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6,467 results for “process”
Publication data of Examining holistic processing strategies in dogs and humans through gaze behavior
<p>This version, compared to the version 1, further includes the figures and tables used in the publication. </p> <p>There is an error in the Table 3 file uploaded. The locations of the column names 'else upper half' and 'else lower half' or the images in the two column cells are switched.</p> <p>Please check the Figure 4 of PloS one version of the paper for correct information. <a href="https://doi.org/10.1371/journal.pone.0317455">https://doi.org/10.1371/journal.pone.0317455</a></p> <p> </p> <p>bioRXiv version: Data of Holistic Processing Strategy in Cross-Species Face Perception between Dogs and Humans </p> <p><a href="https://doi.org/10.1101/2024.06.21.599532">https://doi.org/10.1101/2024.06.21.599532</a></p> <p>&</p> <p>PloS one version: Data of Examining holistic processing strategies in dogs and humans through gaze behavior </p> <p><a href="https://doi.org/10.1371/journal.pone.0317455">https://doi.org/10.1371/journal.pone.0317455</a></p>
Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"
<p>Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>).</p> <p>For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>.</p> <p><code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.</p> <ul> <li><strong>QMC_sobol_samples</strong>: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li><strong>nmae_all_128_ens_T_Rh</strong>: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>
Processed Monthly Datasets from X-SHiELD simulations
<p>Processed monthly datasets during 2020-2021 from X-SHiELD simulations (see Cheng et al. (2022) and Harris et al. (2023) for details on the X-SHiELD model and experiment setups), including mesoscale convective system occurrence frequency, surface precipitation, 500-hPa vertical velocity (ω500), 0-6 km wind shear (shear06), storm relative helicity (SRH), convective available potential energy (CAPE), and convective inhibition (CIN), for both the current climate and a warming scenario with a uniform 4-K increase in sea surface temperature.</p>
Fig 1 in Growth performance, nutrient utilization and survival rate of Clarias gariepinus fed varied inclusion of processed Moringa oleifera diets
Fig 1: Showing Research Location
Process data for development of a digital material shadow for the press hardening route of medium manganese steel
<p>Press-hardened ultra-high strength steel parts are widely used in the automotive sector for their lightweight and safety advantages. Medium-Manganese Steels (MMnS) are being explored as an alternative to boron-manganese steels due to their high strength and ductility after quenching, achieved at lower annealing temperatures thus reducing energy usage and carbon emissions. However, industrial adoption of MMnS is hindered by challenging processing requirements, e.g. in cold-rolling and press hardening. To expedite and improve the process development, data-driven decisions based on process parameters hold promise. Establishing a link between process data and the final produced part necessitates the development of a framework for a Digital Material Shadow (DMS). This paper investigates the development of a DMS framework for the cold rolling and press hardening process chain. In conjunction with conventional data acquisition methods employed for cold rolling, novel data acquisition techniques are introduced specifically tailored for press hardening, ensuring the comprehensive availability of relevant data. Moreover, a data pipeline is implemented to enable automatic processing, visualization, and analysis of process data. To facilitate seamless data linkage across processes in the DMS, an ID-system is introduced. Finally, the developed framework’s validity is demonstrated by creating a DMS for press-hardened MMnS parts, showcasing its potential for practical applications.</p>
Dataset of Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations
<p>Dataset of <em>Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations</em></p>
Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US
<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]: half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
FIGURE 3 in A new approach to the systematics of Laeviprosopon (Brachyura: Homolidae), with remarks on the molting process of early brachyurans
FIGURE 3. Carapace with morphological structures in Laeviprosopon using the type species, L. laeve.
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
Separable processes for live "in-person" and "zoom-like" faces
<p>Increased reliance on Zoom-like (webcam) platforms for interpersonal communications has raised the question of how this new virtual format compares to real face-to-face interactions. This question is also relevant to current models of face processing. Neural coding of simulated faces engages feature-selective processes in the ventral visual stream and two-person live face-to-face interactions engage additional face processes in the lateral and dorsal visual streams. However, it is not known if and/or how live in-person face processes differ from live virtual face processes because the faces and tasks are essentially the same. Current views of functional specificity predict no neural difference between the virtual and live conditions. Here we compare the same live faces viewed both over a video format and in person with measures of functional near-infrared spectroscopy (fNIRS), eye tracking, pupillometry, and electroencephalography (EEG). Neural activity was increased in dorsal regions for in-person face gaze and was increased in ventral regions for virtual face gaze. Longer dwell times on the face, increased arousal indexed by pupil diameter, increased neural oscillation power in the theta band, and increased cross-brain coherence were also observed for the in-person face condition. These findings highlight the fundamental importance of real faces and natural interactions for models of face processing.</p>
Role of laser wobbling welding parameters in dissimilar welding of aluminum and copper alloys: general preliminary process window.
<p>This database explores a wide range of process parameters concerning laser welding of aluminum and copper 0.3 mm thick foils in lap configuration. In particular, this application involves wobbling technique as a dynamic beam shaping for enhancing the formation of a correct interface width in this kind of welding. Given different values of laser power, wobbling tangential speed and wobbling diameter, the results are expressed in terms of weld bead width at the face of the weld bead, weld bead width at the interface between the two sheets and bead penetration depth in the lower sheet. Some trials gave an excessive penetration so that the specimen was cut in two separate parts after the process. This specific type of dissimilar welding is useful for enhancing "hybrid" characteristics of components, where a trade-off between mechanical and electrical or thermal characteristics is needed.</p>
BACI price data for ecoinvent 3.5 processes broken down per region for the year 2012
<p>Price data samples ecoinvent v3.5 for hybridisation based on BACI trade data for the year 2012. </p> <p> </p> <p>Prices samples are taken as thevolume weighted export trade flows for a given region for a given process. </p> <p>This data set was used in the publication "<span>Where is my footprint located? Estimating the geographical variance of Hybrid-</span><span>LCA</span><span> footprints. in the Journal of Industrial Ecology</span>". Doi and link to paper to follow. </p> <p> </p> <p>The zip foler contains pickle files named with the activityUUID_productUUID.pickle which contain a python dictionary with the following entries:</p> <pre>'activityName', 'geography', 'productName', 'cpc', 'unitName', 'pricesByCountry'<br><br>pricesBycountry then contains the available country codes which then contain the following data for each listed region:<br><br>'prices_euro', 'weights', 'price_sample', 'price_baci_mean', 'price_baci_std', 'price_percentiles', 'price_baci_min', 'price_baci_max', 'nr_baci_flows'</pre>
ERP Process
<p><a href="https://www.impactfirst.co/id/c/apa-itu-erp" target="_blank" rel="noopener">ERP (Enterprise Resource Planning) software</a> operates by integrating various business processes and functions into a unified system, streamlining operations, and providing real-time insights across an organization. This centralization is achieved through a modular design, where each module caters to a specific business function such as finance, human resources, supply chain management, inventory, sales, and customer relationship management (CRM). These modules are interconnected, allowing data to flow seamlessly between them, reducing data silos and ensuring that all departments have access to the most up-to-date information.</p> <p>At the core of ERP software is a centralized database that stores all the data from different modules. When a transaction or update occurs in one module, the centralized database ensures that the information is immediately reflected across all relevant modules. For example, when a sales order is processed, the inventory module updates stock levels, the finance module records the revenue, and the supply chain module adjusts procurement schedules as needed. This real-time data synchronization enhances efficiency, accuracy, and decision-making.</p> <p><a href="https://www.impactfirst.co/id/c/software-erp" target="_blank" rel="noopener">ERP systems</a> also include robust analytics and reporting tools that provide insights into business performance. These tools can generate detailed reports and dashboards that help management monitor key performance indicators (KPIs), identify trends, and make informed strategic decisions. By leveraging these insights, organizations can optimize their operations, improve productivity, and drive growth.</p> <p>Furthermore, ERP software supports automation of routine tasks and workflows, reducing manual intervention and minimizing errors. For instance, automated invoicing, payroll processing, and inventory replenishment can save time and resources, allowing employees to focus on more value-added activities. The system’s workflow management capabilities also ensure that business processes are consistent, standardized, and compliant with internal policies and external regulations.</p> <p>Scalability is another significant feature of ERP software. As businesses grow and their needs evolve, ERP systems can be scaled to accommodate new users, additional modules, and increased transaction volumes without disrupting existing operations. This scalability ensures that the ERP system can support the organization’s growth and changing requirements over time.</p> <p>In summary, <a href="https://www.impactfirst.co/id/erp/software-erp" target="_blank" rel="noopener">ERP software</a> works by integrating and automating various business processes through a centralized database, providing real-time data synchronization, comprehensive analytics, and scalable solutions. This integration enhances operational efficiency, improves data accuracy, and supports informed decision-making, ultimately driving business success.</p>
PubChem and ChEMBL-series processed dataset used in Exhaustive local chemical space exploration using a transformer model
<p>PubChem and ChEMBL-series processed dataset used in <span>Exhaustive local chemical space exploration using </span><span>a transformer model</span></p>
Does the processing of black soldier fly larvae meal affect the amino acid solubility in Atlantic salmon (Salmo salar)?
Open the record for dataset details and reuse information.
Processed Data for Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology.
<p>These files include all data used to create Figures in Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology. Raw data provenances and methodological processing are cited in the published manuscript.</p> <p>See README file for metadata information.</p>
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
Codes and data for Software lock-in processing
<p>These codes and data sets are going to be published in:</p> <p>Oppermann, F., Günther, T.: A remote-control datalogger for large-scale resistivity surveys and robust processing of its signals using a software lock-in approach; Geosci. Instrum. Method. Data Syst.</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.