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3,225 results for “Case of study”
Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection.
<p>Data set for the publication Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection. </p>
FIGURE S3 in Accounting for differences in element size and homogeneity when comparing Finite Element Models: Armadillos as a case study
FIGURE S3. Map of Von Mises stress distribution in the 20 FEA models of Cingulata mandibles.
FIGURE 2 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 2. Orthophoto of the slab MPUR NS 34/28. Scale bar is 50 cm.
Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: a case study of London terraced houses
<p>This archive includes the data used (e.g. Time use survey (UK-TUS) data), model files (idf files for running EnergyPlus) and codes for analysis in the paper (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.enbuild.2024.114668" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.enbuild.2024.114668</a>).</p> <p>Files in this archive should include:</p> <ul> <li>Time use survey data analysis</li> </ul> <p>o Main dataset: TUS_activity.zip</p> <p>o Code: TUS_clustering_code.zip</p> <p>o Output: InternalHeatProfile.zip</p> <ul> <li>Building energy modeling </li> </ul> <p>o Main dataset (run in EnergPlus 9.4): IDFfiles.zip</p> <p>o Output: Eplus_output.zip</p> <ul> <li>PostProcess analysis</li> </ul> <p>o Code: QF_analysis_code.zip</p> <p>o Output: QF_output.zip</p> <p> </p> <p>Note: this version currently only includes the outputs of all processes, the main dataset and code will be updated later.</p>
Fig. 4 in Use of ITS-1 to identify Bactrocera dorsalis and Bactrocera occipitalis (Diptera: Tephritidae): a case study using flies trapped in California from 2008 to 2018
Fig. 4. Image of wing of fly (16V457) with Bactrocera occipitalis ITS‑1 sequence.
Fig. 3 in Use of ITS-1 to identify Bactrocera dorsalis and Bactrocera occipitalis (Diptera: Tephritidae): a case study using flies trapped in California from 2008 to 2018
Fig. 3. Image of first Bactrocera occipitalis trapped in California in 1983.
Mapping the potential habitat suitability, and opportunities of bush encroacher species in Southern Africa: a case study of the SteamBioAfrica project.
<p>Senegalia mellifera (Benth) Seigler & Ebinger. , Dichrostachys cinerea (L.) Wight & Arn. and Terminalia sericea Burch. Ex DC., are three important bush encroacher species that contribute to the well-known ecological process named "thicketization" in Southern Africa. This issue has persisted for many years, impacting species distribution, plant communities, soil, and fauna dynamics. According to climate change projections, Southern Africa is expected to become drier and warmer in future scenarios, creating favourable conditions for proliferation of bush encroacher species.</p> <p>In the paper, we analyze the habitat suitability of these bush encroachers through MaxEnt 3.4.4 under three different future climate models and scenarios. Future projections were made using the shared socio-economic pathways (SSPs) for greenhouse gas concentrations, specifically SSP245-585 across two-time horizons: 2041-2060 and 2061-2180. We utilized <a href="https://www.worldclim.org/data/cmip6/cmip6climate.html">WorldClim 1-km resolution climate data</a> from three global Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) that obtained better results than CMIP5 models (Bouramdane 2022; Mmame and Ngongondo 2024): the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1.2-HR), the Hadley Climate Center Earth System Model (UK-ESM1.0-LL) and the Institute Model for Numerical Mathematics (INM-CM5-0). </p>
Leveraging Multi-Agent Systems and Decentralised Autonomous Organisations for Tax Credit Tracking: A Case Study of the Superbonus 110% in Italy
<p>The present dataset was obtained as output of the simulation of Secure Fiscal Credit Model (SCFM) demonstrator. The software, inspectable at the link in the repository url, allows to simulate the workflow and payments involved in the execution of Italian Superbonus 110% fiscal credits model. Two different Scenarios were tested, parameters are reported in the following table.</p> <table> <tbody> <tr> <td> </td> <td><strong>GC payment</strong></td> <td><strong>Tech Asseveration</strong></td> <td><strong>Open</strong></td> <td><strong>Anticipation</strong></td> <td><strong>SAL1</strong></td> <td><strong>SAL2</strong></td> <td><strong>EOW</strong></td> <td><strong>Tech fee</strong></td> </tr> <tr> <td><strong>Scenario 1</strong></td> <td>50%</td> <td>50%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> <tr> <td><strong>Scenario 2</strong></td> <td>25%</td> <td>25%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> </tbody> </table> <p>File named with S1 prefix refer to Scenario 1, while those with S2 prefix refer to Scenario 2.</p>
New insights into the driving mechanism of the mid-Eocene (43 Ma) tectonic transition in the western Pacific margin—A case study of stress field modeling in Pingbei
<p><span>This is the dataset for "</span> <span>New insights into the driving mechanism of the mid-Eocene (43 Ma) tectonic transition in the western Pacific margin—A case study of stress field modeling in Pingbei" including modeling parameters and raw results.</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>
Tab. 4 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
<p>Tab. 4 - Values of <i>q</i> based on Tukey test. The pairs pointed out with asterisks (*) are significantly different.</p><table><tbody><tr><th>Comparison of samples</th><th><b>q</b></th></tr></tbody><tbody><tr><th>RH59 - RH480</th><td>0.5853</td></tr><tr><th>RH59 - WP31 *</th><td>4.8001</td></tr><tr><th>RH59 -WP16 *</th><td>5.9384</td></tr><tr><th>RH59 - UTHAL *</th><td>6.402</td></tr><tr><th>RH480 - WP31</th><td>1.3343</td></tr><tr><th>RH480 - WP16</th><td>1.5047</td></tr><tr><th>RH480 - UTHAL</th><td>2.8947</td></tr><tr><th>WP31 - WP16</th><td>3.4168</td></tr><tr><th>WP31 - UTHAL *</th><td>5.4741</td></tr><tr><th>WP16 - UTHAL</th><td>2.2843</td></tr></tbody></table>
Tab. 3 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
<p>Tab. 3 - Results of ANOVA test. DF, degree of freedom; S 2, variance; F, variance ratio; P, significance level.</p><table><tbody><tr><th><b>Source of variation</b></th><th><b>Deviance</b></th><th><b>DF</b></th><th><b>S</b> <b>2</b></th><th><b>F</b></th><th><b>P</b></th></tr></tbody><tbody><tr><th>Within groups variance</th><td>107.8805</td><td>986</td><td></td><td></td><td></td></tr><tr><th>Common regression slope</th><td>0.226133</td><td>1</td><td>0.226133</td><td>2.075882</td><td><0.01</td></tr><tr><th>Between regression slopes</th><td>0.8000667</td><td>4</td><td>0.200017</td><td>1.836137</td><td><0.05</td></tr><tr><th>Error for coefficient</th><td>106.8637</td><td>981</td><td>0.108933</td><td></td><td></td></tr></tbody></table>
Tab. 2 in The local forms of Zootecus (Gastropoda: Pulmonata: Subulinidae) of Pakistan: an archaeomalacological case study
<p>Tab. 2 - Number of individuals, heights (H), diameters (D), means, roundness indices (H/D), whorl numbers and linear regression coefficient for <i>Zootecus</i> sp. of the five sites.</p><table><tbody><tr><th></th><th><b>WP 16</b></th><th><b>WP 31</b></th><th><b>UTHAL</b></th><th><b>RH 59</b></th><th><b>RH 480</b></th></tr></tbody><tbody><tr><th>Numbers of individuals</th><td>240</td><td>162</td><td>217</td><td>197</td><td>167</td></tr><tr><th>Mean H mm</th><td>10.75</td><td>11.40</td><td>11.57</td><td>11.97</td><td>10.70</td></tr><tr><th>Max H mm</th><td>13.50</td><td>13.30</td><td>14.85</td><td>17.00</td><td>12.90</td></tr><tr><th>Min H mm</th><td>8.00</td><td>9.50</td><td>8.30</td><td>10.00</td><td>8.50</td></tr><tr><th>Delta H mm</th><td>5.50</td><td>3.800</td><td>6.55</td><td>7.00</td><td>4.40</td></tr><tr><th>Mean D mm</th><td>4.69</td><td>4.85</td><td>4.80</td><td>4.19</td><td>4.87</td></tr><tr><th>Max D mm</th><td>6.00</td><td>5.60</td><td>5.50</td><td>4.85</td><td>5.60</td></tr><tr><th>Min D mm</th><td>3.80</td><td>3.90</td><td>4.10</td><td>3.70</td><td>3.50</td></tr><tr><th>Delta D mm</th><td>2.20</td><td>0.95</td><td>1.40</td><td>1.15</td><td>2.10</td></tr><tr><th>Mean roundness H/D</th><td>2.30</td><td>2.30</td><td>2.41</td><td>2.85</td><td>2.19</td></tr><tr><th>Whorl numbers</th><td>8</td><td>8</td><td>8 and 9</td><td>8 and 10</td><td>7 and 8</td></tr><tr><th>Linear regression equation</th><td>y = 0.2493x + 1.9771</td><td>y = 0.1606x + 3.0419</td><td>y = 0.302x + 1.1788</td><td>y = 0.10278x + 2.9634</td><td>y = 0.2122x + 2.5862</td></tr></tbody></table>
Deliverable D1.2 - Supplementary material : Datasets on substance prioritization for subsequent development of target analytical methods for relevant iPM(T) substances in waters in case studies CS#1, CS#2, CS#3 & CS#7.
<p>Additional information on deliverable D1.2. Data sets for substance prioritization and subsequent development of target analytical methods for relevant iPM(T) substances in waters in case studies CS#1, CS#2, CS#3 and CS#7.</p>
Network formation as Narrative: Case Study Data
<p>This data is the companion to a forthcoming paper entitled Network Formation As Narrative In Computer Role-playing Games. It consists of several excel sheets and gephi files with some graphs of social network data from a campaign of Mount & Blade: Warband, a campaign of The Exile Princes, and some hand-curated data on networks presented in the narrative of the early part of Baldur's Gate 3.</p>
Case studies of the usage of the SAFARI platform
<p>The dataset contains the JSON files generated with the Profiler and the charts that visualise the data.</p>
Datasets used for case studies in M-VIDIA paper
We provide the extracted view data from the datasets we used for our case studies. There are three datasets, including the Thyroid Nodule Classification (TNC) dataset, the Single Mouse Oocytes (SMO) dataset, and the Pick-up Single-cell Proteomic Analysis (PiSPA) dataset. The TNC dataset is used to test the performance of applying M-VIDIA for patient diagnosis. The SMO and PiSPA are used to test the performance of M-VIDIA in cell clustering.
A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia
<p>This is the dataset for the NCST project <em>"A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia"</em> by the Georgia Tech research team.</p> <p> </p> <p>Here is the abstract of the research: </p> <p>In this study, a modeling framework for population exposure to traffic-related PM2.5 with high spatiotemporal resolution is proposed and applied to the I-575/I-75 Northwest Corridor (NWC) in Atlanta, GA, for environmental equity analysis. The analyses retrieved trip data from the Atlanta Regional Commission’s (ARC) Activity-Based Model 2020 (ABM2020), after implementing path retention algorithms (Zhao, et al., 2019) to generate individual travel paths for more than 20 million predicted vehicle trips. Emission rates for each link were retrieved from MOVES-Matrix given the ABM link speed and facility type, the ARC’s county-level fleet composition data, and regional fuel properties and I&M program parameters. High-resolution downwind concentration profiles were predicted using EPA’s AERMOD microscale dispersion model with AERMET meteorology profiles for a huge array of receptors. Trip-end locations were derived from the ABM trip data, and the on-road trajectories for each person-trip (vehicle trace data) were derived from the travel paths through network. ABM synthetic household and person data were used in demographic assessment, and linked to representative household latitude and longitude locations in the Epsilon 2019 household demographic dataset. Individual exposure to traffic-related PM2.5 in time and space (average hourly concentration) was assessed by overlaying the second-by-second person location profiles (for 24 hours) against the hourly predicted PM2.5 concentration profiles. The analyses summarize the results across 16 demographic groups and the aggregate population exposure are compared to assess potential impact differences across demographics. High-income households in the corridor were exposed to less traffic-related air pollution as they tended to live further from the freeways. The analyses did not reveal large disproportionate negative impacts on low income groups along this specific corridor, but lager disproportionate negative impacts are expected elsewhere in the metro area due to the spatial clustering of income groups along other corridors. Overall, the research demonstrates the applicability of the modeling framework and describes how the various elements (e.g., link screening, dispersion modeling, path tracing, etc.) are optimized on the supercomputing cluster.</p>
Analysis of Determining the Location of Public Electric Battery Exchange Stations (SPBKLU) using The Buffer Method in The Geographical Information System (GIS) in The Central Jakarta Region (Case Study of PT. XYZ)
<p>Figure 1. The Existing Station Map in the Central Jakarta Area</p> <p><strong><span>Figure 2.</span></strong><span> The Suitability of Battery Replacement Station Location and Closeness to Alfamart Supermarket </span></p> <p><strong><span>Figure 3.</span></strong><span> The Suitability of Battery Replacement Station Location and Closeness to District Office </span></p> <p><strong><span>Figure 4.</span></strong><span> The Suitability of Battery Replacement Station Location in the Central Jakarta Area</span></p> <p><span>Data (The Variable Weight & The Variable Criteria)</span></p>
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
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.