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.
228
datasets available to search
ShareScore release 0.9.0
Dataset results
228 results for “test set”
Phindr3D: Test Data Set 1 (primary mouse cortical neurons)
<p>3D confocal image stacks of primary cortical neurons under different treatment conditions to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP file.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p> <p> </p>
Phindr3D: Test Data Set 2 (human MCF10A breast cancer organoids)
<p>3D confocal image stacks of human MCF10A breast cancer organoids expressing different oncogenes to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP files.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p>
Test set for geodesics
<p>This is a set of 500000 geodesics for the WGS84 ellipsoid; this is an ellipsoid of revolution with equatorial radius <em>a</em> = 6378137 m and flattening <em>f</em> = 1/298.257223563.</p> <p>Each line of the test set gives 10 space delimited numbers</p> <ul> <li>latitude at point 1, φ<sub>1</sub> (degrees, exact)</li> <li>longitude at point 1, λ<sub>1</sub> (degrees, always 0)</li> <li>azimuth at point 1, α<sub>1</sub> (clockwise from north in degrees, exact)</li> <li>latitude at point 2, φ<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>longitude at point 2, λ<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>azimuth at point 2, α<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>geodesic distance from point 1 to point 2, <em>s</em><sub>12</sub> (meters, exact)</li> <li>arc distance on the auxiliary sphere, σ<sub>12</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>reduced length of the geodesic, <em>m</em><sub>12</sub> (meters, accurate to 0.1 pm)</li> <li>the area between the geodesic and the equator, <em>S</em><sub>12</sub> (m<sup>2</sup>, accurate to 1 mm<sup>2</sup>)</li> </ul> <p>These are computed using high-precision direct geodesic calculations with the given φ<sub>1</sub>, λ<sub>1</sub>, α<sub>1</sub>, and <em>s</em><sub>12</sub>. The distance <em>s</em><sub>12</sub> always corresponds to an arc length σ<sub>12</sub> ≤ 180°, so the given geodesics give the shortest paths from point 1 to point 2. For simplicity and without loss of generality, φ<sub>1</sub> is chosen in [0°, 90°], λ<sub>1</sub> is taken to be zero, α<sub>1</sub> is chosen in [0°, 180°]. Furthermore, φ<sub>1</sub> and α<sub>1</sub> are taken to be multiples of 10<sup>−12</sup> deg and <em>s</em><sub>12</sub> is a multiple of 0.1 μm in [0 m, 20003931.4586254 m]. This results in λ<sub>2</sub> in [0°, 180°] and α<sub>2</sub> in [0°, 180°].</p> <p>The contents of the file are as follows:</p> <ul> <li>100000 entries randomly distributed</li> <li>50000 entries which are nearly antipodal</li> <li>50000 entries with short distances</li> <li>50000 entries with one end near a pole</li> <li>50000 entries with both ends near opposite poles</li> <li>50000 entries which are nearly meridional</li> <li>50000 entries which are nearly equatorial</li> <li>50000 entries running between vertices (α<sub>1</sub> = α<sub>2</sub> = 90°)</li> <li>50000 entries ending close to vertices</li> </ul> <p>The values for <em>s</em><sub>12</sub> for the geodesics running between vertices are truncated to a multiple of 0.1 pm and this is used to determine point 2.</p>
LYON19- Lymphocyte Detection Test Set
<p><strong>LYON19</strong></p> <p>The provided test set includes 441 ROIs saved in the .<em>png</em> files, and it is a test set of LYON grand challenge: <a href="https://lyon19.grand-challenge.org/">https://lyon19.grand-challenge.org</a></p> <p> </p> <p><strong>Data Description</strong></p> <p>The test set contains Region of Interests (ROIs) selected from whole-slide images (WSI) of immunohistochemistry (IHC) stained specimens of breast, colon and prostate. Data came from eight different medical centers in the Netherlands. All slides were stained with an antibody against CD3 or CD8. Slides were subsequently digitized with a Pannoramic 250Flash II scanner (3DHistech, Hungary), resulting in WSIs with a spatial resolution of 0.24μm/px. Selected ROIs were saved with full resolution in the .<em>png</em> files. </p> <p>Selected ROIs were representative for most different types of lymphocyte distributions that occur in slides, namely (1) area with regular lymphocyte distribution, (2) clustered cells, and (3) staining or tissue artifacts.</p> <p> </p> <p><strong>Citation:</strong></p> <p>Please reference the following paper if you use LYON19 data for a scientific publication:</p> <p>Swiderska-Chadaj, Zaneta, et al. "<em><strong>Learning to detect lymphocytes in immunohistochemistry with deep learning</strong></em>." Medical Image Analysis (2019): 101547.</p> <p>Link to the paper: <a href="https://www.sciencedirect.com/science/article/pii/S1361841519300829">https://www.sciencedirect.com/science/article/pii/S1361841519300829</a></p>
Data set of a thermal response test on a planar trench collector
<p>This data set provides three different temperature measurements (PT100, fiber optic measurement, thermistors), as well as the determination of the volumetric water content and the bulk electrical conductivity of the subsurface. During the published period, a thermal response test was carried out. On April 28th, at 15:25, the fluid circulation began, and heat injection started at 15:38 on April 28th and continued until May 3rd. The test was conducted with a constant volume flow of 1.00 m³/h and a constant heat injection rate of 0.88 kW.</p>
A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena subject to imposed damage
<p>A data set from an extensive experimental benchmark study of the Hell Bridge Test Arena (HBTA), a full-scale steel bridge subject to imposed damage, has been established. The data set includes organized dynamic response and load measurement data of the bridge under different structural state conditions, where the structural state conditions range from an undamaged (reference) state to known damage states. Furthermore, the data set includes acceleration and strain data from the response monitoring and acceleration data from the load monitoring, where a modal vibration shaker is used as an excitation source. The data is collected in one h5-file (hierarchical data format version 5) with a sampling rate of 100 Hz. Signal processing and resampling of the data has been performed according to the description provided in the references below. The data set is now published in this open-access data repository and can be accessed and downloaded freely. As such, the data set provides an important benchmark to the scientific community within bridge damage detection and SHM.</p>
Google Speech Commands-Musan test set
<p>This noisy speech test set is created from the Google Speech Commands v2 [1] and the Musan dataset[2]. It is introduced in our ICASSP 2022 paper [3]. </p> <p>Specifically, we created this test set by mixing the speech in the Google Speech Commands v2 test set with random noise in the Musan dataset at different signal to noise ratio -12.5,-10,0,10,20,30 and 40 decibel (dB). </p> <p>The Google Speech Commands v2 dataset is under the Creative Commons BY 4.0 license. It could be downloaded at: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz</p> <p>The Musan dataset is under Attribution 4.0 International (CC BY 4.0). It could be downlowned at https://www.openslr.org/17/</p> <p>Citations:</p> <p>[1] Pete Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,” arXiv preprint arXiv:1804.03209, 2018.</p> <p>[2] David Snyder, Guoguo Chen, and Daniel Povey, “Musan: A music, speech, and noise corpus,” arXiv preprint arXiv:1510.08484, 2015.</p> <p>[3] V. A. Trinh, H. Salami Kavaki and M. I. Mandel, "Importantaug: A Data Augmentation Agent for Speech," <em>ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em>, 2022, pp. 8592-8596, doi: 10.1109/ICASSP43922.2022.9747003.</p>
hiPSC 3D immunofluorescence images, test data set 2x2, 10Z
<p>Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a small subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal</p> <p>3 Channels were imaged:</p> <p>- C01: DAPI, nuclear stain</p> <p>- C02: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog</p> <p>- C03: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1</p> <p> </p> <p>This dataset contains 10 Z levels for 4 field of views for those 3 channels, as well as (manually adjusted) metadata files from the Yokogawa CV7000.</p> <p> </p> <p>The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless). </p>
Data set of pup retrieval test in control and V1b vasopressin receptor knockout mice
<div> <div> <div> <p>This repository contains the images and code used in the paper "Computer vision analysis of mother-infant interaction identified efficient pup retrieval in V1b receptor knockout mice".</p> <p>For effective nursing, close contact between lactating mothers and their infants is necessary. However, evaluation of maternal motivation to retrieve pups is challenging, because multiple infants were randomly accessed multiple times in changing background. We applied computer vision and deep learning analysis in this process to precisely calculate maternal behavior. Object detection in an open filed test identified less entry into the center area and less moving distance in virgin female mice lacking V1b vasopressin receptor (V1bKO) than wild-type (WT) mice. Although this character was replicated in a V1bKO mother, a pup retrieval test showed that total distances among a V1bKO mother and infants come closer in a shorter time than with a WT mother. In the medial preoptic area, V1b receptor message was partly detected in galanin- and c-fos-positive neurons after the mother was stimulated by infants. Our deep learning analysis effectively evaluated mother-infant relationship in V1bKO mice.</p> </div> </div> </div>
Test set of geodesics on a triaxial ellipsoid
<p>This is a set of 500000 shortest geodesics on a triaxial ellipsoid. The ellipsoid is defined by</p> <p>$$\frac{X^2}{a^2} + \frac{Y^2}{b^2} + \frac{Z^2}{c^2} - 1 = 0,$$</p> <p>with \(a = \sqrt2\), \(b = 1\), \(c = 1/\sqrt2\) (measured in arbitrary units). (This ellipsoid was studied by A. Cayley, <em>On the geodesic lines on an ellipsoid</em>, Mem. Roy. Astron. Soc. <strong>39</strong>, 31-53, 1872.) Each line of the test set consists of 10 space-delimited numbers</p> <ul> <li>the latitude at point 1, \(\beta_1\) (\(^\circ\), exact)</li> <li>the longitude at point 1, \(\omega_1\) (\(^\circ\), exact)</li> <li>the azimuth at point 1, \(\alpha_1\) (\(^\circ\), accurate to \(10^{-18}{}^\circ\))</li> <li>the latitude at point 2, \(\beta_2\) (\(^\circ\), exact)</li> <li>the longitude at point 2, \(\omega_2\) (\(^\circ\), exact)</li> <li>the azimuth at point 2, \(\alpha_2\) (\(^\circ\), accurate to \(10^{-18}{}^\circ\))</li> <li>the geodesic distance from 1 to 2, \(s_{12}\) (units, accurate to \(10^{-20}\))</li> <li>the reduced length of the geodesic, \(m_{12}\) (units, accurate to \(10^{-20}\))</li> <li>the geodesic scale, \(M_{12}\) (accurate to \(10^{-20}\))</li> <li>the geodesic scale, \(M_{21}\) (accurate to \(10^{-20}\))</li> </ul> <p>Here \(\beta\), \(\omega\), and \(\alpha\), are the <em>ellipsoidal</em> latitude, longitude, and azimuth. For a given \((\beta, \omega)\), the Cartesian coordinates of a point are</p> <p>$$\begin{align}<br> X &= a \cos\omega<br> \frac{\sqrt{a^2 - b^2\sin^2\beta - c^2\cos^2\beta}}<br> {\sqrt{a^2 - c^2}}, \\<br> Y &= b \cos\beta \sin\omega, \\<br> Z &= c \sin\beta<br> \frac{\sqrt{a^2\sin^2\omega + b^2\cos^2\omega - c^2}}<br> {\sqrt{a^2 - c^2}}.<br>\end{align}$$</p> <p>Lines of constant \(\beta\) and \(\omega\) are orthogonal. The azimuth \(\alpha\) of a geodesic is the direction measured clockwise from North (defined as \(\beta\) increasing at constant \(\omega\)). The coordinates are singular at the four <em>umbilical</em> points \(\cos\beta = \sin\omega = 0\). The azimuth of a geodesic jumps by \(\pm\frac12\pi\) on passage through such points and the value for such points is the azimuth on <em>leaving</em> the umbilical point.</p> <p>The geodesics are computed using high-precision inverse calculations with the exact <em>integer</em> values for \((\beta_1, \omega_1)\) and \((\beta_2, \omega_1)\). Any of the other entries reported as an integer is also exact.</p> <p>For most pairs of points, there is a unique shortest geodesic. However</p> <ul> <li>for opposite umbilical points, \(\alpha_1\) and \(\alpha_2\) can take on arbitrary values provided that the ratio \(\tan\alpha_1/\tan\alpha_2\) is maintained;</li> <li>if \(\beta_1 + \beta_2 = 0\) and if \(\cos\alpha_1\) and \(\cos\alpha_2\) have opposite signs, then there is another shortest geodesic with azimuths \(\pi - \alpha_1\) and \(\pi - \alpha_2\).</li> </ul> <p>For a particular \((\beta_1, \omega_1)\) and \((\beta_2, \omega_2)\), additional geodesics of the same length can be trivially generated by swapping the points or by reflecting them in any of the coordinate planes. A non-trivial symmetry is given by swapping <em>just</em> the longitude coordinates; this also results in a geodesic of the same length. The data set has had any such redundant geodesics removed.</p> <p>The data set is sorted according to whether either point</p> <ul> <li>is an umbilical point</li> <li>lies on the middle principal ellipse, with \(\sin\omega = 0\)</li> <li>lies on the middle principal ellipse, with \(\cos\beta = 0\)</li> <li>lies on the major principal ellipse, \(Z = 0\)</li> <li>lies on the minor principal ellipse, \(X = 0\)</li> <li>is near an umbilical point</li> <li>is general (all other points)</li> </ul> <p>Approximately 85% of the entries are with two general points. If only a small set of random test cases is needed, select a random subset with, e.g.,</p> <p> <code>shuf Geod3Test.txt | head -1000 > Geod3Test-samp.txt</code></p> <p> </p>
Test data sets: File A
I am getting an "Entity too large" error when trying to use this file as File A, with File A taxonID: EOL-000002128790. The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: File B
I am getting an "Entity too large" error when trying to use this file as File B, with File B taxonID: BJ5N3. The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: Test data set for "Request Entity Too Large" exception
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: COLTest
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: Amoebozoa Test
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
OME-Zarr hiPSC 3D immunofluorescence images, tiny test set
<p><em>This dataset is intended to be used for automated testing of OME-Zarr processing.</em></p> <p> </p> <p>Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a tiny subset of a larger experiment intended as a test dataset for the <a href="https://fractal-analytics-platform.github.io/">Fractal platform</a> and others experimenting with OME-Zarrs.</p> <p>1 Channel is included:</p> <ul> <li>C01: DAPI, nuclear stain</li> </ul> <p>It is generated from this raw data: <a href="../records/8287221">https://zenodo.org/records/8287221</a></p> <p>This dataset contains 2 Z levels for 2 field of views for this 1 channel, as well as (manually adjusted) metadata files from the Yokogawa CV7000. The data was acquired in the Pelkmans lab in August 2020.</p> <p>The images have been processed using Fractal, the workflow is attached as a json file. It ran with fractal-server==2.3.6, fractal-client==2.0.1, fractal-web==1.4.0 and fractal-tasks-core==1.2.1.</p> <p>Two versions of the OME-Zarr are added here: A 3D version with both Z planes. And a 2D version (MIP of the Z-planes) which also contains label images from cellpose segmentation, measurements and output ROI tables.</p>
DRALOD D1.3 Results of performance testing of the prototype of energy recovery system data set
<p>Data set for delivery D1.3 Results of performance testing of the prototype of energy recovery system </p>
Diversity-Driven Unit Test Generation (Data Set)
<p>The goal of automated unit test generation tools is to create a set of test cases for the software under test that achieve the highest possible coverage for the selected test quality criteria. The most effective approaches for achieving this goal at the present time use meta-heuristic optimization algorithms to search for new test cases using fitness functions defined on existing sets of test<br> cases and the system under test. Regardless of how their search algorithms are controlled, however, all existing approaches focus on the analysis of exactly one implementation, the software under test, to drive their search processes, which is a limitation on the information they have available. In this paper we investigate whether the practical effectiveness of white box unit test generation tools can be increased by giving them access to multiple, diverse implementations of the functionality under test harvested from widely available Open Source software repositories. After presenting a basic implementation of such an approach, DivGen (Diversity-driven Generation), on top of the leading test generation tool for Java (EvoSuite), we assess the performance of DivGen compared to EvoSuite when applied in its traditional, mono-implementation oriented mode (MonoGen). The results show that while DivGen outperforms MonoGen in 33% of the sampled classes for mutation coverage (+16% higher on average), MonoGen outperforms<br> DivGen in 12.4% of the classes for branch coverage (+10% higher average).</p>
ViF-GTAD: A new Automotive Data Set with Ground Truth for ADAS/AD Development, Testing and Validation
<p>A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception data sets. While the most state-of-the-art perception data sets primarily focus on provision of various on-board sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated data-set describes the first publicly available perception measurement data that include not only the on-board sensor information from camera, Lidar and radar with semantically classified objects, but also the high precision ground-truth position measurements enabled by the accurate RTK assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the meta data structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing and validation.</p>
DA-TCA test data sets (KIAPS-GIST)
<p><strong>About the data sets:</strong></p> <p>The test datasets from SMOS/ASCAT will be utilized to validate the efficacy of SMAP data assimilation in numerical weather forecasting. This project is being conducted in collaboration with the Korea Institute of Atmospheric Prediction Systems (KIAPS) and the Gwangju Institute of Science and Technology (GIST) in South Korea.</p> <p><strong>About the project:</strong></p> <p>Soil moisture assimilation from satellites can significantly improve weather prediction accuracy by providing valuable information about the moisture content in the soil. When integrated into numerical weather forecasting models, satellite-derived soil moisture data enhances the representation of land surface processes and interactions between the atmosphere and the Earth's surface.</p> <p>Here's how soil moisture assimilation benefits weather prediction:</p> <ol> <li> <p>Initialization of Models: Accurate initial conditions are crucial for reliable weather forecasting. Satellite-derived soil moisture data serves as an important source of information for initializing models. By incorporating this data, forecast models can start with more realistic representations of the state of the land surface, enabling a better starting point for predictions.</p> </li> <li> <p>Land-Atmosphere Interaction: Soil moisture plays a significant role in land-atmosphere interactions. It influences the partitioning of energy, the transfer of moisture, and the formation of clouds and precipitation. Assimilating satellite-derived soil moisture data allows forecast models to better capture these interactions and improve the representation of feedback mechanisms between the land surface and the atmosphere.</p> </li> <li> <p>Precipitation Forecasting: Soil moisture assimilation also enhances precipitation forecasting. The availability of accurate soil moisture data helps in understanding soil moisture-atmosphere feedback processes, which impact the formation, intensity, and movement of precipitation systems. By incorporating this information into forecasting models, more accurate precipitation predictions can be made.</p> </li> <li> <p>Drought and Flood Monitoring: Satellite-derived soil moisture data aids in monitoring and predicting droughts and floods. Real-time updates of soil moisture conditions can be integrated into forecasting models, allowing for timely and accurate assessments of soil moisture deficits or surpluses. This information is crucial for managing water resources, agriculture, and mitigating the impacts of extreme weather events.</p> </li> </ol> <p><strong>Related code in Github</strong>:</p> <p>https://github.com/Hyunglok-Kim/HydroAI/blob/main/Ex_in_TCA.ipynb</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.