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9,302 results for “DETECT”
A fine-grained dataset named iSOOD for sewage outfalls objective detection in natural environments
<p><strong>Basic Information:</strong></p> <p>The 10481 images in iSOOD were captured using UAVs and handheld cameras by individuals from the river basin in China. Our study has carefully annotated these images to ensure accuracy and consistency. The iSOOD has undergone technical validation utilizing the YOLOv5 objective detection model. The iSOOD have been publicly released after undergoing desensitization, with the goal of promoting interdisciplinary collaboration and accelerating advancements in the intelligence watershed management. We expect that the iSOOD dataset is anticipated to inspire further research on the SOs detection and the control of pollution migration paths, and serve as a fundamental resource for the use of advanced deep learning visual technology in environmental monitoring.</p> <p><strong>Usage Policy:</strong><br>If you plan to use our data in a scientific analysis paper, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.</p>
Monash X band radar case for bushfire detection and a precipitation case used to examine uncertainty in ZDR
<p>Data sets from a mobile X-band radar used to examine uncertainty in ZDR and roHV . Data includes preciptation and wildfire ash clouds. </p> <p>The data format is <span>unprocessed, ungridded (spherical coordinates) and provided using the Eumetnet </span><a href="https://www.eumetnet.eu/wp-content/uploads/2019/01/ODIM_H5_v23.pdf" target="_blank" rel="noopener"><span>ODIMH5 model</span></a><span> of the HDF5 format</span> . </p> <p>The bublication will be May et al, J. Atmos. Oceanic. Tech titled: </p> <p><span>Accuracy of polarimetric radar Z<sub>DR</sub> estimates: Implications for the quantitative observation of meteorological and non-meteorological echoes</span></p>
Data from "Single-molecule fluorescence multiplexing by multi-parameter spectroscopic detection of nanostructured FRET labels"
<p>Source data for all figures; full table of all nucleic acid sequences used; Supplementary Video 1 showing rotating view of Fig. 4e.</p>
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Results of Multi-Perspective Concept Drift Detection on BPIC17 data using Event Knowledge Graphs
<p>Extensive results of multi-perspective concept drift detection performed on the BPI Challenge 2017 dataset retrieved using the related implementation from Github: <a href="https://github.com/multi-dimensional-process-mining/ekg-bpic17-concept-drift-detection-multi-perspective">https://github.com/multi-dimensional-process-mining/ekg-bpic17-concept-drift-detection-multi-perspective</a></p> <p> </p>
Inlist files for "Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars"
<p>Concerned article: "Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars" by Bhattacharya et al. (submitted).<br>Corresponding author: Shatanik Bhattacharya</p> <p>Inlists for the proof-of-concept stellar models used in this project have been provided here for reproducibility.</p> <p>For the red-giant model, the inlist was executed with MESA version r22.05.1 and MESA-SDK version x86 64-linux-22.6.1. Model 500 (age 4.056 Gyr) was used as the RG in this project.</p> <p>For the sub-giant models, the inlist was executed with MESA version r23.05.1 and MESA-SDK version x86 64-linux-22.6.1. Models 345 (age 3.624 Gyr) and 350 (age 3.702 Gyr) were used as the MSG and LSG models respectively.</p>
Raw results of the experiments performed to evaluate irregular repetition slotted ALOHA with multiuser detection
<p>This zip file contains the data files needed to generate the figures of the numerical results presented in [1].</p> <p> </p> <p>[1] M. Fernández-Veiga, M.E. Sousa-Vieira, A. Fernández-Vilas, R.P. Díaz-Redondo. "Irregular repetition slotted ALOHA with multiuser detection: A density evolution analysis". Computer Networks, 234, 109921, 2023.</p>
Study of coded ALOHA with multiuser detection under heavy-tailed and correlated arrivals
<p>This zip file contains the data files needed to generate the figures presented in [1].</p> <p> </p> <p>[1] M.E. Sousa-Vieira, M. Fernández-Veiga. "Study of coded ALOHA with multiuser detection under heavy-tailed and correlated arrivals". Future Internet, 15, 132, 2023.</p> <p> </p>
Open-cup nesters in the Kalahari: incubation and egg-shading behaviour in passerines cannot be detected with temperature dataloggers during hot periods.
<p>The dataset includes the raw data and R scripts necessary to reproduce the analyses and figures explained in the manuscript "<span>Open-cup nesters in the Kalahari: incubation and egg-shading behaviour in passerines cannot be detected with temperature dataloggers during hot periods</span>"<strong>.</strong></p> <p>This dataset includes:</p> <p>- README.docx: Complete explanation of the different raw data used for the statistical analyses, together with the link to the R script where data is analyzed. We recommend to read this document before diving into the data and scripts.</p> <p>- Project data & code: Two separate folders for raw data and code.</p> <p> </p>
Deep learning detects entire multiple-size Lunar craters driven by elevation data and topographic knowledge
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AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error (Dataset)
<p>This upload contains the dataset used in <a href="https://arxiv.org/abs/2401.17879" target="_blank" rel="noopener">AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error</a>.</p> <p>Further details and instructions are provided in the <a href="https://github.com/jonasricker/aeroblade" target="_blank" rel="noopener">project's Github repository</a>.</p>
Genomic profiling of NSCLC tumors with the TruSight Oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel associations between genomic alterations, TMB, and PD-L1
<p>Wallen ZD, Ko H, Nesline MK, Tierno M, Roos A, Schnettler E, Husain H, Sathyan P, Caveney B, Eisenberg M, Severson EA, Ramkissoon SH. <strong>Genomic profiling of NSCLC tumors with the TruSight Oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel associations between genomic.</strong> <em>Front Oncol.</em> 2024 Nov 5;14:1473327. doi: <a href="https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1473327">10.3389/fonc.2024.1473327</a>.</p> <p><strong>ABSTRACT</strong></p> <p><strong>Introduction: </strong>Matching patients to an effective targeted therapy or immunotherapy is a challenge for <br>advanced and metastatic non-small cell lung cancer (NSCLC), especially when relying on assays that test one <br>marker at a time. Unlike traditional single marker tests, comprehensive genomic profiling (CGP) can <br>simultaneously assess NSCLC tumors for hundreds of genomic biomarkers and markers for immunotherapy <br>response, leading to quicker and more precise matches to therapeutics. <strong>Methods: </strong>In this study, we performed <br>CGP on 7,606 patients with advanced or metastatic NSCLC using the Illumina TruSight Oncology 500 (TSO <br>500) CGP assay to show its coverage and utility in detecting known and novel features of NSCLC. <strong>Results: </strong><br>Testing revealed distinct genomic profiles of lung adenocarcinoma and squamous cell carcinomas and <br>detected variants with a current targeted therapy or clinical trial in >72% of patient tumors. Known associations <br>between genomic alterations and immunotherapy markers were observed including significantly lower TMB <br>levels in tumors with therapy-associated alterations and significantly higher PD-L1 levels in tumors with ALK, <br>MET, BRAF, or ROS1 driver mutations. Co-occurrence analysis followed by network analysis with gene <br>module detection revealed known and novel co-occurrences between genomic alterations. Further, certain <br>modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and <br>increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, <br>and genomic alterations in these gene modules. <strong>Discussion:</strong> This study is the largest clinical study to date <br>utilizing the TSO 500. It provides an opportunity to further characterize the landscape of NSCLC using this <br>newer technology and show its clinical utility in detecting known and novel facets of NSCLC to inform treatment <br>decision-making.</p> <p><strong>DATA AVAILABILITY</strong></p> <p>The data and code presented in the study are deposited in this Zenodo repository, accession number <br>13137232 (<a href="https://zenodo.org/record/13137232">https://zenodo.org/record/13137232</a>). Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Further data inquiries can be directed to the corresponding <br>author. </p>
Experts fail to reliably detect AI-generated histological data
<p>This repository contains material related to the paper "<em>Experts fail to reliably detect AI-generated histological data</em>":</p> <ul> <li>Dreambooth parameters (<em>dreambooth_parameters.zip</em>)</li> <li>Images displayed during the study <em>(images.zip)</em></li> <li>Data collected during the survey (<em>results-survey.xlsx</em>)</li> <li>R Code to reproduce results and figures (<em>analysis_code.zip</em>)</li> </ul> <p>Please find our associated work here:</p> <p>Hartung, J., Reuter, S., Kulow, V.A., Fähling, M., Spreckelsen, C., and Mrowka, R. (2024). Experts fail to reliably detect AI-generated histological data. Sci Rep <em>14</em>, 28677. https://doi.org/10.1038/s41598-024-73913-8.</p>
CommRad RF: A dataset of communication radio signals for detection, identification and classification
<div> <div> <div> <div> <p>In today’s world, data is one of the most valuable resources. It’s now easier than ever to collect millions of radio frequency (RF) signals, both well-known and unusual. With such a large amount of data, researchers can create advanced deep learning techniques. This dataset focuses on baseband signals from communication radios. It includes RF signals captured from 27 different radios, using various frequencies and recorded in different environments. Altogether, there are over 2,700 signals in this collection. The signals were captured using an RF receiver that automatically detects and records signals until they disappear. To organize these signals into a library, the receiver was connected to a laptop, which processed and saved the unique RF fingerprints from different devices.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Remote-sensing detectability of airborne Arctic dust - code
<p>The Matlab codes employed in the generation of the figures of the paper "Remote-sensing detectability of airborne Arctic dust", submitted to Atmospheric Chemistry and Physics (https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1057/).</p>
Resources for the paper: "Social Context in Political Stance Detection: Impact and Extrapolation"
<p>This repository contains the resources in our paper <strong>[Social Context in Political Stance Detection: Impact and Extrapolation]</strong><br><em>Ramon Villa-Cox, Evan Williams, Kathleen M. Carley</em></p> <p>In this work, we explore the performance and extrapolation power of political stance-detection models using an existing large-scale weakly-labeled Twitter dataset collected around the 2019 South American Protests [1]. We construct transformer-based user and tweet encoders to embed users in a low-dimensional space using their text and ego-networks. We then train heterogeneous graph attention networks to predict user stances and contrast their ability to extrapolate stance predictions to different country contexts.</p> <p>The protest dataset, which was collected between September 25 and December 24 of 2019, contains 550k labeled users split unevenly across the four countries and contains over 36 million labeled tweets. It contains an additional 1.1 million unlabeled neighbors and 40 million unlabeled tweets. This repository includes the anonymized datasets necessary to reproduce the results and tables of the paper. In addition, we include the corresponding anonymized resources for the new weakly-labeled dataset around the 2020 Chilean Referendum presented in our paper.</p> <p>Following Twitter's January 2023 User Protection Policy update, tweet or user IDs related to sensitive political events cannot be publicly shared. We respect this policy, and only share:</p> <ul> <li>The anonymized user ID, their weak-stance label, the label predicted by each model and the data split (train, validation or test) the user was assigned to.</li> <li>Anonymized user network edges used by the different network classifiers</li> <li>The type of tweet the edge represents (Original, Reply, or Quote)</li> <li>The User Embeddings produced by the User Transformer and which serve as input for the different network models.</li> </ul> <p>This repository is comprised of the following files:</p> <ol> <li>Main_Predictions.7z: Compressed folder containing anonymized user IDs their stance label and each model’s prediction for the country it was trained on. The performance metrics for each model can be obtained based on the test split for each country. This folder includes the results for the Chilean Referendum.</li> <li>Cross_Predictions.7z: Compressed folder containing the results of the cross-country experiments for each anonymized user. The performance metrics for each model, when applied on a different country can be obtained based on each complete file. Users seen during the training of each model are excluded as described in the paper.</li> <li>Tweet_Level_Edgelists.7z: Compressed folder containing anonymized tweet edge lists indicating its interaction type (Original, Reply, or Quote).</li> <li>User_Networks.7z: Compressed folder containing different anonymized user edge lists for each interaction type.</li> <li>Embeddings.zip: Compressed Pytorch tensor files containing the User Embeddings produced by the User Transformer and which serve as input for the different network models. This are provided for the main results and the cross-country and referendum experiments.</li> </ol> <p>The code developed for this study is available at: https://github.com/rvillaco/Protest_Stance_Detection</p>
Annotated Cows in Aerial Images - Test Set for Livestock Detection Models
<p>This dataset contains a test set of aerial images from fields in Juchowo, Poland and Wageningen, the Netherlands, with annotated cows. The annotations are provided in a CSV format containing image file paths, bounding box coordinates (<code>xmin</code>, <code>ymin</code>, <code>xmax</code>, <code>ymax</code>), and labels for the objects (cows). The dataset is intended for evaluating livestock detection models in deep learning, particularly DeepForest.</p> <p>The images and their corresponding bounding box annotations are included in this archive. The test set consists of 10% of the total images, split from the original dataset created by G.J. Franke and Sander Mucher, which is available in [Harvard Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/N7GJYU). This dataset is part of the GenTORE project (<a href="https://www.gentore.eu" target="_new" rel="noopener">https://www.gentore.eu</a>), focusing on precision livestock farming using automated detection and deep learning techniques.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>Franke, G.J., & Mucher, S. (2021). Annotated cows in aerial images for use in deep learning models. Harvard Dataverse. <a href="https://doi.org/10.7910/DVN/N7GJYU" target="_new" rel="noopener">https://doi.org/10.7910/DVN/N7GJYU</a></p> </li> </ul>
GLaD4CD: Global Landslide Dataset for Change Detection
<p>This dataset constitutes one of the outcomes of the Master's Thesis titled 'Landslide identification using deep learning-based change detection and the DeepESDL collaborative cloud platform,' authored by Julia Anna Leonardi, conducted under the supervision of Prof. Maria Antonia Brovelli and Dr. Vasil Yordanov at Politecnico di Milano. The authors extracted the image patches through the DeepESDL platform, which the ESA NoR sponsorship provided access to. </p> <p>The resource contains the .csv database containing landslide information of 174 events, such as the date, location, source, and the dates of the Sentinel-2 images from before and after the event. The sources of the data included in this inventory are:</p> <ul> <li><a href="progettoiffi.isprambiente.it" target="_blank" rel="noopener">Inventario dei Fenomeni Franosi in Italia (IFFI) compiled by ISPRA</a></li> <li>NASA <a href="https://gpm.nasa.gov/landslides/projects.html#GLC" target="_blank" rel="noopener">Global Landslide Catalog (GLC)</a> [1],[2].</li> <li><a href="https://emergency.copernicus.eu/">Copernicus Emergency Management Service</a> (© European Union, 2012-2024) [3]</li> <li>Geological Survey Ireland | <a href="https://www.gsi.ie/en-ie/data-and-maps/Pages/Geohazards.aspx">Geohazards</a> | <a href="https://www.gsi.ie/en-ie/publications/Pages/National-Landslide-Susceptibility-Map.aspx" target="_blank" rel="noopener">National Landslide Susceptibility Map</a></li> <li><a href="../records/7970874" target="_blank" rel="noopener">Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018, and 2019</a>, courtesy of Katy Burrows [4] </li> <li><a href="https://maps.disasters.nasa.gov/download/gis_products/event_specific/2023/turkiye_earthquakes_202302/landslides/">Landslides after 2023 earthquakes in Turkey</a> compiled by the NASA GSFC team</li> <li><a href="https://www.itc.nl/about-itc/centres-of-expertise/centre-for-disaster-resilience/what-we-do/education-training/Landslides-Course-21-25-November/" target="_blank" rel="noopener">University of Twente online landslide course</a></li> <li>Manually extracted sources from [5], [6], [7], [8].</li> </ul> <p>The .zip file contains the TRAIN dataset with the pre-event Sentinel-2 image patches in folder PRE and the post-event Sentinel-2 image patches in folder POST, and the TEST set with 17 bi-temporal pairs in the PRE and POST folders following the above convention and the labels in the form of change maps in the CM folder. In this updated version the full 13-band Sentinel-2 images are published. (In the previous version only 5 bands (B02, B03, B04, B08, and CLM) were available). All the image patches and the ground truth annotations are in the GeoTIFF format. </p> <p>The authors developed the dataset for change detection workflows.</p> <p>Project supported by the ESA Network of Resources Initiative.</p> <p>This work is funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001</p> <p> </p> <p>[1] Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:<a href="https://doi.org/10.1016/j.geomorph.2015.03.016">10.1016/j.geomorph.2015.03.016</a></p> <p>[2] Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:<a href="https://doi.org/10.1007/s11069-009-9401-4">10.1007/s11069-009-9401-4</a></p> <p>[3] Copernicus Emergency Management Service. (n.d.). Retrieved March 12, 2024 from <a href="https://emergency.copernicus.eu/">https://emergency.copernicus.eu/</a></p> <p>[4] K. Burrows, O. Marcand C. Andermann, “Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018 and 2019”. Zenodo, May 25, 2023. doi: <a href="10.5281/zenodo.7970874.">10.5281/zenodo.7970874.</a></p> <p>[5] F. Wang et al., ‘Coseismic landslides triggered by the 2018 Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling factors, and possible failure mechanism’, Landslides, vol. 16, no. 8, pp. 1551–1566, Aug. 2019, doi: <a href="10.1007/s10346-019-01187-7">10.1007/s10346-019-01187-7</a>.</p> <p>[6] P. Amatya, D. Kirschbaum, and T. Stanley, ‘Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method’, Geoscience Data Journal, vol. 9, no. 2, pp. 315–327, 2022, doi: <a href="10.1002/gdj3.145">10.1002/gdj3.145</a>.</p> <p>[7] ]‘Vietnam – At Least 12 Killed in Flash Floods and Landslides in North – FloodList’. Accessed: Feb. 18, 2024. [Online]. Available: <a href="https://floodlist.com/asia/vietnam-floods-landslides-yen-baison-la-august-2017" target="_blank" rel="noopener">https://floodlist.com/asia/vietnam-floods-landslides-yen-baison-la-august-2017</a></p> <p>[8] L. Colombo, ‘Crollata, causa frana, la volta di una galleria lungo la SP72 a Fiumelatte a Varenna - GLI AGGIORNAMENTI’, Lecco Notizie. Accessed: Feb. 18, 2024. [Online]. Available: <a href="https://lecconotizie.com/cronaca/crollata-la-volta-di-una-galleria-lungo-la-sp72-a-fiumelatte-avarenna/" target="_blank" rel="noopener">https://lecconotizie.com/cronaca/crollata-la-volta-di-una-galleria-lungo-la-sp72-a-fiumelatte-avarenna/</a></p>
Splicing Detection and Localization for Speech Deepfakes using Audio Novelty
<p>With the rapid progress of artificial intelligence over the last few years, the possibility of generating highly realistic multimedia content is within everyone's reach. This has paved the way for the creation of deepfakes, synthetic data produced using deep learning techniques that realistically represent people in behaviors not belonging to them. <br>In the audio case, speech deepfakes can be used alone or combined with splicing techniques.<br>This consists of replacing portions of authentic speech with synthetic elements, thereby altering the conveyed message and leading to significant threats.<br>In this work, we address the problem of splicing detection and localization in speech deepfakes.<br>We consider spliced audio tracks created by substituting parts of a pristine speech with synthetically generated segments. We design a system able to detect whether a manipulation takes place and localize it in time.<br>The proposed method, trained exclusively on splicing-free audio tracks, extracts embeddings from the input signal through a sliding window. Then, it employs audio novelty to measure the similarity among consecutive signal sections and uses it to detect and localize splicing points.<br>We evaluate our method on a state-of-the-art dataset as well as a novel, bias-free corpus specifically developed and released in this paper. <br>The proposed approach is benchmarked against multiple baselines for both splicing detection and localization tasks.</p>
Advances in Leukemia Detection and Classification: A Systematic Review of AI and Image Processing Techniques
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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.