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”
Dataset: Relevance and usability evaluation in a data portal for biodiversity research
<p>Supplementary material for a relevance and usability evaluation in a data portal for biodiversity research.</p> <p>Data portal: GFBio (<a href="https://www.gfbio.org">https://www.gfbio.org</a>)</p> <p>Evaluation time:<span> February 2016 (at that time the search index consisted of ~ 2 Mio datasets)</span></p> <p>Eight domain experts rated the Top25 search results of 16 provided search questions on a 7-point-Likert scale from 0 (irrelevant) to 6 (highly relevant). Afterwards, we asked the users to provide and rate up to two own queries.</p> <p><span>The users also rated 28 statements in a subsequent usability evaluation on a 5-point Likert scale from 'completely disagree' to 'highly agree'. For some statements, only binary ratings were given.</span></p>
Datasets to Evaluate Accuracy, Miscalibration and Popularity Lift in Recommendations
<p>This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift in recommender systems. All datasets contain genre/category information in addition to different user group splits:</p> <ol> <li>Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)</li> <li>MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)</li> <li>MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)</li> </ol> <p>'user_events_cats.txt' contains the users' rating/interaction data along with a list of genres/categories assigend to the rated items. The list of categories is given in 'categories.txt'. Additionally, assignments to three user groups that differ in their inclination to popular/mainstream items are provided: LowPop in 'low_main_users.txt', MedPop in 'med_main_users.txt', and HighPop in 'high_main_users.txt'.</p> <p>The format of the three user files are "user,mainstreaminess"</p> <p>The format of the user-events files are "user,item,preference,cats", where different categories are separated by '|'</p> <p>The format of the categories files are "category-name,index", where index refers to the category-id in the user-events files</p> <p>Example Python-code for analyzing the datasets as well as empirical results on calibration, popularity lift and accuracy can be found on GitHub: https://github.com/domkowald/FairRecSys</p>
Dataset for the publication "Evaluation of Voltage Transformers' Accuracy in Harmonic and Interharmonic Measurement"
<p>This is dataset for paper published:</p> <p>G. Crotti, G. D’Avanzo, C. Landi, P. S. Letizia and M. Luiso, "Evaluation of Voltage Transformers’ Accuracy in Harmonic and Interharmonic Measurement," in <em>IEEE Open Journal of Instrumentation and Measurement</em>, vol. 1, pp. 1-10, 2022, Art no. 9000310, doi: 10.1109/OJIM.2022.3198473.</p>
A Policy and Infrastructure Evaluation Model of Commodity Flows through Inland Waterway Ports (Dataset)
<p>The purpose of this project is to guide strategic investment into port capacity through the development of a policy and infrastructure evaluation model of inland waterway commodity flows. A multi-stage stochastic optimization model will be developed to evaluate tradeoffs in strategic, long-term port infrastructure investment with mid-term capacity expansion decisions and provision of complementary highway infrastructure made by public and private stakeholders, and shorter-term operational practices made by shippers and carriers. This work builds on prior MarTREC projects which developed a Multi-Commodity Assignment Problem to estimate annual commodity flows through inland waterway ports from truck Global Positioning System (GPS), marine Automatic Identification System (AIS), and the Lock Performance Management System (LPMS). The proposed project will explore critical extensions of the assignment model: 1) disaggregation of the temporal scope to reflect monthly seasonality among commodities, 2) incorporation of uncertainty related to observed vehicle and vessel movement data, and 3) inclusion of transportation costs. With these extensions the team expects to increase the accuracy and resolution of the commodity-based port throughput estimates and to allow the model to be used to not only describe the current system but to prescribe policy and project investment strategies for public and private sector transportation decision makers. Calibration and validation of the multi-stage optimization model will be done through two case studies. The regional-based study will use historical truck GPS, marine AIS, and LPMS datasets. The national-based study will use data from the Billion Ton Study led by the US Department of Energy. This will ensure a feasible and realistic base-case on which to compare future policy scenarios. This project aligns with MarTREC’s research focus area in Maritime and Multimodal Logistics Management by modeling commodity flows through ports that serve as critical connections for the multimodal freight supply chain.</p>
Dataset for P-FARFAR2 evaluation
<p>Repeated sampling exercises are conducted using different numbers of workers. The shared data capture the distributions of key metrics observed per configuration, so they can be compared against each other for statistical significance.</p>
Datasets for Evaluating Metagenomic Phage Detection Tools
<p>These datasets include sequences that can be used for evaluating computational tools that detect bacteriophage in metagenomes.</p> <p>Datasheets are supplied for each dataset, and a README describes how to extract each dataset. Once the directories are extracted, there is a README in each directory describing the individual dataset.</p>
Dataset and evaluation for HTR models for Latin and French Medieval Documentary Manuscripts
<p><strong>1. Dataset presentation.</strong></p> <p>This is the dataset used to produce the HTR models applied to documentary Latin and French manuscripts presented in the paper: Sergio Torres Aguilar, Vincent Jolivet. <strong>Handwritten Text Recognition for Documentary Medieval<br> Manuscripts. </strong>2022. https://hal.science/hal-03892163</p> <p>The dataset contains mostly charters and registers from the Late-medieval period (12th-15th). The training and evaluation, entailing 1855 pages, 120k lines of text and almost 1M tokens, were conducted using three freely available ground-truth corpora :</p> <p><strong>The Alcar-HOME database </strong>: https://zenodo.org/record/5600884</p> <p><strong>The e-NDP corpus </strong>: https://github.com/chartes/e-NDP_HTR</p> <p><strong>The Himanis project </strong>: https://zenodo.org/record/5535306</p> <p>The final model operates in a multilingual environment (Latin and French) and it is able to recognize several Latin script families (mostly <em>Textualis</em> and <em>Cursiva</em>) in documents produced in ca. 12th - 15th centuries. During the evaluation the models shows an accuracy of <strong>94.01%</strong> on the validation set and a CER (character error ratio) of about <strong>0.12</strong> to <strong>0.17</strong> on four external unseen datasets. A fine-tuning exercise using 10 ground-truth pages can raise these results to a CER between <strong>0.06</strong> to <strong>0.10</strong> respectively.</p> <p> </p> <p><strong>2. Dataset contents .</strong></p> <p>a) <em>GT_list : </em>List containing the GT file names which constitute the training, evaluation and test sets. The images and transcriptions can be downloaded from their original repositories.</p> <p>b) <em>Training :</em> Contains the training and testing results (evaluation and prediction files) presented in the original paper for the two training phases: Regular (Textualis and Cursiva separated training) and Quartiles (mixed training by quartiles).</p> <p>c) <em>Useful_scripts :</em> Scripts to produce the HTR metrics (CER, WER, SER) and plot the model's accuracy.</p> <p>d) <em>Best_model :</em> Contains the best multilingual and multi-script model.</p>
Dataset: A quantitative 1H NMR approach for evaluating the metabolic response of Saccharomyces cerevisiae to mild heat stress
<p>In this study, the effect of growth temperature on the yeast (<em>Saccharomyces cerevisiae</em>) metabolome has been analyzed by one-dimensional proton NMR spectroscopy (<sup>1</sup>H NMR).</p> <p>Yeast cells were grown either at 30 or 37°C. A non-targeted chemometric evaluation of the spectra was performed in order to detect potential biomarkers. Moreover, an exhaustive assignment for most of the detected NMR signals was carried out, corresponding to 38 identified metabolites. Resonances from these identified metabolites were integrated, and univariate and multivariate data analyses were applied on the matrices of these relative concentrations. Observed changes in metabolite concentrations were consistent with the expected process of temperature acclimation, showing alterations in amino acid cellular pools, nucleotide metabolism and lipid composition.</p>
Datasets of evaluated Covalent Organic Frameworks for photocatalysis
<p>We developed a workflow to evaluate covalent organic frameworks (COFs) for photocatalysis. As an input, we chose the CURATED COFs (doi:<a href="https://doi.org/10.24435/materialscloud:z6-jn">10.24435/materialscloud:z6-jn</a>), a database of experimental COFs. We performed density functional theory (DFT) calculations, and post-processed the result with in-house codes available on GitHub (see <a href="https://github.com/bmourino/cof_photocatalysis">GitHub repository</a>). The dataset in cofs_descriptors.csv is the full dataset with all of our computed descriptors, and an application was developed to allow for interactively exploring the results (see <a href="https://github.com/bmourino/cofs_photocat_app">GitHub repository</a>). The bootstrapped_statistics.csv is the result of the statistical analysis with DABEST (Data Analysis with Bootstrap-coupled ESTimation, doi:<a href="https://doi.org/10.1038/s41592-019-0470-3">10.1038/s41592-019-0470-3</a>).</p>
(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America
<p>Dataset for the paper:</p> <p>Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America</p> <p>For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!</p>
Dataset Related to "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller"
<p>Dataset (and programs used to create it) for the publication "Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller":</p> <p>Hannes Tröpgen, Mario Bielert, and Thomas Ilsche. 2023. Evaluating the Energy Measurements of the IBM POWER9 On-Chip Controller. In Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE ’23), April 15–19, 2023, Coimbra, Portugal. ACM, New York, NY, USA, 10 pages. <a href="https://doi.org/10.1145/3578244.3583729">https://doi.org/10.1145/3578244.3583729</a></p> <p>Find additional descriptions of the data in the included readme files.</p> <p> </p> <p>This work is supported in part by the German National High Performance Computing (NHR@TUD).<br> The authors are grateful to the Center for Information Services and High Performance Computing at TU Dresden for providing the Power9 Systems used in the measurements and the support during them.</p>
Digital Tools for Handwriting Proficiency Evaluation in Children - DATASET
<p>Data to support the findings in the conference paper</p> <p>L. G. Dui, E. Calogero, M. Malavolti, C. Termine, M. Matteucci and S. Ferrante, "Digital Tools for Handwriting Proficiency Evaluation in Children," <em>2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)</em>, Athens, Greece, 2021, pp. 1-4, doi: 10.1109/BHI50953.2021.9508539.</p> <ul> <li>Characterization.xls: an Excel file with sheets: <ul> <li>Characterization: <ul> <li>Subject ID</li> <li>School ID</li> <li>Class</li> <li>Age</li> <li>Sex</li> <li>Hand</li> <li>Years since starting writing in cursive</li> </ul> </li> <li>TabletSUS: score for the System Usability Scale referred to writing on tablet</li> <li>PenSUS: score for the System Usability Scale referred to writing on paper with the smart ink pen</li> <li>BVSCO-2: number of graphemes produced when writing on paper or tablet</li> </ul> </li> <li>pen_tablet_data.mat: a Matlab file with indicators and their names, computed with tablet and pen data</li> </ul>
PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks (dataset)
<p>Computational protein-binding studies are widely used to investigate fundamental biological processes and facilitate the development of modern drugs, vaccines, and therapeutics. Scoring functions aim to assess and rank the binding strength of the predicted protein complex. Accurate scoring of protein binding interfaces remains a challenge. PIsToN (evaluating Protein binding Interfaces with Transformer Networks) represents a novel approach to distinguish native-like protein complexes from incorrect conformations. Protein interfaces are transformed into a collection of 2D images (interface maps), each corresponding to a geometric or biochemical property. Pixel intensities represent the feature values. A neural network was adapted from a popular vision transformer (ViT) with several enhancements: a hybrid component to accept empirical-based energy terms, a multi-attention module to highlight essential features and binding sites, and the use of contrastive learning for better ranking performance. The resulting PIsToN model significantly outperforms state-of-the-art scoring functions on well-known datasets.</p> <p>This repository contains proteins and pre-computed interface maps for the PIsToN work.</p>
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
<p>MedleyVox is an evaluation dataset for multiple singing voices separation, which consists of 381 segments (1.1hour), containing 23 songs from MedleyDB v1 and v2 (https://medleydb.weebly.com).</p> <p> </p> <p>MedleyVox contains 1) unison, 2) duet, 3) main vs. rest (folder name 'rest') , and 4) N-singing ('unison' + 'duet' + 'rest', please check our dataloader code for N-singing in https://github.com/jeonchangbin49/MedleyVox/blob/main/svs/data/test_dataset.py) categories explained in our ICASSP 2023 paper. For more details, please check our paper (https://arxiv.org/pdf/2211.07302.pdf) and code repository (https://github.com/jeonchangbin49/MedleyVox).</p> <p> </p> <p>Acknowledgment</p> <p>We are grateful to Rachel Bittner, the author of the original MedleyDB data, for allowing us to publish the MedleyVox dataset.</p> <p> </p> <p>License</p> <p>This work is licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</p>
Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform
<p>Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform<br> =======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, and Francesco Mondada (2023). Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7489244</p> <p> • The associated peer reviewed article that will appear in the International Journal of STEM education : El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curricular Reforms Contributing to Equity? Impact on Student Learning, Perception of the Discipline, and Gender Gaps. arXiv, to appear in the International Journal of STEM Education. https://doi.org/10.48550/arXiv.2306.00820</p> <p>• License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date: May 2nd 2023</p> <p>• Subject: Computer Science; Curricular Reform; Elementary Education; Learning Achievement; Computational Thinking; Perception Survey; Equity; Gender Gaps</p> <p>• Dataset format: CSV</p> <p>• Dataset collection: January 2021 to May 2022</p> <p>• Dataset size : < 100 kB</p> <p>• Dataset content : three excel files. We provide the detailed description of each of the files below. The original questions are available in the associated publication [1]. Please note that these datasets contain missing values due to students either not being present for all data collections or not having the associated teacher-related data.</p> <p>• Abbreviations :<br> - CS : Computer Science<br> - CT : Computational Thinking<br> - PD : Professional Development</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p> </p> <p># References</p> <p>[1] El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curricular Reforms Contributing to Equity? Impact on Student Learning, Perception of the Discipline, and Gender Gaps. arXiv, to appear in the International Journal of STEM Education. https://doi.org/10.48550/arXiv.2306.00820</p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p> </p>
Digital Humanities Retrieval Evaluation Dataset for Art, Architecture and Life Sciences (DHREAAL)
<p>This dataset, titled "Digital Humanities Retrieval Evaluation Dataset for Art, Architecture, and Life Sciences," emphasizes semantic retrieval, including ground truth data for both content-based and instance-based image retrieval. Utilized to compare various promising approaches and refine the evaluation method, this dataset serves as a precursor to a more extensive dataset. Additional details can be found in the dataset's related publication.</p> <p>Currently, the dataset is still under development. In its next iteration, the dataset will include options for retrieving image syntax and temporal aspects, in addition to the semantics.</p>
Global datasets to evaluate a multi-sensor approach for observation of floods
<p><strong>1. Overview</strong></p> <p>This repository contains datasets used to evaluate potential improvements to flood detectability afforded by combining data collected by Landsat, Sentinel-2, and Sentinel-1 for the first time globally. The datasets were produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review.</p> <p><strong>2. Dataset Descriptions</strong></p> <p>There are two datasets included here.</p> <p><strong>(a) A global grid of revisit periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their combination </strong>[GlobalMedianRevisits.zip]</p> <p>A global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.<br> Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.<br> The following equation is used to calculate revisit periods:</p> <p>Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured)</p> <p>Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.</p> <p><strong>(b) Average revisit periods of satellites for flood records in the DFO database </strong>[FloodInfo.zip]</p> <p>Average Revisit Times of Landsat, Sentinel-1, Sentinel-2 and their ensemble are calculated for 5130 flood records in the Dartmouth Flood Observatory's (DFO) flood record database. These were appended to the already existing attributes of the database.</p>
Dataset: Evaluating approximate asymptotic distributions for fast neutrino flavor conversions in a periodic 1D box
<p>In this data package we include two txt files and two zip files.</p> <p>The file Gaussian.txt contains 4 columns. The first column shows the indices of the file numbers contained in the file Gaussian_100.zip. The second, third, and fourth columns list the corresponding <span class="math-tex">\(I_{\bar\nu}\)</span>, <span class="math-tex">\(F_\nu\)</span>, and <span class="math-tex">\(F_{\bar\nu}\)</span>, respectively. </p> <p>The file Maxent.txt contains 5 columns. The first column shows the indices of the file numbers contained in the file Maxent_100.zip while the second column lists the corresponding indices in the file Gaussian.txt. The third, fourth, and fifth columns list the corresponding <span class="math-tex">\(I_{\bar\nu}\)</span>, <span class="math-tex">\(F_\nu\)</span>, and <span class="math-tex">\(F_{\bar\nu}\)</span>, respectively. </p> <p>Files Gaussian_100 and Maxent_100 give the asymptotic simulated outcomes of fast neutrino flavor conversions with the initial Gaussian and maximum-entropy distributions, respectively. Each data file xxxx.dat lists four columns for <span class="math-tex">\(v_z\)</span>, <span class="math-tex">\(P_{ee}(v_z)\)</span>, <span class="math-tex">\(g_\nu(v_z)\)</span>, and <span class="math-tex">\(g_{\bar\nu}(v_z)\)</span>, respectively. We take 100 <span class="math-tex">\(v_z\)</span> grids for all data.</p>
Study Dataset: Shedding Light on CVSS Scoring Inconsistencies: A User- Centric Study on Evaluating Widespread Security Vulnerabilities
<p>This record contains the <strong>study datasets, descriptive results and questionnaires</strong> from the paper "Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security Vulnerabilities" by Julia Wunder, Andreas Kurtz, Christian Eichenmüller, Freya Gassmann and Zinaida Benenson to appear in Proceedings of the 45th IEEE Symposium on Security and Privacy (2024).</p> <p>The pseudonymous <strong>datasets</strong> contain data from the online surveys (main study with 196 participants and follow-up study with 59 participants). The first row gives the question codes and questions, the following rows gives the answers from the participants (see also README.md).</p> <p>We also provide <strong>descriptive results</strong> from the online surveys as PDF and the questionnaires.</p> <p>Please refer to the README.md file and our paper for further details about the data set and study.</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.