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3,915 results for “Parameters”
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
Simulation of the SLR Space Segment Evolution to Improve the Realization of Terrestrial Reference Frames and Determination of Low-Degree Gravity Field Parameters
<p>These are data obtained from simulation studies of the development of the space segment of the SLR technique. Detailed information can be found in Najder et al. (2025). Najder, J., Sośnica, K., Zajdel, R., & Kur, T. (2025). Simulation of the SLR space segment evolution to improve the realization of terrestrial reference frames and determination of low-degree gravity field parameters. <em>Journal of Geodesy</em>, <em>99</em>(6), 46. https://doi.org/10.1007/s00190-025-01971-5</p>
Experimental determination of the hafnium L-subshell fundamental parameters using the holistic approach
<p>This file contains the experimentally determined atomic fundamental parameters for the hafnium L-shells as described in "Experimental determination of the hafnium L-subshell fundamental parameters using the holistic approach" by N. Wauschkuhn, H. Gundlach and P. Hönicke. Fundamental parameters in this file: L-subshell fluorescence yields, L-shell Coster-Kronig factors, L-shell Auger yields, L-subshell photo ionization cross sections up to 23 keV, L-subshell fluorescence prodution cross sections up to 23 keV </p>
Dataset for the fine-tuning of parameters and hyper parameters, and the evaluation of the Scorca agent
<p># Scorca Data<br>This repository contains data collected during multiple tests of the Scorca agent.</p> <p>This data was used to generate the various plots seen in the ICAART paper titled ***Knowledge Modelling, Strategy Designing, and Agent Engineering for Reconnaissance Blind Chess***, and to forge decisions for the agent, i.e. with which strategy to go and which hyperparameters to use. </p> <p>It also contains multiple scripts used to generate the plots in the paper.</p> <p>## Project overview</p> <p>The project is organized into several key directories, each containing specific components of the Scorca agent's testing and data analysis:</p> <p>- /experiments: Contains various experimental data and scripts.<br> - /entropy_comp: Data and scripts related to entropy computation experiments.<br> - /piece_states_removal: Information on experiments involving the removal of piece states.<br> - /sense_comp: Contains sub-directories for adapted entropy, likely senses, and opponent move weight analysis.<br>- /misc_src: Miscellaneous source files and scripts.<br> - /efficiency: Scripts and data related to the efficiency analysis of the agent.<br> - /enemy_bot_movement: Data on enemy bot movement patterns and strategies.<br> - /history: Historical data and analysis scripts.<br> - /naive_entropy: Scripts for naive entropy calculations.<br>- /sense_comp: Sense computation related files.<br> - /all_possible_states: Tracking and analysis of all possible board states.<br> - /likely_senses: Data on the most likely senses used in various game scenarios.<br> - /opp_move_weight: Analysis of opponent move weight in different contexts.</p> <p>For the agent source code, please refer to the Scorca GitHub repository: https://github.com/Robinbux/Scorca.</p> <p>- Contact Robin in case of any inquiry (rb.stoehr@gmail.com)<br>- The licence is CC-BY 4.0.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Data Associated with Chemical Cartography with APOGEE: Two-process Parameters and Residual Abundances for 288,789 Stars from Data Release 17
<p>Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity log(g) in 17 elemental abundances of 288,789 evolved stars from the SDSS APOGEE survey. We fit these corrected abundances as the sum of a prompt process tracing core-collapse supernovae and a delayed process tracing Type Ia supernovae, thus recasting each star's measurements into the amplitudes A_cc and A_Ia and the element-by-element residuals from this two-parameter fit. Here we present the log(g)-calibrated abundances, fit parameters, process amplitudes, and element-by-element abundance residuals of 288,789 stars (310,427 spectra) in APOGEE DR17 that accompany <a href="https://arxiv.org/abs/2403.08067" target="_blank" rel="noopener">the paper</a>.</p> <p>calibration_values_final.dat contains all derived calibration offsets, including the grids of log(g) calibration offsets and zero-point offsets for two-process model analysis. The first five rows of this catalog are reproduced in Table 2 of the paper.</p> <p>logg_calib_example.ipynb is a Jupyter notebook containing Python code to load calibration_values_final.dat, extract the log(g) calibration offsets for specific element, and apply calibration offsets to 10 sample stars.</p> <p>2process_residual_abund_catalog_final.fits is the catalog of 310,427 APOGEE DR17 spectra (288,789 unique stars) containing calibrated abundances, two-process fit parameters, and abundance residuals. A full listing of columns in this catalog is given in Table 5 of the paper.</p> <p>catalog_examples.ipynb is a Jupyter notebook containing Python code to load 2process_residual_abund_catalog_final.fits, cross match with other catalogs (using AstroNN and the APOGEE DR17 Globular Cluster Value-Added Catalog as examples), and make some example plots utilizing the cross-matched data.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></p>
Observed and WRF-simulated near-surface meteorological parameters on selected James Ross Island glaciers during heatwaves in summer 2022/23
<p>The files contain time series of near-surface meteorological conditions observed on Triangular Glacier and Davies Dome on James Ross Island, Antarctica and simulated time series for these glaciers based on the Weather Research and Forecasting (WRF) model output. Observations of 2-m air temperature, 2-m wind speed, net radiation and glacier surface height are available from 01 November 2022 to 16 January 2023 (net radiation is available only on Triangular Glacier). Simulated values of 2-m air temperature, 2-m wind speed, net radiation, sensible and latent heat fluxes are available from 08 November 2022 to 16 January 2023.</p>
Videos of the processed microscope images and time series of the petrophysical parameters from image processing and geochemical simulation and of the measured induced polarisation [Video][Dataset]
<p>Supporting Information for the manuscript <em>Microfluidics and spectral induced polarization for direct observation and petrophysical modeling of calcite dissolution</em> published in Geophysical Research Letters</p> <ul> <li><strong>Data Set S1.</strong> Porosity, water saturation, and calcite sample perimeter from image<br>processing.</li> <li><strong>Data Set S2.</strong> Porosity, water conductivity, and pH from geochemical simulation.</li> <li><strong>Data Set S3.</strong> Real and imaginary components of the complex electrical conductivity at<br>2.5 Hz and CEC from petrophysical modeling.</li> <li><strong>Movie S1.</strong> Dissolution of the calcite sample with the detected contour superimposed in<br>white on the grayscale images. Time, length scale, and flow direction are indicated. In<br>case of problems launching the file, we recommend using VLC Media Player software.</li> <li><strong>Movie S2.</strong> Segmented images of the CO2 bubbles produced by the calcite dissolution.<br>Time, length scale, and flow direction are indicated. In case of problems launching the<br>file, we recommend using VLC Media Player software.</li> </ul>
Net community production, nutrients, and hydrographic parameters in the South China Sea in summer 2017
<p>In summer, the Vietnam Offshore Current (VOC) and the Kuroshio intrusion are two important processes provoking considerable environmental fluctuations in the South China Sea (SCS). Net community production (NCP) is an important proxy of biological pump strength and can be estimated based on the dissolved oxygen to argon ratio (O<sub>2</sub>/Ar) in the mixed layer. To determine the influence of the VOC and Kuroshio intrusion on the NCP in the oligotrophic SCS, we conducted high-resolution underway measurements of O<sub>2</sub>/Ar and hydrographic parameters using membrane inlet mass spectrometry (MIMS, HPR-40, Hiden, UK) and multi-parameter water quality logger (RBR Maestro, Canada) during the cruise in the northeastern SCS in summer 2017. NCP in the mixed layer was estimated using the supersaturation of O<sub>2</sub>/Ar (Delta O<sub>2</sub>/Ar) and gas transfer velocity (k). All the underway observation data were compiled into the 5-min interval. To monitor the nutritive fluctuations induced by the VOC and Kuroshio intrusion, we also collected surface water samples from Niskin bottles at sampling stations for the nutrients analysis; the nutrients were then determined by an auto-analyzer. We divided the cruise into three phases (Phase 1, 2, and 3); Phase 1 was dominated by the Kuroshio intrusion, while Phase 3 was influenced by the VOC. Because the upwelling driven by cyclonic eddies and typhoons could introduce considerable uncertainties to the NCP result, we excluded the data obtained in the upwelling regions.</p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey
<p>The full Table 1 in <em>Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey</em> (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).</p>
Supplementary materials to the paper: Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality
<p>Supplementary materials to the paper:</p> <blockquote> <p>Riccardo Bona, Davide Fantini, Giorgio Presti, Marco Tiraboschi, Isaac Engel and Federico Avanzini. 2022. Automatic Parameters Tuning of Late Reverberation Algorithms for Audio Augmented Reality. In <em>Proceedings of International Conference on Audio Mostly</em>.</p> </blockquote> <p>The supplementary materials include the reverberated audio stimuli employed in the MUSHRA listening test reported in the paper. For each type of audio stimuli (Drums, Sax and Speech) the version reverberated with each of the six target Room Impulse Responses (RIRs) is provided along with the versions reverberated using the reverb matching method proposed in the paper (two different artificial reverberators have been considered: FDN and Freeverb).</p> <p>Further, the reverberation times (<span class="math-tex">\(T_{20}\)</span>) per octave band for each considered RIR are provided.</p>
A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with the preprint "A <em>NICER</em> View of the Massive Pulsar PSR J0740+6620 Informed by Radio-Timing and <em>XMM-Newton</em> Spectroscopy" by Riley et al. (2021; <a href="https://arxiv.org/abs/2105.06980">arXiv:2105.06980 [astro-ph.HE]</a>; submitted to ApJL).</p> <p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p> <p>Please refer to the README for detailed information.</p> <p> </p> <p> </p>
A NICER View of PSR J0030+0451: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with "A <em>NICER</em> View of PSR J0030+0451: Millisecond Pulsar Parameter Estimation" by Riley et al. (2019; <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab481c">DOI: 10.3847/2041-8213/ab481c</a>).</p> <p>Also included are model modules and scripts in the Python language using the X-PSI framework.</p> <p>Please refer to the README for detailed information.</p>
Database of optical parameters for the simulation of perovskite/silicon solar cells
<p>This dataset contains a set of representative optical parameters, i.e. the wavelength dependent complex refractive index (n+ik), for common materials used in perovskite-silicon tandems. In particular: SnO2, Spiro-MEOTAD, CH3NH3PbI2, a-Si:H, undoped c-Si, MgF.<br> The data are stored in the ASCII file “<em>nk_MaterialParameters.txt</em>”, where for each material, we report three data columns: wavelength (in µm), refractive index <em>n</em> and extinction coefficient <em>k</em>.</p>
Performance Criteria and Example Parameter Sets Comparing Different Variants of the Ensemble Kalman Filter as Applied to Volcanology
<p>This dataset contains the results of various Ensemble Kalman Filter (EnKF) inversions in which synthetic GNSS and InSAR observations from an inflating magma system are assimilated into numerical models of rock deformation around a pressurized ellipsoidal magma reservoir. Each inversion uses a different variant of the EnKF, with changes to workflow meta-parameters such as the number of ensemble members or the particular update algorithm used. In particular, each filter variant is evaluated by comparing the final output model to the original synthetic model. The specific performance criteria used include (1) the root mean square error (RMSE) between the model predictions and the assimilated observations, as well as normalized misfit terms measuring the filter's ability to resolve (2) reservoir wall tensile stress, (3) easily-observable unique parameters such as reservoir position and aspect ratio, and (4) difficult-to-derive non-unique parameters such as the specific size and internal pressure of the reservoir. The assimilated data include two different scenarios, one in which inflation is caused by pressurization and another in which it is driven by a lateral reservoir expansion. Both datasets are tested with each EnKF variant. Finally, we include example matrices from within an EnKF update step to demonstrate inter-parameter correlations that develop during the assimilation and how they can be mitigated through randomization.</p>
Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach
<p>This dataset contains the experimentally determined fundamental parameters for the sulfur K-subshells from as shown in the publication with the title "Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach". The paper will be published soon in a peer-reviewd journal.</p> <p>This file contains the following fundamental parameters for sulfur: K-subshell fluorescence yield, K-shell Auger yield, Ka and Kb transition probabilities, K-subshell photo ionization cross sections up to 10 keV, K-subshell fluorescence prodution cross sections up to 10 keV</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.