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1,481 results for “data processing”
Data for Nonequilibrium Process of Conduction Electrons on HEMP
<p>Data for the manuscript Nonequilibrium Process of Conduction Electrons on the High-altitude Electromagnetic Pulse</p>
Dataset: Problem-centred interviews results for Matching Data Life Cycle and Research Processes in Engineering Sciences
<p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Temporal morphodynamic evolution of the Glacier d'Otemma proglacial forefield for melt seasons 2020 and 2021: data collection and post-processing
<p><span>The data included in this dataset concern the continuous geomorphic (orthomosaics, DEMs, inundation maps) and sedimentological (grain-size maps) evolution of the Glacier d’Otemma proglacial margin (Southern-Western Swiss Alps) located at an altitude of ca. 2450 m a.s.l. during summer 2020 and 2021. </span></p> <p><span>Data details and formats are available in the pdf document. Further information on data aquisition and post-processing techniques are available in Mancini et al. (2024).</span></p>
Processing of 10X 500 PBMC single cell ATAC-seq data with SnapATAC2
<p>Input files for SnapATAC2 tutorial on Galaxy</p>
Processed scRNA and scATAC data in DRCTdb
<p><span>Understanding the molecular mechanisms underlying </span><span>genetic</span><span> diseases is challenging due to the involvement of both environmental and genetic factors. Genome-wide association studies (GWAS) have identified numerous genetic loci, but their functional implications remain largely unknown. Single-cell multiomics sequencing has emerged as a powerful tool to study disease-specific cell types and their relationship with genetic variants. However, there is a lack of comprehensive databases for exploring genetic disease-related cell types and their mechanisms across different human tissues. In this study, we present the disease-related cell type database (DRCTdb), a database that integrates GWAS data and single-cell multiomics data to identify disease-related cell types and elucidate their regulatory mechanisms. DRCTdb contains well-processed single-cell multiomics data in 16 studies, encompassing transcriptome and epigenetic information overall 4 million cells within 28 tissues. Through DRCTdb, user can easily browse relationships and regulatory mechanisms between SNPs of 42 genetic disease and cell type in different human tissue based on GWAS and single cell multiomics data. Moreover, DRCTdb also provides data download,</span><span> which</span><span> allowing users to download well-processed <a name="_Int_HEP27DcN"></a>single-cell multiomics data and analysis result from DRCTdb</span></p>
Data from: CoZr nanocomposites in a ceramic-metal AlOx(OH)y/Al matrix with different Co/Zr ratio and its potential for syngas processing
<p>Data from article in Dalton Transaction</p>
Data from: Invertebrate diversity in groundwater filled lava caves is influenced by both neutral and niche-based processes
<p><strong>Aim</strong>: Understanding which factors shape and maintain biodiversity is essential to understand how ecosystems respond to crises. Biodiversity in ecological communities is a result of the interaction of various factors which can be classified as neutral or niche-based. The importance of these processes has been debated, but many scientists believe that both processes are important. Here we examined the importance of neutral vs. niche-based factors for shaping invertebrate communities. We hypothesized that if neutral processes are the main drivers of community structure we would not see any clear relationship between the structure of community and ecological factors. If niche-based processes are important we should see clear relationships between community structure and variation in ecological variables.</p> <p><strong>Location</strong>: Groundwater-filled lava caves near Lake Mývatn, Iceland.</p> <p><strong>Methods</strong>: We collected various ecological variables from these caves. Invertebrate communities were collected on the hard bottom using stone scrubbing and from epibenthic traps. Results: Both communities were species-poor, with low densities of invertebrates, showing the resource-limited and oligotrophic nature of these systems. Unusually for Icelandic freshwater ecosystems, the benthic communities were not dominated by Chironomidae (Diptera) larvae, but rather by crustaceans, mainly Cladocera. The epibenthic communities were not shaped by environmental variables, suggesting that they may be structured primarily by neutral processes. The benthic communities were shaped by the availability of energy, and to some extent pH, suggesting that niche-based processes were important drivers of community structure, although neutral processes may still be relevant. </p> <p><strong>Main conclusions</strong>: The results suggest that both processes are important for invertebrate communities in freshwater, and research should focus on understanding both of these processes. The ponds we studied are representative of a number of freshwater ecosystems that are extremely vulnerable to human disturbance, making it even more important to understand how their biodiversity is shaped and maintained.</p>
Data from: Severity of topsoil compaction controls the impact of skid trails on soil ecological processes
<p>Skid trails are a major management-induced disturbance in temperate forest ecosystems with considerable impact on soil ecological processes that are so far poorly understood. In German forests, skid trails comprise 10 – 20 % of the forest area that is potentially affected by soil compaction through heavy machinery. We systematically investigated the influence of skid trails on physical, chemical, and microbiological soil parameters at 84 paired plots across four Central European forest types. In low mountain forests with steeper topography, skid trails had more drastic effects than in lowland forests. Skid trails in low mountain areas showed a decrease in the C to N ratio of microbial biomass (MBC/MBN), as well as increased microbial (MBC/SOC) and enzyme activities leading to faster carbon turnover (lower C/N, EOC/EN) and increased CO<sub>2</sub> losses (CO<sub>2</sub>/SOC) from the soil. The overall effects of the skid trails in lowland forests were small. On base-poor soils, we found an increase in the MBC/MBN ratio, while skid trails in base-rich lowland soils showed a reduction in CO<sub>2</sub>/SOC, suggesting a proportional increase in soil carbon storage. Regardless of region-specific effects, the relative increase in the bulk density of the fine soil was identified as a 'golden trait' that determined the effects of skid trails on many soil parameters, as shown by negative correlations with SOC, N, MBC, MBN, MBP, MBC/SOC and CO<sub>2</sub>/SOC and positive ones with the activities of certain hydrolytic enzymes.</p> <p>Synthesis and Applications: Our data clearly showed that carbon conversion processes and soil respiration leading to significant carbon and nutrient losses increased significantly on skid trails in low mountain regions with relatively steep slopes, which was in sharp contrast to lowland sites. The strong context dependence of our findings suggests that the mapping of soil conditions in terms of slope, substrate and moisture with high spatial resolution is mandatory to assess the vulnerability of sites to soil compaction by heavy machinery. Based on such vulnerability analysis, negative impacts can be minimized through the designation of permanently fixed skid trails, the technical adaptation of vehicles (e.g., wide base tyres) as well as careful planning and timing of management operations that should be restricted to dry weather and soil moisture conditions or periods of frost.</p>
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>
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>
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>
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>
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>
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>
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>
SCID Multiomics Post-Processed Data and Analysis
<p>In this repository are the post-processed datasets and analytical code for the SCID Multiomics paper. The repository is structured as an installable R package for dependency management and dataset loading; it does not export any functions.</p> <p><strong>Installation</strong></p> <p>The easiest way to install this is to download the repository and install using `devtools::install()`. This will allow the import of various datasets using the `data()` function, upon which many of the analysis scripts depend.</p> <p><strong>Datasets</strong></p> <p>In no particular order, the important datasets are described below:</p> <p>- <strong>intsites</strong>: summary statistics from (Wang et al, Blood, 2010) for timepoints used in this study<br> - <strong>tcr</strong>: Aggregate TCR data from Adaptive Biotechnology's ImmunoSeq pipeline.<br> - <strong>mb</strong>: Metadata for the microbiome sampling timepoints, as well as species data from Metaphlan (not used)<br> - <strong>agg.mb.kz</strong>: Kraken species data for the microbiome samples, after low-complexity filtering<br> - <strong>agg.vp.kz</strong>: Kraken species data for the virome samples, after low-complexity filtering<br> - <strong>card</strong>: Antibiotic resistance gene data from CARD<br> - <strong>subject_ids.csv</strong>: Provides a mapping from the original sample IDs used in the datasets to the ones used in the manuscript.</p> <p>The code for creating these datasets from the original data files are in the `data-raw` directory.</p> <p><strong>Analysis/Figures</strong></p> <p>The analysis code is broken apart by subject and is largely concerned with figure generation. The R<br> scripts are all located in the `inst` folder. To generate all figures, you should run each script in the<br> order specified by the `GenerateFigures.R` file.</p> <p>Figures are output to the `figures` directory, while tables are output to the `tables` directory.</p> <p>Please note: many of the figures used in the manuscript were aesthetically modified after generation (text size, color palette, orientation), precluding exact figure replication</p>
Process plant data integration and querying
<p>This data set includes:</p> <ul> <li>Process plant data derived from heterogeneous sources</li> <li>An OWL ontology of the entities in the data sources</li> <li>Mapping for data in the sources to the ontology (<em>done using Cellfi</em>e,a <em>Protégé plugin. Also save as .json to use</em>)</li> <li>Data integrated from all the sources</li> <li>Sample SPARQL queries (<em>Q5 and Q6</em>)</li> </ul> <p> </p>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables
<p>Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain.<br> Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach.<br> Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility.<br> In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data.</p> <p>This repository contains the observed and simulated data that was used in the description paper.</p> <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.