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9,302 results for “DETECT”
Data from: DNA of diverse adenoviruses detected in Cameroonian rodent and shrew species
Rodent adenoviruses are important models for human disease. In contrast to the over 70 adenovirus types isolated from humans, few rodent adenoviruses are known, despite the vast diversity of rodent species. PCR and Sanger sequencing were used to investigate adenovirus diversity in wild rodents and shrews in Cameroon. Adenovirus DNA was detected in 13.8% of animals (n=218). All detected sequences differ from known adenovirus types by more than 10% on the amino acid level, thus indicating up to 14 novel adenovirus species. These results highlight the diversity of rodent adenoviruses, their phylogeny, and opportunities for studying alternative adenovirus rodent models.
Using Environmental DNA to detect a dangerous carnivore
<p>Negative human-wildlife interactions can be better managed by early detection of the wildlife species involved. However, many animals that pose a threat to humans are highly cryptic and detecting their presence before the interaction occurs can be challenging. Here we describe a method whereby the presence of a dangerous animal, the estuarine crocodile (<i>Crocodylus porosus</i>) was detected using traces of DNA shed into the water, known as environmental DNA (eDNA). <i>C. porosus</i> is present in waterways throughout South-East Asia and Oceania and has been responsible for over a thousand attacks upon humans in the past decade. A critical factor in the crocodile's capability to attack humans is their ability to remain hidden in turbid waters for extended periods, ambushing humans that enter the water or undertake activities around the waterline. Using our eDNA technique in an experimental setting, we could correctly discriminate <i>C. porosus</i> from a closely related sympatric species that does not pose a threat to humans, the freshwater crocodile (<i>Crocodylus johnstoni</i>).<i> </i>Further, we could detect the presence of <i>C. porosus</i> within an hour of its entry and up to 72 hours after it exited. We conclude that eDNA could be a valuable tool for reducing human-wildlife conflict through early detection of the species in question.</p>
Data from: Low second-to-fourth digit ratio predicts indiscriminate social suspicion, not improved trustworthiness detection
Testosterone administration appears to make individuals less trusting, and this effect has been interpreted as an adaptive adjustment of social suspicion, that improved the accuracy of trusting decisions. Here, we consider another possibility, namely that testosterone increases the subjective cost of being duped, decreasing the propensity to trust without improving the accuracy of trusting decisions. In line with this hypothesis, we show that second-to-fourth digit ratio (2D : 4D, a proxy for effects of testosterone in the foetus) correlates with the propensity to trust, but not with the accuracy of trusting decisions. Trust game players (n = 144) trusted less when they had lower 2D : 4D (high prenatal testosterone), but their ability to detect the strategy of other players was constant (and better than chance) across all levels of digit ratio. Our results suggest that early prenatal organizing effects of testosterone in the foetus might impair rather than boost economic outcomes, by promoting indiscriminate social suspicion.
Data from: Studying phenology by flexible modeling of seasonal detectability peaks
1.Many animals and plant species have advanced spring phenology in response to climate warming. The majority of avian phenological studies are based on arrival dates. Consequently knowledge on bird phenology is mainly based on migratory species. In addition, arrival dates of migratory birds may be substantially affected by en-route climate conditions; thus failing to provide good indicators for spring phenology on the breeding grounds. Correlating arrival dates with other phenological data or with environmental covariates may be meaningless in these cases. 2.We propose the date of highest singing activity, quantified by detection probability, as a powerful proxy for breeding phenology that is applicable to migratory and sedentary bird species alike. In contrast to arrival dates, breeding phenology is mainly (non-migrants) or at least partially (migrants) influenced by conditions experienced within the breeding area. 3.We developed a new method for flexible estimation of peak detectability date in spring by combining multi-season site-occupancy with semi-parametric regression modeling (thin-plate splines). We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland. 4.We found substantial differences among species in the date of spring peak detectability: late February to mid-April in sedentary and short-distance migratory species and mid-April to late May in long-distance migrants. Among 10 species with data for >9 years, five showed a trend in detectability peaks towards an earlier spring phenology by nine to 17 days within 10 years. The mean shift over all species was ~3.5 days per 10 years. 5.Our approach is widely applicable, especially for temporally and spatially large-scale data from monitoring or citizen-science programs. Besides using the detectability peak as measure of phenology, the estimated seasonal pattern in detectability can help designing monitoring programs for improved efficiency. Our approach may be applied to any species with pronounced acoustic displays or other behavioral traits strongly influencing detectability during the breeding period. We believe that it can contribute substantially to unraveling how species and communities respond to environmental change.
Data and R script for publication: The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean
<p>This is a www.zenodo.org data and R script upload for publication:</p> <p> </p> <p>Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003</p> <p> </p> <p>Downloadable script: Script_Schmid_Mio_2016_data_upload.R</p> <p>Downloadable data: Schmid_2016_MIO_CGlac.csv</p>
Action recognition and object detection dataset for firearm-related actions
Open the record for dataset details and reuse information.
Thermal Image Dataset for Concealed Handgun Detection
Open the record for dataset details and reuse information.
Parasitic Egg Detection and Classification in Microscopic Images
<div> <p>Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals. Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited. Targeting to automate routine faecal examination for parasitic diseases, this challenge aims to gather experts in the field to develop robust automated methods to detect and classify eggs of parasitic worms in a variety of microscopic images. Participants will work with a large-scale dataset, containing 11 types of parasitic eggs from faecal smear samples. They are the main interest because of causing major diseases and illness in developing countries. We open to any techniques used for parasitic egg recognition, ranging from conventional approaches based on statistical models to deep learning techniques. Finally, the organisers expect a new collaboration come out from the challenge.</p> </div> <h4>Instructions:</h4> <div> <p>Datasets contain 11 parasitic egg types. Each category has 1,000 images.</p> <ul> <li>category_id 0: Ascaris lumbricoides</li> <li>category_id 1: Capillaria philippinensis</li> <li>category_id 2: Enterobius vermicularis</li> <li>category_id 3: Fasciolopsis buski</li> <li>category_id 4: Hookworm egg</li> <li>category_id 5: Hymenolepis diminuta</li> <li>category_id 6: Hymenolepis nana</li> <li>category_id 7: Opisthorchis viverrine</li> <li>category_id 8: Paragonimus spp</li> <li>category_id 9: Taenia spp. egg</li> <li>category_id 10: Trichuris trichiura</li> </ul> <p>Please visit the Challenge Homepage (https://icip2022challenge.piclab.ai/).</p> <p>Results must be submitted to the Leaderboard at the Challenge Homepage (https://icip2022challenge.piclab.ai/submission/).</p> <p>Please cite our paper for the usage after the competition: <a href="https://arxiv.org/abs/2208.06063" target="_blank" rel="noopener">N. Anantrasirichai, T. H. Chalidabhongse, D. Palasuwan, K. Naruenatthanaset, T. Kobchaisawat, N. Nunthanasup, K. Boonpeng, X. Ma and A. Achim, "ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results," IEEE ICIP2022</a>.</p> </div>
Dataset for Insect Detection Remote Sensing
<div># MSU Horticulture farm beehive dataset</div> <div> </div> <p>Dataset associated with journal submission entitled "Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data" by authors T. C. Vannoy, N. B. Sweeney, J. A. Shaw, and B. M. Whitaker.</p> <p>The associated software is archived at <a href="../doi/10.5281/zenodo.10055809" target="_blank" rel="noopener noreferrer">https://zenodo.org/doi/10.5281/zenodo.10055809</a>.</p> <div> </div> <div> </div> <div>The data were collected in June and July of 2022 at the horticulture farm at Montana State University - Bozeman. The data consists of 9977 images taken in front of the beehives.</div> <div> </div> <div>For the data collection process, the lidar was mounted in the back of a U-haul van and pointed in front of the beehives. The lidar was then run at a variety of pan and tilt angles. This created a diverse set of images with varying levels of activity depending on how far the beam was from the beehives, along with some of the sets of images containing stationary targets where the beam was hitting a beehive or plant in the distance.</div> <div> </div> <div>## Organization</div> <div>At the top-level, data are split by the collection date. The next level down, the folders correspond to individual data collection runs; the timestamp at the end of the folder names indicates when the data collection started.</div> <div> </div> <div>Each top-level date folder contains a README file that describes the individual data collection runs.</div> <div> </div> <div>Each data collection folder contains the following files:</div> <div>- `adjusted_data_junecal_volts.mat`: The main data file, which contains all the data and metadata.</div> <div>- `<image-number>_P<pan-angle>_T<tilt-angle>.mat`: The individual images; these files contain the raw data. These are all combined into `adjusted_data_junecal_volts.mat`, so these files are rarely used.</div> <div>- `labels.csv`: The class labels for all data in the folder</div> <div>- `labels.mat`: The class labels converted into label vectors, which are used for machine learning.</div> <div> </div> <div> </div> <div>## Class labels</div> <div>After collecting the data, we manually labelled the bounding box of each insect in each image, then converted the bounding boxes into binary labels that indicate whether a row contains an insect. Each potential insect was labeled with a confidence rating because some bees were more obvious than others. During the labeling process, we found 4671 probable bees. Since we were not able to collect ground-truth data in the field, it is possible that our labels are imperfect: some insects might have been missed, and some non-insects might have been labeled as insects.</div> <div> </div> <div>There is a `labels.csv` file in the root directory, which contains all the labels. Each of the data-collection subdirectories has a `labels.csv` and `labels.mat` file that contain the labels for that run.</div> <div> </div> <div>### Class imbalance</div> <div>Of the 9977 images, 3498 (35.14\%) contain one or more bees. In total, the dataset has 1775906 rows, 11492 (0.647\%) of which contain an insect measurement. Due to sampling jitter in the ADC, most insects span multiple range bins, leading to an increase in the number of rows that were labeled as containing insects. The dataset has a large class imbalance, particularly when looking at how many rows contain insects. </div>
Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks
<p><strong>Dataset description</strong></p><p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p><p>Pollen images of 16 types: ...<strong>images_16_types.zip</strong></p><ul><li>Acer Pseudoplatanus</li><li>Aesculus Carnea</li><li>Alnus</li><li>Anthoxanthum</li><li>Betula Pendula</li><li>Brassica</li><li>Carpinus</li><li>Corylus</li><li>Dactylis Glomerata</li><li>Fraxinus</li><li>Pinus Nigra</li><li>Platanus</li><li>Populus Nigra</li><li>Prunus Avium</li><li>Sequoiadendron Giganteum</li><li>Taxus Baccata</li></ul><p>Pollen video library ...<strong>pollen_video_library.zip</strong></p><ul><li>Each type of pollen is in a separate folder, there may be multiple videos per type.</li><li>In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1].</li><li>Cropped file name structure [Video file name]_[TrackingID]_[Image index of a grain]_[Frame index in video]<ul><li>Example, if a grain has 5 images, the file name would be: Anthoxanthum-grass-20200530-122652_0000000_001_00001.jpg Anthoxanthum-grass-20200530-122652_0000000_002_00002.jpg ... Anthoxanthum-grass-20200530-122652_0000000_005_00005.jpg</li></ul></li></ul><p>Field data over 3 days are gathered in Graz in spring 2020. ...<strong>pollen_field_data.zip</strong></p><p> </p><p><strong>Version 2:</strong></p><p>For experiments of mitigating the distribution shift of pollen identification on field data, there are 5 types selected from field data and manually labeled by the expert. The data are zipped in <strong>"the manual_labeled_field_data_5_types.zip"</strong> </p><p>The "<strong>images_5_types_9010_train.zip</strong>" and "<strong>images_5_types_9010_val.zip</strong>" contain 5 types selected from library data (<strong>images_16_types.zip</strong>), and these correspond to field data. </p><p>The "<strong>images_3_types_for_ablation_study.zip</strong>" contains data on 3 levels of pollen grain hydration. These data are used for the ablation study of model generalization in pollen identification. </p><p>Sample code to load the data and visualize the images is in ...plot_pollen_sample.py. Download and extract the file ...<strong>images_16_types.zip</strong> in the same folder as ...plot_pollen_sample.py to run the example.</p><p><strong>Dependecies:</strong></p><ul><li>opencv</li><li>numpy</li><li>matplotlib</li></ul><p><strong>Credit</strong></p><p>[1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119.</p><p>@inproceedings{namcao2020pollen, title = {Automated Pollen Detection with an Affordable Technology}, author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh}, booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)}, pages={108–119} month = {2}, year = {2020}, }</p>
Test data of published work titled 'Automatic Detection of Colorectal Polyps with Mixed Convolutions and its Occlusion Testing'
<p>Test data of published work titled 'Automatic Detection of Colorectal Polyps with Mixed Convolutions and its Occlusion Testing' includes:</p><ol><li>Gastrointestinal atlas-Colon Polyp dataset (test dataset)</li><li>Supplementary file containing information of Gastrointestinal atlas-Colon Polyp</li></ol><p>For more details: <a href="https://link.springer.com/article/10.1007/s00521-023-08762-z">Automatic Detection of Colorectal Polyps with Mixed Convolutions and its Occlusion Testing | Neural Computing and Applications (springer.com) </a></p>
Low-Cost Microscopic Images for Parasitic Egg Detection
<p>Our dataset contains 10<span><span><span>×</span></span></span> magnification microscopic images with a resolution of 640<span><span><span>×</span></span></span>480 pixels. Four different types of parasitic eggs present: 67 images of <em>Ascaris lumbricoides</em> (AL), 27 images of <em>Hymenolepis diminuta</em> (HD), 32 images of <em>Fasciolopsis buski</em> (FB) and 36 images of <em>Taenia</em> spp. (Tn). The images were obtained from Chulalongkorn University, Thailand, and were labelled by experts. </p>
Dataset for PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python
<p>This dataset was utilized in the research paper titled "PSASpotter: A Tool to Detect the Usage of Platform-Specific APIs in Python" for publication in Mining Software Repositories (MSR). Herein, we present a concise abstract of the study.</p> <p>A <em>platform-specific API</em> is implemented for a particular platform (e.g., operating system), thus, it may not work on other platforms than the target one. Detecting the usage of such APIs is important for supporting software maintenance, as it allows maintainers to be alerted about APIs that could pose potential risks. This paper proposes PSASpotter, a tool to detect the usage of platform-specific APIs in Python systems. PSASpotter also identifies whether the platform-specific APIs are used within a defensive code, such as try/except blocks or if blocks that check the current platform. PSASpotter can support the development of novel empirical studies about the usage of platformspecific APIs in the Python ecosystem. Moreover, the defensive code detected by PSASpotter may contain alternative solutions for unavailable APIs, which can provide insights for software development and testing across multiple platforms. PSASpotter is available at: https://github.com/ricardojob/PSASpotter. </p> <p> </p> <div> <div>PSASpotter is an AST-based tool that detects API usage at the function/method level. Given a Git project, PSASpotter analyzes all Python files and exports details of platform-specific API usage. Specifically, for each usage, PSASpotter reports information about the analyzed project (name and commit), the used API (name and availability), and the usage location (filename, line, and GitHub link). In addition, PSASpotter also reports whether the usage happens within defensive code.</div> </div> <p>The tool <strong>PSASpotter</strong> is publicly available at <a href="https://github.com/ricardojob/PSASpotter">https://github.com/ricardojob/PSASpotter</a>.</p>
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Artifact Dataset)
<p>Dataset for Peer-Reviewed Artifact of 'Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples' (NDSS 2025) </p>
Multimodal Remote Sensing Dataset for Landslide Change Detection in Haiti
<p>The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021. It consists of SAR-optical pairs from Sentinel-1 and Sentinel-2 acquired shortly before and after the event. To encourage for machine and deep learning algorithms, the images are cropped into 1713 patches. Since Sentinel-1 SAR images are acquired both in ascending and descending passes, twice more patches are available in this modality. Due to the mountainous nature of the terrain, the images are provided together with a layover-shadow mask computed as described in [Meier <em>et al</em>, 1993]. This mask is destined to limit the impact of geometrical distortions on the algorithm. Similarly, Sentinel-2 optical images are provided with a cloud and cloud shadow mask. Landslide annotations are extracted from the TODO database from NASA, rasterized and croped similarly to the images. The dataset is aimed to be used whether for monomodal or multimodal approaches, and whether for monotemporal or bitemporal approaches.</p> <p>[Meier et al., 1993] Meier, E., Frei, U., and Nüesch, D. (1993). Precise Terrain Corrected Geocoded Images. SAR Geocoding: Data and Systems, pages 173–185. Publisher: Karlsruhe, Germany: Herbert Wichmann Verlag GmbH.</p>
Recurrence and metastasis detection of breast cancer
<p>This dataset includes clinical data from 126 patients which were pathologically diagnosed as invasive cancer and all received adjuvant trastuzumab treatment. Among them, 31 patients suffered from recurrence or metastasis. In addition, 84 H&E stained whole slide images (WSI) of breast cancer were downloaded from TCGA (https://portal.gdc.cancer.gov/repository/), of which 19 cases have recurrence or metastasis. </p>
Figure 2 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 2 Method selectivity.
Figure 3 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 3 Calibration curves of 1-Gliclazide, 2-Glibenclamide, 3-Glimepiride and 4-Metformin.
Figure 1 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 1 Chromatogram of a standard mixture.
Figure 5 from: Kirakosyan VG, Tsaturyan AH, Poghosyan LE, Minasyan EV, Petrosyan HR, Sahakyan LY, Sargsyan TH (2022) Detection and development of a quantitation method for undeclared compounds in antidiabetic biologically active additives and its validation by high performance liquid chromatography. Pharmacia 69(1): 45-50. https://doi.org/10.3897/pharmacia.69.e76247
Figure 5 "Sugar Balance" sample injection chromatogram.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.