1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - The 'run_series' bash script, with the command to run multiple models at once. The user should modify: ° The input file name: INPUT_FILE=heterogeneity-several-blobs-INSERT-SERIES.prm ° The directory where ASPECT is located: srun --mpi=pmix_v2 $HOME/aspect/aspect-build/aspect --" echo -e $COMMAND | sbatch --job-name gs_${viscosity}_spacing_${blob_spacing} -p hpg2-compute -N 1 -n 32 -t 3-23:59:00 -o output_gs_${viscosity}_spacing_${blob_spacing}.%j -e error_gs_${viscosity}_spacing_${blob_spacing}.%j --constraint 'haswell|skylake' --mem-per-cpu '3gb' --distribution block; - The three parameter files (.prm), one per series; - Two .py files containing the scripts necessary to plot all the figures in the paper</pre>

Electrobyte for Singing Voice Detection

This is a public dataset of 90 copyright-free electronic songs with vocal annotations (sing/no sing).

opencc-by-4.0May 2022View details →

wGrapeUNIPD-DL: an open dataset for white grape bunch detection

National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.

Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip

Dataset accompanying "Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip".

An End-to-End Framework for Detecting and Repairing Potential Vulnerabilities

Nowadays, program development is getting easier and easier as the various IDE tools provide advice on what to write in the program. But it is not enough to implement a solution to a problem; it is also important that the non-functional properties, like the quality or security of the code, are appropriate in all aspects. One of the most widely used techniques to ensure quality is testing. If the tests fail, one can fix the code immediately. However, security issues are unexpected cases when implementing the program, which is why we do not write tests for them in advance. In many cases, security-relevant bugs can not only cause financial loss but also put human lives at risk, so detecting and fixing them is an important step for the reliability and quality of the program. The tool presented in this paper aims to generate automatic code repairs to potential vulnerabilities in the program. By integrating the recommended fixes, one can easily harden the security of their program early in the development process. A case study on six open-source Java subject systems showed that we were able to generate viable repair patches for 57 out of the 81 detected security issues (70%). For certain types (e.g., revealing private references of mutable objects), our tool reached close to perfect performance.

Dataset of very-high-resolution satellite RGB images to train deep learning models to detect and segment high-mountain juniper shrubs in Sierra Nevada (Spain)

This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.

opencc-by-4.0Mar 2022View details →

SocialDisNER corpus: gold standard annotations for detection of disease mentions in Spanish tweets

If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo:  Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, and Martin Krallinger. 2022. <a href="https://aclanthology.org/2022.smm4h-1.48">The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora</a>. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 182–189, Gyeongju, Republic of Korea. Association for Computational Linguistics. <pre><code class="language-json">@inproceedings{gasco2022socialdisner, title = "The {S}ocial{D}is{NER} shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora", author = "Gasco S{\'a}nchez, Luis and Estrada Zavala, Darryl and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Miranda-Escalada, Antonio and Krallinger, Martin", booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.smm4h-1.48", pages = "182--189" }</code></pre>   Introduction: The SocialDisNER corpus of the SMM4H 2022 – Task 10 task focus on the recognition of disease mentions in tweets written in Spanish after selecting primarily first-hand experience of diseases and other health-relevant content (from patient associations, professional healthcare institutions, and through followers of patient association accounts of a diversity of pathologies including rare diseases, mental health, cancer, etc..). SocialDisNER Gold Standard The Gold Standard corpus was manually annotated by medical experts following the <a href="https://doi.org/10.5281/zenodo.6983041">SMM4H-SocialDisNER guidelines</a>. These guidelines were adapted from previous efforts used to annotate patient clinical records and medical literature. It covers rules for annotating mentions of diseases in health-related tweets in Spanish, The training set consists of 5000 tweets written in Spanish and the validation set consists of 2500 tweets written in Spanish. Both sets have been manually annotated by healthcare professionals. The test dataset contains 23430 tweets, although only 2000 will be used to evaluate the systems participating in the task (the rest is background set). We don't plan to publish the test set, but if you want you can test your system from <a href="https://codalab.lisn.upsaclay.fr/competitions/3531">SocialDisNER Codalab</a>. SocialDisNER Large Scale Corpus The large-scale data contains mentions automatically extracted from a set of 85000 tweets. Separate datasets are shown for each entity including diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm, and persons. SocialDisNER co-mention networks We have computed a co-occurrence matrix of the extracted diseases, as well as several co-mention matrices between the disease mentions and the rest of the entities in the large-scale corpora.   File structure: The structure of the corpus is:  <ul> <li>SocialDisNER_Data: <ul> <li>training-validation-data folder <ul> <li>train-valid-txt-files:  folder with training and validation text files. One text file per tweet, the file name corresponds to the tweet id. One sub-directory per corpus split (train and valid). The files named ids_dev_set.txt and ids_train_set.txt contain the list of file identifiers for each of the data splits (validation and train).</li> <li>mentions.tsv: This file contains the manually annotated disease mentions. The file has the following fields: <ul> <li>tweets_id: This is the id of the tweet, using Twitter API you can query the content of the tweet.</li> <li>Begin: This is the position in the tweet where the annotation was found.</li> <li>End: This is the position of the last character of the annotation in the tweet.</li> <li>Type: This is the type of entity found, in our case "ENFERMEDAD".</li> <li>Extraction: This is the literal extraction, in other words, the fragment of text which refers to the annotation. </li> </ul> </li> </ul> </li> <li>test-data folder: <ul> <li>test-data-txt-files: folder with test text files. One file per tweet, the file name corresponds to the tweet id. The folder contains 23430 tweets to be used as test set of the task. Of them, 2000 will be used to evaluate the participating systems.</li> </ul> </li> </ul> </li> </ul>   <ul> <li>SocialDisNER_LargeScale_additionaldata: <ul> <li>socialdisner_diseases: <ul> <li>tweets_txt: Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li>diseases_mentions.tsv: This file contains the automatically annotated disease mentions from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_ENTITY: Each folder with this naming convention contains the following data structure. Corpora have been generated with mentions of diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm and persons <ul> <li>tweets_txt: Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li>ENTITY_mentions.tsv: This file contains the automatically annotated mentions of type “ENTITY” from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_networks: This folder contains tsv files containing the co-mention matrices between the diseases and the rest of the entities of the large-scale socialdisner data. Each file follows the following naming convention: <ul> <li>socialdisner_disease-ENTITY_net.tsv:  The tsv file contains a series of columns and rows corresponding to the mentions used for building the matrix. Each column is separated by “;”. The type of each mention is identified by the label in parentheses of each title. The count represents the number of times that mention x and mention y were found in the same tweet of the large-scale dataset.</li> <li>socialdiser_disease_net.tsv: This tsv file contains the array of socialdisner-disease large-scale corpus co-mentions separated by ";". This file can be loaded into NetworkX to perform disease co-morbidity analysis on the socialdisner-disease large-scale data.</li> </ul> </li> </ul> </li> </ul> Note: In previous versions of the dataset the order of the columns in the mentions.tsv file was not in the correct order. From this version onwards the order is correct and adequate to send the predictions of the task.   For further information, please visit <a href="https://temu.bsc.es/socialdisner/">https://temu.bsc.es/socialdisner/</a> Summary statistics: <table> <caption>Manually annotated data</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">Training set</th> <th scope="col">Development set</th> </tr> </thead> <tbody> <tr> <th scope="row"># tweets</th> <td>5000</td> <td>2500</td> </tr> <tr> <th scope="row"># characters</th> <td>1253431</td> <td>516768</td> </tr> <tr> <th scope="row"># tokens</th> <td>211555</td> <td>84478</td> </tr> <tr> <th scope="row">Avg. char / tweet</th> <td>250.69</td> <td>206.71</td> </tr> <tr> <th scope="row">Avg. tok. / tweet</th> <td>42.31</td> <td>33.79</td> </tr> <tr> <th scope="row"># mentions</th> <td>15173</td> <td>4252</td> </tr> <tr> <th scope="row"># unique mentions</th> <td>4407</td> <td>1413</td> </tr> </tbody> </table>   <table> <caption>Large-scale annotated data (Silver Standard)</caption> <tbody> <tr> <td> </td> <td>Socialdisner-diseases</td> <td>Socialdisner-pharma</td> <td>Socialdisner-morphology_neoplasms</td> <td>Socialdisner-symptoms</td> <td>Socialdisner-professions</td> <td>Socialdisner-Procedures</td> <td>Socialdisnerv-Person</td> <td>Socialdisner-Species</td> </tr> <tr> <td># tweets</td> <td>85077</td> <td>1759</td> <td>8518</td> <td>12624</td> <td>15831</td> <td>11462</td> <td>41033</td> <td>12118</td> </tr> <tr> <td># characters</td> <td>19920670</td> <td>435141</td> <td>2082574</td> <td>3023784</td> <td>4063114</td> <td>2873791</td> <td>10273278</td> <td>2933925</td> </tr> <tr> <td># tokens</td> <td>3236411</td> <td>68269</td> <td>332539</td> <td>521503</td> <td>660071</td> <td>467059</td> <td>1689479</td> <td>486249</td> </tr> <tr> <td>Avg. char / tweet</td> <td>234.15</td> <td>247.38</td> <td>244.49</td> <td>239.53</td> <td>256.66</td> <td>250.72</td> <td>250.37</td> <td>242.11</td> </tr> <tr> <td>Avg. tok. / tweet</td> <td>38.04</td> <td>38.81</td> <td>39.04</td> <td>41.31</td> <td>41.69</td> <td>40.75</td> <td>41.17</td> <td>40.13</td> </tr> <tr> <td># mentions</td> <td>116260</td> <td>1029</td> <td>8943</td> <td>12896</td> <td>18590</td> <td>10080</td> <td>58007</td> <td>14014</td> </tr> <tr> <td># unique mentions</td> <td>16034</td> <td>530</td> <td>541</td> <td>6991</td> <td>3667</td> <td>3841</td> <td>3446</td> <td>1676</td> </tr> </tbody> </table>       Do not share the data with other individuals/teams without permission from the task organizer. Tweets IDs are the primary source of information. Tweet texts are provided as support material. By downloading this resource, you agree to the Twitter <a href="https://twitter.com/en/tos">Terms of Service</a>, <a href="https://twitter.com/en/privacy">Privacy Policy</a>, <a href="https://developer.twitter.com/en/developer-terms/agreement">Developer Agreement</a>, and <a href="https://developer.twitter.com/en/developer-terms/policy">Developer Policy</a>.

Light camera position detection - experiment data

Experiment data measure the accuracy of automatic light fixtures position detection using a single monocular web camera with different resolutions.

opencc-by-4.0Oct 2020View details →

Characterizing Measures for the Assessment of Cluster Analysis and Community Detection

Description. The dataset is constituted of: <ul> <li>`figs.zip`: an archive containing the plot files;</li> <li>`data&results.zip`: an archive containing the necessary data to perform our analysis, as well as result files.</li> </ul> These are the resources used in the following articles: <ol> <li>N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02993542">hal-02993542</a>⟩</li> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Characterizing and comparing external measures for the assessment of cluster analysis and community detection,” IEEE Access 9:20255–20276, 2021. DOI: <a href="http://doi.org/10.1109/access.2021.3054621">10.1109/access.2021.3054621</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-03124118">hal-03124118</a>⟩</li> </ol> Source code. The associated source code is available on GitHub: <a href="https://github.com/CompNet/ExtMeasEval">https://github.com/CompNet/ExtMeasEval</a> Citation. If you use these data, please cite the paper [2]. <code>@Article{Arinik2021,</code> <code>  author    = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code> <code>  title     = {Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection},</code> <code>  journal   = {IEEE Access},</code> <code>  year      = {2021},</code> <code>  volume    = {9},</code> <code>  pages     = {20255-20276},</code> <code>  doi       = {10.1109/access.2021.3054621},</code> <code>}</code>

Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation

The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.

Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy

This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a  sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.

openmit-licenseJul 2022View details →

Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Oxford-IIIT Pet)

Preprocessed dataset for Oxford-IIIT Pet in YOLOv5 format..  Ground truth labels for head bounding boxes, body bounding boxes (derived from segmentation mask).

openmit-licenseJul 2022View details →

Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Tsinghua Dogs)

Preprocessed dataset for Tsinghua Dogs in YOLOv5 format..  Ground truth labels for head bounding boxes, body bounding boxes

dryad40/100

Dataset for: Ultrarapid detection of SARS-CoV-2 RNA using a reverse transcription-free exponential amplification reaction, RTF-EXPAR

A dataset is reported for a rapid isothermal method for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19. The procedure uses an unprecedented reverse transcription–free (RTF) approach for converting genomic RNA into DNA. This involves the formation of an RNA/DNA heteroduplex whose selective cleavage generates a short DNA trigger strand, which is then rapidly amplified using the exponential amplification reaction (EXPAR). Deploying the RNA-to-DNA conversion and amplification stages of the RTF-EXPAR assay in a single step results in the detection, via a fluorescence read-out, of single figure copy numbers per microliter of SARS-CoV-2 RNA in under 10 min. In direct three-way comparison studies the assay has been found to be faster than both PCR and loop-mediated isothermal amplification (LAMP), while being just as sensitive. The assay protocol involves the use of standard laboratory equipment and is readily adaptable for the detection of other RNA-based agents.

opencc-zeroJul 2022View details →

Dataset for Crack Detection in Images of Bricks and Masonry Using CNNs

Dataset for training CNN built from aerial drone images of buildings in Hamburg This dataset contains images extracted from aerial surveillance photos of the <a href="https://www.bing.com/ck/a?!&&p=b45d6a7b67c7b3c6JmltdHM9MTY1ODMzOTc0MCZpZ3VpZD04NTU2MjdiYS1kYjljLTQyOTMtOTFlOC0xYmM0NmE1ZWViOGMmaW5zaWQ9NTIyMQ&ptn=3&hsh=3&fclid=2a810abc-0855-11ed-8af8-d88619cb2403&u=a1aHR0cHM6Ly9kZS53aWtpcGVkaWEub3JnL3dpa2kvU3BlaWNoZXJzdGFkdA&ntb=1">Speicherstadt</a> and <a href="https://www.bing.com/ck/a?!&&p=750d82f6afc564d9JmltdHM9MTY1ODMzOTg0OSZpZ3VpZD1mOWMwYzE5OC01NjU3LTQ1NzMtOGE0YS1mNzYxN2VlOTlmMmEmaW5zaWQ9NTIxNA&ptn=3&hsh=3&fclid=6b854f27-0855-11ed-9794-60e2f5efc96c&u=a1aHR0cHM6Ly93d3cuaGFmZW5jaXR5LmNvbS9pbmZvY2VudGVyL2tlc3NlbGhhdXM&ntb=1">Kesselhaus</a> buildings in Hamburg, provided by the City of Hamburg. Original 834 high resolution images (5472 x 3648 pixels) have been separated into smaller images (227 x 227 pixels) of the size that could be processed using SqueezeNet, a deep Convolutional Neural Network (CNN). This resulted in more than 350 thousand images that had to be subsequently processed automatically to retain images containing solely bricks and mortar and concrete. The final stage contained tedious manual/visual verification of images and their separation into positive (containing cracks) and negative (clear bricks and mortars) sets of images. The final set contains nearly 40 thousand images. Since images extracted from Hamburg buildings contained only specific type of bricks and our intention was to extend the CNN to be able to deal with wider range of brick types as well as concrete surfaces, we added to our training set also images from the following Open Access databases (note that such images required resizing to 227 x 227 pixel size before use): <ul> <li><a href="https://data.mendeley.com/datasets/5y9wdsg2zt/1">Concrete Crack Images for Classification (Mendeley Data)</a></li> <li><a href="https://zenodo.org/record/5108846#.YthGSLbP0bB">Dataset for Crack Detection in Images of Masonry Using CNNs</a></li> </ul> Such a combined data set resulted in over 80 thousand of images. Matlab WebApp Server application based on trained SqueezeNet CNN The integrated database of images has been used to train the SqueezeNet CNN using a method proposed by <a href="https://www.linkedin.com/in/kenta-itakura-b88129202/">Kenta Itakura</a> in his article published on Matlab Central: <a href="https://www.mathworks.com/matlabcentral/fileexchange/75418-classify-crack-image-using-deep-learning-and-explain-why?s_tid=srchtitle">Classify crack image using deep learning and explain "WHY"</a>, which in turn is based on the work of <a href="https://ieeexplore.ieee.org/author/37280177000">Lei Zhang</a> reported in his IEEE article: <a href="https://ieeexplore.ieee.org/abstract/document/7533052">Road crack detection using deep convolutional neural network</a> published at <a href="https://ieeexplore.ieee.org/xpl/conhome/7527113/proceeding">2016 IEEE International Conference on Image Processing (ICIP)</a>. The "Matlab" subfolder contains the complete software to allow building the application to run under Matlab WebApps Server. The provided version of the "netTransfer.mat" file has been compiled for Matlab revision 2020b, but it should also work when compiled for other revisions from 2019a onwards. BTW, the original location of the files was "D:\Cracks (2-class)\". For instructions how to use the provided Matlab files, refer to Matlab instructions at <a href="https://www.mathworks.com/products/matlab-web-app-server.html">MATLAB Web App Server</a> and <a href="https://www.mathworks.com/help/webappserver/getting-started-with-matlab-web-app-server.html?s_tid=CRUX_lftnav">Get Started with MATLAB Web App Server</a>. After producing and uploading the application to the Matlab WebApps Server, the application can be found at http://localhost:9988/webapps/home/ if deployed locally. It can be also deployed on a WEB server, subject to installation of the compliant Matlab Runtime package on the custom server, whcih can be found at <a href="https://www.mathworks.com/products/compiler/matlab-runtime.html">MATLAB Runtimes (mathworks.com)</a>. The important function included in the package is "unscramble.m", which corrects the error that exists in all known revisions of Matlab in uploading images selected by open file function in the Matlab App Designer. The effect is that image is "scrambled beyond recognition" after uploading to the Matlab WebApps Server. Our function de-scrambles such images, converting them into their original form.

Galaxy Training Material - Peak detection with recetox-aplcms

This dataset contains the training data for the recetox-aplcms GTN Tutorial. It includes 3 LC-ESI+-HRMS DDA files containing a mixture of pesticides and 3 GC-EI+-HRMS files from seminal plasma samples.