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6,467 results for “process”
Data and processing code for Depth-averaged Subtidal and Tidal Circulation off of a Rocky Shore
<p>Data and code in order to recreate all figures in "Depth-averaged Subtidal and Tidal Circulation off of a Rocky Shore". </p> <p>JGR_data: all data to remake figures </p> <p>JGR_functions: all functions to remake figures </p> <p>Copy_of_make_JGR_figs.m: script to remake figures </p>
The formation process, mechanism, and attribution of urban impervious surface thermal runoff
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Generation processes and characteristics of submarine landslide-generated tsunami
<p>Wave height data of 8 virtual gauges (P1 - P8) in 15th minute after the landslide occurrence . The sampling interval is 10 seconds.</p>
Pre-processed WRF output, interpolated from gridded data to 74 station locations, in the Midwest U.S.
<p>The dataset includes pre-processed WRF simulation data for the historical period of 1980 to 2022 and future projections from 2058 to 2100 under two climate change scenarios: RCP 4.5 and RCP 8.5. The data has been interpolated from gridded data to 74 station locations in the Midwest United States. Each file contains data for a single station and a single month with a 3-hourly time step. This dataset can be used to generate intensity-duration-frequency (IDF) curves using the procedure published at https://doi.org/10.5281/zenodo.13685451.</p>
Improvements for the Planning Process in the Scrum Method - Case studies datasets
<p>Data for the article Improvements for the Planning Process in the Scrum Method. These are the data inputs for the case studies located in the article.</p>
Computational modeling and analytical validation of singular geometric effects in fault data using a combinatorial approach - Input and processed data
<p>The archive contains the input and processed data for the companion manuscript.</p> <p>The input data contains XYZ coordinates of points documenting the investigated interfaces. The output datasets contain directional data from applying the combinatorial algorithm to point data sets.</p> <p>We have also included .VTU and .PVSM files for visualization of the geological settings in ParaView.</p>
Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture
<p>Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2% to 69.8%, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7%–13.3% and 2.2%–2.4%. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.</p>
Field measurement data of hydrodynamic and morphological processes in the mangrove forest of Lac Bay, Bonaire, Caribbean Netherlands
<p>31-10-2024, Rik Gijsman</p> <p>_________________________________________________________<br>Dataset of field measurement of hydrodynamic and morphological processes in the mangrove forest of Lac Bay, Bonaire, Caribbean Netherlands.</p> <p>_________________________________________________________<br>For more information please see scientific publication:</p> <p>Gijsman, R., Engel, S., van der Wal, D., van Zee, R., Johnson, J., van der Geest, M., Wijnberg, K.M. and Horstman, E.M. (2024). <strong>The Importance of Tidal Creeks for Mangrove Survival on Small Oceanic Islands</strong>. <em>Unpublished Manuscript</em>.</p> <p>_________________________________________________________</p> <p>Dataset contains: </p> <ol> <li>Timeseries data from field instruments: <br> <ol> <li>Atmospheric pressure</li> <li>Water depth</li> <li>Water level</li> <li>Flow velocity</li> <li>Waves</li> <li>Turbidity</li> <li>Rainfall</li> <li>Temperature</li> <li>Wind</li> <li>Tidal creek flows</li> <li>Tidal creek transport</li> <li>Lagoon suspension</li> </ol> </li> <li>Mapped data of field surveys: <ol> <li>Bathymetry and creek profiles</li> <li>Sediment characteristics</li> </ol> </li> <li>Python script for importing and plotting data</li> <li>Overview map with shapefiles of instrument location coordinates</li> </ol> <p>________________________________________<br>Additional notes for your information:</p> <ul> <li>Timeseries data stored in .txt files</li> <li>Visualization of data in .txt files provided with accompanying .png files</li> <li>Timeseries data of Hobo sensors, Rain gauge and KNMI station interpolated between 19-01-2022 00:00:00 and 18-05-2022 23:55:00 (120 days) on 5 minute intervals </li> <li>Timeseries data of other sensors interpolated between 19-01-2022 00:00:00 and 08-03-2022 23:55:00 (49 days) on 5 minute intervals</li> <li>Column headers show instrument station abbreviation</li> <li>Datetime in local timezone of Bonaire (GMT-4)</li> <li>When a property (e.g., water depth) was measured by different instruments, the instrument used is mentioned in the file name, e.g. "water_depth_hobo.txt"</li> <li>Measurements from KNMI (The Royal Netherlands Meteorological Institute) station 990 were included in the dataset and indicated with '_knmi' in the file name. Data obtained from 'https://www.knmi.nl/nederland-nu/klimatologie/uurgegevens_Caribisch_gebied' </li> </ul> <p> </p>
Analysis of Padlet in the educational process on the rock cycle through Data Science
<p><strong><span>Analysis of Padlet in the educational process on the rock cycle through Data Science</span></strong></p>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1 processed with GAMMA
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 52 acquisitions</p> <p>Processor: GAMMA</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p> <p>This dataset is similar to <a href="https://doi.org/10.5281/zenodo.6539952">SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1</a>, but has been pre-processed with GAMMA software. </p>
Screening Process and Eligibility Decision for SR Anticancer Effects Curcumin, Shogaols, and Gingerols
<p>A dataset consisting of list of studies together with their inclusion/exclusion decision for</p> <p>a. Sheet 1: Excluded studies with reason(s) from electronic database search</p> <p>b. Sheet 2: Included studies from electronic database search</p> <p>c. Sheet 3: Decision on full-text screening</p> <p>d. Sheet 4: Decision on studies retrieved from other sources</p>
Processed data of "Observations of Ring Current Proton Fast Local Loss Associated with Deepening Local Minimum in Phase Space Density in Earth's Inner Magnetosphere"
<p>The dataset includs the processed observation data of "Observations of Ring Current Proton Fast Local Loss Associated with Deepening Local Minimum in Phase Space Density in Earth’s Inner Magnetosphere". *.sav files are the phase space densities of protons and the corresponding adiabatic invariants (μ, K, and L*) which are calculated under the T89D magnetic field model (using the observation data measured by the RBSPICE on the Van Allen Probes from 24th to 26th Jun 2017), which are used to plot Figure 1. The data in Processed data for Figures 2-5.zip are the processed data for Figures 2-5. </p>
The processed polarimetric radar product datasets and the MLP model files
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Data, Pre- and Post- processing scripts for shallow-water photogrammetry applications
<h1>Description</h1> <p>This repository contains the data and scripts to reproduce the results of Casella et al. (Remote Sensing, 2024). The scripts are in python, two of them are wrapped in Jupyter Notebooks with explanatory notes.<br><br>Please read the paper for further information on the platform used to collect the data shared in this repository.</p> <h2>Folder structure</h2> <p>The main folder contains two subfolders:</p> <ol> <li><strong>Data</strong>: this folder includes all the original data, and the results of the preprocessing and post-processing notebooks. Note that the original photos, included in the "Camera/all_photos" folder must be unzipped before running the preprocessing Jupyter Notebooks.</li> <li><strong>Precision_Analysis</strong>: this folder includes the digital bathymetric models that were co-registered as described in the paper. The co-registration was done offline with Quantum GIS. In this folder are also stored the results of the "Compare_DBMs.py" script, that makes the precision analysis (differences between co-registered DBMs).</li> </ol> <h2>Installation</h2> <p>Refer to the README.md file for a quick installation guide using Anaconda. </p> <h2>Credits</h2> <p>The code included in this work has been improved with the assistance of ChatGPT, which provided guidance on optimization, debugging, and documentation to enhance clarity and functionality. All the code has been reviewed and supervised by humans to ensure consistency and correctness.</p>
Ophiolites in the Central Asian Orogenic Belt record Cambrian subduction initiation processes
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Experimental data and processing scripts of chirp ENDOR spectra
<p>Experimental data and MATLAB processing scripts for the manuscript:<br><br>"Increased sensitivity in Electron Nuclear Double Resonance spectroscopy with chirped radiofrequency pulses"</p>
Processed DepMap data (.h5mu)
<p><strong>Disclaimer</strong></p> <p>The DepMap data were generated and shared by the Broad Institute of Harvard and MIT and the Sanger Institute.</p> <p>We reprocessed the data and packaged it in a single h5mu file for easier access and to reproduce analyses with Semi-supervised Omics Factor Analysis (SOFA). Please see https://www.biorxiv.org/content/10.1101/2024.10.10.617527v3 for more details on how the data was processed and analysed.</p> <p><strong>Data Usage Policy</strong></p> <p>The Broad Institute publishes its data under the <a href="https://depmap.org/portal/ccle/terms_and_conditions">Terms and Conditions linked here.</a></p> <p>The Sanger Institute publishes its data under the <a href="https://depmap.sanger.ac.uk/documentation/data-usage-policy/">Terms and Conditions linked here.</a></p> <p>The DepMap data are provided under <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 license</a>.</p> <p>Contact <a href="mailto:depmap@broadinstitute.org">depmap@broadinstitute.org</a> or <a href="mailto:depmap@sanger.ac.uk">depmap@sanger.ac.uk</a> for more information</p> <p><strong>Please cite the following when using these data</strong></p> <ul> <li><strong>Drug response</strong>: <a href="https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00274-4?dgcid=raven_jbs_aip_email">Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).</a><strong> </strong>(https://figshare.com/articles/dataset/Pan-cancer_proteomic_map_of_949_human_cell_lines/19345397)</li> <li><strong>Proteomics:</strong> <a href="https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00274-4?dgcid=raven_jbs_aip_email">Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).</a><strong> </strong>(https://figshare.com/articles/dataset/Pan-cancer_proteomic_map_of_949_human_cell_lines/19345397)</li> </ul> <ul> <li><strong>RNA-Seq: <a href="https://aacrjournals.org/cancerres/article/78/3/769/633178/Transcription-Factor-Activities-Enhance-Markers-of">Garcia-Alonso, L. et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 78, 769–780 (2018).</a></strong> (https://cellmodelpassports.sanger.ac.uk/downloads)</li> <li><strong><strong>Methylation</strong>: <a href="https://www.cell.com/fulltext/S0092-8674(16)30746-2">Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).</a></strong> (https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html)</li> <li><strong>Mutation: <a href="https://www.cell.com/fulltext/S0092-8674(16)30746-2">Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).</a> </strong>(https://cellmodelpassports.sanger.ac.uk/downloads)</li> <li><strong>CRISPR-Cas9: <a href="https://www.nature.com/articles/s41467-021-21898-7">Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021)</a>.</strong> (https://score.depmap.sanger.ac.uk/downloads)</li> <li><strong>Metadata</strong> for all cell lines was obtained from: <ul> <li><a href="https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00274-4?dgcid=raven_jbs_aip_email">Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).</a><strong> </strong></li> <li><strong><a href="https://www.nature.com/articles/s41467-021-21898-7">Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021)</a>.</strong></li> </ul> </li> </ul> <p> </p> <p> </p>
Effect of an alkaline reactive on the thermochemical process (pyrolytic) of HIPS
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Data and code of "Unified percolation scenario for the α and β processes in simple glass formers
<p>Molecular dynamics simulation data and post-processing code for "Unified percolation scenario for the α and β processes in simple glass formers"</p>
Social life cycle sustainability assessment of dried tomato products based on material and process selection through multi-criteria decision making
<h3>BACKGROUND</h3> <p>Tomatoes are a significant product of the Mediterranean region and a crucial component of the Mediterranean diet. The formulation of dried tomato products enriched with proteins and bioactive compounds could be a strategic approach to promote adherence to the Mediterranean diet. Six different novel tomato products were analyzed using different protein enrichment sources (pea proteins and leaf proteins) and drying technologies (hot-air dryer, microwave vacuum dryer, and conventional dryer). The novelty of this approach lies in combining product-specific criteria with global societal factors across their life cycles. Using 21 criteria and an analytic hierarchy process (AHP) survey of experts, the social sustainability score for each product was determined through a multi-criteria assessment.</p> <h3>RESULTS</h3> <p>The tomato product's life cycles have minimal regional impacts on unemployment, access to drinking water, sanitation, or excessive working hours. However, they affect discrimination, migrant labor, children's education, and access to hospital beds significantly. The study identified nutritional quality as the top criterion, with the most sustainable design being a tomato bar enriched with pea protein and processed using microwave vacuum drying.</p> <h3>CONCLUSION</h3> <p>The study revealed that integrating sensory and nutrient compounds into social sustainability assessments improves food sustainability and provides a practical roadmap for social life cycle assessments of food products. It emphasized the importance of considering global social issues when reformulating Mediterranean products to ensure long-term adherence to the Mediterranean diet. Incorporating social factors into sustainability scores can also enhance the effectiveness of product information for conscious customers. © 2024 The Author(s). <em>Journal of the Science of Food and Agriculture</em> published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.</p>
ScienceDex guides
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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.