Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,302
datasets available to search
ShareScore release 0.9.0
Dataset results
9,302 results for “DETECT”
Armadillo detections in Northwest Arkansas
<p>As the human footprint upon the landscape expands, wildlife seeking to avoid human contact are losing the option of altering their spatial distribution and instead are shifting their daily activity patterns to be active at different times than humans. In this study, we used game cameras to evaluate how human development and activity were related to the daily activity patterns of the nine-banded armadillo (<i>Dasypus novemcinctus)</i> along an urban to rural gradient in Arkansas, USA during the winter of 2020-2021. We found that armadillos had substantial behavioral plasticity in regard to the timing of their activity patterns; >95% of armadillo activity was nocturnal at six of the study sites whereas between 30 and 60% of activity occurred during the day at 3 other sites. The likelihood of diurnal armadillo activity was best explained by the distance to downtown Fayetteville (the nearest population center) and estimated ambient sound level (both indices of human activity) with armadillos being most active during the day at quiet sites far from Fayetteville. Furthermore, armadillo activity occurred later during the night period (minutes after sunset)at sites near downtown and with higher anthropogenic sound. Anecdotal evidence suggests that the observed activity shift may be in response to not only human activity but the presence of domestic dogs. Our results provide further evidence that human activity has subtle nonlethal impacts on even common, widespread wildlife species. Because armadillos have low body temperatures and basal metabolism, being active during cold winter nights likely has measurable fitness costs. Nature reserves near human population centers may not serve as safe harbors for wildlife as we intend, and managers could benefit from considering these nonlethal responses in how they manage recreation and visitation in these natural areas.</p>
Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
Background <p>Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. </p> Methods and Findings <p>A single-molecule fluorescence immunosorbent assay was used to detect the Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.</p> Conclusions <p>The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</p>
Data from: Detecting macroevolutionary genotype-phenotype associations using error-corrected rates of protein convergence
<p><span>On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype-phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of nonsynonymous-to-synonymous substitution rate ratios and developed the novel metric <em>ω<sub>C</sub></em>, which measures the error-corrected convergence rate of protein evolution. While </span><span><em>ω<sub>C</sub></em></span><span> distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally nontrivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype-phenotype associations, even in lineages that diverged for hundreds of millions of years.</span></p>
Automatic unsupported electromembrane extraction followed by on-line electrochemical detection of diclofenac in urine
<p>Raw voltammograms (including calibration) of data presented in Table 1 of the article entitled "Programmable millifluidic platform integrating automatic electromembrane extraction clean-up and in-line electrochemical detection: A proof of concept"<strong> </strong>published in ACS Sensors (<a href="https://doi.org/10.1021/acssensors.2c01648">10.1021/acssensors.2c01648</a>)</p>
Detection of SARS-CoV-2 in conjunctival secretion and tears in patients with COVID-19 in a tertiary care centre, South India
<p><strong>Aims and objectives</strong>: The purpose of this study is to detect the presence of SAR-CoV-2 viral RNA in conjunctival secretions of COVID-19 patients and to compare the RT-PCR positivity rate for SARS-CoV-2 in conjunctival and nasopharyngeal swabs.</p> <p><strong>Materials and method</strong>: Eighty hospitalised COVID-19 patients whose nasopharyngeal swab tested positive for SARS-CoV-2 by RT-PCR were included in the study. Conjunctival swab was collected from the eyes of these patients and sent for detection of SARS-CoV-2 by RT-PCR method.</p> <p><strong>Results</strong>: <span>Among the eighty patients, 51 (63.7%) were males and 29 (36.3%) were females. The mean age of the patients was 55.93 ± 16.59. Six patients had ocular manifestations. Eleven (13.75%) patients tested positive on conjunctival swab for SARS-CoV-2 viral RNA, and only one of them had ocular manifestations out of the eleven.</span></p> <p><strong><span>Conclusion</span></strong><span>: In our study, the presence of SARS-CoV-2 in conjunctival secretions of COVID-19 patients was detected and this was not dependent on the presence of ocular manifestations or duration of disease. Though the conjunctival positivity is lower compared to the nasopharyngeal swab sampling, ocular surface and secretions can be a potential route of viral transmission.</span></p>
Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Dataset
<p>This contains the dataset for the trigger synthesis subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the trigger_synthesis folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the trigger synthesis track can be found <a href="https://zenodo.org/record/7217600">here</a>.</p>
Trojan Detection Challenge - Evasive Trojans Track Dataset
<p>This contains the dataset for the evasive trojans track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the evasive_trojans folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the evasive trojans track can be found <a href="https://zenodo.org/record/7217616">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Dataset
<p>This contains the dataset for the target label prediction subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the target_label_prediction folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the target label prediction track can be found <a href="https://zenodo.org/record/7217508">here</a>.</p>
Trojan Detection Challenge - Detection Track Dataset
<p>This contains the dataset for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the detection folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the detection track can be found <a href="https://zenodo.org/record/7217508">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Test Set
<p>This contains the test set for the trigger synthesis track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full trigger synthesis track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Trojan Detection Challenge - Evasive Trojans Track Test Set
<p>This contains the test set for the evasive trojans track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full evasive trojans track Zenodo record, which can be found <a href="https://zenodo.org/record/7213753">here</a>.</p>
Trojan Detection Challenge - Detection Track Test Set
<p>This contains the test set for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full detection track Zenodo record, which can be found <a href="https://zenodo.org/record/7213750">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Test Set
<p>This contains the test set for the target label prediction track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full target label prediction track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Methods for structural variant detection with long-read sequencing data
<p>SV calls from different long-read based SV callers on sequencing data. SV results evaluated in Methods for structural variant detection with long-read sequencing data.</p> <p>NA24385_Pacbio_HiFi -> HiFi_L1 in paper</p> <p>NA24385_Pacbio_MtSinai -> CLR_L1 in paper</p> <p>NA24385_Pacbio_CLR_SRX7668835 -> CLR_L2 in paper</p> <p>NA24385_Pacbio_CLR_SRX6719924 -> CLR_L3 in paper</p> <p>NA24385_ONT_Promethion -> Nano_L1 in paper</p>
Detecting anomalies in system logs with a compact convolutional transformer - Data
<p><strong>Detecting anomalies in system logs with a compact convolutional transformer - Data</strong></p> <p>Preprocessed data and a pre-trained model for the Larisch, Vitay, Hamker (2022) publication.</p> <p>The <em>data</em> directory contains the Blue Gene/L data set, after tokenization, shuffling, and splitting in a training and test set (BGL_masked_Xtrain.npy and BGL_masked_Xtest.npy, respectively) and the corresponding labels.<br> Additionally, the BGL_masked_Xtest_uniq.npy and BGL_masked_Ytest_uniq.npy contains the test data, where samples from the training set are removed.</p> <p>The <em>model</em> directory contains a pre-trained compact convolutional transformer (CCT) model. The CCT is trained on the proposed BGL training data and uses a 4x4 convolutional kernel.</p>
How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed
<p>These datasets provide precipitation data over Central Plateau watershed of Iran. Four different variants of the Inverse Distance Weighting (IDW) method are utilized to create these datasets. Two out of four IDW variants are proposed and developed by the authors to enhance the performance of the available standard models.</p> <p> </p> <p>Data Format: xlsx</p> <p>Spatial Resolution: ~0.08˚ & 0.25˚</p> <p>Spatial Coverage: Central Plateau watershed, Iran (48˚07'E to 61˚25'E - 26˚33'N to 37˚27'N)</p> <p>Temporal Resolution: Monthly</p> <p>Temporal Coverage: 2005 - 2015 (Inclusively)</p> <p> </p> <p><strong>How to cite:</strong></p> <p><strong>Arash Ghomlaghi, Mohsen Nasseri & Bardia Bayat (2022): How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed, Hydrological Sciences Journal, DOI: 10.1080/02626667.2022.2124874</strong></p>
Data for: Condition monitoring system for in situ crack detection based on thermal emissions
<p>The advent of packaged infra-red (IR) bolometer detectors has led to thermography-based techniques becoming popular for non-destructive evaluation of aerospace structures. These packaged bolometers are relatively compact and cost about 10% the price of high-resolution IR photovoltaic effect detectors. In this work, a condition monitoring system for in situ crack detection has been presented which utilises an original equipment manufacturer (OEM) microbolometer detector. The proposed system cost approximately 1% the price of a state-of-the-art photovoltaic effect detector system and has the potential to transform the use of IR imaging for condition monitoring in the aerospace industry and elsewhere. The proposed system performs crack detection based on the principles of thermoelastic stress analysis (TSA), which is a well-established non-destructive thermography technique. Proof-of-concept lab tests were performed on open-hole aluminium specimens to compare the performance of the proposed system against an IR photovoltaic effect detector system and demonstrate its potential application for in situ crack detection in industrial environments. It was demonstrated that crack detection is possible from loading waveform signals with frequencies as low as 0.3 Hz. This represents a significant advance in the viability of TSA-based crack detection in large-scale structural tests where loading frequencies are usually lower than 1 Hz.</p> <p> </p>
Secret Neighbor - Blue Detective
Blue Wear Cloth Detective from Secret neighbor Source: Objaverse 1.0 / Sketchfab
Supplementary material 4 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
SRP RNA multiple sequence alignment : Explanation note: Multiple sequence alignment with the SRP RNA sequences of Dumesic et al. (2015; Stereum hirsutum, Heterobasidion irregulare, and Heterobasidion annosum) aligned to our newly generated ITS sequences of Russula and Lactarius.
Supplementary material 3 from: Rosenblad MA, Martín MP, Tedersoo L, Ryberg M, Larsson E, Wurzbacher C, Abarenkov K, Nilsson RH (2016) Detection of signal recognition particle (SRP) RNAs in the nuclear ribosomal internal transcribed spacer 1 (ITS1) of three lineages of ectomycorrhizal fungi (Agaricomycetes, Basidiomycota). MycoKeys 13: 21-33. https://doi.org/10.3897/mycokeys.13.8579
ITS/SRP RNA multiple sequence alignment : Explanation note: Multiple sequence alignment comprising the 63 public ITS1 sequences with SRP RNA found in them, the three newly generated sequences, and the SRP RNA sequences from Dumesic et al. (2015) (Stereum hirsutum, Heterobasidion irregulare, and Heterobasidion annosum).
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.