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6,467 results for “process”
Superalloys fracture process inference based on overlap analysis of 3D models
<h2>Datasets and code utilized in the paper "Superalloys fracture process inference based on overlap analysis of 3D models"</h2> <h2>Code and data description</h2> <h3>Data for 3D reconstruction</h3> <ul> <li>Original SEM images of Fracture A - Fracture D obtained through the collection method in the paper.</li> </ul> <h3>Data for scale calibration</h3> <ul> <li>Original SEM images sequences of marked points 'dot1' and 'dot2' of Fracture A - Fracture D.</li> </ul> <h3>Sharpness score calculation</h3> <ul> <li>'shapeness.m' : Calculating image sharpness using normalized variance equations.</li> <li>'focus_A_data' - 'focus_D_data' : Sharpness scores for all images in the image sequences and their corresponding sample stage coordinates.</li> </ul> <h3>3D fracture models</h3> <ul> <li>Scale calibrated 3D models of Fracture A - Fracture D.</li> </ul> <h3>Description of internal cracks</h3> <ul> <li>Original images and EDS results for an illustration of the regions of internal crack generation in Fracture A</li> </ul> <p> </p> <p> </p>
Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks: dataset
<p>This repository contains the data accompanying the paper "<em>Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks</em>", by Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dedè and Alfio Quarteroni.</p> <p>The associated codes are available in the repository <a href="https://github.com/FrancescoRegazzoni/LDNets">https://github.com/FrancescoRegazzoni/LDNets</a></p>
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Supplementary data to: Large rock and ice avalanches frequently produce cascading processes in High Mountain Asia
<p>This data, focusing on large rock and/or ice avalanche events with severe consequences in High Mountain Asia (HMA), <span>provide a valuable first step toward improved understanding of the frequency, scope, and societal impact of such hazards across HMA</span> (linked to a journal article: Large rock and ice avalanches frequently produce cascading processes in High Mountain Asia, published in Geomorphology in 2024).</p>
Soil respiration processed data at Palazzelli CREA_OFA citrus farm
<p>Soil respiration processed data at Palazzelli CREA_OFA citrus farm</p>
Demand-side flexibility enables cost savings in a Reversible pH-swing electrochemical process for Oceanic Co2 Removal
<p>Raw datasets and codes used in our manuscript of the same title. Figure numbers correspond with the figure numbers in the manuscript.</p> <p><strong>Figure 2: </strong>Process Overview(a) Steps of the proposed, reversible, oceanic CDR process, starting with acidification of the ocean water, followed by vacuum stripping of the CO2, basification of the oceanwater, and return to the ocean to equilibrate with the atmosphere. (b) Corresponding effects on pH and dissolved inorganic carbon (DIC) at each step in the oceanic CDR process. Oceanwater pH decreases from 8.1 during acidification to produce a shift in speciation from HCO3 todissolved CO2, which is phase-separated between step 2-3 to capture pure CO2 which decreases the DIC. Basification negates these changes through the addition of the same concentration of OH− ions as H+ ions added to ocean water during acidification, resulting in pH > 8.1 at the end of basification. CO2-starved ocean water will re-equilibrate with the atmosphere and get buffered back to a neutral-pH to re-enter acidification and the cycle continues.</p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div><strong>Figure 3:</strong> Predicted Performance from 0-D Equivalent Circuit Modeling (Sec. Prediction of current-volatge behavior) for (a,c) acidification and (b,d) basification to determine (a,b) polarization behavior and (c,d) Faradaic efficiencies; including (c) power density produced during acidification.<br>Different mass-transfer conditions and therefore limiting currents are modeled by varying the liquid-side boundary layer thicknesses, (δl). Two different pH-shift conditions are modeled for the low and high pH values at the end of acidification and basification, that are respectively constrained by the concentration of H+ and OH− additions to be equal during both steps – (pHlow, pHhigh): (4,10.7) and (6,9) as described in Sec. S3. Baseline parameter values listed in Table S1 are used in the equivalent circuit models with consideration of desired and competing reactions. Note the differences in the current and potential ranges plotted between (a) acidification and (b) basification steps.</div> <div> </div> <div><strong>Figure 4: </strong>Energy Intensity Comparisons (a-c) Predictions for electrochemical energy intensity normalized by the amount of CO2 captured (Eq. (14)) in (GJ/tCO2 ) as a function of the current density when jbase = jacid (a) without and (b) with competing reactions modeled. Dashed lines on the plot indicates a baseline cell resistance of 2.18 Ω cm2 and the shaded areas are indica-tive of this ohmic resistance in the range of 0 - 5 Ω cm2; the shades of blue are indicative of theliquid-side boundary layer thicknesses, δl, modeled. Electrochemical energy intensities from (a)and (b) where δl = 50 μm are shown in (c), and compared with state-of-the-art ocean water CDRprocess intensities reported by Digdaya et al.,15 Kim et al.,23 and Yan et al.22 The gray filled areashows possible operational points with varying extents of competing reactions with the maximumbounded by competing reactions occurring at mass-transfer limited rates. (d) Overall energy inten-sity (GJ/tCO2 ) including electrochemical and parasitic loads (as described in Sec. Energy IntensityCalculations) for the proposed process without competing reactions, calculated at an industrially-relevant current density of 100 mAcm−2, the baseline cell resistance, and pH shift of 6 to 9.6,compared with electrochemical and thermally-driven direct air capture (DAC) processes with data obtained from Singh et al.;24 error bars for the thermal DAC processes are from Viebahn et al.</div> <div> </div> <div><strong>Figure 5: </strong>Operating Energy Profiles and Effects of Load Shifting (a) Integration of solar and wind electricity production in California, CAISO 2022-2023,40 with predicted energy demand for the proposed oceanic CO2 removal process as a function of varying extents of yearly CO2 captured, 0 – 16 MtCO2,year. (b) Hourly power demand to capture 1 tCO2 per day. The gray curve indicates the process shown in (a), while the red curves shows a constant power demand process occurring at an equivalent energy intensity, i.e., the area underneath both curves are the same. (c) Hourly cost to capture 1 tCO2 per day. The gray curve indicates the hourly cost associated with variable energy process shown in (b) if all produced electricity is sold at market value, while the red curve compares it to the hourly cost associated with constant power consumption process shown in (b). Operating conditions of the proposed, variable process shown in (a-c) include jacid = 75 mA cm−2 and jbase = 150 mA cm−2, while still constraining the same volume of ocean water being treated over a 24-hour day in the individual steps (i.e. tbase = 0.5tacid). The process switches between acidification (occurring early mornings and evenings/nights when electricity price is high) and basification (occurring mid-day when electricity price is low) based on the supply and the cost of electricity purchased from CAISO (Sec S8.5). (d) Electricity cost savings for the proposed process operation compared to a process with constant energy usage at different ratios of tbase to tacid, assuming a time-weighted average operating current density of 100 mA cm−2. Different line styles indicate different proportions of market value at which produced electricity can be sold (0%, 50%, and 100%). For all cases: competing reactions are not considered, a thin boundary layer is assumed (δl = 10 μm), and the pH shift is from 6 to 9.6. Parasitic energy costs are included for CO2 phase separation, but not for pumping as we assume co-location with a desalination plant. Parasitics are assumed to only operate during the basification process, at high enough power to remove all CO2 treated over the day.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Original research data for the paper "Additive Manufacturing of Polymeric Gradient Index Optics via Grayscale Digital Light Processing Vat Photopolymerization Technology"
<p>[CMOS camera data]</p> <p>[conversion maps]</p> <p>[cure kinetics]</p> <p>[GRIN profiles arbitrary]</p> <p>[MonoPrinter grayscale power density]</p> <p>[MonoPrinter print files]</p> <p>[pictures of arbitrary GRINs]</p> <p>[predicted printing param matrices]</p> <p>[refractive index vs conversion]</p> <p>[working curve]</p>
Exploring the Cognitive Effects of Ambiguity in Process Models
<p>This dataset contains the supplementary material for the paper "Exploring the Cognitive Effects of Ambiguity in Process Models", accepted for publication in the proceedings of BPM 2024.</p> <p>The dataset includes:</p> <ul> <li>All process models used in the experiments</li> <li>A report on the process models complexity metrics</li> <li>The list of all tasks and the respective modeling guidelines violations</li> <li>Participants demographics</li> <li>Higher resolution version of the figures in the paper</li> <li>The Python notebook used for the data analysis</li> <li>Process maps generated from the data analysis results</li> </ul>
The a-crystallin chaperones undergo a quasi-ordered co-aggregation process in response to saturating client interactions
<p>This data set included micrographs used for FIJI based morphology analysis of aA-crystallin and aB-crystallin co-aggregates with lysozyme and insulin, and aL-crystallin with lysozyme. Reaction ratios of chaperone:client include 1:0 (apo-state), 4:1, 2:1, and 1:1 for each isoform. Also, included is a spreadsheet containing various data from the paper (turbidity, dynamic light scattering, negative stain EM measurements).</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>
Data for: Passive Daytime Cooling Foils for Everyone: A Scalable Lamination Process Based on Upcycling Aluminum-Coated Chips Bags
<p>Raw data of the measurements presented in the affiliated publication. The data are organized in folders corresponding to the respective characterization techniques. The employed techniques comprise: optical spectroscopy, indoor and field testing of the passive cooling performance, and scanning electron microscopy imaging.</p>
Animation, MESA inlists, and post-processing package for "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants"
<p>Videos, post-processing package for mock ALMA observations, MESA (r23.05.1 with mesasdk-x86_64-linux-23.7.3) inlists and history files, Jupyter notebook and data to reproduce figures of the paper "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants".</p>
Search Protocol for "Conversational Systems for AI-Augmented Business Process Management"
<p>Results obtained from implementing the search protocol devised for the literature survey "<em>Conversational Systems for AI-Augmented Business Process Management</em>".</p> <p>The dataset comprises four spreadsheets, each corresponding to one of the four BPM areas identified in the paper, namely:</p> <ul> <li><em>Descriptive Process Analytics</em>;</li> <li><em>Predictive Process Analytics</em>;</li> <li><em>Prescriptive Process Optimization</em>;</li> <li><em>Augmented Process Execution</em>.</li> </ul> <p>Each spreadsheet consists of multiple sheets:</p> <ul> <li>The first four sheets document the papers collected from each data source (<em>Google Scholar</em>, <em>Scopus</em>, <em>ACM Digital Library</em>, <em>IEEE Xplore</em>) by applying the search strings defined in the paper.</li> <li>"All" reports all the papers obtained in the search.</li> <li>"All(-duplicates)" lists all publications, excluding duplicates.</li> <li>"Inclusion" applies the inclusion criteria defined in the paper to select the works considered in this survey.</li> <li>"Final" comprises the selected papers, representing the outcomes of the search protocol's application.</li> <li>"Results" provides statistical insights into the application of the search protocol for the specific BPM area under analysis."</li> </ul>
Uncoupling of Behavioral and Metabolic Twenty-Four-Hour Rhythms in Reindeer (Current Biology, Meier et al. 2024): Pre-Processed metabolomics data
<p>Pre-processed metabolomics data (peak picking, peak alignment, integration and annotation using XCMS) obtained from untargeted UPLC-MS measurements of reindeer blood plasma.</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>
Combining datasets after Alevin pre-processing - Galaxy Training Material
<p>These are the downsampled datasets for one of the single cell case study tutorials in Galaxy: "Combining single cell datasets after pre-processing". </p>
Supplemental Material: Analysis of video games on the teaching-learning process in children from 3 to 4 years old
<p>Supplementary material of the results obtained in the paper Analysis of video games on the teaching-learning process in children from 3 to 4 years old.</p>
Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling
<p>This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling". The code to generate the models and reproduce the results of the comparative study in the above paper is available on this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a></p> <p><strong>Supplementary data</strong>:</p> <p>- The <strong>trained_models</strong> folder contains the results of the trained models.</p> <p>- The folder <strong>data</strong> contains data needed for for the comparative study for the year 2019 in the paper above. This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.</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>
Supplementary material to "Aligning business processes with regulatory requirements"
<p>Bibliographic collection from Web of Science (March 8, 2024), ScienceDirect (March 8, 2024).</p> <p>The below query is a compositie of 4 queries and yields 42 results </p> <p>https://www.webofscience.com/wos/woscc/summary/88d17377-0bb9-4db5-97d1-bf866c373e6f-ce98ba0a/relevance/1</p> <p>The savedrecs.bib file was created after selecting "full records" on export.</p> <p>Also included is the resulting excelfile containing identified literature slr_logbook.xlsx</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.