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7 results for “testset”
Word Sense Change Testset
<p><strong>Overview</strong></p> <p>This testset consists of 23 terms which have experienced word sense change during the past centuries. The main changes for each term were found using Wikipedia, dictionary.com and the Oxford English Dictionary. We consider major changes in usage as well as changes to sense. In cases where multiple (fine-grained) senses were available, we opted to accept the widest sense. E.g. for the term <em>rock</em> we consider a music sense without any distinction between different types of <em>rock music</em>, because our dataset is unlikely to have fine-grained sense differentiations. If a clear time point cannot be pinpointed, we choose the earliest possible. For comparison purposes we also chose a set of 11 terms that have experienced minimal change during the investigated period, i.e., stable terms.</p> <p> </p> <p><strong>Supplementary material</strong></p> <p><em>1. testset.txt </em></p> <p>Contains a list of all terms and the different change types for each term with a short description of the sense and change.</p> <p> </p> <p><em>2. Files of the kind "TERM.txt"</em></p> <p>The header tells us the term, which clustering coefficient was used, which similarity threshold and which similarity measure.</p> <p>A path starts with "Path:". </p> <p>A unit starts with "UNIT:"</p> <p>and the numbers following indicate 1. the number of years that the unit spans, and then a list of all years that the internal clusters stem from.</p> <p>E.g., UNIT: 83 1785, 1787, 1790, 1793, 1798, 1801, 1823, 1867, spanns 83 years and consists of clusters from year 1785, 1787, 1790 etc.</p> <p>Indentation shows the tree structure, more indentation means lower level branch in the tree.</p> <p>As an example, in AEROPLANE.txt unit UNIT: 23 1908, 1909, 1910, 1911, 1914, 1918, 1930, 1908 is the root node and the unit is related to UNIT: 27 1916, 1919, 1924, 1932, 1942, 1916.</p> <p> </p> <p><strong>Interesting findings</strong></p> <p>The longest units and paths are found for stable terms, e.g., <em>newspaper</em>. These are statistically significantly longer than the average units and paths for terms that later evolve.</p> <p><em>Newspaper</em> has a unit that spans 145 years and the first path spans from 1852 - 2007.</p> <p> </p> <p><em>FLIGHT.txt</em></p> <p>For the term <em>flight</em> we find that the first unit captures a name, <em>Flight & Robson</em> who were organ builders.</p> <p>The second unit (it its own path) represents the <em>flight</em> over a hurdle: UNIT: 28 1868, 1869, 1870, 1877, 1885, 1889, 1890, 1892, 1893, 1894, 1895</p> <p>There is a unit (it its own path) that represents the <em>flight</em> of a cricket ball: UNIT: 29 1938, 1957, 1966</p> <p>Finally, the last path represents <em>flight</em> as in a means of transportation, in particular for holidays, starting with UNIT: 19 1962, 1970, 1973, 1980</p> <p> </p> <p><em>TAPE.txt</em></p> <p>The first path for tape is a path related to <em>sowing tape</em>.</p> <p>Then there is a second path starting with UNIT: 38 1970, 1974, 2007 that takes up the <em>musical tape</em>.</p> <p>The last path end in the same units that the second path ends in, also related to the <em>musical tape</em>.</p> <p>The <em>music tape</em> and the <em>sowing tape</em> should be related because of their shape, but we cannot find any relation as there are few or no overlapping terms.</p> <p> </p>
Annotated images from yeast cell lifespans - Testset- 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 ~ 35 000 images from 50 cell 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 50 timeseries of classes corresponding to the lifespan of the 250 cells.</p> <p>It is related to the trained 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</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>
MIQP-TestSet
<p><strong>Introduction</strong></p> <p>The test set provides a large collection of nonconvex problems consisting of quadratic, binary quadratic and integer quadratic programs.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~803MB.</li> <li>The dataset contains the instances used in the manuscript “Spectral Relaxations and Branching Strategies for Global Optimization of Mixed-Integer Quadratic Programs” by Nohra, Raghunathan and Sahinidis (see Citation section).</li> <li>Files in the unzipped folder: <ul> <li>README.md</li> <li>BoxQP_instances_data/</li> <li>CBQP_instances_data/</li> <li>EIQP_instances_data/</li> <li>QSAP_instances_data/</li> <li>TestSetDescription.pdf.</li> </ul> </li> <li>The format of the test instances are described in TestSetDescription.pdf.</li> </ul> <p><strong>Citation</strong></p> <p>If you use MIQP-TestSet in your research, please cite our paper:</p> <pre><code>@article{doi:10.1137/19M1271762, author = {Nohra, Carlos J. and Raghunathan, Arvind U. and Sahinidis, Nikolaos}, title = {Spectral Relaxations and Branching Strategies for Global Optimization of Mixed-Integer Quadratic Programs}, journal = {SIAM Journal on Optimization}, volume = {31}, number = {1}, pages = {142-171}, year = {2021}, doi = {10.1137/19M1271762}, }</code></pre> <p><strong>Copyright and License</strong></p> <p>The MIQP-TestSet dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2020, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
LastMile-TestSet
<p><strong>Introduction</strong></p> <p>MERL Last Mile Problem Set</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~5.3MB</li> <li>The dataset contains the instances used in the manuscript "Seamless Multimodal Transportation Scheduling" by Raghunathan, Bergman, Hooker, Serra, and Kobori (see Citation section).</li> <li>Files in the unzipped folder: <ul> <li>./README.md</li> <li>express_500_10: Folder with instances.</li> <li>express_750_10: Folder with instances.</li> <li>express_1250_25: Folder with instances.</li> <li>express_1875_25: Folder with instances.</li> <li>ProblemObjectiveInformation.xlsx: Provides information on each problem instance including the optimal solution.</li> <li>regular_1000_10: Folder with instances.</li> <li>regular_1500_10: Folder with instances.</li> <li>regular_2000_10: Folder with instances.</li> <li>regular_2500_25: Folder with instances.</li> <li>regular_3750_25: Folder with instances.</li> <li>regular_5000_25: Folder with instances.</li> <li>regular_5000_50: Folder with instances.</li> <li>regular_7500_50: Folder with instances.</li> <li>regular_10000_50: Folder with instances.</li> </ul> </li> <li>The instances of this problem set are organized in folders with names following the pattern regular_NP_ND and express_NP_ND, where NP and ND are the number of passengers and the number of destinations in the instances of the corresponding folder. Folders that begin with "regular" have uniform mass transportation service for all origins. Folders that begin with "express" have better mass transportation service for certain origins, as described below.</li> </ul> <p><strong>Other Details</strong></p> <p>In each folder, we have files with names following the patterns merl_lastmile_demand_NP_ND_TW_I.txt, merl_lastmile_arrivals_NP_ND.txt, and merl_lastmile_network_NP_ND.txt, where NP and ND are the number of passengers and the number of destinations as before. In addition, TW is the size of the time window (in minutes) and I is the number of the instance (from 1 to 5). Every instance corresponds to a combination of the passenger demands in each file that begins with "merl_lastmile_demand" along with the mass transportation services in the file that begin with "merl_lastmile_arrivals" and the last mile routes to each destination in the file that begins with "merl_last_mile_network". Those latter two files are unique in each folder and they can differ from the corresponding files in other folders that describe instances with the same number of destinations ND.</p> <p>Each file that begins with "merl_lastmile_demand" has the following structure. The first line contains the number N of mass transportation stations used as origin for the passengers. These stations are numbered from 1 to N. The second line contains the number M of locations used as last mile destinations for the passengers. These locations are numbered from 1 to M. The third line contains the number P of passengers. The next P lines describe the relevant information of each passenger as 4 integer numbers: their station of origin, last mile destination, earliest time for arrival at destination, and latest time for arrival at destination. Please note that those times are given in integer units that are multiples of 30 seconds.</p> <p>Each file that begins with "merl_lastmile_arrivals" has the following structure. The first line containts the number N of mass transportation stations used as origin for the passengers. These stations are numbered from 1 to N. The remaining lines describe transportation legs taking each passenger from their station of original to the alighting terminal in which they should board for their last mile trip. Each line describes a mass transit leg as 3 integer numbers: the station of origin, the departure time from the state of origin, and the arrival time at the alighting terminal. In practice, you can assume that all legs with the same arrival time correspond to a single mass transportation service. In the folders that begin with "express", the departure time for certain stations of origin is a very large negative number. That implies that, in practice, that mass transportation service does not serve the corresponding station. Please note that those times are given in integer units that are multiples of 30 seconds.</p> <p>Each file that begins with "merl_lastmile_network" has the following structure. The first line contains the number M of locations used as last mile destinations for the passengers. These locations are numbered from 1 to M. The second line contains the number L of time units for loading and unloading of passengers in the last mile service. This time should be added after the conclusion of every operation of loading or unloading passengers in the last mile service. The third line contains the number R of routes used for last mile service. Those routes are numbers from 0 to R-1. The remaining lines describe those routes by providing the times, relative to departure from the alighting terminal, that it takes for a given route to be in different locations. Each line describes that as 3 integer numbers: the last mile route in use, the last mile location, and the time relative to departure for reaching that location. Every route begins and ends at the alighting terminal, which is denoted as 0. The first line for each route has a time 0 at the alighting terminal and the last line for each route has the time to arrive back at the alighting terminal. Please note that those times as well as the duration L are given in integer units that are multiples of 30 seconds.</p> <p>Certain parameters of the experiments are not described in those files, such as the number and capacity of last mile vehicles. The coefficient alpha that is used to balance between minimizing passenger travel time and number of trips using last mile vehicles is also not given. The values described for those parameters are tested with all instances.</p> <p><strong>Citation</strong></p> <p>If you use LastMile-TestSet in your research, please cite our paper:</p> <pre><code>@misc{raghunathan2022seamless, title={Seamless Multimodal Transportation Scheduling}, author={Arvind U Raghunathan and David Bergman and John Hooker and Thiago Serra and Shingo Kobori}, year={2022}, eprint={1807.09676}, archivePrefix={arXiv}, primaryClass={math.OC} } </code></pre> <p><strong>Copyright and License</strong></p> <p>The LastMile-TestSet dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2020,2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
microPIECE testset
<p>Test set for microPIECE</p> <p><a href="https://github.com/microPIECE-team/microPIECE-testset">Github Repository</a></p>
Mathematical Subjective Questions Handwriting Recognition TestSet
Open the record for dataset details and reuse information.
Toward General Speech Restoration With VoiceFixer - Testsets
<p>This is the test set for the paper VoiceFixer: Toward General Speech Restoration with Neural Vocoder<em>.</em></p> <p>If you found this dataset helpful, please consider citing: </p> <blockquote> <pre>@article{liu2021voicefixer, title={VoiceFixer: Toward General Speech Restoration with Neural Vocoder}, author={Liu, Haohe and Kong, Qiuqiang and Tian, Qiao and Zhao, Yan and Wang, DeLiang and Huang, Chuanzeng and Wang, Yuxuan}, journal={arXiv preprint arXiv:2109.13731}, year={2021} }</pre> </blockquote>
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.