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47,155 results for “evaluations”
Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"
<p>Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in <em>Frontiers in Marine Science</em></p>
Replication data for: "How does Docker affect energy consumption? Evaluating workloads in and out of Docker containers"
<p>Database of raw power measurements and energy summaries for our Docker energy tests.</p> <p>Please cite us if you use this dataset.</p> <p>Schema</p> <pre><code>CREATE TABLE configuration( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE experiment( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE run( id PRIMARY KEY, configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE ); CREATE TABLE measurement( run REFERENCES run(id) ON DELETE CASCADE ON UPDATE CASCADE, timestamp REAL NOT NULL, -- Unix timestamp in milliseoncds power REAL NOT NULL ); CREATE TABLE energy( id PRIMARY KEY REFERENCES run(id), configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE, energy REAL NOT NULL, started REAL NOT NULL, ended REAL NOT NULL, elapsed_time REAL NOT NULL -- in milliseconds );</code></pre>
Genome assemblies for "Versatile genome assembly evaluation with QUAST-LG"
<p>Supplementary data for A. Mikheenko, A. Prjibelski, V. Saveliev, D. Antipov, A. Gurevich. Versatile genome assembly evaluation with QUAST-LG. ISMB 2018 PROCEEDINGS (<em>Bioinformatics </em>journal)</p> <p>Reference genomes of</p> <ol> <li><em>Saccharomyces cerevisiae </em>(yeast) version R64-1-1 </li> <li><em>Caenorhabditis elegans </em>(worm) version WBcel235</li> <li><em>Drosophila melanogaster </em>(fruit fly) version BDGP6</li> </ol> <p>And<em> de novo</em> genome assemblies of</p> <ol> <li>Yeast_PB (<em>S. cerevisiae</em>, genome size: 12.1 Mb): Canu, FALCON, Flye, MaSuRCA, Miniasm (from Illumina pair-ends and PacBio SMRT)</li> <li>Yeast_NP (<em>S. cerevisiae</em>, genome size: 12.1 Mb): Canu, Flye, MaSuRCA, Miniasm (from Illumina pair-ends and Oxford Nanopore)</li> <li>Worm_PB (<em>C. elegans</em>, genome size: 100.3 Mb): Canu, FALCON, Flye, MaSuRCA, Miniasm (from Illumina pair-ends and PacBio SMRT)</li> <li>Fly_MP (<em>D. melanogaster</em>, genome size: 137.6 Mb): ABySS2, MaSuRCA, Meraculous, Platanus, SOAPdenovo2, SPAdes (from Illumina pair-ends and mate-pairs)</li> <li>Human_MP (<em>H. sapiens</em>, genome size: 3.1 Gb): UpperBound assembly only (from Illumina pair-ends and mate-pairs)</li> <li>Human_NP (<em>H. sapiens</em>, genome size: 3.1 Gb): UpperBound assembly only (from Illumina pair-ends and Oxford Nanopore)</li> </ol> <p>Each pack (items 1-4) is accompanied with the upper bound assembly created with QUAST-LG (for computing theoretical limits on assembly correctness and completeness for a particular genome and set of reads). For more information, interactive QUAST-LG reports, and links to <em>de novo</em> assemblies of the human datasets please visit http://cab.spbu.ru/software/quast-lg/ or write to <em>quast.support@cab.spbu.ru.</em></p>
Data and R script for 'Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Evaluating the cyclic ratio schedule as an assay of feeding behaviour in the European starling (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
Evaluation of Heracleum sosnowskyi Manden. survivial after snow cover removing in early spring
<p>The results of an experiment on the effect of snow cover removing on the areas occupied by Heracleum sosnowskyi stands in the early spring period. The experimental (impact) and control plots located in the Syktyvkar city suburb (Komi Republic, Russia).</p> <p>Most of calculation were performed in R. Find the file "FrozenHogweed_R_script.r" for calculation reproducing.</p>
Supporting dataset for: Repository optimisation & techniques to improve discoverability and web impact : an evaluation
<p>This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a>. </p> <ul> <li>Macgregor, G. (2018). <em>Repository optimisation techniques to improve discoverability and web impact: an evaluation</em>. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: <a href="https://doi.org/10.17868/65389/">https://doi.org/10.17868/65389/</a></li> </ul> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.</p>
Datasets for "BB correction" algorithm evaluation and analysis
<p>The datasets contain the raw and processed data relevant to the "BB (black body) correction" method, an algorithm to correct for scattering and systematic bias in quantitative neutron imaging from experimental measurements. Five CT datasets imaging various hydrogenous and non hydrogenous materials are uploaded. The raw data folders for each dataset contains the sample projections over the tomographic range as well as the image used for referencing: the dark current, the open beam files and the additional "BB images", obtained with an interposed grid of neutron absorbers (BBs) from which scattering and bias components are estimated. The processed folders contain the CT reconstruction with and without the BB correction. All processing was done with the MuhRec open source software (https://zenodo.org/record/1311698#.W30exxixVtg)</p>
Evaluating Multi-Tenant Live Migrations Effects on Performance
<p>Results for Evaluating Multi-Tenant Live Migrations Effects on Performance article (Coopis 2018)</p> <p>Framework used to generate this data available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms</a></p> <p>Code for data interpretation available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms/coopis2018/xp_paper.ipynb</a></p> <p>Files description :</p> <ul> <li>png images : BPM process schemas used for the experimentations (AdditionalApproval, HumanTask and M3Process)</li> <li>xp1.csv : data for the <em>Migration duration </em>experiment</li> <li>xp3.csv : data for the <em>Migration effects on migrated tenant</em> and <em>Migration effects on co-located tenants</em> experiments</li> </ul>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Laboratory Tests
<p>A study was conducted to evaluate the repeatability of the Flexibility Index of asphalt mixtures obtained according to AASHTO TP-124-16. Three asphalt concrete samples were mixed and compacted using the Superpave Gyratory Compactor in one laboratory. The samples were then cut to specific dimensions for semi-circular bend testing based on the AASHTO Specifications at a single laboratory using a dedicated cutting equipment. The samples were randomized and distributed equally among three different testing labs.</p> <p>The process was repeated three times and in some instances the rate of loading was varied.</p> <p>This experiment allowed to study the repeatability of the the Flexibility Index</p>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Field Material
<p>The data contained herein is part of a study conducted with support from the Utah Department of Transportation. In the study, seven asphalt mixtures from across the state of Utah were collected at the plant (prior to delivery) and at laydown (prior to compaction). The mixtures were sealed in metal containers and brought to three different laboratories where the asphalt mixtures were compacted using a Superpave gyratory compactor into cylinders. Each cylinder was cut using a masonry saw to create semi-circular samples with a notch in the middle based on the specification from AASHTO T124-16. The samples were tested following the procedures outlined in the specification with some exceptions where the loading rate was changed. The results were used to developed specification limits.</p>
RAGE - Data Final Evaluation RAGE Components
<p><strong>General description: </strong>Data from the final (summative) evaluation of RAGE game components (assets)</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology<br> Geographical information: Europe<br> Time information: October-November 2018</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: RAGE software components for applied games development<br> Evaluation variables: usability, usefulness, relevance, game engineering, benefits, cost effectiveness, quality of support material</p> <p><strong>Instruments:</strong> Usability Metric for User Experience - UMUX (Finstad, 2010); items adapted from perceived usefulness scale (Davis, 1989; Davis and Venkatesh, 2004); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209200">10.5281/zenodo.1209200</a>, <a href="https://doi.org/10.5281/zenodo.1209204">10.5281/zenodo.1209204</a></p>
RAGE - Data Evaluation JobQuest pilot 2
<p><strong>General description: </strong>Data from the second (summative) Evaluation of the JobQuest game - pilot round 2</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing<br> PsycINFO Classification: 3530 Curriculum & Programs & Teaching Methods</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology, RANDSTAD<br> Geographical information: France<br> Time information: June-August 2018</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group: </strong>target groups - demand side - end users - adults - unemployed, temporary workers</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: JobQuest game<br> Methodology/design: Pre-post test design<br> Evaluation variables: usability; user experience - usefulness, enjoyment; job search efficacy</p> <p><strong>Instruments:</strong> questionnaire items adapted from Career Self Efficacy Scale - CSES (Solberg et al., 1994), Job Seeking Self-Efficacy Scale - JSS (Barlow, Wright, & Cullen, 2002), Game User Experience Satisfaction Scale - GUESS (Phan, Keebler, & Chaparro, 2016), Intrinsic Motivation Inventory - IMI (Ryan, 1982)</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills - understanding, remembering, applying, creating; affective skills - interpersonal, intrapersonal<br> ESCO skills: provide assistance with job search (http://data.europa.eu/esco/skill/7903f946-aeff-4024-b146-8e48018d4eb9), prepare for job interview (http://data.europa.eu/esco/skill/929e89f9-ac69-4bcb-9eea-37257aa5aff9)<br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.1209194">10.5281/zenodo.1209194</a>.</p>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
Initial evaluation of the MOVING platform
<p>This resource contains information about the laboratory study carried out as an initial evaluation of the MOVING platform.</p> <p>It contains the information gathered from the questionnaires, and qualitative notes taken during the study for two different use cases. The design of the study and the results of the analysis can be found in the deliverable 1.3 "Initial evaluation, updated requirements and specifications"</p> <p><a href="http://moving-project.eu/deliverables/">http://moving-project.eu/deliverables/</a></p>
CVD2014 - A database for evaluating no-reference video quality assessment algorithms
<p>The CVD video database is developed to provide an useful tool for researchers in the validation and developing processes of no-reference (NR) objective video quality assessment (VQA) algorithms. It consists of 234 videos from five different scenes captured by 78 different cameras (mobile phones, compact camera, video camera, SLR). The subjective experiments are conducted following the Single-Stimulus (SS) procedure to collect ratings of video quality.</p> <p><strong>Setup</strong></p> <p>We implement our experiments according to the Single Stimulus methodology using VQone MATLAB toolboxon high quality monitors (Eizo ColorEdge CG241W) with 1920x1200 pixel resolution in a dark room (ambient light < 20 lux). Video stimuli were displayed at their original size of VGA (640 x 480) or HD (1280 x 720). The subjects viewing distance (80 cm) was controlled by a string hanging from a ceiling and they were instructed to keep their head steady next to it. The monitors were calibrated to according to sRGB (target values were: 6500 K, 80 lux, and gamma 2.2) using EyeOne Pro calibrator (X-rite co.). The laboratory setup is showed in the figure below.</p> <p><strong>Subjects</strong></p> <p>Subjects (n = 30, 30, 28, 33, 30, 32 and 27 for Tests 1 - 7 respectively) were naïve in a sense that they did not study or work with image quality or related fields. They were recruited through student mailing lists consisting mainly humanities and behavioral science students. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward.</p> <p><strong>Procedure</strong></p> <p>Subjects evaluated one video sample at a time and all video samples of one scene were presented in a row. The order of video samples and scenes was randomized. Subjects had the option to view video samples again as many times as they wanted.</p> <p><strong>Data</strong></p> <p>The results are processed and reported in the form of Mean Opinion Score (MOS) for the tested video samples. In addition, we provide the whole raw data from the subjective experiments instead of just pre-calculated mean opinion scores from each video sample. This allows further analyses to be made by those who wish to use this database and gives them better opportunity to utilize the data to its full potential.</p> <p>Realignment study (test 7) contains the data from the additional study in which the mappings from the test and scene specific quality scales (test 1-6) to the global quality scale were formed. The global scale is valuable when studying and developing VQA algorithms. With the global scale, all of the samples (234 video samples in the case of the CVD2014) are in the same scale, and the performance analysis for algorithms can be conducted with a high number of samples.</p> <p><strong>If you use this database in your research, we kindly ask that you follow The Copyright notice below and cite the following paper:</strong></p> <p> </p> <p>M. Nuutinen, T. Virtanen, M. Vaahteranoksa, T. Vuori, P. Oittinen and J. Häkkinen, "CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms," in <em>IEEE Transactions on Image Processing</em>, vol. 25, no. 7, pp. 3073-3086, July 2016. doi: 10.1109/TIP.2016.2562513</p> <p> </p> <p> </p> <p>-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------</p> <p>Copyright (c) 2014 The University of Helsinki<br> All rights reserved.</p> <p>Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University of Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission.</p> <p>The database and our paper are to be cited in the bibliography as: M. Nuutinen, T. Virtanen, M. Vaahteranoksa, T. Vuori, P. Oittinen and J. Häkkinen, "CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms," in <em>IEEE Transactions on Image Processing</em>, vol. 25, no. 7, pp. 3073-3086, July 2016.<br> doi: 10.1109/TIP.2016.2562513</p> <p>-----------------------------------------------------------------------------</p> <p>LIMITATION OF LIABILITY</p> <p>UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE.</p> <p>THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.</p> <p>THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW.</p> <p>-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------</p>
Evaluating data-flow coverage in spectrum-based fault localization
<p>This release contains files with the results of the experiment comparing the use of data- and control-flow spectra for Spectrum-based Fault Localization. It also has instructions to run Jaguar to perform experiments. The subject programs used in the experiment are public available in our GitHub repository.</p>
List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists
<p>Linked lists represent a countable number of ordered values, and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID, a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.</p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties
<p>This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests.</p> <p>The approach is described in <a href="http://dx.doi.org/10.1145/3297280.3297500 ">http://dx.doi.org/10.1145/3297280.3297500 </a></p> <p>DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them.</p> <p>Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out <a href="http://dx.doi.org/10.5445/IR/1000046878">http://dx.doi.org/10.5445/IR/1000046878</a> for a reference on the used specification language.</p> <p>For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.</p> <p> </p>
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</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.