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412 results for “verification”
Database on Certified Reference Materials measured with PAT tools for validation and verification purposes
<p>The H2020 PAT4Nano project aims to develop and demonstrate Process Analytical Technologies (PAT) tools for nanosuspension characterization which have sufficiently high resolution, accuracy, and speed, for real-time industrial process monitoring and control. Real time monitoring is desired for example to obtain: small, high precision, specialty batch of materials, processing monitoring of nucleation/growth/milling of materials at different scales (lab, pilot, production), and for producing feedback loops (adapt T, pH, etc.,) needed for process control.<br> Laser diffraction (LD), Spatially Resolved Dynamic Light Scattering (SR-DLS), Cross-Correlation Dynamic Light Scattering (CC-DLS), Ultrasound Nanoparticle Sizer (UNPS), Raman, and Transmission Electron Microscopy (TEM) are the main PAT tools used in this project. For validation and verification purposes of these measurement techniques, polystyrene and silica samples (200 and 1000 nm particle size) were selected as (Certified) Reference Materials ((C))RMs) by the consortium partners. The results described in this database are particle size measurements using PAT methods in an offline mode. The particle size and particle size distribution data are presented as the D10, D50 and D90 and PDI/span measured with each PAT tool.<br> Raman spectra of the CRMs are presented as well. Here, particle size data was extracted by using chemometric software. Lastly, TEM images of the CRMs are included in the database to cross-correlate and cross-validate the results of the spectroscopic and scattering PAT tools.</p>
Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect - Complementary Data
<p>This repository contains the UMC and SPARX EA data used in the paper:</p> <p>Experimenting with Formal Verification and Model-based Development in Railways: the case of UMC and Sparx Enterprise Architect</p> <p>by Davide Basile, Franco Mazzanti and Alessio Ferrari.</p>
Dataset for : A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
<p>We present a novel solution combining Large Language Model (LLM) capabilities with Formal Verification strategies to falsify and automatically repair software vulnerabilities. Initially, we employ Bounded Model Checking (BMC) to locate the software vulnerability and derive a counterexample. Relying on mathematical proofs, counterexamples provide evidence that the system behaves incorrectly or contains a vulnerability, thereby preventing the generation of false positive alerts. The counterexample that has been detected, along with the source code, are provided to the LLM engine. Our approach involves establishing a specialized prompt language for conducting code debugging and generation to understand the vulnerability's root cause and repair the code. Finally, we use BMC to verify the corrected version of the code generated by the LLM. As a proof of concept, we create \esbmcai based on the Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained Transformer model, specifically gpt-3.5-turbo, to detect and fix errors in C programs. We generated a dataset comprising $1{,}000$ C code samples, each consisting of $20$ to $50$ lines of C code. Experimental results show that our proposed method achieved an impressive success rate of up to $80$\% in repairing vulnerable code, encompassing buffer overflow, arithmetic overflow, and pointer dereference failures. To our knowledge, \esbmcai represents the first proposal for a pioneering initiative to integrate a Large Language Model (LLM) with software model checking. We advocate that this automated approach has the potential to incorporate into the software development lifecycle's continuous integration and deployment (CI/CD) process. </p> <p> </p> <p>The uploaded dataset contains 1000 codes, each comprising 20 to 50 lines of C code generated with gpt-3.5-turbo. The material also consists of a version of ESBMC statically compiled with all dependencies, a classifier script, and the output file.</p> <p> </p> <p> </p>
WRF Forecast Data used for Verification of multi-resolution model forecasts of heavy rainfall events of 23rd-26th August 2017 over Nigeria
<p>A deterministic Weather Research and Forecasting model version 4.2 forecast of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) within the period 23<sup>rd</sup>-26<sup>th</sup> August 2017 over Nigeria. The model was setup to perform two nested domain simulations with 18 (parent domain), 6 and 2 km (hereafter WRF18, WRF6 and WRF2) horizontal resolutions. The outer domain covers West Africa and the innermost domain, which runs at convection-permitting scale, focuses on Nigeria. When interpreting the results, it is worthy of note that the data has been regridded to 18 km, which is 3 x the grid scale for WRF6 and 9 x the grid scale for WRF2. This means that there is a fair degree of smoothing that has been applied using a bilinear regridding process to get the models onto a level playing field. Only WRF18 retains its native grid and has not benefited from any additional smoothing.</p> <p>The WRF model setup is similar to the study of Gbode et al. (2019; DOI: https://doi.org/10.1007/s00704-018-2538-x) in terms of the model physics combination used in the model simulations. The parameterization schemes used are the Goddard (GD) WRF model microphysics (MP), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) and the Bett-Miller-Janjic (BMJ) cumulus convection (CU) parameterization schemes. This combination was found to reproduce realistic rainfall and temperature relative to gridded observations over West Africa. The GD is a six-class microphysics with graupel and modifications for ice/water saturation. MYJ is a local closure scheme that predicts turbulent kinetic energy and the BMJ CU is a profile adjustment scheme that relaxes both deep and shallow profiles toward a reference profile without explicit updraft, downdraft, or cloud entrainment. However, the CU scheme was turned off in the 2 km domain to explicitly represent convection.</p>
Verification Witnesses from Verification Tools (SV-COMP 2020)
<p>This data set contains verification witnesses produced by tools for software verification that participated in the 9th International Competition on Software Verification (SV-COMP 2020).</p>
Word-in-Context Target Sense Verification
<pre>Formally, WiC is framed as a <strong>binary classification</strong> task. Each instance in WiC-TSV consists of a target word <em>w</em> with a corresponding target sense <em>s</em> represented by either its definition (subtask 1) or its hypernym/s (subtask 2), and a context <em>c</em> containing the target word <em>w</em>. The task aims to determine whether the meaning of the word <em>w</em> used in the context <em>c</em> matches the target sense <em>s</em>. In the following table there are some examples from the dataset. </pre> <p> </p> <p>Subtasks</p> <p> WiC-TSV has <strong>three subtasks</strong> - participants can submit results in any of the subtasks:</p> <p>Subtask 1: Definitions</p> <p>In Subtask 1 systems make use of <strong>definitions</strong> for deciding whether the target word in context corresponds to the given definition or not.</p> <p>Subtask 2: Hypernyms</p> <p>In Subtask 2 systems make use of <strong>hypernymy</strong> information for deciding whether the target word in context is a hyponym of the given hypernym or not.</p> <p>Subtask 3: Definitions + Hypernyms</p> <p>In subtask 3 systems can make use of <strong>both</strong> sources of information, i.e., definitions and hypernyms.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - volume filtering verification
<p>For each of the four ROIs (R1 to R4 in file names) we pick few sub-regions (annotated in R?_legend.png) and show the changes between sections (temporally encoded) from initial (registered) input, marked "1" in video to the final result of volume filtering, marked "5" in video.</p> <p>Subregion number j in ROI number i is bears the video file name "R<i>_verification_volume_-_region_<j>_FullHD.mov".</p>
Setup of the TPV16 benchmark for dynamic rupture verification
<p>Verification benchmark for simulations with dynamic rupture and local time stepping with SeisSol, version Shaking Corals. More details can be found in the publication Uphoff et al. "Extreme scale multi-physics simulations of the tsunamigenic 2004 Sumatra megathrust earthquake", 2017 and on the project homepage (http://www.seissol.org/).</p>
Rotating Shallow Water Verification Suite Output
<p>The Rotating_Shallow_Water_Verification_Suite_Output directory contains the output obtained by running a verification suite of shallow water test cases for the barotropic solver of ocean models using two types of spatial discretizations: a mimetic finite volume method based on the TRiSK scheme and a discontinuous Galerkin spectral element method (DGSEM); and a variety of time-stepping methods. The associated code can be cloned from the Github repository <a href="https://github.com/siddharthabishnu/Rotating_Shallow_Water_Verification_Suite.git">Rotating_Shallow_Water_Verification_Suite</a>. The output, which is organized into the two directories MPAS_Ocean_Shallow_Water_Output and DGSEM_Rotating_Shallow_Water_Output corresponding to the two spatial discretizations, includes:</p><ul><li>text files containing the exact solution and the numerical error of the various test cases at specific instants of time along with their plots; and </li><li>text files containing the L2 error norm with respect to the exact solution for refinement in both space and time, and the L2 norm of the difference in the numerical solution for refinement only in space and only in time, along with the convergence plots.</li></ul>
Supplementary materials for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone"
<p>This is a ReadMe for the supplementary material for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone" written by Rina Noguchi and Wataru Nakagawa.</p> <p>----------------------<br>[ReadMe.txt]<br>ReadMe text file.</p> <p>[FigS1.png]<br>This figure is a supplementary figure which appeared as "Figure S1" in the main text.<br>Caption: Figure S1. Examples of conduits (dashed green lines) and loser conduits (solid magenta lines) were observed in the experiments with original and contrast-enhanced images.</p> <p>[FigS2.png]<br>This figure is a supplementary figure which appeared as "Figure S2" in the main text.<br>Caption: Figure S2. Relationships between the thickness of poured heated syrup and (A) mass losses caused by baking soda decomposition, (B) number of conduits, (C) total conduit area, (D) average conduit area, (E) number of failed conduits, and (F) sum number of conduits and failed conduits. Each plot and error bar represents the average and standard deviation in three repeated experiments, respectively. The red plots and error bars show the 350 g of heated syrup case, which performed ten repeated experiments to verify the reproducibility. Note that horizontal error bars are derived from the difficulty of strict heated syrup-pouring control.</p> <p>[Experimental_datasheet.xlsx]<br>This EXCEL file includes two sheets: a mass loss change log and a summary of experimental results.</p> <p>[movie/SSS_X_x15.mp4]<br>These MP4 files are fast-forward movies (x15) for each experiment. SSS = the amount of poured hearty syrup (g), and X = round in each condition.<br>----------------------</p> <p>For more details, please refer to a research paper "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone".</p> <p>If you have any questions, please send an e-mail to:<br>r-noguchi@env.sc.niigata-u.ac.jp<br>or<br>flugel555@gmail.com<br>.<br>(R. Noguchi)</p>
Improving Formal Verification with Portfolio-based Runtime Techniques: Dataset
<p>Evaluation Dataset of the Scientific Students’ Association Report titled Improving Formal Verification with Portfolio-based Runtime Techniques</p>
Combining Horizontal Strain DAS and Local Seismic Stations in a Full Waveform Attribute Stacking Detector/Locator Algorithm: Verification Test for the Thorbjörn, Iceland, 2020 Unrest Episode
<p>We present a waveform stacking-based earthquake catalog of the seismicity unrest episode in the Svartsengi fissure swarm close to Mt. Thorbjörn, SW Iceland, which started in January 2020 and was still ongoing in January 2021. The magmatic unrest produced more than 5 earthquake swarms comprising thousands of individual events each. We were able to combine local and regional seismic networks with 6 months recording of a 17 km long distributed acoustic sensing (DAS) fibre optical cable with a channel resolution of 4 m. The kHz DAS data were downsampled to 200 Hz and stacked every 64 m. The catalog is based on a migration-based detector / locator technique as for instance implemented in Lassie (Pyrocko). In the accompanying we demonstrate the robustness in a wide variety of applications in seismology. For this dataset, we have extended Lassie to efficiently combine linear ultra-dense sensor arrays with sparse seismological networks.</p>
ECHAM6 verification without top 10 layers synchronization and with less physics schemes
<p>Verification of ECHAM6 nudging module when ECHAM6 has synchronized to its own outputs.</p> <p>Some physics schemes are switched off as the following namelist was used.</p> <p> </p> <p>&physctl<br> LCOVER = .false.<br> lphys=.true.<br> lconv=.false.<br> lgwdrag=.false.<br> lrad=.true.<br> lsurf=.false.<br> lvdiff=.false.<br> lcond=.false.<br> /</p> <p> </p> <p>Files and description:</p> <ul> <li>ndg_197102.nc <ul> <li>ECHAM6 grb output converting to netcdf format as a reference data for ECHAM6 nudging module</li> </ul> </li> <li>diff_t_197102_gp_rmse.nc <ul> <li>Spatial root mean square errors on all levels and at all outputs (6 hourly).</li> <li>RMSE between ECHAM6 output (echam6_nudging_grb_T63_197102.01_echam) and reference data (ndg_197102.nc)</li> </ul> </li> <li>namelist.echam <ul> <li>namelist used to run ECHAM6 nudging case, in which nudging is expected to only apply to layer 11 to 47.</li> </ul> </li> <li>Four ECHAM6 outputs when nudging module is switched on <ul> <li>echam6_nudging_grb_T63_197102.01_echam</li> <li>echam6_nudging_grb_T63_197102.01_echam.codes</li> <li>echam6_nudging_grb_T63_197102.01_nudg</li> <li>echam6_nudging_grb_T63_197102.01_nudg.codes</li> </ul> </li> <li>Two log files generated by ECHAM6 executable <ul> <li>r_nudging_err</li> <li>r_nudging_log</li> </ul> </li> </ul>
ECHAM6 verification without top 10 layers synchronization
<p>Verification of ECHAM6 nudging module when ECHAM6 has synchronized to its own outputs.</p> <p>Files and description:</p> <ul> <li>ndg_197102.nc <ul> <li>ECHAM6 grb output converting to netcdf format as a reference data for ECHAM6 nudging module</li> </ul> </li> <li>diff_t_197102_gp_rmse.nc <ul> <li>Spatial root mean square errors on all levels and at all outputs (6 hourly).</li> <li>RMSE between ECHAM6 output (echam6_nudging_grb_T63_197102.01_echam) and reference data (ndg_197102.nc)</li> </ul> </li> <li>namelist.echam <ul> <li>namelist used to run ECHAM6 nudging case, in which nudging is expected to only apply to layer 11 to 47.</li> </ul> </li> <li>Four ECHAM6 outputs when nudging module is switched on <ul> <li>echam6_nudging_grb_T63_197102.01_echam</li> <li>echam6_nudging_grb_T63_197102.01_echam.codes</li> <li>echam6_nudging_grb_T63_197102.01_nudg</li> <li>echam6_nudging_grb_T63_197102.01_nudg.codes</li> </ul> </li> <li>Two log files generated by ECHAM6 executable <ul> <li>r_nudging_err</li> <li>r_nudging_log</li> </ul> </li> </ul>
Multifunctional Polymer Composites for Automatable Induction Heating with Subsequent Temperature Verification
<p>This data upload contains the metadata and datasets underlying the manuscript: "Multifunctional Polymer Composites for Automatable Induction Heating with Subsequent Temperature Verification".</p> <p>A description of the uploaded data is found in the README.txt.</p>
Simulation data for the step-by-step verification of PIC-MCC codes from VKI and LPP
<p>This dataset contains the results of PIC-MCC simulations of a 2D plasma in a box in 7 test cases with different boundary conditions. The results can be used to verify the correct implementation of PIC and MCC routines. The simulations are performed using the LPPic code, developed at the Laboratoire de Physique des Plasmas (LPP) at École polytechnique, and with Pantera, developed at the von Karman Institute for Fluid Dynamics (VKI).</p> <p><strong>CrossSections.zip</strong> contains the cross-sections used in the simulations in tabulated form. The first column is the collision energy in the center-of-mass frame in [eV]. The second column is the cross-section in [m²]. The cross-section for elastic e−H and electron impact excitation are taken from the Morgan database on LXCat (<a href="https://nl.lxcat.net">https://nl.lxcat.net</a>). The cross sections for H+−H elastic and charge exchange collisions are taken from the tabulated data in Schultz <em>et al.</em>. The ionization cross section corresponds to the fit of Janev <em>et al.</em>, but the energy has been scaled down by a factor of 10 for practical reasons.</p> <p><strong>DensityTemporalProfiles_xxx.csv</strong> contain the temporal evolution of the mean electron and ion density, normalized to the initial density. The data from VKI also contains the corresponding confidence interval estimates.</p> <p><strong>DensitySpatialProfiles_xxx.csv</strong> contain the spatial profiles along the x-coordinate (x in [0,1]m) of the electron and ion densities at the lat time step (t = 5 μs), spatially avergaed in the interval 0.4 m < y < 0.6 m. The data from VKI also contains the corresponding confidence interval estimates.</p> <p><strong>ResultsComparison.zip </strong>contains the temporal history of many domain-integrated quantities (number of particles, total momentum, energies, ...) for each run of each test case using both codes.</p> <p><strong>ErrorQuantification.zip</strong> contains the results of the same integrated quantities, in addition to a final snapshot of the simulation domain, for each test case at three progressive levels of refinement, obtained using the VKI code. These data has been used to compute the confidence intervals for temporal and spatial quantities.</p> <p><strong>DensityConvergenceRate.pdf </strong>contains plots of the effective convergence rate for the mean density of electrons and ions as a function of time for all test cases, computed using the VKI code.</p>
Verification of library complexity in the HEK-Cas9 sublibraries - sequence data of the generated sublibraries A and B
<p>Sequence data of the generated HEK-Cas9 sublibraries A and B, linked to the manuscript 10.1128/mbio.01925-24: The <em>Bordetella</em> effector protein BteA induces host cell death by disruption of calcium homeostasis by Martin Zmuda, Eliska Sedlackova, Barbora Pravdova, Monika Cizkova, Marketa Dalecka, Ondrej Cerny, Tania Romero Allsop, Tomas Grousl, Ivana Malcova, and Jana Kamanova</p>
Dataset supporting publication: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."
<p>DATASET suporting: "A novel ROM methodology to support the estimation of the energy savings under the Measurement and Verification protocol."</p> <p>Piccinini, Alessandro; Hajdukiewicz, Magdalena; D'Angelo, Letizia; Blanes, Luis Miguel; Keane, Marcus M.</p> <p>This paper presents a novel Reduced Order grey box Model (ROM) methodology, based on a ResistorCapacitor (RC) network, which supports the creation of the baseline energy consumption and the estimation of energy savings due to Energy Conservation Measures (ECMs) under the Measurement and Verification protocol. Within this scope, a description of the RC network, including a calculation of the parameters’ needed to execute the ROM, are presented. This ROM methodology is demonstrated on an educational building located in Sant Cugat, Spain as part of the H2020 GEOFIT project. The results presented in this paper demonstrate that the ROM is sufficiently accurate for the creation of the baseline energy consumption and for estimating the energy savings of different ECMs.</p>
I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge
<p><strong>Dear Users,</strong></p> <p><strong>Data is password protected, to get password all you need to do is register using below link. Note that data is free of Cost </strong></p> <p><a href="https://forms.gle/1gsVhJaJYT4mBp83A">Click here for Registration</a></p> <p>Speaker Verification (SV) is a task to verify the claimed identity of the claimant using his/her voice sample. Though there exists an ample amount of research in SV technologies, the development concerning a multilingual conversation is limited. In a country like India, almost all the speakers are polyglot in nature. Consequently, the development of a Multilingual SV (MSV) system on the data collected in the Indian scenario is more challenging. With this motivation, the Indic- Multilingual Speaker Verification (I-MSV) Challenge 2022 has been designed for understanding and comparing the state of-the-art SV techniques. For the challenge, approximately 100 hours of data spoken by 100 speakers has been collected using 5 different sensors in 13 Indian languages. The data is divided into development, training, and testing sets and has been made publicly available for further research. The goal of this challenge is to make the SV system robust to language and sensor variations between enrollment and testing. In the challenge, participants were asked to develop the SV system in two scenarios, viz. constrained and unconstrained. The best system in the constrained and unconstrained scenario achieved a performance of 2.12% and 0.26% in terms of Equal Error Rate (EER), respectively.</p>
Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp)
<p>MERV-Jp is the dataset of meteorological forcing and multi-model runoff simulation in 135 (ver1.1) / 87 (ver2.0) Japanese basins, and contributes to carrying out a large sample rainfall-runoff simulation in Japan. In addition, MERV-Jp can be used as a benchmark to evaluate user's hydrological modeling. <br> The detailed description of MERV-Jp can be found at "Y. Sawada, S. Okugawa and T. Kimizuka (2022): Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins, Hydrological Research Letters, 16, 73-79" (https://doi.org/10.3178/hrl.16.73).</p>
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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.