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6,467 results for “process”
Illustration of an inverted growth process
<p>Illustration of a basic inverted growth process. The semiconductor layers of interest are first grown on a semicondutor substrate. Then the full structure is attached to another substrate that acts as holder (glass, for example) and then the original substrate is removed.</p> <p>The video was created with Blender 2.79b. I attach the Blender file so you can create other views, change the colors, camera movements, etc. </p>
Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science
<p>Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science</p> <p> </p> <ul> <li>info.txt contains a general description of how the data was processed and the main results.</li> <li>pipeline.tar contains the data pipeline used to process the e-MERLIN observations</li> <li>imaging.py is the script used to produce the final images</li> <li>CY6213_images.tar contain the final images of the target source (not corrected by calibration factor, described in the imaging script).</li> </ul> <p> </p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data
<p>Codes and processed data to reproduce the analysis discussed in: </p> <p>Wegmann <em>et Al.</em>,<strong> CellSIUS provides sensitive and specific detection of rare cell<br> populations from complex single cell RNA-seq data</strong>, Genome Biology 2019 (Accepted)<br> </p>
[Dataset] Software Process Line as an Approach to Support Software Process Reuse: a Systematic Literature Review
<p>Dataset of a Systematic Literature Review on Software Process Line as an Approach to Support Software Process Reuse</p> <p>Please, read README.txt file before go through dataset.</p>
Measurements of Ice Nucleating Particles in Beijing, China - Data and processing code
<p>Dataset needed to replicate findings published in the journal article "Measurements of Ice Nucleating Particles in Beijing, China, published in the Journal of Geophysical Research. The dataset contains the following:</p> <p>1. Data files containing raw data from a Continuous Flow Diffusion Chamber - Ice Activation Spectrometer (CFDC-IAS), in comma-delimited format).</p> <p>2. Data and processing files for analysis of backward air trajectories as an Igor Pro 8 packed experiment package file. Igor Pro is available from www.wavemetrics.com and a free 30-day trial version can be used to export data to other formats.</p> <p>3. Data and processing files for analysis of CFDC, APS and meteorological data, including data in the form of waves, as part of an Igor Pro 8 packed experiment package.</p>
Data for the publication "Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation"
<p>Michibata et al. (2019), currently under peer-review for publication in <em>Geoscientific Model Development</em>, incorporated a diagnostic tool for warm rain microphysics into the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011; Swales et al., 2018), designed to evaluate model representations of aerosol–cloud–precipitation interactions at a fundamental process-level. The tool automatically generates two diagnostics related to warm rain microphysics during COSP execution in a host model. One is the contoured frequency by optical depth diagram (CFODD), which visualizes a cloud-to-rain microphysical vertical structure (Suzuki et al., 2015). The other diagnostic is a global map of warm rain fraction classified as non-precipitating clouds (< –15 dBZ<sub>e</sub>), drizzling clouds (–15 < dBZ<sub>e</sub>< 0), and precipitating clouds (0 < dBZ<sub>e</sub>).</p> <p>This repository contains the MIROC6/COSP2 input data and A-Train satellite statistics used in Michibata et al. (2019). A sample of the post-processing scripts for visualization using the GrADS software is also included in this repository.</p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</p>
Effect of the Increased Nursing Attrition Rate on Nursing Administration Process during the Covid-19 Pandemic in a Selected Tertiary Care Hospital
<p><span>During<span> </span>the<span> </span>COVID-19<span> </span>outbreak,<span> </span>healthcare<span> </span>professionals,<span> </span>particularly<span> </span>nurses,<span> </span>were<span> </span>more<span> </span>prone to<span> </span>diseases.<span> </span>Globally<span> </span>attrition<span> </span>rate<span> </span>was<span> </span>high<span> </span>among<span> </span>nurses<span> </span>and<span> </span>during<span> </span>the<span> </span>pandemic,<span> </span>it<span> </span>increased because<span> </span>of<span> </span>various<span> </span>reasons<span> </span>such<span> </span>as<span> </span>the<span> </span>risk<span> </span>of<span> </span>infection,<span> </span>occupational<span> </span>and<span> </span>psychological<span> </span>stress, causing risk to their loved ones. This led to a chaotic situation where nurse managers were forced to implement specific strategic plans to deal with increased nurse attrition. This study aims<span> </span>to<span> </span>describe<span> </span>the<span> </span>impact<span> </span>of<span> </span>nurse<span> </span>attrition<span> </span>rate<span> </span>on<span> </span>nursing<span> </span>administration<span> </span>during<span> </span>COVID-19 at a selected tertiary care hospital. The research approach adopted in this study is descriptive cross-sectional. A total sample of 66 nurses involved in nursing administration. The data is collected through a structured questionnaire and the nurse attrition data during the COVID-19 pandemic period was collected from the interview method during the survey. Statistical tests used were frequency, percentage, mean, Standard Deviation (S.D). The study showed that there is a moderate impact of increased nurse attrition on nursing administration during the COVID-19 pandemic. The study led to the identification of gaps that need to be addressed in a similar crisis.</span></p>
2001_2019_MODIS_Fourier Processed_1k_ER
<p><strong>Overview:</strong></p> <p>This is a set of images produced by Temporal Fourier Analysis of global MODIS data</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p> </p> <p><strong>Abstract: </strong></p> <p>MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area.</p> <p>This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm.</p> <p>A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)<br> Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility<br> </p> <p><strong>File naming scheme:</strong></p> <p><br> The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.<br> <br> The next two characters identify the channel:<br> 03 - middle infra-red<br> 07 - daytime land surface temperature<br> 08 - nighttime land surface temperature<br> 14 - NDVI: Normalised Difference Vegetation Index<br> 15 - EVI: Enhanced Vegetation Index<br> <br> The last two characters of each file name denote the output from Fourier processing:<br> a0 - mean<br> mn - minimum<br> mx - maximum<br> a1 - amplitude of annual cycle<br> a2 - amplitude of bi-annual cycle<br> a3 - amplitude of tri-annual cycle<br> p1 - phase of annual cycle<br> p2 - phase of bi-annual cycle<br> p3 - phase of tri-annual cycle<br> d1 - variance in annual cycle<br> d2 - variance in bi-annual cycle<br> d3 - variance in tri-annual cycle<br> da - combined variance in annual, bi-annual, and tri-annual cycles<br> vr - variance in raw data</p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br> 8-day and 16-day from 2001 to 2019</p> <p><br><strong>Pixel values</strong></p> <p>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <p><br><strong>Source: </strong><br>MODIS NASA :MOD11A2 and MOD13A2</p> <p><br><strong>Software used:</strong><br>Codes for modelling are in Python and C++<br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
Groundwater monitoring with an underwater DAS1 cable, processed dataset
<p>This repository contains data to reproduce figures from the article "Groundwater monitoring with an underwater DAS cable " by Destin Nziengui Bâ, Aurélien Mordret, Olivier Coutant, Camille Jestin and Ludovic Bodet </p>
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
Replication Package for the paper "Conversing with business process-aware Large Language Models: the BPLLM framework"
<p>Replication Package for the research paper "<em>Conversing with business process-aware Large Language Models: the BPLLM framework</em>".</p> <p>The package includes the process models, the questions (and expected answers), the results of the qualitative evaluation, and the Hugging Face links to the fine-tuned versions of Llama 3.1 8B employed in the quantitative evaluation of the framework.</p> <p>In particular, the process models are:</p> <ul> <li>The natural language Directly-follows graph (DFG) of the Food Delivery process: <em>food_delivery_activities.txt</em> for the definition of the activities and <em>food_delivery_flow.txt</em> for the sequence flow.</li> <li>The BPMN model of the Food Delivery, E-commerce, and Reimbursement processes: <em>ecommerce.bpmn</em>, <em>food_delivery.bpmn</em>, and <em>reimbursement.bpmn</em>.</li> </ul> <p>The datasets with the questions and the expected answers are:</p> <ul> <li><em>1_questions_answers_not_refined_for_DFG.csv</em> ;</li> <li><em>1.1_questions_answers_refined_for_DFG.csv</em> ;</li> <li><em>2_questions_answers_not_refined.csv</em> ;</li> <li><em>3_questions_answers_refined.csv</em> ;</li> <li><em>4_questions_answers_different_processes.csv</em> ;</li> <li><em>5_questions_answers_similar_processes.csv</em> ;</li> <li><em>6_questions_answers_refined_ft.csv</em> .</li> </ul> <p>The complete results of the qualitative evaluation are contained in the file <em>qualitative_experiments_results.pdf</em>.</p> <p>The Hugging Face links to the fine-tuned versions of Llama 3.1 8B are reported in <em>hf_links_finetuned_models.pdf</em>.</p>
A Bioconductor workflow for processing, evaluating and interpreting expression proteomics data
<p>Files for users of the workflow "A Bioconductor workflow for processing, evaluating and interpreting expression proteomics data". Files include Proteome Discoverer (v2.5) processing and consensus workflows for both TMT and LFQ expression proteomics data. Also provided are the output .txt files of a corresponding Proteome Discoverer identification search, as required for users to follow the workflow themselves. For raw data please refer to PRIDE. Appendix is provided as a PDF.</p>
Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications
<p>This is the data supporting the manuscript "Optimizing the Shelling Process of InP/ZnS Quantum Dots Using a Single-Source Shell Precursor: Implications for Lighting and Display Applications".</p> <p>Abstract</p> <p>InP/ZnS core/shell quantum dots (QDs), recognized as highly promising heavy-metal-free emitters, are increasingly utilized in lighting and display applications. Their synthesis in a tubular flow reactor enables production in a highly efficient, scalable, and reproducible manner, particularly when combined with a single-source shell precursor, such as zinc diethyldithiocarbamate (Zn(S2CNEt2)2). However, the photoluminescence quantum yield (PLQY) of QDs synthesized with this route remains significantly lower compared to those synthesized in batch reactors involving multiple steps for the shell growth. Our study identifies the formation of absorbing, yet non-emissive ZnS nanoparticles during the ZnS shell formation process as a main contributing factor to this discrepancy. By varying the shelling conditions, especially the shelling reaction temperature and InP core concentration, we investigated the formation of pure ZnS nanoparticles and their impact on the optical properties, particularly PLQY, of the resultant InP/ZnS QDs through UV-vis absorption, steady-state and time-resolved photoluminescence (PL) spectroscopy, scanning transmission electron microscopy (STEM) and analytical ultracentrifugation (AUC) measurements. Our results suggest that process conditions, such as lower shelling temperatures or reduced InP core concentrations (resulting in a lower external surface area), encourage the homogeneous nucleation of ZnS. This reduces the availability of shell precursors necessary for an effective passivation of the InP core surfaces, ultimately resulting in lower PLQYs. These findings explain the origin of persistently underperformed PLQY of InP/ZnS QDs synthesized from this synthesis route and suggest further optimization strategies to improve their emission for lighting and display applications.</p> <p>The data are sorted per techniques used for characterization. Information about the measurement details can be found in README files attached to each technique folder.</p>
Processed Data for "Improving Gene Regulatory Network Inference using Dropout Augmentation"
<p>Here are the processed dataset that are used in the manuscript "Improving Gene Regulatory Network Inference using Dropout Augmentation"</p>
Dataset for "Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator"
<p>The dataset represents the experimental data for publication "<span>Scaling of ultrashort-pulsed laser structuring processes for electromobility applications using a spatial light modulator</span>"</p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
Process-structure-property map for organic solar cells
<p>This archive contains:<br> - 1708 morphologies generated using Cahn-Hilliard equation for two parameters: blend ratio in range (0.5-0.63) and chi/interaction parameter (2.3-4.0). Blend ratio and chi are the processing conditions. Morphologies are given in two formats: plt files (srcdata folder) - raw data from simulations, and txt file (data folder) in the row-wise format for Graspi. For quick visualization morphologies are visualized and stored in folder figs.</p> <p>The dataset contains:<br> Five folders:<br> - srcdata: the source data with all plt files (1708 files) generated by Cahn Hilliard equation solver<br> - data: the data used by graspi to compute descriptors (these are txt files stored as row-wise array, the volume fraction has been segmented using tools of graspi)<br> - logs: 1708 log files with the descriptors generated by graspi (native C++ version)<br> - figs: 1708 png files with visualized morphologies</p> <p>Two tables (in comma separated values format):<br> - Combined PSP.csv (combined data on process-structure-property maps), where process information consists of three variables: PHI, CHI, NN (volume fraction, interaction parameter and time step index)<br> - AllPropertiesCurated.csv - File with results from EDD - see reference below for more details</p> <p>One shell script: extractDesc.sh to build CombinedPSP.csv table from three sources: filename (contains info about PHI, CHI, NN), descriptors (graspi and logs), and Jsc (from AllPropertiesCurated.csv)</p> <p>More details on the dataset: Wodo, O., J. Zola, . Pokuri, P. Du, and B. Ganapathysubramanian. "Automated, high throughput exploration of process–structure–property relationships using the mapreduce paradigm." Materials discovery 1 (2015): 21-28.</p> <p> </p>
Processed data used in transcriptome- metabolome-wide association study
<p>Processed_RNASeq_RPKM.txt contains RPKM levels for 45484 genes quantified in 555 individuals from RNA-Seq of lymphoblastoid cell lines (LCLs).</p> <p>Processed_NMRpeaks_baseline.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1276 bins quantified in 555 individuals from urine samples taken at baseline. NMR spectra were acquired at 300 K on a Bruker 16.4 T Avance II 700 MHz NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) using a standard 1H detection pulse sequence with water suppression. The spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>Processed_NMRpeaks_followup.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1289 bins quantified in 315 individuals from urine samples taken during follow-up. NMR spectra were acquired with an Avance III HD 600 NMR spectrometer. Spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>More details on the data set can be found in Sönmez Flitman et al. (doi: https://doi.org/10.1101/2020.05.22.110197).</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.