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470 results for “Evaluation dataset”
Dataset for the manuscript "In silico evaluation of variant calling methods for bacterial whole genome sequencing"
<p>Input data and associated analysis code for reproducing results reported in the manuscript.</p>
Dataset D2 used to evaluate HycDemux
<p>Dataset D2 used to evaluate hycdemu.</p>
Dataset D1 used to evaluate HycDemux
<p>Dataset D1 used to evaluate hycdemu</p>
Dataset for "Adding the SMT solver OpenSMT2 to the JavaSMT Framework and Evaluation using CPAchecker"
<p>Benchmark data and code examples for the thesis. See README.md files for the exact locations.</p>
Dataset #2 for paper "A Close-Range Photogrammetric Surface Scanner and its Evaluation"
<p>This dataset is supplementary material to the submission “A low-cost close-range photogrammetric surface scanner”, by Panagiotis Koutlemanis, Xenophon Zabulis, Nikolaos Stivaktakis, Nikolaos Partarakis, Emmanouil Zidianakis, and Ioanna Demeridou. The dataset contains 3D reconstructions that show the results of a close-range photogrammetric approach and its implementation using a low-cost CNC device. </p>
A reply to the critical evaluation of the Oscillayers methods and dataset
Open the record for dataset details and reuse information.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Evaluation Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.<br><br>The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lower TA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Evaluation Dataset
The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image 8-Day Running Mean V5.3 Evaluation Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a evaluation product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.<br><br>The evaluation Version 5.3 is identical to the Version 6.0 validated release with the exception that Version 5.3 uses the Version 5 L1B antenna temperatures (TA) as input. The V6 L1B TA uses a lowerTA threshold for RFI exclusion. Until the full back-processing of V6.0 is complete, the evaluation Version 5.3 can and should be used instead. Version 5.3 has been processed from the beginning of the SMAP mission to the end of 2023, and each data file is available in netCDF-4 file format.Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 5.3 release is the smoothed salinity product with a spatial resolution of approximately 70 km.
Datasets Used in Evaluation Of RNAi And CRISPR Technologies By Large Scale Gene Expression Profiling In The Connectivity Map
GEO Series GSE106127. Homo sapiens. 0 samples. Type: Expression profiling by array.
Evaluation of single cell classifiers for single cell RNA-seq datasets
GEO Series GSE128982. Homo sapiens; Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Common Mitochondrial Deletions in RNA-Seq: Evaluation of Bulk, Single-Cell and Spatial Transcriptomic Datasets
GEO Series GSE226663. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Other.
Data-Driven Domain Discovery (D4) - Evaluation Datasets
<p>Data used for the evaluation of our data-driven domain discovery algorithm (D4). In our evaluation, we used four different datasets from two repositories - <a href="https://opendata.cityofnewyork.us/">NYC Open Data</a> and <a href="https://opendata.utah.gov/">State of Utah Open data</a>. Both repositories were downloaded using the <a href="http://api.us.socrata.com/api/catalog/v1">Socrata Open Data API</a> on Nov. 22nd 2016 and on Sep. 27th 2019, respectively. The downloads contained 1114 datasets and 1953 datasets, respectively.</p> <p>NYC Open Data contains datasets from NYC agencies such as Department of Education and Department of Finance. Each dataset is labeled using 13 different labels. Based on these labels, we obtained three datasets: (a) Education, (b) Finance (using labels Economy and Finance) and (c) Services which includes all tables that are not in (a) or (b).</p> <p>The published data has been pre-processed using D4 (as described in the <a href="https://github.com/VIDA-NYU/domain-discovery-d4">README</a> file). For our evaluation we only considered columns where the majority of distinct terms are text. The published datasets contain the column metadata file (columns.tsv), the index of unique terms across all datasets in the two repositories (term-index.txt.gz), and the set of equivalence classes derived from the term index (compressed-term-index.txt.gz). We also include files containing terms for 25 ground truth domains that we used to evaluate our algorithm.</p> <p> </p>
Utah Open Data - D4 Evaluation Datasets
<p><a href="https://opendata.utah.gov/">State of Utah</a> data used for the evaluation of our data-driven domain discovery algorithm (D4). This repository contains 1766 tables that were downloaded on 2019-09-27 from the <a href="http://api.us.socrata.com/api/catalog/v1">Socrata Open Data API</a>.</p> <p> </p>
Websites Quality In Use Evaluation Dataset
<p>This dataset contains a number of computed dynamic analysis metrics related to quality in use for the 5,000 most popular websites.</p>
NYC Open Data (Services) - D4 Evaluation Datasets
<p><a href="https://opendata.cityofnewyork.us/">NYC Open</a> Services data used for the evaluation of our data-driven domain discovery algorithm (D4). This repository contains 692 tables that were downloaded on 2016-11-22 from the <a href="http://api.us.socrata.com/api/catalog/v1">Socrata Open Data API</a>.</p> <p> </p>
TAU Urban Acoustic Scenes 2020 3Class, Evaluation dataset
<p>TAU Urban Acoustic Scenes 2020 3Class development dataset consists of 10-seconds audio segments from 10 acoustic scenes grouped into <strong>three major classes</strong> as follows:</p> <ul> <li>Indoor scenes - <em>indoor</em>: airport, indoor shopping mall, and metro station</li> <li>Outdoor scenes - <em>outdoor</em>: pedestrian street, public square, street with medium level of traffic, and urban park</li> <li>Transportation related scenes - <em>transportation</em>: travelling by a bus, travelling by a tram, travelling by an underground metro</li> </ul>
Mobile flux plane dataset for the paper Evaluating the Mobile Flux Plane (MFP) Method to Estimate Methane Emissions using Large Eddy Simulations (LES)"
<p>This dataset contains the mobile flux planes generated from the WRF-LES simulations at the sampling distances of 10m, 20m, 30m, 40m, 50m, 75m, 100m, 125m, and 150m downwind of different well pads. Each flux plane is 400 m long and 1 km high with 81 vertical levels. </p>
ClairCity Evaluation Dataset
<p>Meta data and data entries for the evaluation of ClairCity</p>
Intelligent Java Dataset Construction and Visualization Evaluation for Reliable Software Development
<p>These are four datasets used in the paper "An Intelligent Java Method Name Recommendation Framework via Two-phase Neural Networks." They are the Liukui dataset, New-100, New-400, and RQ5.</p>
DCASE 2024 Challenge Task 10 Evaluation Dataset: Acoustic-based Traffic Monitoring
<h3><strong>Directory structure:</strong></h3> <p><strong>locX.zip</strong><br>|----- test [<em>test flac files inside</em>]</p> <p> </p> <p><strong>NOTE:</strong> We split zip files for large size folders (i.e., loc1, loc3, loc6). Please make sure to download <em><strong>all splits</strong></em> before unzipping the data! For example, run </p> <p><code>zip -s 0 loc6.zip --out unsplit-loc6.zip</code></p> <p><code>unzip unsplit-loc6.zip</code></p> <p>in your terminal once you have downloaded <code>loc6.z01</code>, <code>loc6.z02</code> and <code>loc6.zip</code>.</p> <p> </p> <h3><strong>Contact:</strong></h3> <p>If there is any question, please contact:</p> <ul> <li>Luca Bondi: <a href="mailto:luca.bondi@us.bosch.com">luca </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:luca.bondi@us.bosch.com"> bondi@us (dot) bosch (dot) com</a></li> <li>Shabnam Ghaffarzadegan: <a href="mailto:shabnam.ghaffarzadegan@us.bosch.com">shabnam </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> ghaffarzadegan@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:shabnam.ghaffarzadegan@us.bosch.com"> com</a></li> <li>Wei-Cheng (Winston) Lin: <a href="mailto:winston.lin@us.bosch.com">wei-cheng </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> lin@us </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> bosch </a><a href="mailto:luca.bondi@us.bosch.com">(dot)</a><a href="mailto:winston.lin@us.bosch.com"> com</a></li> </ul> <p> </p> <h3><strong>Acknowledgement:</strong></h3> <p><em>This work has partially received funding from the European Union's Horizon 2020 research and innovation programme under the Marie </em><em>Skłodowska</em><em>-Curie grant agreement No. 956962 and from the European Research Council under the European Union's Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA (no. 773268). This work reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.</em></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.