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9,302 results for “DETECT”
A real-time PCR assay to detect predation by spiny dogfish on Atlantic cod in the western North Atlantic Ocean.
Conventional observations show spiny dogfish (<i>Squalus acanthius</i> Linnaeus) rarely eat Atlantic cod (<i>Gadus morhua</i> Linnaeus; 0.02% of stomachs) in the northwestern Atlantic Ocean. Critics express concern that digestion may limit species-level prey identification, and with recovery from overfishing, dogfish populations may be suppressing cod by competition or predation. This study applied a real-time PCR TaqMan assay to identify cod in dogfish stomachs collected by cooperating fishing boats during normal trawling operations (May 2014 – May 2015; Gulf of Maine, Georges Bank). Conventional methods observed 51 different prey taxa and nearly 1600 individual prey items, but no cod were observed. Cod DNA was detected in 31 (10.5%) of the dogfish stomachs, with a higher percentage of these from the homogenate of amorphous, well-digested prey and stomach fluids (20 stomachs or 65%) than from discrete animal tissues (11 stomachs or 35%). Re-examination of photographs of these 11 tissue samples revealed one whole, partially-digested fish that could be recognized in hindsight as cod. Cod DNA was observed in dogfish stomachs year round: in January (1 of 1 trip), February (1 of 1), May (1 of 3), June (0 of 1), July (3 of 4), August (1 of 2), and October (3 of 3). Although these data suggest higher interaction rates between dogfish and cod than previously observed, addressing the population consequences of this predator-prey relationship requires a robust sampling design, estimates of digestion rates by dogfish to account for complete degradation of DNA sequences, and consideration for dogfish scavenging during fishing operations.
Figure 1 from: Susilawati NM, Halimah E, Saidah S (2021) Pharmacists' strategies to detect, resolve, and prevent DRPs in CKD patients. Pharmacia 68(3): 619-626. https://doi.org/10.3897/pharmacia.68.e65136
Figure 1 Article Selection Scheme.
Pancreatic cancer detection and characterization: state of the art and radiomics.
<p>We uploaded the images of the manuscript: Granata V, Grassi R, Fusco R, Galdiero R, Setola SV, Palaia R, Belli A, Silvestro L, Cozzi D, Brunese L, Petrillo A, Izzo F. Pancreatic cancer detection and characterization: state of the art and radiomics. Eur Rev Med Pharmacol Sci. 2021 May;25(10):3684-3699. doi: 10.26355/eurrev_202105_25935. PMID: 34109578.</p>
Effect of neurological screening on early dementia detection in southern Italy
<p>Objective</p> <p>Population screening can facilitate early diagnosis of dementia and improve disease management. This study examined the effects of a screening campaign for neurodegenerative disorders on the early diagnosis of dementia using 2-year follow-up data.</p> <p>Methods</p> <p>A 5-day screening campaign was conducted that comprised neurological, neuropsychological and other specialist examinations. Identification of alterations during the neurological examination was followed-up by further diagnostic examinations to confirm the neurological impairment.</p> <p>Results</p> <p>Neurological alterations were observed in 39% of the screened subjects, who were mostly diagnosed with mild cognitive impairment and referred to a dementia and cognitive disorders centre. Suspicion of neurological impairment was a risk factor for inclusion in a specific neurological ambulatory follow-up and a condition for exemption from payment for medical examinations.</p> <p>Conclusions</p> <p>Neurodegenerative screening initiatives should include subjects selected by general practitioners. It would be useful to create a network including primary care physicians and cognitive disorder centres. Telemedicine tools (e.g., teleconsulting) could also be used to facilitate early diagnosis.</p>
Thai depression detection dataset and baseline models
<p>Depression detection dataset and baseline models. If you are only interested in the dataset, download data.zip.</p> <p>Please cite: Hämäläinen, M., Patpong, P., Alnajjar, K., Partanen, N. & Rueter, J. (2021) Detecting Depression in Thai Blog Posts: a Dataset and a Baseline. In <em>Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)</em>.</p>
Emerging Saccharomycotina yeast pathogens: Detection and susceptibility profiles
<p>This dataset includes the Supplementary material of the PhD thesis titled:</p> <p><strong>Emerging Saccharomycotina yeast pathogens: Detection and susceptibility profiles</strong></p> <p>by Aimilia Aikaterini Stavrou.</p>
A Multi-Rater Benchmark for Perineuronal Nets Detection and Counting in Fluorescence Microscopy Images
<p>Dataset of fluorescence microscopy images of mice brain slices stained against perineuronal nets (PNNs). The dataset is composed of two subsets: a large single-rater subset (PNN-SR) and a smaller multi-rater subset (PNN-MR).</p> <ul> <li>PNN-SR consists of 25 images having different sizes ranging from 8184×6163 to 15120×9477 pixels. Among all the images, there are roughly 34k annotated PNNs, varying from a few dozens to some thousand per image, dot-annotated by a single human rater. <br> </li> <li>PNN-MS comprises 12 microscopic images of 2000×2000 pixels representing different portions of a mouse brain, with a total of 2,532 dot-annotated PNNs. The annotation procedure has been performed by seven different raters.</li> </ul>
LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning
<p>LMHLD collects remote sensing images of five satellite sensors in seven areas of the world: Wenchuan, China (2008); Rio de Janeiro, Brazil (2011); Gorkha, Nepal (2015); Jiuzhaigou, China (2015); Taiwan, China (2018); Hokkaido, Japan (2018); Emilia-Romagna, Italy (2023), a total of 25,365 patches by setting different patch sizes according to different landslide scale.</p> <p>Specifically, patch sizes vary across different areas: 32 for image segmentation in Emilia-Romagna, Italy and Gorkha, Nepal; 64 in Rio de Janeiro, Brazil; 128 in Jiuzhaigou, China and Hokkaido, Japan; and 224 in Wenchuan and Taiwan, China. All the above patches constitute LMHLD, a large-scale multi-source high-resolution heterogeneous landslide dataset.</p> <p><strong><em>LMHLD.rar</em></strong> includes all the Train, Validation, Test data used for the experiments.</p> <p>If you use our data, please cite our work published in <strong><em>IEEE Transactions on Geoscience and Remote Sensing</em></strong>. Or if you have any other questions about LMHLD, please contact us: <strong>guan.ting.liu2000@gmail.com</strong>.</p> <p><strong>Paper:</strong> Liu G, Wang Y, Chen X, et al. LMHLD: A Large-scale Multi-Source High-Resolution Landslide Dataset for Landslide Detection based on Deep Learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025. (<strong>DOI: </strong><a href="https://doi.org/10.1109/TGRS.2025.3619062" target="_blank" rel="noopener">10.1109/TGRS.2025.3619062</a>)</p>
Evaluation of misbehavior detection mechanisms in Maat using VeReMi
<p>This is the evaluation data for this paper: https://arxiv.org/abs/1804.06701</p> <p>The analysis scripts for this data, as well as a brief explanation on what's what can be found here:<br> https://github.com/VeReMi-dataset/VeReMi-popper/tree/master/analysis</p> <p>This data was directly derived from the VeReMI dataset. Because the derivation scripts run on a cluster and are quite closely tied to that cluster, we decided to also publish the raw data. For processing, I split the data into 3 groups (low, medium, and high) of 75 tgz files that correspond to different densities (see paper for details). Basically this corresponds to A_R0_* to A_R14_*, A_R15_* to A_R29_* and A_R30_ to A_R44_* (this can also be clearly seen by the file size). If you store these in ./low, ./med, ./high respectively, you'll end up with the same graphs as published here:</p> <p>https://github.com/vs-uulm/securecomm2018-misbehavior-evaluation</p> <p>For the paper, we used a sub-set of these graphs.</p>
Dataset for "Detection and characterization of wind-blown charged sand grains on Titan with the DraGMet/EFIELD experiment on Dragonfly"
<p>Codes, datasets and supplementary information for the following paper:</p> <p>Chatain A., Le Gall A., Berthelier J.-J., Lorenz R.D., Hassen-Khodja R., Lebreton J.-P., Joly-Jehenne T. and Déprez G., <em>Detection and characterization of wind-blown charged sand grains on Titan with the DraGMet/EFIELD experiment on Dragonfly.</em> Icarus 391 (2023) 115345. https://doi.org/10.1016/j.icarus.2022.115345 ; open access: https://doi.org/10.48550/arXiv.2211.01502</p> <p>About numerical simulations and laboratory tests of the ability of Dragonfly/EFIELD to detect moving charged grains.</p> <p>Files and folders:</p> <p>-EFIELD_Supporting_Information.pdf : Supporting Information to the published paper</p> <p>-SImulation.zip : data and codes to plot the results of the numerical simulations presented in the paper</p> <p>-Experiments.zip : data and codes to plot the results of the lab experiments presented in the paper</p> <p>-Transfer_Function.zip : data and codes to plot the transfer function of the prototype electrode</p>
Fig. 1 in The detection of an alien pest, the cotton leaf roller Haritalodes decorata (Lepidoptera: Crambidae), on the Black Sea Coast of Russia
Fig. 1. Adults of Haritalodes derogata. A – male, B – female. Reared from Hibiscus
AnoShift: A distribution shift benchmark for unsupervised anomaly detection
<p>Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models. The existing benchmarks are focused on supervised learning, and to the best of our knowledge, there is none for unsupervised learning. Therefore, we introduce an unsupervised anomaly detection benchmark with data that shifts over time, built over Kyoto-2006+, a traffic dataset for network intrusion detection. This type of data meets the premise of shifting the input distribution: it covers a large time span (10 years), with naturally occurring changes over time (eg users modifying their behavior patterns, and software updates). We first highlight the non-stationary nature of the data, using a basic per-feature analysis, t-SNE, and an Optimal Transport approach for measuring the overall distribution distances between years. Next, we propose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing splits. We validate the performance degradation over time with diverse models, ranging from classical approaches to deep learning. Finally, we show that by acknowledging the distribution shift problem and properly addressing it, the performance can be improved compared to the classical training which assumes independent and identically distributed data (on average, by up to 3% for our approach).</p>
Example mineral detection outputs
<p>Example output for Carbon Mapper mineral detection algorithm version 1 based on AVIRIS-NG flight line ang20200712t201415 available from the AVIRIS-NG data portal. </p>
Variety differentiation: Development of a powerful CRISPR DETECTR method for the detection of single nucleotide polymorphisms (SNPs)
<p>Supplemental information: Variety differentiation: Development of a powerful CRISPR DETECTR method for the detection of single nucleotide polymorphisms (SNPs). Excel sheets show the raw fluorescence data and calculated sample/NTC and P/N ratios described in the manuscript.</p>
FIRE14 Detection of SOurce COde Re-use
<p>This data was used for the PAN shared task on source code re-use detection at FIRE2014. </p> <p>Please find the task description at https://pan.webis.de/fire14/pan14-web/index.html.</p> <p><strong>THIS DATA</strong></p> <p>For the training phase we provide an annotated corpus including with the programming language extensions. It includes information about whether a text fragment has been re-used and, if it is the case, what its source is.</p> <ul> <li>The collection consists of source codes written in Java and C.</li> <li>Re-use is commited in both programming languages but ONLY at monolingual level.</li> <li>The Java collection contains 259 source codes from 000.java to 258.java.</li> <li>The C collection contains 79 source codes from 000.c to 078.c.</li> <li>Relevance Judgements represent re-use in both directions(a→b and b→a)</li> </ul> <p>In the test phase the only annotation that will be provided in the corpus is the programming language extensions.</p> <ul> <li>It is divided by programming language (C/C++ and JAVA) so you do not need any pre-process to identify the programming language of the source codes.</li> <li>Each programming language folder contains 6 folders (A1, B1, B2, C1 and C2) that contains a specific scenario with monolingual re-use.</li> <li>There is not re-use between scenarios so you just need to look for re-used cases among the source code files inside each folder.</li> <li>The name of the files consists of the name of the task which they belong plus an identifier. For example, file "B10021" belongs to scenario B1 and its identifier number is 0021.</li> <li>It could not exist re-use between source codes that belong to different scenarios. For example, you do not have to submit a re-used case between files "B10021" and "B20013". The first one belongs to scenario B1 but the second one belongs to B2.</li> </ul>
HST Ultraviolet Detection of Hot White Dwarf Companions to Blue Lurkers in M67
<p>We present the results of our Hubble Space Telescope far-ultraviolet survey of the blue lurkers (BLs) in M67. We find evidence for two white dwarf companions among the BLs that are indicative of mass transfer from an evolved companion, one in WOCS 14020 and the other in WOCS 3001. The cooling ages of the white dwarfs suggest that mass transfer in these systems occurred ~300--540 Myr and ~600--900 Myr ago, respectively. The rotation periods and cooling ages of the BLs are consistent with spin-up and subsequent single-star spin-down models, and binary evolution models yield plausible evolutionary pathways to both BLs via highly non-conservative mass transfer. We conclude that the BLs are lower-luminosity analogues to the classical blue stragglers.</p> <p>These are the inlists and starting models corresponding to our best-fit MESA models to both WOCS 14020 and WOCS 3001.</p>
Traces, Metrics, and Logs for Anomaly Detection and Root Cause Localization in Microservices
<p>Here are the data used in our paper published at ICSE 2023: </p><p>"Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data".<br><br>Please make sure to cite our paper whenever you use the data in your research:<br><br>@inproceedings{DBLP:conf/icse/LeeYCSL23, author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Michael R. Lyu}, title = {Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data}, booktitle = {45th {IEEE/ACM} International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, May 14-20, 2023}, pages = {1750--1762}, publisher = {{IEEE}}, year = {2023}, url = {https://doi.org/10.1109/ICSE48619.2023.00150}, doi = {10.1109/ICSE48619.2023.00150}, timestamp = {Wed, 19 Jul 2023 10:09:12 +0200}, biburl = {https://dblp.org/rec/conf/icse/LeeYCSL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }</p>
data for: Inconsistencies Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation
<p>Supplementary material for the paper: Inconsistencies Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation</p> <p>In the "gold" sheets there are the requirements: originals requirements are marked with "1" in the third column, mutants are marked with "0". </p>
Facile Electrodeposition-Based Chemosensors Using PANI and C-Hybrid Nanomaterials for the Selective Detection of Ammonia and Nitrogen Dioxide at Room Temperature - Dataset
<p>Sensor systems for monitoring indoor air quality are vital for the precise quantification of the mechanisms which lead to the deterioration of human health, with a typical person spending an average of 20 h a day in an enclosed space. Thus, a series of layered chemoresistive sensors, obtained by the facile electrodeposition of carbon nanomaterial-enhanced PANI composites, have been tested for the selective detection of two core indoor pollutants: ammonia and nitrogen dioxide. The sensors were tested with respect to sensitivity and selectivity to the target gasses, with performance being assessed based on response linearity and repeatability at room temperature. Of the tested sensors, two have been identified as having an adequate performance on ammonia, with sensitivities of up to 96.99% and resolutions of up to 0.85 ppm being observed, while on nitrogen dioxide, despite the successful sensor having a lower sensitivity, 10.71%, it has shown high resolution, 1.25 ppm, and linearity over a large concentration domain. These high performances highlight the viability of multi-layers chemosensors based on the electrodeposition of nanomaterial-enhanced conductive polymers for the detection of pollutant gasses, with finetuning of the detection layer allowing the accurate monitoring of a wide range of gasses.</p>
Quicklook mosaic of all type-III bursts detected by SHARAD, based on cross-detections with STEREO/WAVES and Wind/WAVES
<p>Quicklook mosaic of all type-III bursts detected by SHARAD, based on cross-detections with STEREO/WAVES and Wind/WAVES.</p>
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.