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35 results for “Process Flow”
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Country-ocean-moisture-flows-reconciled-with-ERA5-reanalysis obtained processing Lagrangian moisture connections
<p>The dataset "Reconciled global atmospheric moisture flows between countries/oceans and subcontinents" presents tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance, and provides robust estimates of terrestrial moisture recycling and net moisture flows to support global water governance analysis.</p> <p>This repository is supplement to a study by De Petrillo & Fahrländer et al. (2025), which describes the development of the reconciliation framework, includes a perfromance analysis of the method and shows an exemplary case study on the published data. </p> <p>The atmospheric moisture flows are sourced from the UTrack atmospheric moisture flow dataset by Tuinenburg et al. (2020a) (dataset access: Tuinenburg et al., 2020b) and reconciled with ERA5 precipitation and evaporation data (Hersbach et al., 2020) on the mean annual basis in the period 2008-2017, by means of a post-processing framework, based on the Iterative Proportional Fitting (IPF) algorithm.</p> <p>NOTE: The final dataset is available in form of bilateral matrices (country/ocean and subcontinent/ocean) and in form of direct flows (flow edges). Supporting material to read the dataset is in the folder "List" . Processed ERA5 data (where the precipitation-evaporation annual balance is met) and input data to generate the figures are also available.</p> <p>References:</p> <p>De Petrillo, E., Fahrländer, S., Tuninetti, M., Andersen, L.S., Monaco, L., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric moisture flows to close the global freshwater cycle.<em> </em><em>Commun Earth Environ <strong>6</strong>, 347 (2025). </em><a href="https://doi.org/10.1038/s43247-025-02289-y">https://doi.org/10.1038/s43247-025-02289-y</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020a). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., Staal, A. (2020b): Global evaporation to precipitation flows obtained with Lagrangian atmospheric moisture tracking. PANGAEA, <a href="https://github.com/ObbeTuinenburg/UTrack_global_database">https://doi.pangaea.de/10.1594/PANGAEA.912710</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p> <p> </p>
Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>PSO is used to identify the best solution from collection of solution. It is a computational<br> method that optimizes a problem by iteratively trying to improve a candidate solution with regard to<br> a given measure of quality. PSO optimizes a problem by having a population of candidate solutions,<br> here dubbed particles, and moving these particles around in the search-space according to simple<br> mathematical formulae over the particle's position and velocity. Each particle's movement is<br> influenced by its local best known position but, is also guided toward the best known positions in<br> the search-space, which are updated as better positions are found by other particles. This is expected<br> to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no<br> assumptions about the problem being optimized and can search very large spaces of candidate<br> solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found.<br> The following Figure 5 explains the basic flow of PSO process.</p>
Process Flow Diagram of Cardiac Surgery Pathway
<p><em><strong>This image is supplementary material for:</strong></em></p> <p>Currie, C. S.M. Monks, T. (2018) <em>Modelling Diseases: Prevention, Cure, and Management.</em> In M. Rabe, A. Juan, N. Mustafee, A. Skoogh, & B. Johansson (Eds.). Proceedings of the Winter Simulation Conference 2018. Gothenburg, Sweden.</p> <p><em>Relevant to:</em></p> <p><strong>Section 4.1.4. Spend some Time Observing How the Operations of the Disease Pathway are Managed</strong></p> <p>The description of the disease pathway obtained in the initial meeting may be idealized. It is advised that modelers spend at least a few hours in the one or more services in the real pathway to observe how it operates and to interview on-the-ground workforce. Observations of the system under pressure are useful to understand the complexities of the pathway and will reveal aspects that have been missed or that operate differently from described. This Figure illustrates a cardiac surgery pathway at a large hospital that operated on patients from a network of smaller hospitals in the UK. The diagram itself was primarily constructed in an initial meeting with a group of cardiac surgeons. Observation of the system and shadowing of specialist cardiac nurses revealed two differences from the idealized model. Firstly, patients could deteriorate and ‘bounce back’ from post-operative care to higher levels of care. Secondly, in times of pressure, the post operative beds were used flexibly to accommodate patients waiting for surgery and those who had already undergone surgery.</p>
Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)
<p>Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors".</p> <p>DOI: 10.1016/j.cej.2018.10.131</p> <p>Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor.</p> <p>Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination.</p> <p>Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Data from: The relevance of gene flow with wild relatives in understanding the domestication process
<p><span><span><span>The widespread use of genomic tools has allowed for a deeper understanding of the genetics and the evolutionary dynamics of domestication. Recent studies have suggested that multiple domestications and introgression are more common than previously thought. However, the ability to correctly infer the domestication process depends on having an adequate representation of wild relatives. Cultivated maize (<i>Zea mays</i> spp. <i>mays</i>) is one of the most important crops in the world, with a long and a relatively well documented history of domestication. The current consensus points towards a single domestication event from teosinte <i>Zea mays</i> spp. <i>parviglumis</i> from the Balsas Basin in Southwest central Mexico. However, the underlying diversity of teosintes from lowlands <i>Z. mays</i> spp<i>. parviglumis</i> and highlands <i>Zea mays</i> spp. <i>mexicana</i>) was not taken into account in early studies. We used 32,739 SNPs obtained from 29 teosinte populations and 43 maize landraces to explore the relationship between wild and cultivated members of <i>Zea.</i> We then inferred levels of gene flow among teosinte populations and maize, the degree of population structure of <i>Zea mays</i> subspecies, and potential domestication location of maize. We confirmed a strong geographic structure within <i>Z. mays </i>spp. <i>parviglumis</i> and documented multiple gene flow events with other members of the genus, including an event between <i>Z. mays</i> spp. <i>mexicana </i>and maize. Our results suggest that the likely ancestor of maize may have been domesticated not in the Balsas Basin as previously thought, but in Jalisco or in the Pacific coast, and that different populations of the teosinte subspecies have contributed to modern maize's gene pool. Our results points towards a long period of domestication marked by gene flow with wild relatives, making domestication a long and ongoing process. </span></span></span></p>
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
<p>Motivation: Sap flow sensors are crucial instruments to understand whole-tree water use. The lack of direct calibration of the available methods on large trees and the application of several data-processing procedures may jeopardize our understanding of water uptake dynamics by increasing the uncertainties around sensor-based estimates. We directly compared the heat ratio method (HRM) sap flow measurements to water uptake measured gravimetrically using the cut-tree method on a large mature aspen tree to quantify those uncertainties for ten consecutive days.</p> <p>Dataset: In this dataset, we provide sap flux density (ten-minutes intervals; g.cm-2.hr-1; corrected for wounding and sapwood thermal diffusivity) obtained from four HRM sap flow sensors installed at 2.5 m high on the focus tree (20 m tall, 60 years old trembling aspen in the boreal mixedwood region of Alberta) between July 18th and August 22nd 2017. We present the code and data (weather data from neighboring weather station) used to calculate whole-tree sap flux (L.hr-1) from each of the individual sensors using different methods of radial integration of sap flux density across the sapwood area estimated via different calculations, as well as different zero-flow corrections used. The cut-tree procedure was applied to the focus tree, and gravimetric measurements of water uptake (ten-minutes intervals) were made using a recording scale. We directly compared the different estimates of hourly, daily and cumulative sap flows obtained with gravimetric measurement of water uptake. We present the code providing the statistical analysis and results reported in the associated publication (Merlin, M., Solarik, K.A., Landhäusser, S.M. Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. 2020. Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2020.107926)</p>
Database with characterization factors for material flow accounting (material footprint) for process-based LCA for ecoinvent 3.7.1 and 3.8 in openLCA
<p>This dataset contains extend material-specific material footprint indicators (raw material input RMI and total material requirement TMR) compiled by Mostert and Bringezu (2019) to process-based LCA in form of the databases ecoinvent 3.7.1 and 3.8 (Wernet et al. 2016) with the openLCA software. The scope of the material flows covered here largely fits to the materials list used for the Mineral Resource Scarcity indicator of the ReCiPe life cycle impact assessment (LCIA) method (Huijbregts et al. 2016).</p>
Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow
<p>Fig. S1 Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015).</p> <p>Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013).</p> <p>Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C.</p> <p>Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units.</p> <p>Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.</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>
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
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Ultrahigh-throughput cross-flow filtration of solution-processed 2D materials enabled by porous ceramic membranes
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Data from: The relevance of gene flow with wild relatives in understanding the domestication process
Open the record for dataset details and reuse information.
Data from: Effects of the landscape on boreal toad gene flow: does the pattern-process relationship hold true across distinct landscapes at the northern range margin?
Understanding the impact of natural and anthropogenic landscape features on population connectivity is a major goal in evolutionary ecology and conservation. Discovery of dispersal barriers is important for predicting population responses to landscape and environmental changes, particularly for populations at geographic range margins. We used a landscape genetics approach to quantify the effects of landscape features on gene flow and connectivity of boreal toad (Bufo boreas) populations from two distinct landscapes in Southeast Alaska (Admiralty Island, ANM, and the Chilkat River Valley, CRV). We used two common methodologies for calculating resistance distances in landscape genetics studies (resistance based on least-cost paths and circuit theory). We found a strong effect of saltwater on genetic distance of CRV populations, but no landscape effects were found for the ANM populations. Our discordant results show the importance of examining multiple landscapes that differ in the variability of their features, in order to maximize detectability of underlying processes and allow results to be broadly applicable across regions. Saltwater serves as a physiological barrier to boreal toad gene flow and affects populations on a small geographic scale, yet there appear to be few other barriers to toad dispersal in this intact northern region.
A Novel Flow Shop Batch Scheduling Model for Subtractive Processing in Electronic Product Manufacturing
<p>The data is randomly generated based on real manufacturing enviroment. The data is used to evaluate the performance of algorithms proposed in our research.</p>
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
<p>Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.</p> <p> </p> <p>ET_fluxes.csv<br>- Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations</p> <p>GW_levels.csv<br>- Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment</p> <p>Q_runoff.csv<br>- Observed specific discharge [mm/d] at the LJO catchment outlet</p> <p>THETA_kenttarova.csv<br>- Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations</p> <p>THETA_spatial.csv<br>- Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment</p>
Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"
<p>These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics".</p> <p>These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.</p>
Bank erosion process and distribution along channel caused by multiple debris flow surges
<p>This dataset contains point cloud data and Excel table data, which together describe the bank retreat process of 5 types of bank soils caused by multiple debris flow surges</p>
Kinematic Process and Characteristic of Root-Induced Water Flow Using a Visualizing Point Source Infiltration Experiment
<p><span>The video is the temporal results of the 2-D point source infiltration experiment in this paper. ‘G-’ represents the granular experimental group, and ’RG-’ represents the root-granular experimental group. The injection rate of G-1 and RG-1 is 0.25 mm/min, corresponding to the boundary flow rate of 0.03 ml/min. </span></p> <p><span>In this experiment, the glass beads have a particle diameter ranging from 0.6-0.8 mm. The selected root has diameters ranging from 1.3-1.5 mm.</span></p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.