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1,956 results for “test data”
STIR ROOT Consistency Test Data
<p>Data generated by `ROOT_STIR_consistency` for the `test_view_offset_root` test. This data is pre-generated GATE data of point sources, measured by a GATE geometry (similar to the GE Discovery 690), that is to be used to test the allignment between STIR and GATE crystal positions.</p>
LiBforSecUse Data Release - Impedance spectra of life cycle tests of commercial 18650 cells
<p>The EMPIR project LiBforSecUse aimed to develop empirical measurement models to estimate the residual capacity of second-use Li-ion battery cells with impedance-based measurement and evaluation methods. The models have been established based on a series of life cycle tests of commercial 18650 (graphite/NMC) cells including regular impedance spectroscopy and capacity measurements. The measured data are made publicly available here. They can be downloaded to verify the models established within the project and they may be used for further investigations. However, the user is asked to pay tribute to the project and the researchers providing the data by citing this data source. A pdf file is added to give more detailed information on the data.</p>
Gauge structure of the Einstein field equations in Bondi-like coordinates: convergence tests data
<p>This dataset is the result of the runs performed for the paper "Gauge structure of the Einstein field equations in Bondi-like coordinates". The data can be used to reproduce the convergence plots, as well as to compare with the data obtained if one performs the same runs independently.</p> <p> </p> <p>To perform independently the runs that produce the data, the PITTNullCode included has to be used. More details on how to proceed with this can be found in the ancillary files of https://arxiv.org/abs/2111.14794 at the directory "anc/numerics/README".</p>
Unit Test Trace Analysis Data
<p>In order to find performance changes at code level, Peass (https://github.com/DaGeRe/peass) uses unit tests as proxy for the performance of a software. The performance of a unit test may not change if the called source stays the same. Therefore, PeASS' regression test selection identifies which tests to execute. For further analysis of the performance changes, the changed source is analysed and methods, which are called and which contain source code changes, are extracted.</p> <p>This dataset provides the results of the regression test selection and source code analysis of Apache Commons Compress, CSV, DBCP, fileupload, imaging, IO, JCS, numbers, pool and text and httpcomponents-core.</p> <p>To execute the analysis, execute the following steps:</p> <pre><code class="language-bash">tar -xvf peass_metadata_commons.tar # extract folder chmod +x getCalls.sh # Make script executable, since Zenodo provides scripts without x flag ./getCalls.sh # Execute analysis</code></pre> <p>This consumes a significant amount of hard disc space and contains long filenames; therefore execution on eCryptfs might yield problems.</p>
Location-based augmented reality (LBAR) spatial data test
<p>This repository gathers video data (screen capture) collected on a field test conducted on the 11th of May 2022, at the HEIG-VD in Yverdon-les-Bains, Switzerland.<br> <br> The goal of the test was to submit LBAR interfaces to different sources of spatial data. The 5 conditions compared were:<br> <br> 1) ARCore interface (visual odometry) fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 2) ARCore interface (visual odometry) fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 3) A-Frame + LBAR.js interface fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 4) A-Frame + LBAR.js interface fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 5) A-Frame + LBAR.js interface fed with position and orientation data provided by an external Inertial Navigation Station Xsens MTi-680g (IMU + GNSS/RTK).</p>
Test data for jga-analysis per-sample workflow
<p>Test data for jga-analysis per-sample workflow.</p> <p>Please see:</p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis">https://github.com/biosciencedbc/jga-analysis</a></p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl">https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl</a></p>
Data associated with the manuscript "Simple statistical models can be sufficient for testing hypotheses with population time series data"
<p>This is a revised version of the archive of R code and data used in the manuscript, <em>Simple statistical models can be sufficient for testing hypotheses with population time series data. </em>The data are in three files. <em>etodata1.csv</em> and <em>etodata2.csv</em> contain two versions of the same data for shoal-dwelling fishes in the Etowah River and associated environmental covariates. <em>knz_dat</em> contains data for small mammals collected in the Konza Prairie Biological Station and associated environmental covariates. The R code consists of four primary files that call nine auxiliary files. CaseStudy1-main_code and CaseStudy2-main_code are the primary files for running the two case studies. Simulations1 and Simulations2 are the files for running the two batteries of simulations. We thank the Konza Prairie Biological Station and Konza Prairie Long-Term Ecological Research Program supported by the National Science Foundation (DEB-1440484) for collecting and providing access to mammal community data. More details are in the manuscript and supporting information. </p>
Full dataset of several mechanical tests on an S355 steel sheet as reference data for digital representations
<p>The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI.</p> <p>The data were generated in the frame of the digitization project Innovationplatform Material<em>Digital </em>(PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.</p>
Experimental data collected during first-phase testing of the Ground CO2 Mapper
<p>The various Excel files included in this dataset report data from tests performed to assess the technical capabilities of the Ground CO2 Mapper, a newly developed tool that can be used to help reduce uncertainty in the mapping of geological or anthropogenic CO2 leakage from the ground surface. These files include data from a number of laboratory experiments as well as tests performed at a controlled release site and a natural site where geological CO2 is released over a large area. This dataset was used to create the various figures presented in the article "Development and testing of a rapid, sensitive, high-resolution tool to improve mapping of CO<sub>2</sub> leakage at the ground surface" by Graziani, Beaubien, Ciotoli and Bigi to be published in Applied Geochemistry.</p>
RobotReviewer evaluation data (new test set)
<p>Includes 3,324 openly available PDFs (<em>rct_pdfs.zip</em>) with risk-of-bias annotations (<em>robotreviewer_eval_data.json</em>) from Cochrane systematic reviews. This data has not been used in the development of RobotReviewer and in this way represents a new, unseen test set. For each PDF/pubmed ID, Cochrane topics are also provided (<em>robotreviewer_topics.json</em>).</p>
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
<p>Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological datasets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in datasets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological datasets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded dataset on palaeognathid birds. We find that in small, simulated datasets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger datasets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a dataset, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space.</p>
Data release for Tests of General Relativity with GWTC-3
<p>This is the full posterior samples release of the following analyses reported in the paper <a href="https://arxiv.org/abs/2112.06861">Tests of General Relativity with GWTC-3</a> from the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration:</p> <ul> <li>Inspiral-merger-ringdown consistency test (Sec IV B): <strong><em>IGWN-GWTC3-TGR-v1-imr.zip</em></strong></li> <li>Lorentz invariance violation test (Sec VI): <strong><em>IGWN-GWTC3-TGR-v1-liv.zip</em></strong></li> <li>Parametrized tests of general relativity (Sec V A): <strong><em>IGWN-GWTC3-TGR-v1-par.zip</em></strong></li> <li>Ringdown test (Sec VIII A): <em><strong>IGWN-GWTC3-TGR-v1-rin.zip</strong></em></li> <li>Spin-induced quadrupole moment test (Sec V B): <em><strong>GWN-GWTC3-TGR-v1-sim.zip</strong></em></li> </ul> <p>Each zip file contains HDF5 files that can either be read directly with standard HDF5 tools or using PESummary (<a href="https://docs.ligo.org/lscsoft/pesummary/">https://docs.ligo.org/lscsoft/pesummary/</a>). The curated data set used for producing the figures and table in the paper can be found in <a href="https://dcc.ligo.org/LIGO-P2100456/public">https://dcc.ligo.org/LIGO-P2100456/public</a>.</p> <p>If you make use of these data or software in your own work, please include the following acknowledgment.</p> <blockquote> <p>LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), and Japan Society for the Promotion of Science (JSPS), National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan. Unless otherwise specified, the contents of this release are licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.</p> </blockquote>
LPSE data for ray-based CBET test cases
<p>This dataset contains field data from LPSE simulations for the purpose of validating ray-based CBET models. The input parameters required to replicate these results are given in the Physics of Plasmas paper "Validation of ray-based cross-beam energy transfer models." All of the data is stored in HDF5 files. Each file has three data sets: Ez, xAxis, and yAxis (the 1-D datset does not have yAxis).</p> <p>Here is an example of the Matlab code to open and plot one of the 2-D files:</p> <pre><code>filename = 'two_beam_at_caustic.h5'; hInfo = h5info(filename ); data = h5read(filename , '/Ez'); xAxis = h5read(filename , '/x_axis'); yAxis = h5read(filename , '/y_axis'); figure(1); clf; imagesc(yAxis, xAxis, data), colorbar, axis xy </code></pre> <p>Here is an example of the Python code to open and plot one of the 2-D files:</p> <pre><code class="language-python">import h5py import matplotlib.pyplot as plt filename = 'two_beam_at_caustic.h5' f = h5py.File(filename, 'r') Ez = list(f["Ez"]) x_axis = list(f["x_axis"]) y_axis = list(f["y_axis"]) plt.figure() plt.pcolormesh(x_axis,y_axis,Ez) plt.colorbar() plt.show() </code></pre> <p> </p>
Synthetic and field data to test RASE performance
<p>Texts 1 and 3 are synthetic data and labels; Texts 2 and 4 are field data and labels.<br> The data dimension is N*8640, where N is the number of collection days, and 8640 is the number of data sampling points.</p>
Replication Data for "Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades"
<p>In this research (publication included in the package), we have used author-assigned keywords as a quantitative data source for understanding the connections between keywords and research topics in software testing research, based on a large sample of studies from Scopus.</p> <p>We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. This package contains the map and network files used to perform our analyses, as well as the publication sample.</p>
Data from: Can extreme climatic events induce shifts in adaptive potential? A conceptual framework and empirical test with Anolis lizards
<p>Multivariate adaptation to climatic shifts may be limited by trait integration that causes genetic variation to be low in the direction of selection. However, strong episodes of selection induced by extreme climatic pressures may facilitate future population-wide responses if selection reduces trait integration and increases adaptive potential (i.e., evolvability). We explain this counter-intuitive framework for extreme climatic events in which directional selection leads to increased evolvability and exemplify its use in a case study. We tested this hypothesis in two populations of the lizard <em>Anolis scriptus</em> that experienced hurricane-induced selection on limb traits. We surveyed populations immediately before and after the hurricane as well as the offspring of post-hurricane survivors, allowing us to estimate both selection and response to selection on key functional traits: forelimb length, hindlimb length, and toepad area. Direct selection was parallel in both islands and strong in several limb traits. Even though overall limb integration did not change after the hurricane, both populations showed a non-significant tendency toward increased evolvability after the hurricane despite the direction of selection not being aligned with the axis of most variance (i.e., body size). The population with comparably lower between-limb integration showed a less constrained response to selection. Hurricane-induced selection, not aligned with the pattern of high trait correlations, likely conflicts with selection occurring during normal ecological conditions that favor functional coordination between limb traits, and would likely need to be very strong and more persistent to elicit a greater change in trait integration and evolvability. Future tests of this hypothesis should use G-matrices in a variety of wild organisms experiencing selection due to extreme climatic events. </p>
Reference atmospheres, surface and instrument data used in the test experiments presented in Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82)
<p>The supplied archive includes a set of ASCII files defining the reference atmospheric and surface states that are the basis of the simulation experiments presented in the paper of Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82). Along with atmospheric and surface data, we also supply the seasonal variability and mismatch errors used in the test experiments presented in that paper, the noise error covariance matrices anticipated for FORUM and IASI-NG spectra, and some details of the retrieval setup. The archive includes a README file summarizing the contents of the various files provided.</p>
Acceleration Data at Various Locations on Vehicle On Four Post Test Rig over Different Roads and at Different Tyre Pressures
<p>Dataset of acceleration data at various locations on sport utility vehicle on four post test rig over different roads and at different tyre pressures. This dataset can be used for driving comfort evaluation. </p>
Test data for MultiColorSPR
<p>Dataset to test the developed workflow of multi-color single particle reconstruction. The associated software is available on GitHub: https://github.com/christian-7/MultiColorSPR </p>
Data from: Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment
<p>The role of spontaneous mutations in evolution depends on the distribution of their effects on fitness. Despite a general consensus that new mutations are deleterious on average, a handful of mutation accumulation experiments in diverse organisms instead suggest that of beneficial and deleterious mutations can have comparable fitness impacts, i.e., the product of their respective rates and effects can be roughly equal. We currently lack a general framework for predicting when such a pattern will occur. One idea is that beneficial mutations will be more evident in genotypes that are not well adapted to the testing environment. We tested this prediction experimentally in the laboratory yeast <em>Saccharomyces cerevisiae</em> by allowing nine replicate populations to adapt to novel environments with complex sets of stressors. After >1000 asexual generations interspersed with 41 rounds of sexual reproduction, we assessed the mean effect of induced mutations on yeast growth in both the environment to which they had been adapting and the alternative novel environment. The mutations were deleterious on average, with the severity depending on the testing environment. However, we find no evidence that the adaptive match between genotype and environment is predictive of mutational fitness effects.</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.