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470 results for “Evaluation dataset”
Semi-simulated dataset of indel calling tool evaluation
<p>The semi-simulated datasets used in Ning Wang, et al. </p> <p>100bp_5X_500_Venter_read1.fq.gz & 100bp_5X_500_Venter_read2.fq.gz: 5X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>100bp_30X_500_Venter_read1.fq.gz & 100bp_30X_500_Venter_read2.fq.gz: 30X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>250bp_30X_500_Venter_read1.fq.gz & 250bp_30X_500_Venter_read2.fq.gz: 30X coverage, 250bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>100bp_60X_500_Venter_read1.fq.gz & 100bp_60X_500_Venter_read2.fq.gz: 60X coverage, 100bp read length semi-simulated paired-end sequencing FASTQ files.</p> <p>Haplotype_1_no_gap.fa & Haplotype_2_no_gap.fa: the semi-simulated dipoid human genome hg19 chromosome 1 and chromosome 2, including HuRef indels used in Ning Wang, et al. In order to use ART (fastq simulator) to generate simulated fastq fiels, the gaps (Ns) of genome were removed.</p> <p>chr1_chr2_variants_truthset.txt: types, position, size and genotype of HuRef indels used in Ning Wang, et al. </p>
CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems
<p>This dataset contains python repositories mined on GitHub on January 20, 2021. It allows a cross-domain evaluation of type inference systems. For this purpose, it consists of two sub-datasets, each containing only projects from the web or scientific calculation domain, respectively. Therefore we searched for projects with dependencies to either <a href="https://numpy.org/">NumPy</a> or <a href="https://flask.palletsprojects.com/en/2.0.x/">Flask</a>. Furthermore, only projects with dependencies to <a href="http://mypy-lang.org/">mypy</a> were considered, because this should ensure that at least parts of the projects have type annotations. These can be used later as ground truth. Further details about the dataset will be described in an upcoming paper, as soon as it is published it will be linked here.<br> The dataset consists of two files for the two sub-datasets. The web domain dataset contains 3129 repositories and the scientific calculation domain dataset contains 4783 repositories. The files have two columns with the URL to the GitHub repository and the used commit hash. Thus, it is possible to download the dataset using shell or python scripts, for example, the pipeline provided by <a href="https://github.com/saltudelft/many-types-4-py-dataset">ManyTypes4Py</a> can be used.<br> If repositories do not exist anymore or are private, you can contact us via the following email address: bernd.gruner@dlr.de. We have a backup of all repositories and will be happy to help you. </p>
Evaluation of a Novel 8-Channel RX Coil for Speech Production at 0.55 T DATASET.
<p>This dataset contains raw imaging MRI data used in SNR evaluation for 4 subjects. For all scans, volumetric data of the upper airway was obtained using a 3D spoiled gradient echo sequence with either a speech coil, a head coil, or an integrated body coil. Imaging parameters were: flip angle = 10, TE = 5 ms, TR = 10 ms, FOV = 32x32x16 cm^3, resolution = 2.5 x 2.5 x 5 mm^3. Pre-scan noise information for each scan is also included.</p> <p> </p> <p> </p>
Dataset created in the context of the project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field"
<p>The project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field", whose website is https://datause.es/, is a project funded by the Ministry of Science and Innovation - State Research Agency, with reference PID2019-105708RB-C22.</p> <p>Within the framework of the project, a bibliographic search is carried out in all thematic categories of the Web of Science (WoS) related to agriculture and related areas. The search equation included the following categories:</p> <p><strong>WC </strong>= (FOOD SCIENCE TECHNOLOGY OR PLANT SCIENCES OR FORESTRY OR AGRICULTURAL ENGINEERING OR AGRONOMY OR HORTICULTURE OR AGRICULTURE DAIRY ANIMAL SCIENCE OR AGRICULTURE MULTIDISCIPLINARY OR AGRICULTURAL ECONOMICS POLICY) </p> <p>This data set shows the distribution of journals and the quartile they occupy in each of the thematic categories in 2019, with the aim of serving researchers in this area and for future data mining.</p>
Landfill: An open dataset of code smells with public evaluation
<p>Code smells are symptoms of poor design and implementation choices that may hinder code comprehension and possibly increase the change- and fault-proneness of source code. Several techniques have been proposed in the literature for detecting code smells. These techniques are generally evaluated by comparing their accuracy on a set of detected candidate code smells against a manually-produced oracle. Unfortunately, such comprehensive sets of annotated code smells are not available in the literature, with only a few exceptions. This dataset provides 243 instances of five types of code smells identified from 20 open-source software projects. In particular, it contains a SQL file with the information concerning such instances and a zip file with their source code.</p>
dataset related to article "On the evaluation of edgeless diode detectors for patient-specific QA in high-dose stereotactic radiosurgery"
<p>The database contains the comparison table between calculated doses and values actually measured by the edgeless detectors and an example of dose planar distribution as exported from the radiotherapy Treatment Planning System.</p> <p> </p>
Evaluating 3D Human Motion Capture using Apple ARKit against the Vicon System: A Dataset
<p><strong>A journal paper which was published in Applied Sciences gives detailed information about the dataset.</strong></p> <p>Reimer, L.M.; Kapsecker, M.; Fukushima, T.; Jonas, S.M. Evaluating 3D Human Motion Capture on Mobile Devices. Appl. Sci. <em>(2022)</em></p> <p><a href="https://www.mdpi.com/2076-3417/12/10/4806">https://www.mdpi.com/2076-3417/12/10/4806</a></p> <p>Please cite the corresponding paper when using the dataset.</p> <p> </p> <p>A dataset containing anonymized exercise data for eight exercises from ten subject. The exercise data was recorded with two iPads 11" (2021 version, Apple Inc., Cupertino, CA, USA) and a Vicon system. The two iPads were positioned frontal and in a 30° angle to the left side of the subject. The Vicon system used 14 cameras and captured the motion using the Full-body Plug-in-gait model.</p> <p>The dataset contains 220 files, 22 per subject. The structure of the dataset contains 10 folders, one per subject. Each folder contains two subfolders: ARKit and Vicon. Each ARKit folder holds two CSV files. Each Vicon folder holds 16 files, two per exercise: a .csv file with the motion data and a .xcp file containing meta data about the recording, including the camera setup and start/stop timestamps.</p> <p>Du to export problems, the ARKit files for the Side View do not always contain all joint data. The upper body joints are only available for three out of the ten subjects for the Side View.</p>
Embeddings models for Buddhist Sanskrit: Evaluation Datasets
<p>Evaluation Dataset used for the study published as Embeddings models for Buddhist Sanskrit, <em>LREC 2022 proceedings</em>. It contains a semantic similarity dataset and an analogy dataset, as well as the published study and a ReadMe file containing the guidelines used for scoring semantic similarity and some notes about the manual scoring task.</p> <p> </p> <p>The evaluation datasets have been prepared by Ligeia Lugli, Bruno Galasek-Hul, Luis Quiñones and Jai Paranjape</p> <p> </p>
EvaNIL: silver standard dataset for large-scale NIL entity linking evaluation
<p>The EvaNIL dataset can be used to train or evaluate approaches developed for NIL entity linking. It was built from several Biomedical and Life Sciences corpora:</p> <ul> <li>PubMed DS</li> <li>CRAFT corpus</li> <li>MedMentions</li> </ul> <p>These corpora contain entities associated with knowledge base concepts. To build the EvaNIL dataset, we assumed that those knowledge base concepts did not exist in the respective knowledge bases, so each entity is associated instead with the direct ancestors of those original concepts.</p> <p>The EvaNIL dataset is divided into 6 partitions including annotations from several knowledge bases:</p> <ul> <li>"medic" (CTD-MEDIC)</li> <li>"ctd_anatomy" (CTD-Anatomy)</li> <li>"ctd_chemicals" (CTD-Chemicals)</li> <li>"chebi" (ChEBI)</li> <li>"go_bp" (GO-Biological Process)</li> <li>"hp" (HPO)</li> </ul> <p> </p>
Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models [dataset]
<p>This data package supports empirical analysis of national electricity system transitions and retrospective evaluation of electricity system models in 1990–2019 in 31 European countries, including the EU27, Switzerland, Iceland, Norway, and the United Kingdom. The data package covers two types of content. Firstly, we provide an annotated list of 528 original data sources and references relevant for retrospective electricity system modeling with emphasis on open-access sources. Secondly, we provide a total of 1359 processed and harmonized data files in a format that is suitable as inputs to electricity system models. Four types of data files are included for each country: (i) a country file documenting national demand and economic data, (ii) technology files describing techno-economic data for each major generation technology in the country's electricity mix, (iii) resource files describing fuel prices and CO2 emissions for each fuel, and (iv) load profiles describing 24-hour national load curves for each available year. We provide these data files as comma-separated files to enable their wider reuse for retrospective evaluation of models as well as for empirical analyses of the European electricity system transitions. </p>
Datasets for "Evaluating the performance of a Picarro G2207-i analyser for high-precision atmospheric O2 measurements"
<p>These data files contain the data used in the manuscript "Evaluating the performance of a picarro G2207-i analyser for high-precision atmospheric O2 measurements" submitted to Atmospheric Measurement Techniques. </p> <p>WAO_G2207i_O2_calibrated_AM_all : Calibrated O2 measurements from the G2207-i during the no-drying, partial-drying, and full-drying periods at WAO, for both the water-corrected and non-water corrected outputs</p> <p>CRAM_lab_run1_noRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, without reference tank correction</p> <p>CRAM_lab_run1_wRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied</p> <p>CRAM_lab_run2_noRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, without reference tank correction</p> <p>CRAM_lab_run2_wRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied</p>
Raw Datasets of: "Evaluating the Cost-Effectiveness of Plastic Surgery Based on the Satisfaction with Quality of Life and Nasolabial Appearance in Thai Preteens with Cleft Lip and Palate"
<p>A cross-sectional study with 30 Thai preteen CLP patients aged between 8 to 12-year-old was undertaken between 2017 and 2019 at Tawanchai center. Their satisfaction with QoL was reflected through an interview and a questionnaire with the cooperation of their parents as their caregivers while satisfaction with their nasolabial appearance was from the evaluation of five experts who compared their photos with those of normal faces to determine correlation values of the nasolabial appearance from 5-rating scales using Fleiss’s kappa coefficient scores.</p>
ASDF Evaluation Dataset + Corner Clamp Base
<p>Evaluation data for ASDF and Corner Clamp Base Training Data</p> <p> </p> <blockquote> <p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p> </blockquote>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Benchmark datasets for RDF load time evaluation (RiverBench)
<div> <p>Datasets to be used for reproducing the RDF load time benchmark, using the code here: <a href="https://github.com/Ostrzyciel/rdf4led-riverbench">https://github.com/Ostrzyciel/rdf4led-riverbench</a></p> <p>The datasets were obtained from <a href="https://w3id.org/riverbench/v/2.0.1/profiles/flat-triples" rel="nofollow">RiverBench profile <code>flat-triples</code> version 2.0.1</a>. <strong>The detailed licensing and authorship information for each individual dataset is available on <a href="https://w3id.org/riverbench/v/2.0.1/datasets" rel="nofollow">RiverBench's website</a>.</strong> The most restrictive license that applies to any of the datasets is CC BY-SA.</p> <p>Benchmark results: <a href="https://doi.org/10.5281/zenodo.12087112">https://doi.org/10.5281/zenodo.12087112</a></p> </div>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
SLF Evaluation Dataset
<p>This dataset was constructed from the test set split of the VoxCeleb 2 dataset (<a href="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html">VoxCeleb</a>). The VoxCeleb 2 test set contains 118 speakers each in several different videos. To develop this dataset, only one video per speaker was selected. A face image was also extracted from the video, as well as, a low resolution face image (8x8). Age, gender and ethnicity of the person in the face image were determined using the “DeepFace” library, a face recognition and facial attribute analysis library.</p> <p>This dataset can be used to evaluate speech2face, speech conditioned face generation and speech conditioned face super-resolution systems.</p>
TUT Rare sound events, Evaluation dataset
<p>TUT Rare Sound events 2017, evaluation dataset consists of source files for creating mixtures of rare sound events (classes baby cry, gun shot, glass break) with background audio, as well a set of readily generated mixtures and recipes for generating them.</p> <p>The "source" part of the dataset consists of two subsets:</p> <ul> <li>background recordings from 15 different acoustic scenes,</li> <li>recordings with the target rare sound events from three classes, accompanied by annotations of their temporal occurrences.</li> </ul> <p>The mixture set consists of two 1500 mixtures (500 per target class, with half of the mixtures not containing any target class events). </p> <p>The collection of the background recording data has been financially supported by European Research Council under the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.</p>
ALN Reasoner Evaluation Dataset
<p>Dataset for evaluation of Description Logics reasoners in the Attributive Language with unqualified Number restrictions (ALN). The dataset consists in two sections: a) for standard reasoning tasks, 1398 knowledge bases with expressivity up to ALN, extracted from the 2014 OWL Reasoner Evaluation Workshop competition dataset; b) for non-standard reasoning tasks, 4 knowledge bases, each with a different number of "request" and "resource" individuals.</p>
Annotated dataset for sub-shot segmentation evaluation
<p>This dataset was created for evaluating the performance of the developed motion-driven approach for fine-grained temporal segmentation of user-generated videos, that is reported in [1].</p> <p>Given the fact that user-generated videos are most commonly captured without interruption using a single camera, thus being single-shot videos, their fine-grained temporal segmentation aims to identify visually coherent parts (called sub-shots) that correspond to individual actions taking place during the video recording, such as left camera panning, camera zoom-out or tracking of a moving object.</p> <p>Based on the above, the created dataset contains the ground-truth sub-shot segmentation for 33 single-shot videos. This ground-truth segmentation was created by human annotation of the sub-shot boundaries for each video, where each boundary indicates the end of a visually and temporally contiguous activity of the video recording device and the start of the next one (e.g. a downward camera tilting that is followed by a camera zoom-in). Overall, our dataset contains 674 sub-shot transitions.</p> <p>The set of videos can be divided in three parts:</p> <ol> <li><em>Own Videos</em>: 15 single-shot videos of total duration 6 minutes, which contain clearly defined fragments that correspond to several video recording activities;</li> <li><em>Amateur Videos</em>: 5 single-shot amateur videos of total duration 17 minutes, found on the YouTube platform;</li> <li><em>Movie Excerpts</em>: 13 single-shot parts of known movies of total duration 46 minutes which represent professional video content.</li> </ol> <p>The videos of the first part were recorded in the external spaces of our (CERTH-ITI) facilities using an iPhone 5 smart-phone. These videos and their ground-truth are freely provided through the "Provided files" section below.</p> <p>The videos of the second and third part can be found on the YouTube platform (links in the "Video collection" section below). For these videos we only provide the ground-truth data again through the "Provided files" section below.</p> <p>For each video of the dataset we provide:</p> <ul> <li>The video ID;</li> <li>The video filename for "Own videos" and the YouTube video name and URL for "Amateur videos" and "Movie Excerpts" (accessed on 12/11/2017);</li> <li>The video frame-rate that directly affects the ground-truth annotation; the videos of the second and third part should be downloaded at the corresponding frame-rates (default values when using the KeepVid web application - <a href="https://keepvid.com/">https://keepvid.com/</a>) to match the created annotations.</li> </ul> <p>The above information is included in the provided "video_collection_info.pdf" file.</p> <p>The needed files for using the created dataset are in the provided compressed "dataset.zip" file. After unpacking the compressed file a structure of directories will be generated. In this structure:</p> <ol> <li>The "videos/own_videos" directory contains: a) the video files of the first part of the dataset, and b) the "list.txt" file which lists the video id, the filename and the frame-rate for each video of the "Own videos" collection, in a tab-separated format.</li> <li>The "videos/amateur_videos" directory contains the "list.txt" file which lists the video id, the filename and the frame-rate for each video of the "Amateur videos" collection, in a tab-separated format.</li> <li>The "videos/movie_excerpts" directory contains the "list.txt" file which lists the video id, the filename and the frame-rate for each video of the "Movie excerpts" collection, in a tab-separated format.</li> <li>The "ground_truth" directory contains 33 txt files (one per video) where each file is named after the ID of the corresponding video and includes the annotation of the sub-shots in the video; each line of this file corresponds to the starting frame of a sub-shot of the video (using a zero-based index of frames).</li> <li>The "evaluation" directory contains a simple Matlab script, called "eval_segm.m", that was used for evaluating the results of the sub-shot segmentation analysis.</li> <li>A readme file that contains the documentation of the dataset (i.e. the information that is available in this webpage).</li> </ol> <p>CERTH-ITI holds the copyright of the “<em>Own videos</em>” part of the dataset. These videos can be freely used for academic/research purposes only and their public playback is not allowed. CERTH-ITI does not own the copyright of the “<em>Amateur Videos</em>” and “<em>Movie Excerpts</em>” parts of the dataset. The use of these videos must abide by the YouTube copyright policy (<a href="https://www.youtube.com/yt/about/copyright/">https://www.youtube.com/yt/about/copyright/</a>). Any users of these videos must accept full responsibility for the use of these parts of the dataset, including but not limited to the use of any copies of copyrighted videos that they may create from the dataset.</p> <p>If you use this dataset, please cite the following scientific work:</p> <p>[1] K. Apostolidis, E. Apostolidis, V. Mezaris, <em>"A motion-driven approach for fine-grained temporal segmentation of user-generated videos"</em>, Proc. 24th Int. Conf. on Multimedia Modeling (MMM2018), Bangkok, Thailand, Feb. 2018.</p> <p> </p> <p> </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.