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.
61
datasets available to search
ShareScore release 0.9.0
Dataset results
61 results for “detection efficiency”
Dataset of paper "Real Driving Emissions—Event Detection for Efficient Emission Calibration"
<p>This dataset includes signal traces and resulting data used in the publication "Real Driving Emissions—Event Detection for Efficient Emission Calibration", MDPI Gases, 2024 (doi: 10.3390/gases4030010).</p> <p>The time trace data is available in a frequency of 1 Hz.</p> <p>The general vehicle specifications are listed below. For further information please refer to the paper.</p> <table> <tbody> <tr> <td> <p><strong>Criteria</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Criteria</strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p>Vehicle mass</p> </td> <td> <p>> 2000 kg</p> </td> <td> <p>Drivetrain</p> </td> <td> <p>All-wheel drive (AWD)</p> </td> </tr> <tr> <td> <p>Engine</p> </td> <td> <p>Inline 4 cylinder</p> </td> <td> <p>Gearbox</p> </td> <td> <p>Automatic Transmission (AT)</p> </td> </tr> <tr> <td> <p>Max. power</p> </td> <td> <p>> 180 kW</p> </td> <td> <p>EATS</p> </td> <td> <p>Three-way catalytic converter (TWC) and Gasoline particulate filter (GPF)</p> </td> </tr> <tr> <td> <p>Max. torque</p> </td> <td> <p>> 300 Nm</p> </td> <td> <p>Fuel</p> </td> <td> <p>Gasoline</p> </td> </tr> </tbody> </table> <p> </p> <p>Description of files:</p> <ul> <li><strong>Data_Measurement_NOx_Events_HistoryInfluence</strong>: Profile of examplary cycle in 1 Hz with time [s], speed [km/h], NOx mass flow [g/s], downstream lambda sensor voltage (U_HEGO) [V], flag fuel cut-off (b_fco) [-], distance specific NOx intensity for window layout (20 %, 50 % and 80 % History) [g/km], flag for critical window intensity (b_SE for 20 %, 50 % and 80 % history) [-]. (Refers to figure 9 and 10)</li> <li><strong>Data_NOx_RelativePositionOfEmissionIntensityPeak</strong>: Map of relative position of NOx intensity peak within the detected event based on the window layout. The position is displayed over the Window Duration [s] in rows and the History Share [%] in columns. (Refers to figure 8)</li> <li><strong>Data_PN_RelativePositionOfEmissionIntensityPeak</strong>: Map of relative position of PN intensity peak within the detected event based on the window layout. The position is displayed over the Window Duration [s] in rows and the History Share [%] in columns. (Refers to figure 7)</li> <li><strong>Data_NOx_WindowDuration_NumberEvents_AverageDuration_20percHistory</strong>: Overview of statistics for number of detected events in an examplary cycle and average duration of these events for different window durations and a window layout that considers 20 % history for NOx emissions. (Refers to figure 6)</li> <li><strong>Data_PN_WindowDuration_NumberEvents_AverageDuration_20percHistory</strong>: Overview of statistics for number of detected events in an examplary cycle and average duration of these events for different window durations and a window layout that considers 20 % history for PN emissions. (Refers to figure 5)</li> <li><strong>Data_Threshold_Statistics_NOx</strong>: Data for statistical evaluation of the threshold impact for event detection for the NOx emissions. The data are binned at a step size of 0.0001 g/km from 0 to 0.06 g/km (Threshold). For these bins, the detectable share of samples (relative) is listed for only urban samples, rural samples, motorway samples and total number of samples. (Refers to figure 4)</li> <li><strong>Data_Threshold_Window_Speed_Distance_Intensity_NOx</strong>: Data for threshold analysis. Contains the information of the analysis windows for each measurement sample that show a distance of > 0.002 km. For each window, the average window speed [km/h], the travelled window distance [km] the distance specific NOx emissions [g/km] and the integrated NOx emissions within this window [g] are displayed. (Refers to figure 3)</li> <li><strong>Overview_FinalEvent_Duration</strong>: Contains a list with the number of events and the duration of the events for the final event detection results with the named threshold values</li> <li><strong>Overview_FinalEvent_Profiles</strong>: Contains a list with the explicit data of the final event set. The time based data are displayed in a 1 Hz frequency over rows. The first column names the ID of the event, then the time in the cycle [s], the vehicle speed [km/h], the flag fuel cut-off (b_fco) [-], the downstream lambda sensor voltage (U_HEGO) [V], the Lambda value [-] and the NOx mass flow [g/s] are displayed.</li> </ul>
Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"
<p>Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection".</p> <p>Arxiv paper link: <a href="https://arxiv.org/abs/1806.06773">https://arxiv.org/abs/1806.06773</a></p> <p>Supplementary information and code for the paper: <a href="https://github.com/ronggong/musical-onset-efficient">https://github.com/ronggong/musical-onset-efficient</a></p> <p><strong>Content:</strong></p> <ol> <li>ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation</li> <li>jingju dataset train test split filenames.xlsx: train and test split filename list</li> </ol> <p><strong>Citation:</strong></p> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <p><strong>Contact:</strong></p> <p>Rong Gong: rong.gong<at>upf.edu</p>
Data from: Efficient summary statistics for detecting lineage fusion from phylogeographic datasets
<p>Aim: Lineage fusion (merging of two or more populations of a species resulting in a single panmictic group) is a special case of secondary contact. It has the potential to counteract diversification and speciation, or to facilitate it through creation of novel genotypes. Understanding the prevalence of lineage fusion in nature requires reliable detection of it, such that efficient summary statistics are needed. Here we report on simulations that characterized the initial intensity and subsequent decay of signatures of past fusion for 17 summary statistics applicable to DNA sequence haplotype data.</p> <p>Location: Global.</p> <p>Taxon: Diploid out-crossing species.</p> <p>Methods: We considered a range of scenarios that could reveal the impacts of different combinations of read length versus number of loci (arrangement of DNA sequence data), and whether or not pre-fusion populations experienced bottlenecks coinciding with their divergence (historical context of fusion). Post-fusion gene pools were sampled along 10 successive time points representing increasing lag times following merging of sister populations, and summary statistic values were recalculated at each.</p> <p>Results: Many summary statistics were able to detect signatures of complete merging of populations after a sampling lag time of 1.5 Ne generations, but the most informative ones included two neutrality tests and four diversity metrics, with ZnS being particularly powerful. Correlation was relatively low among the two neutrality tests and two of the diversity metrics. There were clear benefits of many short (200-bp × 200) loci over a handful of long (4-kb × 10) loci. Also, only the latter genetic dataset type was showed impacts of bottlenecks during divergence upon the number of informative summary statistics.<br> Main conclusions: This work contributes to identifying cases of lineage fusion, and advances phylogeography by enabling more nuanced reconstructions of how individual species, or multiple members of an ecological community, responded to past environmental change.</p>
Data from: Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas
Open the record for dataset details and reuse information.
Data from: Compared with conventional PCR assay, qPCR assay greatly improves the detection efficiency of predation
Open the record for dataset details and reuse information.
Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
Open the record for dataset details and reuse information.
Data from: Efficient summary statistics for detecting lineage fusion from phylogeographic datasets
Open the record for dataset details and reuse information.
Increasing efficiency and reducing bias in the detection of seed-dispersal interactions based on mist-netted birds
Open the record for dataset details and reuse information.
Ichthyoplankton metabarcoding: an efficient tool for early detection of invasive species establishment
Open the record for dataset details and reuse information.
Data from: Metabarcoding on planktonic larval stages: an efficient approach for detecting and investigating life cycle dynamics of benthic aliens
High-throughput sequencing (HTS) technologies offer new promise to support surveillance programs targeting marine non-indigenous species (NIS). Metabarcoding might surpass traditional monitoring methods, for example through its ability to detect rare species, a key feature in early detection of NIS. Another interest of this approach is the identification of organisms difficult to identify based on morphology only (e.g., early developmental stages), making it relevant in the context of management programs. Because many marine benthic NIS have a bi-phasic bentho-pelagic life cycle, targeting their pelagic larval stages in zooplankton may allow early detection and assessment of their establishment and potential spread. We illustrate this approach with an analysis of bulk-DNA retrieved from a time-series of zooplankton samples collected over 22 months in one bay in Brittany (France). Using HTS of amplicons obtained with two markers (COI and 18S) and a metabarcoding approach, 12 NIS were identified and their temporal larval dynamics were monitored. Importantly, we chose to focus on a closed list of species, from four metazoan classes encompassing 52 NIS reported within the study area or nearby seas, with molecular references available or obtained locally for 42 of them. The use of a custom-designed database allowed the detection of three NIS otherwise not detected when using public databases. Interestingly, NIS known to have a short-lived larval stage were detected (e.g., the bryozoan Bugula neritina or the tunicate Corella eumyota). For two molluscs Ruditapes philippinarum and Crepidula fornicata, metabarcoding results were compared to those obtained using traditional methods (i.e., barcoding of individual larvae and morphology, respectively) to show the reliability of the approach in detecting and assessing the extent of their reproductive periods. Our results also revealed that the Pacific oyster Crassostrea gigas, a notorious invasive species, failed to reproduce in the study bay, showing that metabarcoding on larval stages also provides information regarding the establishment success (or failure) of NIS. While metabarcoding has its limitations and biases, this study demonstrates its effectiveness for surveillance of targeted NIS, notably to support management strategies like the European Marine Strategy Framework Directive (MSFD).
syslrn: Learning What to Monitor for Efficient Anomaly Detection [Dataset]
<p>This repository includes the dataset for the paper:</p> <p><em><a href="http://doi.org/10.1145/3517207.3526979">D. Sanvito, G. Siracusano, S. Santhanam, R. Gonzalez, R. Bifulco</a></em><br> <strong><em><a href="http://doi.org/10.1145/3517207.3526979">syslrn: Learning What to Monitor for Efficient Anomaly Detection </a></em></strong><br> <em><a href="http://doi.org/10.1145/3517207.3526979">ACM EuroMLSys 2022</a></em></p> <p>The dataset contains two directories at the root level:</p> <ul> <li><em><strong>raw_dataset</strong></em></li> <li><strong><em>processed_dataset</em></strong></li> </ul> <p>Each folder in the <strong><em>raw_dataset</em> </strong>directory contains the raw monitoring data used to generate the graph associated to a single experiment together with additional metadata files.<br> Each folder in the <strong><em>processed_dataset</em> </strong>directory contains the graph associated to a single experiment as a set of three CSV files: two for the graph edges (<em>pid_childof_pid_df.csv</em> and <em>pid_speakswith_pid_df.csv</em>) and one for the graph nodes (<em>proc_df.csv</em>).<br> We provide below a code snippet to parse a graph from <strong><em>processed_dataset</em> </strong>directory.</p> <p>In both folders the name of each sub-folder is based on the following schema: <strong><em>[SCENARIO]_[W]wl/test_[TEST_ID]</em></strong> where:</p> <ul> <li><em>[SCENARIO]</em> reports the target component for the failure injection (<em>cinder_failure</em>, <em>neutron_failure</em>, <em>nova_failure</em>). <em>ff</em> indicates instead a failure-free execution</li> <li><em>[W]</em> reports the number of concurrent workloads</li> <li><em>[TEST_ID] </em>reports the ID of the specific failure scenario injected (same ID selected by the OpenStack failure injection framework [1] )</li> </ul> <p>Each experiment includes the following data in the <strong><em>raw_dataset</em></strong> sub-folders:</p> <ul> <li><em>audit_raw_logs_[TEST_ID]/</em>: raw audit monitoring data</li> <li><em>bpf_tools_[TEST_ID]/</em>: raw ebpf tools monitoring data</li> <li><em>instance-[INSTANCE_ID]/</em>: workload-specific metadata files, e.g. stdout/stderr (generated by the OpenStack failure injection framework [1] )</li> <li><em>logs_workload_[TEST_ID]/:</em> OpenStack application logs</li> <li><em>perf_tools_[TEST_ID]/</em>: raw perf tools monitoring data</li> <li><em>audit_filtered_[TEST_ID].log:</em> audit data pre-processed by <em>ausearch</em> (e.g. numerical entities are resolved to symbols)</li> <li><em>failure_[TEST_ID].info</em>: metadata information about the specific failure scenario (generated by the OpenStack failure injection framework [1] )</li> <li><em>timestamps_[TEST_ID]:</em> timing information</li> </ul> <p><em>[1] D. Cotroneo, L. De Simone, P. Liguori, R. Natella, N. Bidokhti - How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform [ACM ESEC/FSE 2019]</em></p> <p> </p> <p>Example: parsing a graph from <strong><em>processed_dataset</em> </strong>directory</p> <pre><code class="language-python">import pandas as pd import networkx as nx def parse_csv(path): processes_df = pd.read_csv('%sproc_df.csv' % path, index_col=0).reset_index(drop=True) speakswith_edges_df = pd.read_csv('%spid_speakswith_pid_df.csv' % path, index_col=0) speakswith_edges_df['type'] = 'speaksWith' childof_edges_df = pd.read_csv('%spid_childof_pid_df.csv' % path, index_col=0) childof_edges_df['type'] = 'childOf' return processes_df, pd.concat([speakswith_edges_df, childof_edges_df], ignore_index=True) def make_graph(nodes_df, edges_df): G = nx.MultiGraph() for _, node in nodes_df.iterrows(): G.add_node(node.pid, **node) for _, edge in edges_df.iterrows(): G.add_edge(edge.pid1, edge.pid2, type=edge.type) return G PATH = 'processed_dataset/ff_1wl/test_1/' nodes_df, edges_df = parse_csv(PATH) G = make_graph(nodes_df, edges_df) nx.draw_networkx(G, node_size=10, with_labels=False)</code></pre> <p> </p> <p>If you use this dataset for your research, please cite the following paper:</p> <pre><code>@inproceedings{sanvito2022syslrn, title={syslrn: Learning What to Monitor for Efficient Anomaly Detection}, author={Sanvito, Davide and Siracusano, Giuseppe and Santhanam, Sharan and Gonzalez, Roberto and Bifulco, Roberto}, booktitle={2nd European Workshop on Machine Learning and Systems (EuroMLSys '22)}, year={2022}, address = {Rennes, France}, publisher = {ACM}, month = apr, } </code></pre>
The data used in correspondence on the article entitled "Reply: Correspondence on NanoVar's performance outlined by Jiang T. et al. in 'Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation'."
<p>The data used in correspondence on the article entitled “Reply: Correspondence on NanoVar’s performance outlined by Jiang T. et al. in ‘Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation’.”. It contains the benchmarking results evaluated by Truvari. The running log files of NanoVar are also included in this repository.</p>
Evaluation of PreveCol's Efficiency As a Second Line Method for the Early Colorectal Cancer Detection
ClinicalTrials.gov study NCT06703632. IPD Sharing: NO. Countries: 2. Publications: 4.
Quantitative Detection Efficiency of UDFF for Nonalcoholic Fatty Liver Disease
ClinicalTrials.gov study NCT05802199. IPD Sharing: UNDECIDED. Countries: 2. Publications: 13.
Data from: Metabarcoding on planktonic larval stages: an efficient approach for detecting and investigating life cycle dynamics of benthic aliens
Open the record for dataset details and reuse information.
Data from: Hide and seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence
Open the record for dataset details and reuse information.
EFFICIENT GENERATION OF RECEIVER OPERATING CHARACTERISTICS FOR THE EVALUATION OF DAMAGE DETECTION IN PRACTICAL STRUCTURAL HEALTH MONITORING APPLICATIONS
<p>Experimentally measured guided wave signal on a pipe over temperature cycles, used in the paper to construct synthetic datasets to evaluate performance of damage detection methods.</p>
Mixed-species flocking is associated with low arthropod detectability and increased foraging efficiency by Yungas forest birds in Argentina
<p class="CuerpoB"><span>Mixed-species flocks presumably provide birds with antipredator and foraging benefits. The foraging benefits hypothesis predicts that a reduction in arthropod abundance will trigger flocking activity; however, flocking activity may also be influenced by the difficulty of detecting arthropods, a seldom explored possibility. We found that environmental traits (temperature and foliage density) combined with arthropod abundance explained arthropod detection by birds in the Yungas foothill forest of NW Argentina. Prey detection was inversely related to ambient temperature and foliage density while positively associated with arthropod abundance. Based on this result, we built a </span><span>structural equation model </span><span>using a latent proxy variable for arthropod detectability, arthropod crypsis, integrating ambient temperature, foliage density, and proportion of immature arthropods. This model allowed us to compare the relative importance of arthropod abundance and the difficulty in detecting prey items as predictors of flocking propensity. After two years of studying 129 mixed-species flocks, 1,344 bird foraging sequences, and 25,591 arthropod captures,</span> <span>we found that the flocking propensity of birds was only significantly correlated with arthropod detectability and not with arthropod abundance. </span>Flocking propensity peaked when the arthropod community was comprised of proportionately more immature and non-flying arthropods, the temperature was low, and the foliage cover was more dense; all factors are contributing to a low arthropod detectability. <span>Finally, we evaluated whether joining mixed-species flocks provided foraging benefits such as increased foraging efficiency. Individuals benefited from joining flocks by an average increase of their prey-capture attempt rate of 40%, while the search rate increased by 16%. Our results add a new perspective on the drivers of mixed-species flocking by showing that the capacity to find prey items may have a more significant effect than prey abundance per se.</span></p>
Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images
<p>Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This research develops an efficient low-cost deep learning-based methodology to identify cracks on bridges using computer vision-based techniques and deep learning. The Convolutional Neural Networks (CNN) deep learning method is used to identify cracks from images. In this research, a programmable drone is developed that can fly along a pre-defined trajectory. A large volume of images was collected from local bridges and pavements using drones. The collected images were preprocessed and divided into around forty thousand 256 by 256-pixel sub-images and fed into the CNN model. Data augmentation techniques are applied to increase the number of images in some cases. Parameters of the selected CNN model were optimized to obtain the best configuration. To evaluate the performance of the method, images from a different local bridge were used for testing. Research results show that with the optimized CNN model, cracks in the images can be identified efficiently and accurately. The developed methodology can also category the cracked image as slight, moderate, or severe cracking based on a pre-defined quantification index. The research outcome of this project has the potential to automate crack damage identification of bridge key components in a cost-effective manner. Also, the developed methodology is expected to facilitate crack damage identification for other transportation infrastructures, e.g., pavement and traffic sign structures.</p>
A Deep Learning-Based Approach for Efficient Detection and Classification of Local Ca²⁺ Release Events in Full-Frame Confocal Imaging
<p>U-Net implementation and project code: <a href="https://github.com/dottipr/sparks_project">https://github.com/dottipr/sparks_project</a></p> <p>GUI's github repository: <a href="https://github.com/r-janicek/xytCalciumSignalsDetection">https://github.com/r-janicek/xytCalciumSignalsDetection</a></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.