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6,467 results for “process”
In-vivo video microscopy of the rupturing process of thin blood vessels in transparent fish during contact with a blunt indenter
<p>To clarify the mechanism of bruise injuries caused by blunt impact, in vivo microscopy was performed. A flat ended indenter made of transparent acrylic was loaded onto the lateral side of the tail region of anesthetized fish. The process of the rupture of thin blood vessels was recorded in two specimens.</p> <p>This data set includes two types of files; (1) mp4 files of the in vivo microscopy; (2) pdf files to explain the mp4 files.</p> <p><span>The mp4 files are the original data of “Fujikawa, T., Yamada, Y. In vivo video microscopy of the rupturing process of thin blood vessels to clarify the mechanism of bruising caused by blunt impact: an animal study. BioMed Eng OnLine 23, 94 (2024). https://doi.org/10.1186/s12938-024-01284-2.”</span></p>
Process Integration Multi-Actor Multi-Criteria Optimization (PI-MAMCO) Framework: Case Study on Palm Oil Based Complex (POBC)
<p>The dataset includes the complete workflow for the PI-MAMCO method implementation, demonstrated on a POBC integration case study. The files are structured as follows:</p> <ol> <li> <p><strong>NOTEBOOKS Folder</strong>: This folder contains the Jupyter notebooks that constitute the entire workflow of our case study. These notebooks import necessary model equations, data, and functions from the "MODEL" folder. Running these notebooks with the correct Python package versions will reproduce the exact results presented in our submitted article.</p> </li> <li> <p><strong>MODEL Folder</strong>: This folder includes all model equations, data, and functions required by the Jupyter notebooks. The scripts in this folder are integral to executing the PI-MAMCO method as outlined in our research.</p> </li> <li> <p><strong>OUTPUTS Folder</strong>: The results generated by running the notebooks are saved in this folder. This includes all outputs reported in our manuscript as well as additional results not included in the paper.</p> </li> <li><strong>OUTPUTS_GENERATED Folder</strong>: The files contained in this folder are the expected results from executing the case study notebook.</li> </ol> <p><em>Reproducibility</em>: To ensure the reproducibility of our results, please ensure that you are using the correct versions of the required Python packages. Detailed instructions for setting up the Python environment are provided within the notebook and the readme.txt.</p> <p>Contained are also outputs from the <a href="https://www.sciencedirect.com/science/article/pii/S0957417422003931" target="_blank" rel="noopener">IRenE procedure</a>, used for the literature review (<em>01_extracted_keywords.xlsx</em> ; <em>02_sampling_results.csv</em>)</p>
Probabilistic Data Generating Process-based Crop Type Map for the EU 2010-2020
<h3>General Description</h3> <p>This dataset consists of probabilistic crop type maps for the EU-28 for the years 2010-2020 that distinguish 28 crop types at 1km resolution (EPSG:3035). The maps were generated using the Data Generating Process-based procedure developed by Baumert, Heckelei and Storm (2024) [<em><span><a href="https://doi.org/10.1016/j.ecoinf.2024.102836">https://doi.org/10.1016/j.ecoinf.2024.102836</a></span></em>]. We refer to this paper for details on the generation and validation of the maps. The code used to create the maps including a detailed list of the input data can be found here: <a href="https://github.com/JoBaumert/Probabilistic_Crop_Mapping_EU">GitHub - JoBaumert/Probabilistic_Crop_Mapping_EU</a> . </p> <h3>Downloadable Data</h3> <p>The file “EU_expected_crop_shares.zip” consists of 11 raster files, one for each year from 2010 – 2020. The raster files indicate the expected shares for each of the 28 distinguished crop types in a grid cell for the entire EU-28 (see readme.txt contained in the zipped folder). Note that this raster file does not contain uncertainty information.</p> <p>The other 28 zip files contain the entire crop map ensemble (i.e., including uncertainty information), each for one of the EU countries and the United Kingdom. Each of those zip files contain 11 raster files, one for each year from 2010 – 2020. Each raster file has 2830 bands: the first two bands indicate the weight of the cell (proportional to the utilized agricultural area in a cell) and the estimated number of agricultural fields in a cell, respectively. The next 28 bands indicate the expected shares for each of the 28 crops in the respective cell. The remaining 2800 bands compose the crop type map ensemble, i.e., 100 simulated crop shares for each of the 28 crops. The zipped country folder also includes a csv file named “bands” that describes which band refers to which crop. Note that all crop shares were multiplied by 1000 when writing them to the raster files (saving them as integers requires less storage capacity), i.e., if a crop share is 0.325 or 32.5% it will appear as 325 in the raster files. </p> <p>The distinguished crops are (with abbreviation used in "bands.csv"):</p> <ul> <li>Apples and other fruits, nuts and berries (APPL+OFRU)</li> <li>Barley (BARL)</li> <li>Citrus fruits (CITR)</li> <li>Durum wheat (DWHE)</li> <li>Flowers and ornamental plants (FLOW)</li> <li>Grassland (GRAS)</li> <li>Maize (both green maize as well as grain maize, LMAIZ)</li> <li>Rape and turnip (LRAPE)</li> <li>Nurseries (NURS)</li> <li>Oats (OATS)</li> <li>Other cereals (OCER)</li> <li>Other permanent crops (OCRO)</li> <li>Other forage plants (OFAR)</li> <li>Other industrial plants (OIND)</li> <li>Olives (OLIVGR)</li> <li>Rice (PARI)</li> <li>Potatoes (POTA)</li> <li>Pulses (PULS)</li> <li>Fodder roots and brassicas (ROOF)</li> <li>Rye (RYEM)</li> <li>Soybeans (SOYA)</li> <li>Sugar beets (SUGB)</li> <li>Sunflowers (SUNF)</li> <li>Soft/common wheat (SWHE)</li> <li>Other oilseeds and fibre crops (TEXT)</li> <li>Tobacco (TOBA)</li> <li>Fresh vegetables, melons, strawberries (TOMA+OVEG)</li> <li>Vineyards (VINY)</li> </ul> <p> </p> <p> </p>
Train_clean_processed
<div>The datasets used in this project were sourced from the following reference:</div> <div> </div> <div>Ruiz Puentes, P.; Henao, M. C.; Cifuentes, J.; Muñoz-Camargo, C.; Reyes, L. H.; Cruz, J. C.; Arbeláez, P.</div> <div>Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence.* Membranes 2022, 12 (7), 708.</div> <div>DOI: [https://doi.org/10.3390/membranes12070708](https://doi.org/10.3390/membranes12070708)</div>
atlantis_silica_processed
<div> <div> <div>Reference:</div> <div> </div> </div> </div> <div> <div> <div>Spicer, V.; Krokhin, O. V.</div> <div>Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography. Comparison of Separation Selectivity between Bare Silica and Bonded Stationary Phases. J. Chromatogr. A 2018, 1534, 75–84, DOI: [10.1016/j.chroma.2017.12.046](https://doi.org/10.1016/j.chroma.2017.12.046)</div> </div> </div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div>
luna_hilic_processed
<div> <div> <div>Reference:</div> <div> </div> </div> </div> <div> <div> <div>Spicer, V.; Krokhin, O. V.</div> <div>Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography. Comparison of Separation Selectivity between Bare Silica and Bonded Stationary Phases. J. Chromatogr. A 2018, 1534, 75–84, DOI: [10.1016/j.chroma.2017.12.046](https://doi.org/10.1016/j.chroma.2017.12.046)</div> </div> </div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div>
luna_silica_processed
<div> <div> <div>Reference:</div> <div> </div> </div> </div> <div> <div> <div>Spicer, V.; Krokhin, O. V.</div> <div>Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography. Comparison of Separation Selectivity between Bare Silica and Bonded Stationary Phases. J. Chromatogr. A 2018, 1534, 75–84, DOI: [10.1016/j.chroma.2017.12.046](https://doi.org/10.1016/j.chroma.2017.12.046)</div> </div> </div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div>
misc_dia_processed
<div> <div> <div>Reference:</div> <div> </div> </div> </div> <div> <div> <div>Rosenberger, G.; Koh, C. C.; Guo, T.; Röst, H. L.; Kouvonen, P.; Collins, B. C.; Heusel, M.; Liu, Y.; Caron, E.; Vichalkovski, A., et al.</div> <div>A Repository of Assays to Quantify 10,000 Human Proteins by SWATH-MS.* Sci. Data 2014, 1, 140031, DOI: [10.1038/sdata.2014.31](https://doi.org/10.1038/sdata.2014.31)</div> </div> </div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div>
xbridge_amide_processed
<div> <div> <div>Reference:</div> <div> </div> </div> </div> <div> <div> <div>Spicer, V.; Krokhin, O. V.</div> <div>Peptide Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography. Comparison of Separation Selectivity between Bare Silica and Bonded Stationary Phases. J. Chromatogr. A 2018, 1534, 75–84, DOI: [10.1016/j.chroma.2017.12.046](https://doi.org/10.1016/j.chroma.2017.12.046)</div> </div> </div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div>
yeast_unmod_processed
<div> <div> <div> <div>Reference:</div> <div> </div> </div> </div> <div>Nagaraj, N.; Kulak, N. A.; Cox, J.; Neuhauser, N.; Mayr, K.; Hoerning, O.; Vorm, O.; Mann, M.</div> <div>System-Wide Perturbation Analysis with Nearly Complete Coverage of the Yeast Proteome by Single-Shot Ultra HPLC Runs on a Bench Top Orbitrap. Mol. Cell. Proteomics 2012, 11 (3), M111.013722 DOI: [10.1074/mcp.M111.013722](https://doi.org/10.1074/mcp.M111.013722)</div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div> </div>
Test_clean_processed
<div> <div>The datasets used in this project were sourced from the following reference:</div> <br> <div>Ruiz Puentes, P.; Henao, M. C.; Cifuentes, J.; Muñoz-Camargo, C.; Reyes, L. H.; Cruz, J. C.; Arbeláez, P.</div> <div>Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence.* Membranes 2022, 12 (7), 708.</div> <div>DOI: [https://doi.org/10.3390/membranes12070708](https://doi.org/10.3390/membranes12070708)</div> </div>
scx_processed
<div> <div> <div>Reference:</div> <div> </div> </div> </div> <div> <div> <div>Gussakovsky, D.; Neustaeter, H.; Spicer, V.; Krokhin, O. V.</div> <div>Sequence-Specific Model for Peptide Retention Time Prediction in Strong Cation Exchange Chromatography. Anal. Chem. 2017, 89 (21), 11795–11802, DOI: [10.1021/acs.analchem.7b03436](https://doi.org/10.1021/acs.analchem.7b03436)</div> </div> </div> <div> </div> <div> <div> <div>All datasets were implemented and curated from:</div> <div> </div> </div> </div> <div> <div> <div>Ma, C.; Ren, Y.; Yang, J.; Ren, Z.; Yang, H.; Liu, S.</div> <div>Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal. Chem. 2018, 90 (18), 10881–10888. DOI: [10.1021/acs.analchem.8b02386](https://doi.org/10.1021/acs.analchem.8b02386)</div> </div> </div>
Experimental dataset for the publication "Characterisation of Microstructure and Special Grain Boundaries in LPBF AlSi10Mg Alloy Subjected to the KoBo Extrusion Process"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>KOBO.ang - KOBO-processed sample</p> <p>LPBF_condition.ang - As-built sample</p> <p>TKD data:</p> <p>TKD_KOBO.ang - KOBO-processed sample</p> <p>HRTEM images:</p> <p>Fig. 12(b).dm3 - KOBO-processed sample</p>
Table S1: Processed, filtered, and supplemented data
<p>Cleaned and processed data used for analysis in "β-Lactamase Diversity in <em>Acinetobacter Baumannii</em>" by Andrew R. Mack, Andrea M. Hujer, Maria F. Mojica, Magdalena A. Taracila, Michael Feldgarden, Daniel H. Haft, William Klimke, Arjun B. Prasad, and Robert A. Bonomo. Derived from National Center for Biotechnology Information databases on September 9th, 2024.</p>
Simulated Object-Centric Event Logs (OCEL 2.0) for Order-to-Cash, Procure-to-Pay, Hiring, and Hospital Patient Lifecycle Processes
<p>This dataset contains simulated object-centric event logs for four distinct business processes: <strong>Order-to-Cash (O2C)</strong>, <strong>Procure-to-Pay (P2P)</strong>, <strong>Hiring</strong>, and <strong>Hospital Patient Lifecycle</strong>. Each process is designed to reflect realistic workflows, encompassing multiple object types and capturing key activities, decision points, and process dynamics. The dataset is aimed at providing a rich source of data for process mining, analysis, and modeling activities.</p> <p>1. <strong>Order-to-Cash (O2C)</strong>:<br> The O2C process simulates an end-to-end business flow starting from customer order placement to payment receipt. It includes diverse activities such as order approval, fulfillment, invoice generation, and payment processing, involving object types like Customers, Orders, Products, and Invoices. The dataset captures variability through random decisions, synchronization between departments, and workarounds in credit checks and inventory adjustments. Attributes such as customer tiers, order values, and shipment statuses add further depth, allowing for detailed analysis of this complex process.</p> <p>2. <strong>Procure-to-Pay (P2P)</strong>:<br> The P2P process simulates the procurement lifecycle, from requisition creation to payment of suppliers. Key activities include purchase order creation, three-way matching, goods receipt, and payment processing. The event log records object types such as Purchase Requisitions, Purchase Orders, Suppliers, and Invoices. Variability is introduced through approval decisions, batching, and potential mismatches in the matching process. The dataset represents the inherent complexities of real-world procurement operations, including batching and synchronization issues between different process stages.</p> <p>3. <strong>Hiring Process</strong>:<br> The hiring process log tracks the recruitment lifecycle, from job requisition creation to onboarding. It includes object types like Candidates, Job Requisitions, Recruiters, and Interviewers. The process covers activities such as resume screening, interviews, assessments, and offer management. Variability in the hiring process is introduced through random delays, candidate decisions, and background check durations. Batching occurs in stages like resume screening and onboarding, while synchronization challenges arise during interview scheduling.</p> <p>4. <strong>Hospital Patient Lifecycle</strong>:<br> This log represents the lifecycle of patients within a hospital, capturing interactions with multiple resources such as physicians, beds, and medical equipment. The process begins with pre-admission activities, followed by diagnosis, treatment, and discharge. The dataset includes object types like Patients, Physicians, and Medical Equipment, with attributes related to patient demographics and event severity. The process reflects the dynamic nature of hospital operations, including synchronization of resources and the occurrence of workarounds in case of delays or resource unavailability.</p> <p>Each process simulation captures high variability, synchronization issues, and batching, making this dataset suitable for analyzing real-world operational challenges. The logs provide a comprehensive view of complex workflows, supporting advanced analysis, including object-centric process mining.</p> <p>This description will provide the necessary details about the dataset, highlighting its structure, purpose, and potential uses for researchers and process analysts.</p> <p>Object-centric event logs conceived and simulated by the <strong>o1-preview-2024-09-12</strong> LRM, using the https://github.com/fit-alessandro-berti/llm-ocel-simulator project.</p> <p> </p> <p> </p>
Processed cancer data used in Williams, Oliphant & Au et al. 2024
<p>Processed cancer data from TCGA, PCAWG, METABRIC and Nik-Zainal et al. Processed to have copy number calls in 0.5Mb bins across the genome.</p>
1.Figure cut off value of NLRP3 calculation process by R 2.Normality check results and Variance homogeneity.
Open the record for dataset details and reuse information.
Processed NILM Datasets for Research: IAWE, REDD, and UKDALE
<p>This dataset includes the original IAWE, REDD, and UKDALE datasets, as well as the pre-processed tensor data used in the research paper. The files with the .h5 extension are the original datasets, while the .npy files are the tensorized data used in the paper.</p>
Table S1: Processed, filtered, and supplemented data
<div> <p>Cleaned and processed data used for analysis in "β-Lactamase Diversity in <em>Pseudomonas aeruginosa</em>" by Andrew R. Mack, Andrea M. Hujer, Maria F. Mojica, Magdalena A. Taracila, Michael Feldgarden, Daniel H. Haft, William Klimke, Arjun B. Prasad, and Robert A. Bonomo. Derived from National Center for Biotechnology Information databases on September 9th, 2024.</p> </div>
Reproducibility Case Study and Survey: Machine Learning-based Additive Manufacturing Process Monitoring and Quality Prediction
<p><span>Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). However, the reproducibility of the proposed ML-based AM monitoring systems in published works has not been investigated due to a lack of evaluation methods. In the paper 'Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing,' we propose a reproducibility investigation pipeline and conduct two case studies to validate the pipeline. This dataset records the data generated by one of the case studies. This dataset also contains the reproducibility survey results.</span></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.