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.
470
datasets available to search
ShareScore release 0.9.0
Dataset results
470 results for “Evaluation dataset”
Evaluating Risk Progression in Mental Health Chatbots with Escalating Prompts Dataset
<p>Excel dataset for "Evaluating Risk Progression in Mental Health Chatbots with Escalating Prompts" manuscript. </p>
Diagnostic accuracy of nutritional screening tools in patients with digestive system tumors: A meta-analysis and bayesian evaluation dataset
Open the record for dataset details and reuse information.
Child 3D anthropometry evaluation datasets
Open the record for dataset details and reuse information.
Fresh snow, peak accumulation, and ablation-Sun Cup snow surface datasets for evaluation of geometry aerodynamic roughness code
Open the record for dataset details and reuse information.
Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents
Open the record for dataset details and reuse information.
Dataset related to publication "In silico evaluation of the thermal stress induced by MRI switched gradient fields in patients with metallic hip implant"
<p>The datasets reported in the figures of the article "In silico evaluation of the thermal stress induced by MRI switched gradient fields in patients with metallic hip implant", published on Phys. Med. Biol. 64 245006, 2019.</p> <p>This work focuses on the in silico evaluation of the energy deposed by MRI switched gradient fields<br> in bulk metallic implants and the consequent temperature increase in the surrounding tissues. The results show that the gradient coils can generate local increases of temperature up to some kelvin when acting without radiofrequency excitation. Hence, their contribution in general should not be disregarded when evaluating patients’ safety.</p>
NYC Open Data (Finance) - D4 Evaluation Datasets
<p><a href="https://opendata.cityofnewyork.us/">NYC Open</a> Finance data used for the evaluation of our data-driven domain discovery algorithm (D4). This repository contains 176 tables that were downloaded on 2016-11-22 from the <a href="http://api.us.socrata.com/api/catalog/v1">Socrata Open Data API</a>.</p> <p> </p>
NYC Open Data (Education) - D4 Evaluation Datasets
<p><a href="https://opendata.cityofnewyork.us/">NYC Open</a> Education data used for the evaluation of our data-driven domain discovery algorithm (D4). This repository contains 201 tables that were downloaded on 2016-11-22 from the <a href="http://api.us.socrata.com/api/catalog/v1">Socrata Open Data API</a>.</p> <p> </p>
MiRoR10 - P1 - Dataset A systematic review finds that spin or interpretation bias is abundant in evaluations of ovarian cancer biomarkers
<p>This dataset is related to "A systematic review finds that spin or interpretation bias is abundant in evaluations of ovarian cancer biomarkers" (Mona Ghannad, Maria Olsen, Isabelle Boutron, Patrick M. Bossuyt)</p>
Dataset: Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 – 2019)
<p>A data set of evaluations of mixed and augmented reality in 2009–2019.</p>
Synthetic Datasets for Evaluation of Spa-Typing Methods
<p>This dataset contains 3 sets of wgsim simulated reads (paired-end, 100 bp length) of MRSA.</p> <p>Synthetic dataset A contains reads sampled from a reference genome where the X region was artificially replaced with the t032 spa-type sequence. The reads contain no errors.</p> <p>Synthetic dataset B contains reads sampled from a reference genome where the X region was artificially replaced with the t032 spa-type sequence. The reads contain point mutation errors.</p> <p>Synthetic dataset C contains reads sampled from a reference genome where the X region was artificially replaced with a specific spa-type sequence. The reads contain point mutation errors.</p>
TAU Urban Acoustic Scenes 2020 Mobile, Evaluation dataset
<p>TUT Urban Acoustic Scenes 2020 Mobile development dataset consists of 10-seconds audio segments from 10 acoustic scenes:</p> <ul> <li>Airport - <em>airport</em></li> <li>Indoor shopping mall - <em>shopping_mall</em></li> <li>Metro station - <em>metro_station</em></li> <li>Pedestrian street - <em>street_pedestrian</em></li> <li>Public square - <em>public_square</em></li> <li>Street with medium level of traffic - <em>street_traffic</em></li> <li>Travelling by a tram - <em>tram</em></li> <li>Travelling by a bus - <em>bus</em></li> <li>Travelling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul>
Multimodal Biomedical Dataset for Evaluating Registration Methods (patches from TMA Cores)
<p>The dataset consists of 206 aligned Bright-Field (BF) and Second-Harmonic Generation (SHG) images. All images are of the same size of 834x834 pixels. The training set contains 40 image pairs. A validation set 1 of 25 image pairs for tuning the hyperparameters for the training. A validation set 2 of 7 image pairs for tuning the registration method. The test set for evaluation: 134 image pairs (times two).</p> <p>For each image pair in the test set, a random rotation up to +/-30 degrees and random translations in x and y for up to 100px were applied. For each image pair that was transformed for the test set, we apply the transformation to both modalities and keep their corresponding reference images as well as transformed images, in order to allow for registration using each modality as a reference and the other as a floating image. The metadata.csv file includes the coordinates of the four corners of the reference image as well as the coordinates of the corners of the transformed image after displacement. The origin of this coordinate system is the upper left corner of the reference image, given by (0,0).</p> <p>Example of naming convention in the test set:</p> <ul> <li>R_1B_F8_BF.tif: reference image in Bright-Field</li> <li>R_1B_F8_SHG.tif: reference image in Second-Harmonic Generation</li> <li>T_1B_F8_BF.tif: transformed image in Bright-Field</li> <li>T_1B_F8_SHG.tif: transformed image in Second-Harmonic Generation.</li> </ul> <p>Where the transformations applied to T_1B_F8_BF.tif and T_1B_F8_SHG.tif are identical and hence the coordinates of the corners in metadata.csv are valid for both image pairs.</p> <p>The metadata.csv file contains the following columns:</p> <ul> <li>Filename: identifier for image pair</li> <li>X1_Ref: x-coordinate of upper left corner of reference image</li> <li>Y1_Ref: y-coordinate of upper left corner of reference image</li> <li>X2_Ref: x-coordinate of lower left corner of reference image</li> <li>Y2_Ref: y-coordinate of lower left corner of reference image</li> <li>X3_Ref: x-coordinate of upper right corner of reference image</li> <li>Y3_Ref: y-coordinate of upper right corner of reference image</li> <li>X4_Ref: x-coordinate of lower right corner of reference image</li> <li>Y4_Ref: y-coordinate of lower right corner of reference image</li> <li>X1_Trans: x-coordinate of upper left corner of transformed image</li> <li>Y1_Trans: y-coordinate of upper left corner of transformed image</li> <li>X2_Trans: x-coordinate of lower left corner of transformed image</li> <li>Y2_Trans: y-coordinate of lower left corner of transformed image</li> <li>X3_Trans: x-coordinate of upper right corner of transformed image</li> <li>Y3_Trans: y-coordinate of upper right corner of transformed image</li> <li>X4_Trans: x-coordinate of lower right corner of transformed image</li> <li>Y4_Trans: y-coordinate of lower right corner of transformed image</li> <li>Displacement: Mean Euclidean distance between reference corner points and transformed corner points</li> </ul> <p>The data set was originally produced by the authors of <em>Aligned Collagen Is a Prognostic Signature for Survival in Human Breast Carcinoma</em> (<a href="https://www.sciencedirect.com/science/article/pii/S0002944010002336">https://www.sciencedirect.com/science/article/pii/S0002944010002336</a>). The registered and non-registered sub-image pairs in this data set were created by Johan Öfverstedt and Elisabeth Wetzer.</p>
CGRE Framework Dataset - A Dataset automatically generated to evaluate OCR Software on Webdocuments
<p><strong>Description</strong><br> The provided dataset was generated by the <a href="https://github.com/Drizzy3D/CGRE">CGRE Framework.</a><br> It was generated as a part of a bachelor thesis and used to evaluate the Tesseract OCR Software on webdocuments.</p> <p><strong>CGRE_dataset.zip:</strong><br> <em>1. crawl.json</em><br> This file contains crawling results from the alexa.com Top 50 most used webpages in the US from the 7th June 2020.<br> The crawling was done specifically for styling information only.</p> <p><em>2. html</em><br> The generated webdocuments can be found in this directory.<br> They are based on the crawled styling information.<br> The levels of the directory are used to store the different styling attributes.<br> Every directory is named by the used value for a specific styling attribute.<br> Every word is placed in a span html element.</p> <p><em>3. dataset</em><br> The rendered webdocuments can be found in this directory as png files.<br> They were rendered using the Chromium Embedded Framework (CEF) and contain corresponding labels.<br> The labels are in the same directory with the same name as the corresponding rendered webdocument, just as txt files.<br> The labels contain "word\t(left,top,width,height)\n" lines.<br> "(left,top,width,height)" is the bounding box of a span element containing a word.<br> "word" is the word in the bounding box.</p> <p><em>4. dataset_tesseract_complete</em><br> This directory contains the Tesseract results on the dataset as txt files.<br> The structure is analogue to the dataset.<br> The txt files contain analogue to the dataset "word\t(left,top,width,height)\n" lines.</p> <p><em>5. evaluation</em><br> The results of the evaluation of Tesseract on the dataset.<br> To evaluate the localisation of words by Tesseract, the Intersection Over Union metric was used, with different threshold values (0.5, 0.6, 0.7, 0.8, 0.9).<br> To evaluate the determination of words by Tesseract, a normalized Levenshtein distance metric was used, with different threshold values (0.5, 0.6, 0.7, 0.8, 0.9).<br> The times were measured by using this system:<br> Ubuntu 20.04, AMD Ryzen 5 1600 CPU, AMD Radeon RX Vega 56 GPU, 16 GB DDR4 RAM with 2400 MHz<br> The different threshold values are stored in the filenames.<br> You can find the results in the csv files.<br> Every line contains the results for a specific webdocument.<br> The txt files contain calculated precision and recall values.</p>
The evaluation dataset of the AI composition recognition competition, CSMT2020
<p>The evaluation dataset contains 4000 MIDI files with exact configurations of development dataset with two exceptions: 1) A number of melodies composed by human composers are added, some of which are published, and some of which are composed for this competition. The music style of the human composed melodies are the same as the styles of music in the training set. This was confirmed by musicologists. 2) There are a number of melodies generated by algorithms with minor algorithmic or parameter changes compared to the algorithms in the development dataset.</p> <p>The development dataset:</p> <p><a href="https://zenodo.org/record/3944685#.Xza9DegzY2x">https://zenodo.org/record/3944685#.Xza9DegzY2x</a></p> <p>The website of the challenge:</p> <p><a href="http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020/">http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020</a> (Chinese instruction)</p> <p><a href="https://ai-composition-recognition2020.github.io/english.html">https://ai-composition-recognition2020.github.io/english.html</a> (English instruction)</p>
Evaluation dataset for urban climate simulation
<p>The dataset was used in the following papers:</p> <p>(1) Li, Z., Zhou, Y., Wan, B., Chung, H., Huang, B., & Liu, B. (2019). Model evaluation of high-resolution urban climate simulations: using the WRF/Noah LSM/SLUCM model (Version 3.7. 1) as a case study. <em>Geoscientific Model Development</em>, <em>12</em>(11), 4571-4584.</p> <p>(2) Li, Z., Wan, B., Zhou, Y., & Wong, H. (2020). Incoming data quality control in high-resolution urban climate simulation: Hong Kong-Shenzhen area urban climate simulation as a case study using WRF/Noah LSM/SLUCM model (Version 3.7. 1). <em>Geoscientific Model Development Discussions</em>, 1-13.</p>
A Dataset for Evaluating Blood Detection in Hyperspectral Images
<p>The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.</p>
Experiment Dataset for L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094
<p>This dataset provides the results of the validation experiments for transient, steady-state and response time distribution<br> analysis published in L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094.</p>
SIDIRE: Synthetic Image Dataset for Illumination Robustness Evaluation
<p>SIDIRE is a freely available image dataset which provides synthetically generated images allowing to investigate the influence of illumination changes on object appearance. The images are renderings of 3D coin models with different material BRDFs and levels of texturedness. Thus, the dataset makes it possible to directly evaluate the influence of these conditions on the performance of image recognition without introducing a bias due to different objects used between image sets. The dataset has been used for evaluation in [1].</p> <p><strong>Usage</strong></p> <p>The dataset is freely available for non-commercial research use. Please cite our paper [1] when using the dataset for your research.</p> <p><strong>Technical Details</strong></p> <p>Full Image Dataset</p> <p>The full image dataset consists of images of 14 coin models which have been rendered using the open-source graphics software <a href="http://www.blender.org">Blender</a>. For each model, twelve sets of 500×500 images with 65 illumination directions were rendered where each set represents one out of four material BRDFs and one out of three texture density levels. Material BRDFs are intended to represent different levels of specularity starting from a Lambertian material with zero specularity up to specular intensity values of 0.25, 0.50 and 1.00. The first texture density level shows no texture and thus represents the set of textureless objects. For the remaining two levels synthetically generated textures were used. The camera image plane is placed parallel to the coin and light source positions are defined by their azimuth angle φ and elevation angle λ. We used eight levels of λ with eight levels of φ each to produce 64 images. The 65th image is rendered with the light placed at the camera position (i.e. λ=90°).<br> In the provided RAR-file, all the 65 images of a specific model, specularity level and texturedness level are contained in separate directories. For instance, the directory ‘texture_level0\Ref_level2\2874-back’ contains the images of the model ‘2874-back’ rendered without texture and a specularity of 0.50.</p> <p><strong>Patch Dataset</strong></p> <p>The patch dataset contains 50000 matching patch pairs for every of the 12 subsets of SIDIRE. It can be used to generate groups of feature distances by means of true and false patch pairs, in the same manner as, e.g., Matthew Brown’s <a href="http://phototour.cs.washington.edu/patches/default.htm">patch dataset</a>. Please see [1,2] for a detailed description of the evaluation scheme of patch pair databases.<br> The patches have a size of 64×64 and are arranged in images of size 3200×3200. Thus, every image contains 2500 patches where corresponding patches are placed side by side. The patches of the 12 subsets are contained in directories indicating their texture density and reflectance level, e.g. patches rendered without texture and a specularity of 0.50 are contained in the directory ‘tex0_ref2’.<br> </p> <p><strong>References</strong></p> <p>[1] Zambanini S., Kampel M. “Evaluation of Low-Level Image Representations for Illumination-Insensitive Recognition of Textureless Objects”, <em>International Conference on Image Analysis and Processing – ICIAP’13</em>, Naples, Italy, September 2013. (<a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13.pdf">pdf</a>, <a href="https://cvl.tuwien.ac.at/wp-content/uploads/2014/12/iciap13_supp1.pdf">supplementary material</a>)<br> [2] Brown, M., Gang Hua, Winder, S., “Discriminative Learning of Local Image Descriptors”, <em>Pattern Analysis and Machine Intelligence, </em> vol.33, no.1, pp.43-57, 2011.</p>
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
<p>Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.</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.