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.
121
datasets available to search
ShareScore release 0.9.0
Dataset results
121 results for “IQ”
Portable/on site devices (i.e. Specim IQ) spectral data on spice/pepper
<p>The dataset from the analysis of spices with portable/on-site devices (i.e. Specim IQ) based on light spectroscopy. Measurements are taken for the authentication of spice (i.e. pepper) using an on-site/portable/handheld device as part of WP3 (Task 3.1): Implementation of innovations in food authenticity. Measurements (reflectance values) are averaged per sample and only the final average spectral data is provided in the Excel sheets. The data is useful for anyone working with spectral data and its use for the authentication of spices.</p>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
GE Discovery TOF MI PET NEMA IQ projector benchmark listmode data
<p>## LIST0000.BLF</p> <p>listmode file from GE Discvoery MI PET/CT containing all acquired emission events (HDF5)<br> of a single bed position NEMA IQ phantom acq.</p> <p>## corrections.h5</p> <p>file containing all quantitative corrections estimate using GE's duetto tool box (HDF5)</p> <p>- correction_lists/sens -> sensivity value for acquired events<br> - correction_lists/atten -> attenuation value for acquired events<br> - correction_lists/contam -> additive contaminations (randoms + scatter) for all acquired events<br> - all_xtals/atten -> attenuation values for all possible crystal combinations<br> - all_xtals/sens -> sensitivity values for all possible crystal combinations<br> - all_xtals/xtal_ids -> all possible crystal combinations</p> <p> </p>
NEMA IQ phantom raw data GE 4-ring DMI
<p><strong>NEMA IQ phantom raw data of the 4-ring acquired on a GE DMI PET/CT at three different count levels</strong></p> <ul> <li>raw and pre-processed sinograms of NEMA IQ phantom acquisition with 3e6, 1e7 and 1e8 prompt counts</li> <li>for each count (noise) level we provide 5 data sets that were unlisted from a very long listmode file with 1e9 prompt counts</li> <li>each unlisted data set (5 for each count level) contains:<br> - subfolder "raw" with the original unlisted emission sinogram + dicom header<br> - subfolder "duetto_workdir" with the pre-processed emission and correction sinograms<br> - subfolder "duetto_workdir/offline3D" example TOF-OSEM reconstruction in dicom format</li> <li>attenuation CT "CTAC" in dicom format</li> <li>high resolution CT "CT_detail" in dicom format</li> <li>the ratio of the activity concentrations of the "hot" spheres and the background was 4.82</li> </ul> <p> </p>
Dataset: IQ Global Equity R&D Leaders ETF (WRND) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: IQ U.S. Large Cap R&D Leaders ETF (LRND) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iQIYI, Inc. (IQ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Mediso SCP PET NEMA IQ phantom
<p>NEMA iQ Phantom™ with Ge68</p> <p>includes hot sphere and cold sphere with internal diameters of 10, 13, 17, 22, 28, and 37 mm</p> <p>acquired with a Mediso AnyScan SCP<br>* 60 mins <br>* 1 bed position</p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-08-13) #33
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Traminer (Gewürztraminer). The image captured at 2021-08-13 (#33) and it was processed using Specim IQ Studio.</p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-07-23) #20
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Traminer (Gewürztraminer). The image captured at 2021-07-23 (#20) and it was processed using Specim IQ Studio.</p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-08-13) #17
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Xinomavro. The image captured at 2021-08-13 (#17) and it was processed using Specim IQ Studio.</p>
BY70-2 UHF telemetry beacon IQ recording 2020-07-12
<p>This is a recording the UHF beacon of the satellite BY70-02 (also known as BY02). The satellite transmits a beacon every 35 seconds at a frequency of 436.200 MHz. The recording was made on 2020-07-12 11:39:50 UTC from Tres Cantos (Madrid), Spain (approximately latitude 40.595865ºN, longitude 3.699069ºW).</p> <p>The recording has been made using an Arrow 7 element yagi and a FUNcube Dongle Pro+ connected to the antenna via a 1m coaxial cable run. The antenna was always held in the horizontal polarization (with respect to the local horizon) and aimed by hand.</p> <p>The recording is in the RAW format used by the Linrad software. This consists of a 41 byte header followed by the stream of IQ samples as little endian 16 bit signed integers. The sample rate is 192ksps and the centre frequency 436.152kHz, so that the signal of interest is at 48kHz within the passband.</p> <p>Doppler correction has been applied using the TLE</p> <p>1 45857U 20042B 20193.85379087 .00000513 00000-0 81606-4 0 9993<br> 2 45857 98.0133 266.8074 0010022 223.4288 136.6116 14.76202005 1284</p>
DATASET: IQ SAMPLES OF LTE, 5G NR, WI-FI, ITS-G5, AND C-V2X PC5
<div> <div> <div> <h1>Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5</h1> <p>The dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset collected was used for technology recognition and traffic charchterization model. More details on the structure and performance of the model can be found <a href="https://www.sciencedirect.com/science/article/abs/pii/S2214209622001103">here</a>.</p> <p> </p> </div> </div> </div> <div> <div> <div> <h1>Description of Dataset Collection</h1> <h2>LTE</h2> <p>For LTE dataset collection, <a href="https://www.srslte.com/" target="_blank" rel="nofollow noopener">srsRAN</a>, an open-source SDR platform, is used. This LTE SDR implementation is used to collect samples for the LTE dataset. The indoor testbed setup used to collect our dataset consists of one eNB host PC and one UE host PC. Each host PC is connected to a USRP X310 board, which is used as the RF front end. We use the latest srsRAN version 21.04, which is installed on each host PC. For the LTE dataset collection, the FDD mode with a 10 MHz bandwidth and a 5.9 GHz center frequency is used for the down-link traffic. For the LTE dataset, the traffic load was varied between 5 and 50 Mbps, and the MCS used was varied by manually configuring from MCS index 1 to 28 .</p> <h2>5G NR</h2> <p>The <a href="https://openairinterface.org/" target="_blank" rel="nofollow noopener">OpenAirInterface</a> SDR solution is used for 5G NR dataset collection. OpenAirInterface is an open source SDR platform that provides a 3GPP compliant implementation of eNB, UE, and EPC. The OpenAirInterface SDR solution also includes a 5G non-stand alone (NSA) mode which supports 5G networks by using existing 4G infrastructure. This SDR-based 5G network setup is used to collect the IQ samples for the 5G NR dataset. For the 5G NR dataset collection, a 1:1 static TDD configuration is used for up-link and down-link traffic in NSA mode. Numerology 1 is used at 10 MHz bandwidth and center frequency of 5.9 GHz. For the 5G NR dataset, the MCS used was varied by manually configuring different values ranging from MCS index 1 to 28, and the traffic load was varied between 5 and 50 Mbps.</p> <h2>Wi-Fi</h2> <p>For the WiFi dataset collection, the <a href="https://github.com/open-sdr/openwifi" target="_blank" rel="nofollow noopener">openwifi</a> SDR solution is used. openwifi is an open-source full-stack IEEE 802.11 a/g/n SDR implementation based on the Xilinx Zynq System on Chip that includes a Field Programmable Gate Array (FPGA) and an ARM processor. For our dataset, we used an IEEE802.11n access point and a client connected to it. WiFi traffic generated cover a wide range of traffic loads, i.e., 10–200 packets per second (pps), with packet sizes ranging from 500 to 1500 bytes. The MCS used was varied by manually configuring the MCS index value between 0 and 7.</p> <h2>ITS-G5 and C-V2X PC5</h2> <p>The <a href="https://www.marquez-barja.com/images/papers/vehicuularcomms21-openaccess-version.pdf" target="_blank" rel="nofollow noopener">CAMINO</a> framework was used for the ITS-G5 and C-V2X PC5 dataset collection. CAMINO is a core framework for managing the V2X communication technologies, including ITS-G5, C-V2X PC5 and C-V2X Uu (5G/4G). The CAMINO framework is used to dynamically generate standardized C-ITS service packets, including Cooperative Awareness (CA), Decentralized Environmental Notification (DEN), and Infrastructure to Vehicle Information (IVI) message packets. The CAMINO software is implemented on the infrastructure deployed as part of the <a href="https://ieeexplore.ieee.org/document/9621229" target="_blank" rel="nofollow noopener">Belgian Smart Highway testbed</a>. The Smart Highway is a testbed deployed by IMEC on the E313 highway, near Antwerp, Flanders. The Smart Highway testbed consists of eight RSUs and 2 OBUs. Each RSU and OBU includes a general purpose CPU running the CAMINO software, and Cohda MK5 and MK6c modules, which are COTS ITS-G5 and C-V2X modules respectively. In our dataset collection, RSU4 is used as a transmitter, and a USRP N310 connected to RSU3 is used to capture and store the samples. To represent a wide range of traffic characteristics, different packet sizes of 300B for CA, 300B and 600B for DEN, and 600B for IVI packets were used at inter-packet intervals of 20, 50, 100, and 200 seconds. The MCS used were varied by manually changing the MCS index value in the configuration files of the Cohda devices. The MCS index of ITS-G5 varied from 0 to 7. For the C-V2X PC5, the MCS is varied between 0 and 20.</p> </div> </div> </div> <div> </div>
A Dataset of IQ samples in Indoor Jamming Scenarios
<p>This dataset includes physical-layer radio information (IQ samples) acquired from indoor communications affected by different types of jamming techniques. Specifically, it includes data acquired from 7 different Software Defined Radios (SDRs), i.e., the USRP Ettus Research X310, operating in an office environment while the transmitter and receiver communicates without the Line of Sight (nLoS). Each experiment is characterized by a transmitter, a receiver, and a jammer. While the hardware of the transmitter and the receiver are kept the same for all the experiments, the hardware of the jammer is changed adopting 5 different radios of the same model and brand. The dataset includes different jamming types, e.g., no jamming (silent), tone (sinusoidal), and Gaussian noise. Moreover, the dataset includes different transmission distances and jamming power levels. In each experiment, a pre-determined sequence of bits ([0, 255]) has been modulated using the BPSK scheme, and then stored, at the receiver, as a 2-columns matrix of raw I/Q samples.</p>
ve2wu 20 Meters IQ data
<p>Flex-Radio SDR-6700 20 Meters IQ Data</p> <p>1478 Covey Hill Road, FRANKLIN, QC CANADA Square FN35AA</p> <p>Antenna 6 over 6 over 6 stacked Yagi Array beaming Europe</p>
VE2WU - 40 Meter IQ Data Eclipse
<p>Flex-Radio SDR-6700 40 Meter IQ data</p> <p>1478 Covey Hill Road,, FRANKLIN, QC Canada Sector FN35AA</p> <p>Antenna 6 over 6 over 6 el Stacked Array on 235 ft Rotating tower Beaming Europe</p>
WE9V Widband IQ data High Bands 15M, 17M, and 20M during solar eclipse
<p>Chad Kurszewski</p> <p>Amateur Radio Callsign: WE9V</p> <p>Station Location: 42.554568 N, 88.050680 W</p> <p>Receiver: Software Radio Laboratory LLC | QS1R, with DX Engineering RPA-1 16dB pre-amplifier</p> <p>Antenna: 1/4 wave 30M Groundplane (vertical, omnidirectional), ground mounted</p> <p>Recorded IQ data for 96kHz bandwidth, 15M (21 MHz), 17M (18 MHz), and 20M (14 MHz), one file every 15 minutes for each band</p>
WE9V Widband IQ data Low Bands 30M, 40M, 80M and 160M during solar eclipse
<p>Chad Kurszewski</p> <p>Amateur Radio Callsign: WE9V</p> <p>Station Location: 42.554568 N, 88.050680 W</p> <p>Receiver: Software Radio Laboratory LLC | QS1R, with DX Engineering RPA-1 16dB pre-amplifier</p> <p>Antenna: 1/4 wave 30M Groundplane (vertical, omnidirectional), ground mounted</p> <p>Recorded IQ data for 96kHz bandwidth, 30M (10 MHz), 40M (7 MHz), 80M (3.5 MHz) and 160M (1.8 MHz), one file every 15 minutes for each band</p>
Trisat-R IQ Recordings 2023_0831_0903
<p>SDR sharp recording IQ wav files.</p> <p>Bandwidth: 250kHz,</p> <p>Location JN76EG,</p> <p>TLE:</p> <p>1 53108U 22080D 22364.59820797 -.00000003 00000-0 00000-0 0 9997<br> 2 53108 70.1723 18.3848 <a>0013109 224.7552</a> <a>135.2050</a> 6.42152596 10829</p>
Control-IQ Observational (CLIO) Post-Approval Study
ClinicalTrials.gov study NCT04503174. IPD Sharing: NO. Countries: 1. Publications: 1.
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.