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6,452 results for “Application”
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p> <p>Please, refer to the readme.txt for the description.</p>
A Metadata Application Profile for KOS Vocabulary Registries
<p>This paper reports on the end-products of a Dublin Core Application Profile for KOS Resources (KOS-AP) developed by a DCMI Task Group, which builds on work done by the NKOS group during the last decade. Included are user scenarios, a FRBR-based domain model, the attributes of KOS <i>work, expression, </i>and <i>manifestation</i> and the associated metadata elements defined in the context of user tasks. With the testing of real KOS systems that involve multiple editions, languages, delivery formats, and derivations, (such as the Dewey Decimal Classification 22nd edition and of the ASIS Thesaurus 3rd edition), the authors identified some unique issues related to KOS descriptions, for example the designation of a '<i>work'</i> in theory and practice, and the shift of management level from the whole KOS expression to concept- and label-based electronic systems. This KOS-AP effort is becoming more meaningful with the fast development of Linked Data, the success of which depends heavily on the sharing of standardized value vocabularies. The theoretical exploration, the conceptual model, and the core elements to be used for describing and accessing KOS products can be implemented by KOS registries and used in microdata of KOS Websites, as well as be adopted for use by other types of specifications that share common characteristics of KOS, such as frequently updated, translated, and derived handbooks, technical manuals, and schemas. </p>
Dataset of application to "Fondos de Cultura" in Chile for 2017. Segmented by production phase.
<p>Dataset of application to "Fondos de Cultura" in Chile for 2017. Segmented by production phase. </p>
Application of Machine Learning on MARCUS Aerosols to Remove Ship Stack Contamination
<p>contact qingniu@ou.edu for more info please</p>
Case Study Scenarios (CSS) 3 for Biomass Burning for Limb Nadir application
Open the record for dataset details and reuse information.
Fig. 1 in Fig. 2 in Fishing for Tigers: A Method for Collecting Tiger Beetle Larvae Holds Useful Applications for Biology and Conservation
Fig. 1. Degree of dimorphism for all body and leg traits. Trait means ranged from 0.1– 39.6% larger in males than females. Results for tests of significance of difference in trait size are found in Table 1.
Fig. 2 in Fig. 2 in Fishing for Tigers: A Method for Collecting Tiger Beetle Larvae Holds Useful Applications for Biology and Conservation
Fig. 2. Frequency distribution of canonical discriminant scores for male and female Laccophilus maculosus. Female mean 5 0.315, male mean 5 20.587.
Detecting Usage Protocol Violations in Deep Learning Applications
<p>This repo contains a replication package of the "Detecting Usage Protocol Violations in Deep Learning Applications" paper.</p><p>In the paper, we use two benchmarks: </p><ul><li>The first benchmark we used from NeuraLint can be found under the director name Benchmark1/SOSamples</li><li>The second benchmark we used from Humbatova et al. can be found under the director name Benchmark2/SOSamples</li></ul><p>To reproduce the results in the paper: </p><ul><li>Download the NeuralStateAnalysis Zip file.</li><li>Extract the file and go to the NeuralStateAnlaysis directory.</li><li>( Optional ) Install the requirements by running 'Pip install requirements.txt.' N.B: The requirements.txt file is already in this repo.<ul><li>To run NeuralState on Benchmark1:<ul><li>Go to Benchmark1/SOSamples directory,</li><li>Open any of the programs you want to run,</li><li>Set the path: Path-to-folder/NeuralStateAnalysis/</li><li>Then, do 'python program_id.' Since the 'NeuralStateAnalysis(model).debug()' call is already present in all programs, you'll be able to reproduce results.</li></ul></li><li>To run NeuralState on Benchmark2:<ul><li>Go to Benchmark2/SOSamples directory,</li><li>Open any of the programs you want to run,</li><li>Set the path: Path-to-folder/NeuralStateAnalysis/</li><li>Then, do 'python program_id.' Since the 'NeuralStateAnalysis(model).debug()' call is already present in all programs, you'll be able to reproduce results.</li></ul></li></ul></li></ul>
Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States (Supplementary Material)
<p>Supplementary Material to the manuscript entitled "Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States" by the same authors.</p>
Leveraging Hardware Probes and Acceleration for Testing Heterogeneous Applications
<p>The part of data and code for HFuzz: Leveraging Hardware Probes and Acceleration for Testing Heterogeneous Applications</p>
CAIRT/IASI-NG+S5/CAIRT+IASI-NG+S5 FL2S results of Case Study Scenario (CSS) 9 for filament for limb-nadir application
<p>Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 9 (only Ozone files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG + Sentinel 5 (Sentinel 5 only during day time), and the combined product CAIRT+IASI-NG+Sentinel 5.</p>
Selection and application of agri-environmental indicators to assess potential technologies for nutrient recovery in agriculture (Data Sets)
Open the record for dataset details and reuse information.
Dataset of 'Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids'
<p>As a dataset of the paper "Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids" , this dataset includes all configuration files of the idealized case and realistic test on the Arctic Ocean and the source code.</p>
Adsorption of N, He, and Ne on CGe nanoribbons for sensing and optoelectronic applications
<p>Research into nanomaterials yields numerous exceptional applications in contemporary science and technology. The subject of this investigation is a one-dimensional nanostructure, six atoms wide, featuring hydrogen-functionalized edges. The theoretical foundation of this study relies on Density Functional Theory (DFT) and is executed through the utilization of the Vienna Ab initio Simulation Package (VASP). The outcomes demonstrate the stability of adsorption configurations, along with the preservation of the hexagonal honeycomb lattice. The pristine configuration, characterized by a wide bandgap, is well-suited for optoelectronic applications, whereas adsorption configurations find their application in gas sensing. Nitrogen (N) adsorption transforms the semiconducting system into a semi-metallic one, with the spin-up state displaying semiconductor characteristics and the spin-down state exhibiting metallic attributes. The intricate multi-orbital hybridization is explored through the analysis of partial states. While the pristine system remains non-magnetic, N adsorption introduces a magnetic moment of 0.588 μ<sub>B</sub>. Examination of charge density differences indicates a significant charge transfer from N to the CGe substrate surface. Optical properties are systematically investigated, encompassing the dielectric function, absorption coefficient, and electron-hole density. Notably, the real part of the dielectric function displays negative values, a result that holds promise for future communication applications.</p>
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
Application of Artificial Intelligence in the Development of Smart Cities: An Analysis of Trends and the Research Agenda
Open the record for dataset details and reuse information.
Epidermal club cell densities in fathead minnows: assessment of method and application to a case study
<p><strong>Abstract</strong></p> <p>Many fishes possess epidermal club cells that are the presumptive source of chemical alarm cues and also play a role in innate immune defense. Club cell density has been estimated in some studies but a standardized method for quantifying these cells is lacking.</p> <p>Here, we assess the repeatability of estimating club cell density in fathead minnows (<em>Pimephales promelas</em>). Thin-sectioned histological samples of fathead minnows were stained and mounted on slides and then digitally scanned for scoring. We estimated epidermal area using the segment tool in ImageJ to simulate the traditional method of scoring microscope slides using an ocular micrometer, where epidermal area was estimated by the lengths of straight-line segments of epidermal thickness and length of the tissue sample. The second approach measured epidermal area using the freehand tool in ImageJ.</p> <p>The R<sup>2</sup> value for repeated estimates of club cell density (club cells/mm<sup>2</sup>) ranged from 0.959 and 0.969, depending on the method used to estimate epidermal area. Measurement error in estimates of epidermal area was greater than measurement error in cell counts and the freehand tool was more repeatable than the segment tool as a method to measure epidermal area.</p> <p>We applied these methods to test differences in club cell densities between two sources of fathead minnows; wild-caught fish versus lab-reared fish provided by the Environmental Protection Agency. Lab-reared fish had higher densities of club cells than wild-caught fish did, likely reflecting differences in body condition.epidermal club cells</p>
Data from: Acoustic and electrical properties of Fe-Ti oxides with application to the deep lunar mantle
<p>The overturn of titanium-rich mantle cumulates has been invoked to explain the structure and dynamics of the Moon. These dense cumulates are stable at the core-mantle boundary (CMB) and could explain field anomalies inferred from geophysical studies. We report the first acoustic and electrical experiments on natural ilmenite-rutile aggregates up to 4.5 GPa and 1920 K. Seismic velocities show a weak pressure and temperature dependence, with Vs ~4.2 (+/-0.2) km/s and Vp ~ 8.0 (+/-0.2) km/s at the CMB conditions. Conductivity increases by a factor > 10<sup>4</sup> over 373-1920 K and is >10<sup>3</sup> S/m above 1573 K. Seismic and electrical mixing models of Fe-Ti oxides - olivine rocks based on our results indicate that field velocity and conductivity estimates are reproduced satisfactorily with 3-16% Fe-Ti oxides and 20% melt. Interactions between this Ti-rich and melt-bearing layer and the adjacent core likely affect the cooling and magnetic history of the Moon.</p>
Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"
<p>Databases generated for manuscript "<strong>Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures</strong>"</p> <p>There are two zip files containing generated databases:</p> <p>The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location. </p> <p>The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin. </p> <p> </p>
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</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.