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228 results for “test set”
hiPSC 3D immunofluorescence images, tiny test set
<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 Fractal: https://github.com/fractal-analytics-platform/fractal</p> <p>It is a subset of this dataset: https://zenodo.org/record/7057076</p> <p>1 Channel is included:</p> <p>- C01: DAPI, nuclear stain</p> <p> </p> <p>This dataset also contains a small Fractal workflow for standard processing</p> <p> </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.</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>
The BrukerAPI test data set (Bruker ParaVision v6.0.1)
<p>This data set contains data from 45 experiments performed using the Bruker ParaVision v6.0.1 software. The purpose of this data set is to test the unified application programming interface to the Bruker data format - the <strong>BrukerAPI</strong>. See the software <a href="https://github.com/isi-nmr/brukerapi-python">repository</a> for more information.</p> <p>The data was measured using the following pulse sequences:</p> <ul> <li>FLASH.ppg</li> <li>FLASHAngio.ppg</li> <li>IgFLASH.ppg</li> <li>MGE.ppg</li> <li>MSME.ppg</li> <li>RARE.ppg</li> <li>FAIR_RARE.ppg</li> <li>RAREVTR.ppg</li> <li>RAREst.ppg</li> <li>MDEFT.ppg</li> <li>FISP.ppg</li> <li>FLOWMAP.ppg</li> <li>DtiStandard.ppg</li> <li>EPI.ppg</li> <li>FAIR_EPI.ppg</li> <li>CASL_EPI.ppg</li> <li>DtiEpi.ppg</li> <li>T1_EPI.ppg</li> <li>T2_EPI.ppg</li> <li>T2S_EPI.ppg</li> <li>SPIRAL.ppg</li> <li>DtiSpiral.ppg</li> <li>UTE.ppg</li> <li>UTE3D.ppg</li> <li>ZTE.ppg</li> <li>CSI.ppg</li> <li>FieldMap.ppg</li> <li>SINGLEPULSE.ppg</li> <li>NSPECT.ppg</li> <li>EPSI.ppg</li> <li>PRESS.ppg</li> <li>STEAM.ppg</li> <li>ISIS.ppg</li> <li>CPMG.ppg</li> <li>RfProfile.ppg</li> </ul> <p>The data was measured by the NMR group at the Institute of Scientific Instruments of the CAS, v. v. i.</p> <p>Change Log:</p> <ul> <li>Version 1.0.0 <ul> <li>Initial release</li> </ul> </li> <li>Version 1.1.0 <ul> <li>Compressed NumPy arrays were added to each data set to enable data reading tests</li> </ul> </li> </ul> <p> </p>
Dynamic testing of a four-storey building with reinforced concrete and unreinforced masonry wall: Data set
<p>This paper presents a publically available data set recorded during the shake-table test of a structure with reinforced concrete (RC) and unreinforced masonry (URM) walls. The shake-table test, performed at the TREES laboratory of EUCENTRE (Pavia, Italy), was part of a larger research initiative at EPFL (Lausanne, Switzerland) that addresses the seismic behaviour of mixed RC-URM structures. The half-scale test unit was subjected to several shakings of different intensity levels. The paper presents the geometry of the test unit, the properties of the construction materials, the instrumentation, and outlines the organization of the recorded data. Two sets of data are available: the unprocessed data and a second set of processed data where conventional and optical measurements are synchronised. This second set contains also some derived data, which allows to quickly plot key quantities such as base shear and top displacement. The aim of the paper is to provide all information required by the reader for analysing the test data and using it for validation purposes of numerical and mechanical models. The performance of the test unit is described in a companion paper.</p>
Real testing sets for Visual Affordance Segmentation of hand-occluded objects
<p>[<a href="https://arxiv.org/abs/2308.11233">arXiv</a>] [<a href="https://apicis.github.io/projects/acanet.html">webpage</a>] [<a href="https://github.com/SEAlab-unige/acanet">code</a>] [<a href="https://doi.org/10.5281/zenodo.8364197">trained model</a>][<a href="https://doi.org/10.5281/zenodo.5085800">mixed-reality data</a>]</p> <p>RGB images with the corresponding affordance annotation to test affordance segmentation models. Images are selected from two datasets for hand-object pose estimation: <a href="https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/hand-object-3d-pose-annotation/">HO-3D</a> and <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CCM</a>.</p> <p>For HO3D we selected 150 frames from the dataset and enriched the annotation of the hand and object segmentation masks with new annotations specific for the affordance segmentation problem.</p> <p>For CCM we selected 150 frames from the dataset and created the annotation specific for the affordance segmentation problem. The forearms and hands in contact with the offered container are annotated. </p> <p>File names are formatted as: <em><videoname>_<framenumber>.png</em></p> <p>Segmentation classes values:</p> <ul> <li> 0: background</li> <li> 1: graspable</li> <li> 2: contain</li> <li> 3: arm</li> </ul> <p> </p> <p><strong>References. </strong></p> <p><strong>Affordance segmentation of hand-occluded containers from exocentric images</strong><br>T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br>IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023</p> <pre><code>@inproceedings{apicella2023affordance, title={Affordance segmentation of hand-occluded containers from exocentric images}, author={Apicella, Tommaso and Xompero, Alessio and Ragusa, Edoardo and Berta, Riccardo and Cavallaro, Andrea and Gastaldo, Paolo}, booktitle={IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year={2023}, } </code></pre> <p><strong>HOnnotate: A method for 3D Annotation of Hand and Objects Poses<br></strong>S. Hampali, M. Rad, M. Oberweger, V. Lepetit<strong><br></strong>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020</p> <pre><code>@inproceedings{hampali2020honnotate, title={Honnotate: A method for 3d annotation of hand and object poses}, author={Hampali, Shreyas and Rad, Mahdi and Oberweger, Markus and Lepetit, Vincent}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3196--3206}, year={2020} }</code></pre> <p><strong>CORSMAL Containers Manipulation (1.0) [Data set]</strong><br>A. Xompero, R. Sanchez-Matilla, R. Mazzon, and A. Cavallaro<br>Queen Mary University of London. <a href="https://doi.org/10.17636/101CORSMAL1"><u>https://doi.org/10.17636/101CORSMAL1</u></a></p> <p> </p> <p><strong>License. </strong>Creative Commons<strong> </strong>Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)</p> <p><strong>Enquiries, Question and Comments. </strong>For enquiries, questions, or comments, please contact <a href="mailto:tommaso.apicella@edu.unige.it">Tommaso Apicella</a>.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Similarity data set used to test Synchronous Growth Changes (SGC) on dendrochronological data using tree-ring series from the ITRDB
<p>Dataset used to test the SGC, SSGC and AGC in:</p> <div> <div>Visser, RM. 2021 On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree-ring data sets. <em>Archaeometry</em> 63(1): 204–215. DOI: <a href="https://doi.org/10.1111/arcm.12600">https://doi.org/10.1111/arcm.12600</a>.</div> </div> <p>The dataset contains the database used in this study</p> <ul> <li><em>itrdb_structure.sql</em> described the structure of the database (PostgreSQL/PostGIS)</li> <li>Tables <ul> <li><em>GC_??_tbl</em> are tables with ?? denoting the continent (see below) containg the comparisons between tree-ring series and the growth changes <ul> <li>The following columns are present: <ul> <li>ID1 and ID2: These are the ID's of the series compared.</li> <li>SGC: Synchronous Growth Changes</li> <li>SSGC: Semi Synchronous Growth Changes</li> <li>Overlap: the number of tree-rings compared</li> </ul> </li> <li>Data files with values in each table. The continents are as defined in the ITRDB (https://www.ncei.noaa.gov/access/paleo-search/?dataTypeId=18) <ul> <li>GC_af_tbl_202005 (Africa)</li> <li>GC_as_tbl_202005 (Asia)</li> <li>GC_au_tbl_202005 (Australia)</li> <li>GC_ca_tbl_202005 (Canada)</li> <li>GC_eu_tbl_202005 (Europe)</li> <li>GC_mx_tbl_202005 (Mexico)</li> <li>GC_sa_tbl_202005 (South America)</li> <li>GC_us_tbl_202005 (North America)</li> </ul> </li> </ul> </li> <li><em>headers</em>: <ul> <li>The following columns are present: <ul> <li>continent: two letter code of the continent (ITRDB)</li> <li>filename: orginal filename as deposited in the ITRDB</li> <li>line_nr: line number of the header</li> <li>header_text: text of the header related to the line number</li> </ul> </li> <li>Datafile: headers_201905222007.csv</li> </ul> </li> <li><em>names</em>: <ul> <li>The following columns are present: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>name_orig: orginal name of the tree-ring series as deposited in the ITRDB</li> <li>name_new: the IDs of the tree-ring series were replaced with a two‐letter code for the continent (AF, AS, AU, CA, EU, SA, US) and a sequence code to prevent duplicate IDs. These are used as ID1 and ID2 in the tables <em>GC_??_tbl</em></li> </ul> </li> <li>Datafile: names_201905240643.csv</li> </ul> </li> </ul> </li> <li>file: <em>geo_location_201906250635.csv</em> <ul> <li>Contains the locations related to each site in the database</li> <li>The following columns: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>continent: two letter code of the continent (ITRDB)</li> <li>lat: latitude</li> <li>long: longitude</li> <li>geom_point: WGS84 coordinates expressed as well-known text (WKT)</li> </ul> </li> </ul> </li> </ul> <p>For the related code, see also: </p> <p>Ronald Visser. (2022). Code and data related to semi-synchronous growth changes and the similarity of tree-ring patterns (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7157738</p> <p>Or: https://github.com/RonaldVisser/SGC</p>
FM-Tools Release 2.2: Data Set of Metadata about Tools for Formal Methods (SV-COMP 2025, Test-Comp 2025)
<h1>Collection of Information about Formal-Methods Tools</h1> <h2>Motivation</h2> <p>There are many tools available that implement formal-methods approaches. This repository collects meta data about the tools, such that it becomes easier to reuse, integrate, and cooperate with formal-methods tools.</p> <p>A <a href="https://www.sosy-lab.org/research/pub/2024-Podelski65.Find_Use_and_Conserve_Tools_for_Formal_Methods.pdf">description</a> of the structure of this repository can be found in an article.</p> <p>A <a href="https://fm-tools.sosy-lab.org/">formatted listing</a> of some of the data in this repository can be found on a generated web site.</p> <p>A <a href="https://fm-tools.sosy-lab.org/schema.html">schema definition</a> of the data files in this repository can be found on a generated web site.</p>
The Data Set for the Publication "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer"
<p>In the scope of the ambitious EU goals of carbon neutrality in 2050, building energy efficiency is one of the crucial segments. To ensure a faster energy transition process, innovations are needed at various built environment-related sectors - starting from building components up to urban level energy management advancements.</p> <p>Phase change material (PCM) enriched building components allow to shift the existing paradigm - to make a switch from the static building components to dynamic ones able to take an active part in building energy balance by ensuring energy storage in the building thermal envelope.</p> <p>The data set presented here supports the paper "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer". The design of the façade module, experimental setup, used equipment, the plan of the experiment, and obtained results are described in the paper. The provided data set provides heat-flux and average temperature (in PCM) measurements.</p>
Annotated images from yeast cell lifespans - Training & Test sets - DetecDiv (id01)
<p>This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below).</p> <p>It contains ~250 000 images from 250 cellular lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image.</p> <p>Besides, this folder also contains a .mat file containing 250 timeseries of classes corresponding to the lifespan of the 200 cells.</p> <p> </p> <p>The dataset used for training (200 cellular lifespans) is in the folder /trainingset, while the dataset used for testing (50cellular lifespans) is in the folder /testset</p> <p>It has been used to train the network <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a></p> <p><a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p> </p> <p><strong>Data type</strong>: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat)</p> <p><strong>Microscopy data type</strong>: Brightfield images with 3 stacks, each stack representing a color of RGB.</p> <p><strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS</p> <p><strong>Cell type</strong>: Budding yeast wild type cell (BY4742)</p> <p><strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat</p> <p><strong>Image size</strong>: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
MELA Dataset: A Benchmark for Mediastinal Lesion Analysis (Test Set)
<p>MELA dataset is a benchmark for developing algorithms on mediastinal lesion analysis. We hope this large-scale dataset could facilitate the research and application of automatic mediastinal lesion detection and diagnosis. </p> <p>MELA dataset contains 1100 CT scans collected from patients with one or more lesions in the mediastinum. The MELA dataset is split into a subset of 770 CT scans for training, a subset of 110 CT scans for validation, and a test set of 220 CT scans for evaluation.</p> <p>This is the Test Set of MELA dataset, including 220 CTs. Files include:</p> <ol> <li>Test1.zip: 110 CTs in NII format (nii.gz).</li> <li>Test2.zip: 110 CTs in NII format (nii.gz).</li> </ol>
RobotReviewer evaluation data (new test set)
<p>Includes 3,324 openly available PDFs (<em>rct_pdfs.zip</em>) with risk-of-bias annotations (<em>robotreviewer_eval_data.json</em>) from Cochrane systematic reviews. This data has not been used in the development of RobotReviewer and in this way represents a new, unseen test set. For each PDF/pubmed ID, Cochrane topics are also provided (<em>robotreviewer_topics.json</em>).</p>
ClinSpEn-CC (Clinical Cases) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Cases sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with three columns: document number, line number and English line. The direction of this sub-track is EN>ES. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
ClinSpEn-OC (Ontology Concepts) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Ontology Concepts sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: concept number and English concept. The direction of this sub-track is EN>ES. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
ClinSpEn-CT (Clinical Terms) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Terms sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: term number and Spanish term. The direction of this sub-track is ES>EN. The used terms were directly extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
chloroExtractor test set #1
<p>The data set comprises the 1 million paired end reads of the SRA data set SRR5216995.</p> <p>The original SRA data set can be downloaded from ftp://ftp-trace.ncbi.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR521/SRR5216995/SRR5216995.sra</p> <p>The data set was generated via:</p> <blockquote> <p>wget ftp://ftp-trace.ncbi.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR521/SRR5216995/SRR5216995.sra<br> fastq-dump -v -L info --split-files -X 1000000 SRR5216995.sra</p> <p>md5sum *.fastq<br> 51244d493e0459b22f23ce38ba2252a1 SRR5216995_1.fastq<br> da45378160cc306fcdfb700dbbaad0f6 SRR5216995_2.fastq<br> <br> # compress into a tar.bz2 archive<br> tar cjf SRR5216995_1M.tar.bz2 SRR5216995_1.fastq SRR5216995_2.fastq</p> <p># md5sum of archive might differ due to time stamp/compression differences, but md5 checksums for<br> # individual fastq files should match</p> </blockquote>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Helsinki-NLP/WMT16-test-enfi: WMT16 test set for Finnish-English
<p>This package provides the test set for the news translation task at WMT 2016 for Finnish-English and the machine-translated submissions by the University of Helsinki.</p>
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
TROTS - The Radiotherapy Optimisation Test Set
<p>The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset.</p> <p> </p> <p>The set as present in this version is of date 13 May 2019. Updated versions of the Scripts and other extensions can be found at the following pages:</p> <p> </p> <p>Persistent page with links: <a href="https://hdl.handle.net/1765/116520">Erasmus University Rotterdam Library</a></p> <p>Mirror project page: <a href="http://www.sebastiaanbreedveld.nl/trots">TROTS Mirror</a></p> <p>Main publication: <a href="https://dx.doi.org/10.1016/j.dib.2017.03.037">S. Breedveld & B. Heijmen, Data for TROTS - The Radiotherapy Optimisation Test Set, Data in Brief 12 (2017) 143-149 </a></p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
ScienceDex guides
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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)
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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.