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6,467 results for “process”
Integrative processing in artificial and biological vision predicts the perceived beauty of natural images
<p><em>Data, code, and materials for Nara & Kaiser (2023). </em></p> <p><em>Preprint: </em><a href="https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1">https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1</a></p> <p>Paper: <a href="https://doi.org/10.1126/sciadv.adi9294">https://doi.org/10.1126/sciadv.adi9294</a></p> <p>In this version (v2) of the repository, we:</p> <ul> <li>fixed an error in the fMRI data, where only the data from one participant, instead of all participants was uploaded previously - now all data are available,</li> <li>added brain masks (extracted by SPM) for each participant, and</li> <li>added realignment parameter text files (created by SPM) to the functional data for each participant.</li> </ul>
Data from: A qualitative analysis of an Aβ-monomer model with inflammation processes for Alzheimer's disease
<p>We introduce and study a new model for the progression of Alzheimer's disease incorporating the interactions of Aβ-monomers, oligomers, microglial cells and interleukins with neurons through different mechanisms such as protein polymerization, inflammation processes and neural stress reactions. In order to understand the complete interactions between these elements, we study a spatially-homogeneous simplified model that allows to determine the effect of key parameters such as degradation rates in the asymptotic behavior of the system and the stability of equilibriums. We observe that inflammation appears to be a crucial factor in the initiation and progression of Alzheimer's disease through a phenomenon of hysteresis, which means that there exists a critical threshold of initial concentration of interleukins that determines if the disease persists or not in the long term. These results give perspectives on possible anti-inflammatory treatments that could be applied to mitigate the progression of Alzheimer's disease. We also present numerical simulations that allow to observe the effect of initial inflammation and concentration of monomers in our model.</p>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Data supporting "Transition between mechanical and geometric controls in glacier crevassing processes".
<p>The folders:</p> <ul> <li><strong><em>results_Geometrical_Regime</em></strong></li> <li><strong><em>results_Mechanical_Regime</em></strong></li> <li><strong><em>results_3D</em></strong></li> <li><strong><em>results_varyingLengthSlope</em></strong></li> <li><strong><em>results_varyingVelocity</em></strong></li> </ul> <p>contain the data for spacing and depth of the crevasses in the different simulations. Each simulation folder is named after the ice thickness H and cohesion c. For example, H300c2 means an ice thickness of 300m and a cohesion of 2MPa.</p> <p>The simulation files contain data measured every 10 frames. Empty files imply two possibilities:<br>-the crevasse(s) are away from the window measurement (the window is approximately 1500m wide around the obstacle).<br>-No crevasses are observed or at least no distinguishable enough.</p> <p>**<br>The python code <strong><em>readDataMeasuredInSimulations.py</em></strong> use the <strong><em>readDataPP</em></strong> class to plot the measures from the previous folders.<br>All the figures from the paper can be reproduced using this code.</p> <p>**</p> <p>The full simulation results cannot be transfered as a supplementary material as it is too large (20Go for each simulation). However we provide two full result files: <strong>H300_c0_4.abc</strong> and <strong>H100_c2.abc. </strong>We provide an example of the init.lua file used to launch a simulation with given parameters and configurations. The configurations used in the simulations are all provided in the geometry.zip file.</p> <p>A version of the MPM numerical model can be found in a previous publication at<strong> https://www.nature.com/articles/s43247-021-00179-7. </strong></p> <p> </p>
Milling process energy consumption
<p>This dataset is about a CNC machine performing milling processes<br>Each observation means one particular milling process with the machine</p> <p>Axis - the machine's cutting tool was moving along axis X/Y/Z<br>Feed [mm/min] - the cutting tool moved at this speed<br>Path [mm] - the cutting tool moved this distance</p> <p>Energy requirement [kJ] - energy consumption of the process </p>
Chimpanzee food processing video archive (Waibira community, Budongo Forest, Uganda)
<p>This is a video archive containing data (n = 474 videos) on food processing techniques and other behaviours used by the chimpanzees in the Waibira community, Budongo Forest, Uganda. Each video is labelled with data on date, location, individuals and behaviours present (including non-processing behaviours). </p> <p>Also included is the ethogram used to categorise processing behaviours as they are labelled in the archive, and 19 exemplar clips demonstrating some of these behaviours. This archive is intended to be used as a data ark for future interdisciplinary research. While the videos themselves cannot be made publicly accessible, those interested in collaborations may browse the available videos and contact the researchers to request access to specific videos. </p>
Simulations from a max-stable process on a 64 x 64 grid
<p>Simulations from a max-stable process to reproduce the results in Section 5.2 of "Spatial Bayesian Neural Networks", https://doi.org/10.1016/j.spasta.2024.100825. For details on the process, spatial locations, etc., please refer to the paper.</p>
Beyond the Surface: Exploring Ancient Plant Food Processing through Confocal Microscopy and 3D Surface Texture Analysis
<p>This repository contains the raw data and code to reproduce the analyses presented in the paper "Beyond the Surface: Exploring Ancient Plant Food Processing through Confocal Microscopy and 3D Surface Texture Analysis" by Zupancich et al.</p> <p>The repositiory includes:</p> <ul> <li>CSV files containing the raw data of 3D surface measurement of experimental active and passive tools utilised in processing cereals and legumes.</li> <li>Rmarkdown files of the code utilised to perform the analyses</li> </ul>
Dataset for paper: The Promise and Challenges of Using LLMs to Accelerate the Screening Process of Systematic Reviews
Open the record for dataset details and reuse information.
AMD AI Research Focused on Data Processing Efficiency
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Mesolithic Plant Processing Unveiled: Multiscale Use-Wear Analysis of the Ground Stone Tools from Vlasac (Serbia)
<p>This repository contains the raw data and code to reproduce the analyses presented in the paper "Mesolithic Plant Processing Unveiled: Multiscale Use-Wear Analysis of the Ground Stone Tools from Vlasac (Serbia)" by Zupancich et al.</p> <p>The repositiory includes:</p> <ul> <li>CSV files containing the raw data of:</li> <li>3D surface measurement of experimental and archaeological ground stone tools</li> <li>Use wear spatial distribution</li> <li>Ground stone tools techno-morphological data</li> <li>HTML files of the code utilised to perform the analyses</li> </ul>
Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
Dataset of bank soil parameters and stochastic modelling of bank erosion processes in the Middle Yangtze River
<p><span>A probabilistic process-based model of bank erosion has proposed, </span><span>embedding the probability distributions of</span><span> different bank soil parameters. <span><span> The dataset includes the spatial distribution characteristics of critical shear stress, friction angle, and cohesion. The prediction results analyzed the effects of soil erosion resistance capacity and the variability of shear strength parameters in the simulation of bank erosion processes, obtaining the probability of mass failure and the distributions of bank erosion width. Additionally, the study investigated the effect of varying water content on bank erosion modeling and further analyzed how considering more influencing factors in the model affects prediction uncertainty and accuracy. Moreover, the relationship between the variability of these factors and river morphology was discussed. The dataset provides the aforementioned prediction results, and relevant plots were generated using MATLAB or Python, with the associated plotting code also uploaded.</span></span></span></p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Supporting molecular simulations data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3"
<p>Supplementary data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3: <a href="https://doi.org/10.1126/sciadv.abe3326">10.1126/sciadv.abe3326</a>"</p>
ND250 as a prediction error signal in orthographic processing: insights from the comparison of handwritten and printed words
<p><span>This dataset contains electroencephalography (EEG) recordings and behavioral data from a study investigating the neural mechanisms of visual word recognition in native Chinese speakers. The study used a color decision task, where participants viewed printed and handwritten Chinese single-character words varying in lexical frequency (high-frequency vs. low-frequency). The primary aim was to examine the N250 ERP component, a 250-ms difference in brain activity observed between certain word types, and determine whether it reflects activation of the orthographic lexicon or a prediction error signal during orthographic processing. The findings suggest that the N250 is related to prediction error, providing support for the Interactive Account of orthographic processing.</span></p>
In situ plankton footage for testing image processing routines
<p>This dataset contains 12 video files collected from the deep-focus plankton imager (DPI) version of the in situ ictyoplankton imaging system (ISIIS). These files are intended to be used for testing, validating, and comparing computational pipelines. These videos were collected in the Northern Gulf of Alaska as part of an NSF-sponsored field campaign (NGA LTER).</p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
The processing results, dataset and original codes of Coral reef PtCloud segmentation model (based on proposed underwater camera systems)
<p><strong># Data Introduction</strong><br>The proposed camera system includes three operation modes: Surface Mode, Horizontal Mode, and Curved Mode.</p> <ul> <li><strong>Surface Mode</strong>: Provides 3D reconstructions (point cloud), DEM, and orthophoto maps of the seabed (covering a line of 70m in length).</li> <li><strong>Horizontal Mode</strong>: Provides 3D reconstructions (point cloud and mesh) and orthophoto maps of a single transect line.</li> <li><strong>Curved Mode</strong>: Offers 3D reconstructions (mesh) of various targets, including artificial coral reefs, coral reefs with snails, and coral reefs with starfish.</li> </ul> <p>All mesh results are saved in <code>.FBX</code> format, and point cloud results are saved in <code>.TXT</code> format.</p> <p><strong># Code Introduction</strong><br>The backbone of our point cloud segmentation model is the KPConv model.<br>If you encounter any issues during code deployment, please refer to the original KPConv repository (<a href="https://github.com/HuguesTHOMAS/KPConv" target="_new" rel="noopener">Original Code: https://github.com/HuguesTHOMAS/KPConv</a>).<br>We provide our modified code (customized for our task) along with the complete point cloud dataset.</p>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</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.