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470 results for “Evaluation dataset”
Ground truths for dataset search using similarity methods generated from a user evaluation
<p>The dataset contains ground truths for 6 different use cases and 10 levels of agreement among 10 users participating in a user evaluation of dataset search using similarity methods in data catalogs. The data contains collections linking to dataset metadata available in https://doi.org/10.5281/zenodo.4433464.</p>
Test_S: a synthesised dataset for evaluating few-shot Bioacoustic events detection system (DCASE 2021 Task5)
<ul> <li>The database consists of : <ul> <li>DC class and MEL class </li> <li>dense bioacoustic event like official Evaluation Set in each audio</li> </ul> </li> </ul>
Dataset for "Evaluation Methods and Replicability of Software Architecture Research Objects"
<p># Content<br> In this package, please find the following content:</p> <p>* Investigated Papers.bib<br> A BibTeX file with all papers investigated in the paper "Evaluation Methods and Replicability of Software Architecture Research Objects"<br> * Raw-Data-Table Content-Data.html and Raw-Data-Table Meta-Data.html<br> Tables with the raw data as extracted during the systematic literature review<br> * Colection of Data Visualizations.pdf<br> Multiple visualizations of the raw data for analysis. A copy of summary.pdf as described below.<br> * Data and Visualization<br> Contains:<br> - The data as CSV files,<br> - scripts for creating visualizations<br> - *.awk -- Awk scripts are used to create the corresponding of the *.csv files in data<br> - *.rb -- Ruby scripts to build the respective figures in figs as *.tex files<br> - make-all.sh -- A script to call all other scripts for creating diagrams and the summary<br> - make-paper-figures.sh -- A script to build "paper-figures.pdf" with all diagrams used in the accompanying paper<br> - A documentation of the contained scripts (Data and Visualization/README.md)<br> - summary.pdf -- A collection of diagrams (as built by make-all.sh)<br> - paper-figures.pdf -- A collection of all diagrams as used in the accompanying paper (as built by make-paper-figures.sh and make-all.sh)<br> * Wiki/<br> A copy of the wiki used during data extraction.<br> Constains:<br> - descriptions of all data items<br> - the process description<br> - the taxonomy used for data extraction</p> <p><br> # Reproduction<br> You can reproduce the visualizations with the following commands in a UNIX command line environment.</p> <p>> cd "Data and Visualization"<br> > ./make-paper-figures.sh<br> > ./make-all.sh</p> <p>The requirements are:<br> * A UNIX command line environment (e.g., bash) with awk installed<br> * Ruby (>2.5)<br> * latex (e.g., tex-live)</p> <p>The command "./make-paper-figures.sh" produces the file “paper-figures.pdf”, which contains all diagrams that are used in the paper.<br> The command "./make-all.sh" produces the file "summary.pdf", which contains diagrams used for data analysis, and the file "paper-figures.pdf". All figures describing the results in the paper are also in "summary.pdf".<br> These commands each take about 2 ("/make-paper-figures.sh") / 8 ("./make-all.sh") minutes to run on current standard laptop (Intel i5-8250U, 16 GB memory).<br> Calling the commands produces many log statements (information and warnings), which show the progress and can be ignored.</p>
An evaluation dataset for the skills of CMIP5 and CMIP6 models in simulating climate of China
<p>General circulation model (GCM) simulations archived by the Coupled Model Intercomparison Project (CMIP) are crucial tools for climate science. However, with various GCM results simulated by different countries and institutions, researchers have difficulty in choosing appropriate models for their unique study area. To this end, this dataset provieds Tayler skill scores of 28 GCMs in simulating temperature and precipitation of 631 reference sites across China under daily, monthly and seasonally scales. These scores are calculated based on the observations of meteorological stations and historical simulations of GCMs during 1970-2005. </p> <p>The dataset is very important for researchers to select locally appropriate GCMs. For example, researchers concerned with climate change of Beijing could firstly download the GCMs with sound performance at station 54511 (i.e., NorESM2-LM, INM-CM5-0 and MPI-ESM1-2-LR for temperature and NorESM1-M, IPSL-CM5A-LR and INM-CM4 for precipitation), and then conduct the further works of downscaling.</p>
Evaluating Feature Attribution Methods in the Image Domain: High-Dimensional Datasets
<p>Here you can find the versions of the Places-365 and Caltech-256 datasets used in the paper Evaluating Feature Attribution Methods in the Image Domain.</p>
DCASE 2022 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" for the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring"><strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>"</a>.</p> <p> </p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following three papers. </p> <ul> <li>Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [<a href="https://arxiv.org/abs/2206.05876">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
TAU Urban Acoustic Scenes 2022 Mobile, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2022 Mobile evaluation dataset consists of 1-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>Traveling by a tram - <em>tram</em></li> <li>Traveling by a bus - <em>bus</em></li> <li>Traveling by an underground metro - <em>metro</em></li> <li>Urban park - <em>park</em></li> </ul>
Assessing Word Similarity Metrics for Traceability Link Recovery - Evaluation Dataset
<p>This dataset includes all data that was used for the evaluation of my bachelor's thesis:</p> <p><em>Assessing Word Similarity Metrics for Traceability Link Recovery</em></p> <p>The following files correspond to the following data sets from the evaluation:</p> <ul> <li>cc-en-300.tar.gz corresponds to fastText's cc.en.300.bin embedding</li> <li>crawl-300d-2M-subword.tar.gz corresponds to fastText's crawl-300d-2M-subword.bin embedding</li> <li>wiki-news-300d-1M-subword.tar.gz corresponds to fastText's wiki-news-300d-1M-subword.bin embedding</li> <li>wordnet.tar.gz corresponds to the WordNet 3.1 semantic network</li> <li>sewordsim.tar.gz corresponds to SEWordSimDB's vector similarity database</li> <li>glove_cc_840B_300d.tar.gz corresponds to GloVe's CC vector embedding</li> <li>glove_wikigiga_300d.tar.gz corresponds to GloVe's 300 dimensional WIGI vector embedding</li> <li>glove_wikigiga_200d.tar.gz corresponds to GloVe's 200 dimensional WIGI vector embedding</li> <li>glove_wikigiga_100d.tar.gz corresponds to GloVe's 100 dimensional WIGI vector embedding</li> <li>glove_wikigiga_50d.tar.gz corresponds to GloVe's 50 dimensional WIGI vector embedding</li> <li>glove_twitter_200d.tar.gz corresponds to GloVe's 200 dimensional TWTR vector embedding</li> <li>glove_twitter_100d.tar.gz corresponds to GloVe's 100 dimensional TWTR vector embedding</li> <li>glove_twitter_50d.tar.gz corresponds to GloVe's 50 dimensional TWTR vector embedding</li> <li>glove_twitter_25d.tar.gz corresponds to GloVe's 25 dimensional TWTR vector embedding</li> <li>eval_results.tar.gz contains the detailed evaluation results for each configuration of all measures</li> </ul> <p>The licenses of all data sets are included in their respective files.</p> <p>Some of these data sets are .sql files. To use these files to reproduce the evaluation, they need to be imported into a sqlite3 database. The version of ArDoCo used for the evaluation is only able to work with sqlite3 databases and not with sql files.</p>
Dataset for: Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts
<p>Points clouds of Fjallsjökull calving front, as derived by a Raspberry Pi and a Unoccupied Aerial Vehicle. For each sensor, eight sub-sections are analysed. Point clouds are provided in .las format. Each sub-section is generated within its own spatial reference (matching that of the other sensor to allow for comparison). </p>
Implementation and evaluation of a multilingual search pilot in the Europeana digital library (dataset)
<p>The dataset contains the data required to reproduce the experiments done in the paper "Implementation and evaluation of a multilingual search pilot in the Europeana digital library", published in the 26th International Conference on Theory and Practice of Digital Libraries (<a href="http://tpdl2022.dei.unipd.it/">TPDL'22</a>). In that work we implemented a pilot applying query translation to English from the Spanish version of the website in order to surface results that have English metadata associated with them. The dataset is also available at <a href="https://rnd-2.eanadev.org/share/crosslingual_SpanishPilot/">https://rnd-2.eanadev.org/share/crosslingual_SpanishPilot/</a>, and it is organized in three main folders:</p> <ul> <li><strong>sample</strong>: stratified sample of 300 queries queries issued from the Europeana Spanish portal from 1st<br> December 2020 to 28th February 2021.</li> <li><strong>evaluation.translations: </strong>manual annotation of the quality of the identification of the language of the queries using Google Cloud Translation API, and the quality of the translation obtained using Google plus the CEF translation service (eTranslation).</li> <li><strong>evaluation.search_retrieval: </strong>manual annotation of the relevancy of the (binary) relevance of the documents that are retrieved by one system but not by the other (current monolingual version vs pilot) in their top ten.</li> </ul>
Supporting dataset for drafted article Evaluation of Multi-step Forecasting Models
<p>Supporting dataset for drafted article Evaluation of Multi-step Forecasting Models</p>
DATASET - Evaluation of a novel inorganic scintillator for applications in LDR brachytherapy using both TE-cooled and room temperature SiPMs
<p>This dataset includes the raw data and the analyzed data which are included in the conference paper entitled: "Evaluation of a novel inorganic scintillator for applications in LDR brachytherapy using both TE-cooled and room temperature SiPMs".</p> <p> </p> <p>Agnese Giazc, Simona Comettic, Romualdo Santoroc, Peter Woulfea,b, Massimo Cacciac, and Sinead O’ Keeffeb<br> aDepartment of Medical Physics, Galway Clinic, Galway H91 HHT0, Ireland; bOptical</p>
EU-TeachPaaS Resource Evaluation Dataset
<p>Dataset from user evaluation of educational resources and materials co-created in the EU-TeachPaaS project.</p>
Dataset used for paper "Evaluating simplifications of subsurface process representations for field-scale permafrost hydrology models"
<p>No description provided.</p>
How long is the last mile? Evaluating successful malaria elimination trajectories [Dataset]
<p>Annual malaria case series were sought for 56 successful elimination programmes through an extensive non-systematic review of published and unpublished documents, including searches of PubMed (https://pubmed.ncbi.nlm.nih.gov/), Google Scholar (scholar.google.com), Google Books (books.google.com), WHO’s Institutional Repository for Information Sharing (https://apps.who.int/iris), online surveillance databases, and the authors’ existing collection of malaria-related books and reports. Several of these elimination programmes were implemented in regions that today have disputed jurisdictional claims or countries that no longer exist; their inclusion is to understand the impact of the historical programmes implemented there and no statement on their current geopolitical context is intended.</p> <p>When annual case totals were classified by origin, the number of locally acquired (i.e., autochthonous) cases was recorded separately from the number of imported cases. If not classified (as is typical until case incidence falls to very low levels), all reported cases were assumed to be locally acquired. Where locally acquired cases were further subclassified, indigenous and cryptic cases were tallied together, while introduced or induced cases were not included in case totals since such cases may arise sporadically in response to importation and are not considered to jeopardize elimination. </p> <p>The first year in which zero indigenous or locally acquired cases were recorded was taken as the termination point of the case series, regardless of whether sporadic or resurgent local transmission occurred subsequently. While this definition of elimination is looser than that required for certification by WHO (i.e., three years with no indigenous transmission), it permits us to examine a wider range of elimination experiences, including some where success was not maintained. Secondary re-elimination efforts (where a year with zero local cases was achieved, malaria subsequently resurged, and then elimination was again achieved), as in the case of Mauritius or the former republics of the USSR, were not included. </p> <p>Sources for the malaria case data are enumerated in the accompanying Word document. For questions or clarifications, please contact Justin Cohen at jcohen@clintonhealthaccess.org.</p>
EVALUATION OF RADAR-BASED PRECIPITATION DATASETS FOR APPLICATIONS IN THE LOUISIANA COASTAL MASTER PLAN
<p>A suite of hydrological and ecological models is used in the Louisiana Coastal Master Plan (CMP) Integrated Compartment Model (ICM) to assess potential benefits of restoration and protection projects. A main driving input for these models is precipitation. Since precipitation is considered a major source of freshwater in coastal Louisiana (50-60 inches/year), accurate information about its magnitudes and spatial and temporal distributions is critical for successful implementation of the ICM and other CMP-related modeling studies. Accurate information on precipitation is also critical for establishing reliable and representative water budget analyses for coastal Louisiana. In contrast to the sparse availability of rain gauges, weather radars can provide high-resolution rainfall estimates with excellent spatial resolutions and coverage over coastal Louisiana. The potential value of radar-based precipitation products has not been capitalized on yet by the CMP studies. Louisiana is covered by several National Weather Service (NWS) NEXRAD radar stations, with the two radar stations located near Slidell and Lake Charles being the most relevant to coastal Louisiana. The main objective of the graduate research project is to perform a regional-scale assessment of radar-rainfall datasets available over coastal Louisiana and evaluate whether they can be directly used by the CMP studies. Unlike past assessment studies, which were geared towards short-term applications (e.g., flood prediction and forecasting), the focus of this study will be on assessing accuracy aspects that are of direct relevance to the CMP applications. The study will examine (a) the reproducibility of inter-annual and intra-annual variability by the radar dataset, (b) the representation of extreme rainfall events, and (c) the representation of spatial distributions of annual and seasonal rainfall across various regions of the coast with distinctly different climatic regimes. The study will provide the CMP with quantitative assessment on the advantages and limitations of radar-based precipitation datasets and whether they can be readily used by the ICM models. This research will also provide insight on the levels of uncertainties in the radar-rainfall datasets and the implications for these uncertainties for the ICM models.</p> <p> </p> <p>Please make sure you work with the latest version of this data set only (check the panel on the right for versioning).</p>
TUT Sound events 2016, Evaluation dataset
<p>TUT Sound events 2016, evaluation dataset consists of 10 audio recordings from two acoustic scenes:</p> <ul> <li>Home (indoor), 5 recordings, totaling 17:49</li> <li>Residential area (outdoor), 5 recordings, totaling 17:49</li> </ul>
TUT Acoustic scenes 2017, Evaluation dataset
<p>TUT Acoustic Scenes 2017, evaluation dataset consists of 10-seconds audio segments from 15 acoustic scenes:</p> <ul> <li>Bus - traveling by bus in the city (vehicle)</li> <li>Cafe / Restaurant - small cafe/restaurant (indoor)</li> <li>Car - driving or traveling as a passenger, in the city (vehicle)</li> <li>City center (outdoor)</li> <li>Forest path (outdoor)</li> <li>Grocery store - medium size grocery store (indoor)</li> <li>Home (indoor)</li> <li>Lakeside beach (outdoor)</li> <li>Library (indoor)</li> <li>Metro station (indoor)</li> <li>Office - multiple persons, typical work day (indoor)</li> <li>Residential area (outdoor)</li> <li>Train (traveling, vehicle)</li> <li>Tram (traveling, vehicle)</li> <li>Urban park (outdoor)</li> </ul> <p>Each acoustic scene has 108 segments totaling 18 minutes of audio.</p>
TUT Sound events 2017, Evaluation dataset
<p>TUT Sound events 2017, evaluation dataset consists of 8 audio recordings from a single acoustic scene:</p> <ul> <li> Street (outdoor), totaling 29:09</li> </ul>
Dataset to determine the prevalence of delirium in belgian nursing homes by a cross-sectional evaluation
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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)
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.