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6,467 results for “process”
Figure 1: Rubicon KO Starvation and Mitophagy HaloTag processing assay [FINAL]
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Data and analysis process for Microbiome processing of organic nitrogen input supports growth and cyanotoxin production of Microcystis aeruginosa cultures
<p>Data and analysis process for manuscript titled "Microbiome processing of organic nitrogen input supports the growth and cyanotoxin production of Microcystis aeruginosa cultures"</p>
BSC Post-processed Seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the seasonal climate prediction system SEAS5 (ECMWF).</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term. In contrast, a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: European Center for Medium-Range Weather Forecasts (ECMWF) SEAS5 and post-processed by BSC.</p> </li> <li> <p>Issue frequency: Monthly (~15th of each month)</p> </li> <li> <p>Lead times: months 1 to 3 (e.g. For a forecast initialised in June, forecasts will be monthly averages for July, August and September). The initialization date is indicated in the name of each file (e.g. 20210701). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, incoming solar radiation</p> </li> <li> <p>Ensemble size: 51 members</p> </li> <li> <p>Postprocessing: Downscaling from original (1°x 1°) resolution to 0.1°x 0.1° for three domains and monthly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ’douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p> </p> <p>For each prediction, there are several files containing the seasonal variables values, probabilities, definition of the categories and skill scores.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20210701.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (1st of the month).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20210701.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (1st of the month).</p> <p>It contains the 51 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania-percentiles_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33th percentile is the boundary between below normal and normal, and the 66th percentile is the boundary between the normal and above normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <p><strong> </strong></p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania-skill_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the month for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p>
Processed Data for Ring Current Oxygen Ion ANN Model
<p>Processed data, including:</p> <p>the coordinate of Van Allen Probe B in SM coordinates.</p> <p>Geomagnetic indices (Sym-H, SME and F10.7).</p> <p>log10 O+ fluxes, here the unit of 38 keV and 52 keV O+ (measured by HOPE) is s-1 cm-2 ster-1 keV-1, the unit of other energy channels (measured by RBSPICE) is s-1 cm-2 MeV-1.</p>
Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis - Supplementary Material
<p>Supplementary Material for paper: "Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis", submitted to BPMDS 2024</p>
Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment
<p>This is a dataset used in the paper entitled "Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment" by Maina et al., 2024</p>
Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
<p>The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework.</p>
Dataset for "Time-Resolved Trigger Processes Leading to the Plinian Eruptions at Sakurajima Volcano, Japan"
<p>Dataset 1: Core compositions of plagioclase phenocrysts<br>Dataset 2: Core and rim compositions of orthopyroxene phenocrysts<br>Dataset 3: Core and rim compositions of clinopyroxene phenocrysts<br>Dataset 4: Core compositions of olivine phenocrysts in the 1914–1915 lava<br>Dataset 5: Chemical compositions of microlite-bearing groundmass glass of the 1914 Plinian pumice<br>Dataset 6: Core and rim compositions of magnetite phenocrysts<br>Dataset 7: Compositions of magnetite inclusion and hosting plagioclase<br>Dataset 8: Chemical compositions of magnetite microlites in the 1914 Plinian pumices</p>
Source Data for the publication "Sub-100-fs energy transfer in coenzyme NADH is a coherent process assisted by a charge-transfer state"
<p>Molecular Structures for solvated NADH. </p> <p>The folder "QMMM_OPTIMIZED_STRUCTS" contains the pdb files of the six representatives for the three conformational clusters obtained after REMD used in the Supplementary Information.</p> <p>The folder "SOLVENT_ENSEMBLE_AROUND_FIXED_SOLUTE" conatins AMBER RESTART files for 200 solvent configurations around two cluster reps displayed in Figure 2 of main manuscript. </p> <p>The folder "PARAMETERS_FOR_MLMCTDH" contains the input file, operator file and parameters for ML-MCTDH dynamics for the structures shown in Main Manuscript and Supplementary. </p>
Experimental Data - Physicochemical Properties of 20 Ionic Liquids Prepared by the Carbonate-Based IL (CBILS) Process
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Global patterns and drivers of coupling between anammox and denitrification processes across inland aquatic ecosystems
<p>Here, we reported a dataset regarding global anammox and denitrification rates from inland aquatic ecosystems. </p>
Single-neutron transfer on 47K(d.p): Processed data of experiment e793s
<p>This is the calibrated, sorted and processed data of experiment e793s (<a title="E793S_19 - Proton-neutron interactions across the N = 28 shell closure via 47K(d,p) 48K, and implications for the most neutron-rich phosphorus" href="doi.org/10.26143/GANIL-2021-E793S_19" target="_blank" rel="noopener">10.26143/GANIL-2021-E793S_19</a>), wherein the single-neutron transfer reaction 47K(d,p) was performed in inverse kinematics using a radioactive isotope beam. This data file formatted as a TTree, readable using CERN's ROOT Data Analysis Framework.</p> <p>The analytical code to process this data is also available (<a title="Single-neutron transfer on 47K(d.p): nptool framework for the analysis of e793s" href="doi.org/10.5281/zenodo.13748333" target="_blank" rel="noopener">10.5281/zenodo.13748333</a>).</p>
Simulation of Southern Ocean cloud processes using the high-resolution regional UK Met Office Unified Model with interactive aerosols
<p>Code and model data used to create the figures for Price et al., 2024, JGR (in prep.).</p>
nf-core/marsseq: systematic pre-processing pipeline for MARS-seq experiments
<p>Datasets used for analysis using <a href="https://github.com/nf-core/marsseq">https://github.com/nf-core/marsseq</a>. Code is available at <a href="https://github.com/brickmanlab/proks-et-al-2023">https://github.com/brickmanlab/proks-et-al-2023</a>. </p>
processed scRNA-seq data for Neuwirth & Malzl et al. 2024
<p>This dataset contains the analysed scRNA-seq data used in Neuwirth & Malzl et al. 2024 as AnnData objects. You can find the code used to produce and analyse this data on <a href="https://github.com/menchelab/Neuwirth_Malzl_et_al_2024">GitHub</a>. All data was preprocessed using cellranger v6.0.1 with GRCh38 3.0.0 as reference.</p> <h3><strong>Psoriasis and Sarcoidosis PBMC data</strong></h3> <p><strong><em>pbmc.scps.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered data of Psoriasis and Sarcoidosis patient blood as well as healthy controls (Psoriasis data was generated within this study; Sarcoidosis data was reprocessed from 10.1016/j.immuni.2023.01.014)<br><strong><em>tcells.pbmc.scps.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of Psoriasis and Sarcoidosis PBMC data</p> <h3><strong>Atopic dermatitis skin data</strong></h3> <p><strong><em>tissue.ad.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered data of healthy and atopic dermatitis patient skin (reprocessed from 10.1126/science.aba6500)<br><strong><em>tcells.tissue.ad.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of atopic dermatitis data</p> <h3><strong>Psoriasis and Sarcoidosis skin data</strong></h3> <p><strong><em>tissue.scps.integrated.annotated.h5ad</em></strong>: contains scVI-integrated and celltypist-annotated data from Psoriasis and Sarcoidosis patient skin as well as healthy controls (Psoriasis data was reprocessed from 10.1126/science.aba6500; Sarcoidosis data was reprocessed from 10.1016/j.immuni.2023.01.014)<br><strong><em>tcells.tissue.scps.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of Psoriasis and Sarcoidosis skin data<br><strong><em>tregs.tissue.scps.integrated.annotated.h5ad</em></strong>: contains scVI-integrated and SAT1 status annotated regulatory T cell subset of Psoriasis and Sarcoidosis skin data<br><em><strong>tregs.tissue.scps.integrated.milo.h5ad</strong></em>: basically same as above but with cell neighborhood overrepresentation analysis on top</p> <h3><strong>IBD colon data</strong></h3> <p><strong><em>tissue.uc.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered data of Crohn's disease and ulcerative colitis patients as well as healthy controls (reprocessed from 10.1126/sciimmunol.abb4432)<br><strong><em>tcells.tissue.uc.integrated.clustered.h5ad</em></strong>: contains scVI-integrated and Leiden-clustered T cell subset of IBD data</p> <h3><strong>Raw and unfiltered data</strong></h3> <p><strong><em>inflammatory_disease.h5ad</em></strong>: contains the raw, unfiltered and unprocessed data of all the files above (i.e. combined cellranger output) and is the source data file of all analyses in this study. If you just want the untouched data this is what you want to use.</p>
Model code and output for "Transformation Processes in the Oder Lagoon as seen from a Model Perspective"
<p>This dataset contains the numerical model output files that were used in the analysis, the numerical model input files that were used to produce the model simulation, and the specific source code for the model components presented in "Transformation Processes in the Oder Lagoon as seen from a Model Perspective", submitted to Biogeoscience.</p> <p>data1.tar is the direct diagnostic model output. data2.tar are condensed data which are used for the analysis.</p> <p>forcing.tar contains data to run the model. Most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time (1995) is provided for the first year of the simulation. This dataset does not include the coastdat atmospheric data, which can be downloaded directly from https://www.wdc-climate.de/ui/entry?acronym=coastDat-2_COSMO-CLM.</p> <p>src.tar contains the model source code.</p>
Data For Process Spoel Data
<p>Data for article: 'Floating large wood dynamics during a flood: insights from a drone survey and machine learning'</p> <p>Github: https://github.com/janbertoo/process_spoel_data</p>
Processed data files - Muysers et al.
<p>Pre-processed 1P data for the extraction of functional cell types during task learning.</p>
The HEC method processing code and output for constrained precipitation extremes reveal unequal future socioeconomic exposure
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The metre-scale roughness of asteroid (101955) Bennu from the OSIRIS-REx Laser Altimeter: Processed OLA v21 pointclouds for RMS deviation (L = 0.2 m to L = 20.0 m) [Dataset]
<p>Pointclouds:</p> <ul> <li>Pointcloud (.pcd) files for OLA RMS deviation calculation with fields: <br> <ul> <li>'Location': node points (XYZ points on Bennu's surface)</li> <li>'Intensity': height delta between the node and a neighbouring point at distance L from the node. 'Height' is defined as height above an orthogonally regressed plane fit to points within 2L of the node. </li> </ul> </li> </ul> <p> Tables:</p> <ul> <li>2020_06_bt_final_ma.xlsx: <ul> <li>Crater catalog from Bierhaus et al (2022)</li> </ul> </li> <li>bierhaus2023_craters_gt5m_lt80m_olav21_roi_flags.xlsx: <ul> <li>Subset of catalog from Bierhaus et al. (2022) with craters between 5 m - 80 m diameter used for interior-exterior roughness ratio in Bierhaus et al. (2023) and Rossmann et al. (2024).</li> </ul> </li> </ul> <p> </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.