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9,302 results for “DETECT”
Fish eDNA detections in ports mirror fishing fleet activities and highlight the spread of non-indigenous species in the Mediterranean Sea. Environmental DNA Metabarcoding DATASET
<p>Environmental DNA metabarcoding data set. Environmental samples were collected in 2018 (one sample replicate of 2L surface water) and 2019 (four replicates of 1 L surface water) from several Mediterranean ports and data were generated using 12S rRNA and mitochondrial COI. For each sampling campaign (2018 and 2019) and marker two sheets are presented (with the same numeration): the first one represents all the ASV/MOTUs after the bioinformatics pipelines; the second one shows the curated assignment. The last two sheets are the OTU tables (presence-absence).</p>
Flower Detection Dataset
<p>Flower Detection Dataset by iPhone XR with flower annotations. A total of 103 images were acquired from various distances. Also, the subsets used to train, validate and test some deep learning models are available in three.</p> <p>The augmented images and their annotations are stored in an archive with the following structure:</p> <p><em>FlowerDetection/</em></p> <ol> <li>label_map.pbtx</li> <li><em>test/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> <li><em>train/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> <li><em>val/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> </ol> <p><br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_1533.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_1533.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.</p>
Grapevine Bunch Detection Dataset
<p>Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area and position.</p> <p>The images and their annotations are stored in an archive with the following structure:</p> <p><em>GrapevineBunchDetection/</em></p> <ol> <li>label_map.pbtx</li> <li><em>test/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> <li><em>train/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> <li><em>val/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> </ol> <p><br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_20220721_101616.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_20220721_101616.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.</p>
Grapevine Bunch Condition Detection Dataset
<p>Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area, position, and class. The classes present in this dataset indicate the condition of each grape bunch</p> <p>The images and their annotations are stored in an archive with the following structure:</p> <p><em>GrapevineBunchConditionDetection/</em></p> <ol> <li>label_map.pbtx</li> <li><em>test/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> <li><em>train/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> <li><em>val/</em> <ul> <li><em>images/</em></li> <li><em>labels/</em></li> </ul> </li> </ol> <p><br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_20220721_101616.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_20220721_101616.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.</p>
Explanation Needs in App Reviews: Taxonomy and Automated Detection
<p><strong>Replication package for our paper submission to RE 2023</strong></p> <p>It contains the following files:</p> <ol> <li>Our dataset of 5,564 app reviews that we manually labeled with respect to the tags "explanation need present" and "explanation need not present".</li> <li>Code/notebooks that we used for the training and evaluation of our explanation need detection approaches</li> <li>.bin file of our best performing model</li> </ol>
Proteome-wide structure-based accessibility analysis of ligandable and detectable cysteines in chemoproteomic datasets
<p>Covalent drug discovery, in particular targeting reactive cysteines, has undergone a resurgence over the past two decades, demonstrated by recent clinical successes of covalent inhibitors for high-priority cancer targets. Reactive cysteine profiling, first pioneered by the Cravatt lab, has emerged in parallel as a powerful approach for proteome-wide on- and off-target profiling. Thus far however, structural analysis of liganded cysteines has been restricted to experimentally determined protein structures. We combined AlphaFold-predicted amino acid side chain accessibilities for >95% of the human proteome with a meta-analysis of thirteen public cysteine profiling datasets, totalling 40,070 unique cysteine residues, revealing accessibility biases in sampled cysteines primarily dictated by warhead chemistry. Analysis of >3.5 million cysteine-fragment interactions further suggests that exposed cysteine residues are preferentially targeted by elaborated fragments and drug-like compounds. We finally propose a framework for benchmarking coverage of ligandable cysteines in future cysteine profiling approaches, considering both selectivity for high-priority residues and quantitative depth. All analysis and produced resources (freely available at <a href="http://www.github.com/TateLab">www.github.com/TateLab</a>) are readily extendable to reactive amino acids beyond cysteine, and related questions in chemical biology.</p>
Nanopore Sequencing of Double-Stranded RNA (dsRNA) for Plant Virus and Viroid Detection
<p>Thi file contain results of 24 grapevines leaf samples analyzed using dsRNA-MiSeq (Illumina Miseq) and dsRNAcD sequencing (ONT nanopore), that were used in the following article ''<strong>Nanopore Sequencing of Double-Stranded RNA (dsRNA) for Plant Virus and Viroid Detection'' </strong> submitted in Frontiers in Microbiology </p>
TensorFlow Flower Detection Dataset
<p>TensorFlow Flower Dataset (https://www.tensorflow.org/datasets/catalog/tf_flowers) is a public open-access flower dataset for classification with images characterized by distinct flower types, backgrounds, and resolutions. TensorFlow Flower Detection Dataset consist of the same images with the associated annotations in the YOLO format which can be utilized for flower detection. We use the CVAT software to annotate more than 3 500 images, which resulted in more than 14 000 bounding boxes.</p> <p>The images and annotations are stored in an archive with the following structure:</p> <p><em>TensorFlowFlowerDetectionDataset/</em></p> <ol> <li><em>labels/</em></li> <li><em>images/</em></li> </ol> <p>Each annotation file links to its image by the file name, so if an image is named <strong>"2431737309_1468526f8b.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"2431737309_1468526f8b.txt"</strong> in YOLO format.</p>
qPCR data for "An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources"
<p>qPCR data to accompany the manuscript "An innovative passive sampling approach for the detection of cyanobacterial gene targets in freshwater sources"</p>
Person detection using UWB and Monocular camera (With LiDAR ground-truth)
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt</code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
Detection of Ore-forming Elements Migrated from Rock and Soil to Vegetation Leaves using Hyperspectral Data
<p>In this paper we present the detection of ore-forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, A layer) and vegetation (root, stem, leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore-forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.</p>
Machine activity detection evaluation dataset.
<p>Dataset for the evaluation of the one-shot time series machine state detector.</p>
Data for: Nestling mouth colors mediate parental favoritism but minimally influence detectability
<p>Young animals often solicit care from their parents using behaviors and morphologies collectively termed begging. Many nestling altricial birds add to their familiar postures and vocalizations by displaying colorful mouthparts, hypothesized to enhance their visual conspicuousness and/or signal information about individual quality. We evaluated these hypotheses with three experimental manipulations of the flange tissue of house sparrow (<em>Passer domesticus</em>) nestlings. We allowed parents to interact with nestlings that appeared to have either carotenoid-rich or carotenoid-poor flanges, and then with nestlings that appeared similar in carotenoid-richness but differed in either brightness (total light reflected) or ultraviolet richness. These three features of flange color comprise much of the variation observed both within and among species. Neither our manipulation of apparent carotenoid- nor UV-richness shaped parents' ability to transfer prey efficiently to nestlings, a proxy for how visually detectable nestlings were. We found some evidence that brightness shaped parents' ability to transfer large prey items. Parents preferentially allocated prey to nestlings that appeared carotenoid-rich, but did not bias allocation when we manipulated brightness or UV reflectance. These patterns suggest that nestlings displaying carotenoid-rich flanges benefit from parental favoritism, and that any role of brightness in offspring-parent interactions comes via detectability benefits. UV reflectance does not seem to shape parental care. Efforts to understand mouth color in the context of offspring-parent communication and visual signal evolution may benefit from considering the component parts of color, as they may function, be constrained, and evolve independently.</p>
Data from: Ready to detect a reversal of time's arrow: a psychophysical study using short video clips in daily scenes
<p><span>It is generally believed that time flows in one direction and that a reversal of time's arrow would render the external world nonsensical. We </span><span>evaluated</span><span> our ability to tell the direction of time's arrow in a wide range of dynamic scenes in our daily life by presenting 360 video clips in the correct or incorrect direction</span><span>. </span><span>Participants, who judged the direction in a speeded manner, erred in 39% of trials when a video was played in reverse</span><span>,</span><span> but in </span><span>only </span><span>9% when it was played normally. Due to the bias favouring the "forward" judgement, reaction was generally faster for the forward response. However, </span><span>the</span><span> reaction became paradoxically faster and more synchronous for the detection of reversal in some critical occasions such as forward motion</span><span>, </span><span>free fall, diffusion, division, and addition of materials by hand. Another experiment with a fraction of the video clips revealed that reversal replay of these videos provided instantaneous evidence strong enough to overtake the forward judgment bias. We suggest that our brain is equipped with a system that predicts how the external organisms behave or move in these critical occasions and that the prediction error of the system contributes to the fast "reversal" detection. </span></p>
Supplementary material for: Inconsistency Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation
<p>Supplementary material including both data and annotations. For each document we present:</p> <ol> <li>annotated results of the chatGPT answers;</li> <li>annotated results of the manual analysis</li> <li>The requirements and the grund truth pairs. Originals requirements are marked with "1" in the third column, mutants are marked with "0". </li> </ol>
Supplementary material for: Inconsistency Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation
<p>Supplementary material including both data and annotations. For each document we present:</p> <ol> <li>annotated results of the chatGPT answers;</li> <li>annotated results of the manual analysis</li> <li>The requirements and the grund truth pairs. Originals requirements are marked with "1" in the third column, mutants are marked with "0". </li> </ol>
Person detection using UWB and Monocular camera (With LiDAR ground-truth) 0.7m/s
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt_2 </code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
MODELS -- Qvire (CulturalAI) -- Detecting quiremarks
<p>The weights of the models trained on the task of quiremark detection on medieval manuscripts. Specifically this archive contains the model weights of two detection approaches used in our work represented by different architectures (Faster RCNN and ViT), each of which is further represented by a set of five weights corresponding to the resulting models trained when performing the final evaluation on all 59 manuscripts in the form of 5-fold cross validation.</p> <p>The specific manuscripts used to train/eval the models in each fold are also specified in this archive.</p>
DATA -- Qvire (CulturalAI) -- Detecting quiremarks
<p>The dataset comprises 59 medieval manuscripts from the municipal library of Saint-Omer that contain quiremarks we wish to detect. This data was used to train 2 main approaches for quiremark detection.</p>
PSSC dataset: Improving spam detection in Persian SMS by providing a comprehensive dataset
<p>Text messaging is widely regarded as one of the most frequently used methods of text communication among mobile phone users. The affordability and widespread use of texting make it an attractive option for advertisers and spammers. Unfortunately, this has resulted in a large volume of spam messages received by users, lowering their overall satisfaction with mobile phone usage. A significant obstacle in the field of Persian SMS spam classification and removal is the lack of a sufficient and diverse database of both legitimate (ham) and spam SMS messages in Persian. To solve the issue of the limited database of ham and spam Persian SMS messages, in this research a comprehensive database was established by gathering 4,389 text messages from various sources and labeling them.<br><br><br>سالاری, محمدحسین و شایگان, محمدامین,1402,بهبود تشخیص هرزپیامک در پیامک های فارسی با ارائه یک پایگاه داده جامع,اولین کنفرانس ملی آنالیز داده ها,یاسوج,,,https://civilica.com/doc/1670746</p><p> </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.