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.
101
datasets available to search
ShareScore release 0.9.0
Dataset results
101 results for “signal detection”
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification
<p>Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.</p>
Unsupervised New Physics detection at 40 MHz: h+ -> tau nu Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of h+ -> tau nu decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: h^0 -> tau tau Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of h^0 -> tau tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: LQ -> b tau Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of Leptoquarks -> b tau decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Unsupervised New Physics detection at 40 MHz: A -> 4 leptons Signal Benchmark Dataset
<p>Unsupervised New Physics detection at 40 MHz data challenge</p> <p>Signal Benchmark Dataset consisting of A -> 4 leptons decays produced in collision events (simulation of LHC 13 TeV proton-proton collisions) pre-filtered by a requirement of a muon or electron with 23 GeV transverse momentum. Data format description available on the data challenge web page: https://mpp-hep.github.io/ADC2021/</p>
Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplification and flow cytometry
<p>This dataset contains the raw data that were used for the publication entitled, "Ultrasensitive detection of cancer-associated nucleic acids and mutations by primer exchange reaction-based signal amplificaiton and flow cytometry" published in Biosensors and Bioelectronics on 5 October 2024.</p>
Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"
<p>This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal.</p> <p><strong>Contents:</strong></p> <ul> <li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li> </ul> <p><strong>Results based on the data have been presented in the paper:</strong><br> Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation</p> <p><strong>To cite these data sets please use the following:</strong><br> Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668</p>
Data and code for "A Signal-Detection Approach to Modeling Forgiveness Decisions"
<p>Contains the data and code that supplements the journal article, "A Signal-Detection Approach to Modeling Forgiveness Decisions".</p> <p>Article doi: 10.1016/j.evolhumbehav.2016.06.004</p>
Figure 6. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 6. - Strobe models of five positions along the canonical variates indicated in Fig. 5. CV-1, CV-2, and CV-3 axes account for 79.5% of the observed between-species shape variation. Landmarks and semi-landmarks are superimposed in the figure to the right of each sequence to express the magnitudes and directions (arrows) of shape trends. In all models, the mesial margin of the coxa is depicted to the left, the lateral margin to the right.
Supplementary material 1: Appendix from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Voucher data for specimens use din morphometric analyses and supplementary figures of Canonical Variates scatterplots
Figure 5. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 5. - Results of the CVA of coxal shape data for all eight species, showing the subspaces formed by the first three discriminant axes, which together account for more than 79% of observed between-group shape variation. Within each subspace plot the black circles represent the coordinate locations for each of the five along-axis shape models depicted in Fig. 6.
Figure 4. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 4. - Scatterplots of Procrustes PCA scores for coxal shape data. The first two shape variation axes (top) together account for 62.63% of the observed shape variation; PC-2 and PC-3 axes (bottom) together account for 27.58% of the observed shape variation.
Figure 3. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 3. - Thereuopoda longicornis scatterplot of coxal shape data along the discriminant subspace formed by the first two CV axes, which together account for 74.17% of observed between-group shape variation.
Figure 2. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 2. - Landmarks (L1-L10) used in morphometric analysis. Diagonal line to L1 is the longest line from anterolateral to posteromedial corners of the coxa. Spine-bristles numbered 1-4 (blue) from interior to exterior. Throughout text, left and right coxae refer to dorsal orientation (inverted 180° relative to this ventral view).
Figure 1. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 1. - Ventral view of head and forcipules of Thereuopoda longicornis placed in a standard horizontal position. BM 1952.9.8.574-575, Kuching, Sarawak, Malaysia.
Signal-to-Noise Ratios, DYNAMITE Posteriors, and Combined Detectability Plots for 173 TESS Multi-Planet Systems
<p>Collection of figures for 173 TESS Multi-Planet Systems. Each figure (titled with the system's TOI number) has 2 rows of planets, corresponding to results from the clustered period model (upper) and period ratio model (lower) predictions for an additional planet in each system. The left-most column of the plots show the signal-to-noise ratios (SNR) of simulated transits for planets with a given radius and period in the corresponding TESS light curve. The middle column shows the dot product of the radius and period posteriors generated by DYNAMITE using TESS light curves up to and including Sector 76. The right-most column shows the detectability (SNR) modulated by the DYNAMITE posteriors, showing the location in (P, Rp) space where an additional planet in this system is most likely to be detected under each model.</p>
Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021
<p>This dataset is part of the available dataset for <em>Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021</em>, available at https://physionet.org/static/published-projects/cpsc2021/paroxysmal-atrial-fibrillation-events-detection-from-dynamic-ecg-recordings-the-4th-china-physiological-signal-challenge-2021-1.0.0.zip (last accessed today 2022-07-22).</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International Public License:</p> <p>Permissions:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Conditions:</p> <ul> <li> <p>Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> </li> </ul> <p>Limitations:</p> <ul> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</li> </ul> <p>For more information about the license, check: https://creativecommons.org/licenses/by/4.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the training and test set folders are used</li> <li>Only the files *.hea, *.dat and *.atr are used, being the *.dat and *.atr converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>
Experimental characterization of TERIPHIC 4-fold and 8-fold InP photodiode arrays - Detection of 50Gbaud signals with NRZ and PAM-4 modulation formats
<p>The data sets refer to the results from the experimental characterization of the TERIPHIC 4-fold and 8-fold InP-photodiode arrays, which operate in the O-band. The experimental setup consisted of a commercially available off-the-shelf (COTS) laser source, a COTS Mach-Zehnder modulator, and the TERIPHIC PD arrays. For the purposes of the experiments, signals with NRZ and PAM-4 modulation formats and 50 Gbaud symbol rates were generated, transmitted, and detected by the TERIPHIC photodetectors. Two different subassemblies were evaluated. The first subassembly had four InP photodiodes and the second eight. The generated photocurrents were sampled by a real-time oscilloscope with a 256 Gbaud sample rate. The data sets are actually the samples extracted from the oscilloscope. The data sets have a filename in the format “Symbol rate#Modulation formats#PDn#Transmission length#version#Subassemblyn.bin”, where<br> Symbol rate = 50 Gbaud<br> PDn, for n= 1,2, ...,8<br> Transmission length = B2B (0 km), 2 km, and 10 km<br> Subassembly for n=1 and 2<br> <br> The data sets are parts of the Open data management plan of the TERIPHIC ICT project (GA No. 825502)</p>
Experimental data analyzed in: Signal detection models as contextual bandits
<p>Signal detection theory (SDT) has been widely applied to identify the optimal discriminative decisions of receivers under uncertainty. However, the approach assumes that decision-makers immediately adopt the appropriate acceptance threshold, even though the optimal response must often be learned. Here we recast the classical normal-normal (and power-law) signal detection model as a contextual multi-armed bandit (CMAB). Thus, rather than starting with complete information, decision-makers must infer how the magnitude of a continuous cue is related to the probability that a signaller is desirable, while simultaneously seeking to exploit the information they acquire. We explain how various CMAB heuristics resolve the trade-off between better estimating the underlying relationship and exploiting it. Next, we determined how naïve human volunteers resolve signal detection problems with a continuous cue. As anticipated, a model of choice (accept/reject) that assumed volunteers immediately adopted the SDT-predicted acceptance threshold did not predict volunteer behaviour well. The Softmax rule for solving CMABs, with choices based on a logistic function of the expected payoffs, best explained the decisions of our volunteers but a simple midpoint algorithm also predicted decisions well under some conditions. CMABs offer principled parametric solutions to solving many classical SDT problems when decision-makers start with incomplete information.</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.