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722 results for “use case”
Figure 3 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 3. Results of discriminant analysis.
A case study on the impact of interplanetary coronal mass ejection on the Martian O(1S) 557.7 nm dayglow emission using ExoMars TGO/NOMAD-UVIS observations: First Results
<p>This study presents the impact of interplanetary coronal mass ejection (ICME) on the O(1S) 557.7 nm dayglow emission in the Martian atmosphere using instruments onboard ExoMars-TGO and MAVEN spacecrafts.</p>
Example results for use-cases of DAVOS toolkit (dependability assessment, verification, optimisation and selection of hardware models)
<p>This dataset exemplifies the results that can be obtained for several basic experimentation scenarios by means of DAVOS toolkit, available under MIT licence at https://github.com/IlyaTuzov/DAVOS</p> <p>Particular experimentation scenarios are:</p> <p>- Dependability assessment (LEON3 processor core)<br> - Dependability benchmarking of implementation alternatives (MC8051 processor core)<br> - Dependability-aware design space exploration (when implementing PIC core by Xilinx ISE toolkit)</p> <p><br> <strong>Installation steps.</strong></p> <p>1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p>– search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html<br> </p>
Dealing with discontinuous meteorological forcing in operational ocean modelling: a case study using ECMWF-IFS and GETM (v2.5)
<p>Data used in manuscript "Dealing with discontinuous meteorological forcing in operational ocean modelling: a case study using ECMWF-IFS and GETM (v2.5)" for Geoscientific Model Development. </p> <p>Uploaded data includes GETM output and used source code (FABM+GOTM+GETM).</p>
Dengue and microcephaly cases in Brazil used in Carvalho et al.
<p>Datasets used in the analysis of the manuscript entitled "Association of past dengue fever epidemics with the risk of Zika microcephaly at population level in Brazil" by Carvalho et al. (submitted).</p> <p>dengueBR2001-2014.csv contains the number of reported dengue cases by month in a selection of Brazilian microregions.</p> <p>microBR2014-2016.csv contains the cumulated number of reported microcephaly cases between 2014 and 2016 in a selection of Brazilian microregions.</p>
Figure 1 in Using body condition index can be an unreliable indicator of fitness: a case of sand lizard Lacerta agilis Linnaeus, 1758 (Sauria: Lacertidae)
Figure 1. Correlation between lizards' body length and body mass in the 3 age groups.
Fig. 4 in The Use of Testate Amoebae in Monitoring Peatland Restoration Management: Case Studies from North West England and Ireland
Fig. 4. Lead content (ppm) of the top metre of peat at Holcroft Moss.
Figure 3 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 3. Modal progression of individual cohorts for C. coralloides in Acapulco, Guerrero, Mexico.
Figure 1 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 1. Monthly frequency of length of C. coralloides in Acapulco, Guerrero, Mexico.
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.
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</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.
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>
Automatic Complaints Classification in E-Commerce: A Case Study Using CRISP-DM
<p>The growth of e-commerce has been remarkable in recent years, driven by increasing consumer demand for attention and quick responses. Given the large volume of transactions and complaints accompanying this increase, automating the classification of these complaints can help quickly route them to the appropriate departments. This paper presents a computational approach using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to automate the complaints screening process. We categorized 600 real complaints from three e-commerce platforms in Brazil. The learning model was trained progressively, using an initial set of 25 complaints in each category. The classification model obtained an accuracy of 85\% and an average of over 80\% across all relevant metrics, including precision, recall, and F1-Score. The results confirmed the effectiveness of the developed model for automated complaint classification in e-commerce, providing a computational strategy that improves the customer service process and allows for quicker problem resolution.</p>
Data from: How "simple" methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: a case study using the lake whitefish
Reduced representation (RRL) sequencing approaches (e.g., RADSeq, genotyping by sequencing) require decisions about how much to invest in genome coverage and sequencing depth (library quality), as well as choices of values for adjustable bioinformatics parameters. To empirically explore the importance of these "simple" decisions, we generated two independent sequencing libraries for the same 142 individual lake whitefish (Coregonus clupeaformis) using a nextRAD RRL approach: (1) A small number of loci and low sequencing depth (library A); and (2) more loci and higher sequencing depth (library B). The fish were selected from populations with different levels of expected genetic subdivision. Each library was analyzed using the STACKS pipeline followed by three types of population structure assessment (FST, DAPC and ADMIXTURE) with iterative increases in the stringency of sequencing depth and missing data requirements, as well as more specific a priori population maps. Library B was always able to resolve strong population differentiation in all three types of assessment regardless of the selected parameters. In contrast, library A produced more variable results; increasing the minimum sequencing depth threshold (-m) resulted in a reduced number of retained loci, and therefore lost resolution at high -m values for FST and ADMIXTURE, but not DAPC. FST and DAPC were robust to varying the population map and increasing the stringency of missing data requirements. In contrast, ADMIXTURE was unable to resolve strong population differentiation when increasing these same parameters in library A. Similarly, when examining fine scale population subdivision, library B was robust to changing parameters but library A lost resolution depending on the parameter set. We used library B to examine actual subdivision in our study populations. All three types of analysis found complete subdivision among populations in Lake Huron, ON and Dore Lake, SK, Canada using 10,640 SNP loci. Weak population subdivision was detected in Lake Huron with fish from sites in the north-west, Search Bay, North Point and Hammond Bay, showing slight differentiation. Overall, we show that apparently simple decisions about library quality and bioinformatics parameters can have potentially important impacts on the interpretation of population subdivision. Although costly, the early investment in a high-quality library and more conservative stringency settings on STACKS parameters lead to a final dataset that was more consistent and robust when examining both weak and strong population differentiation.
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
ARTICONF Crowd Journalism Use Case final version - Focus Group Dataset
<p>This dataset corresponds to a focus group and beta testing session conducted on the ARTICONF crowd journalism use case final version. The members participating in this discussion are from various sectors and industries. </p>
Data from dissertation: 'Landscape and Aboveground Biomass Dynamics of Brazilian Savanna using airborne LIDAR and MapBiomas datasets : case study of Rio Vermelho Watershed, Brazil'
<p>This dissertation was submitted to University of Manchester as part of MSc GIS program</p> <p>This repository contains:</p> <p>1) Contains the R language code used in the dissertation (CHM_&_LiDAR_metric.R; Landscape_metric.R; Generalized_Linear_Model.R; Random_Forest_Model.R).</p> <p><br> 2) Canopy Height Model (CHM) and 56 LiDAR metric raster files with a resolution of 1m (CHM_&_LiDAR_metric_2014.zip; CHM_&_LiDAR_metric_2018.zip), the original LiDAR data come from Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State.</p> <p><br> 3) AGB raster files with a resolution of 10m (AGB_2014.tif; AGB_2018.tif; AGB_dynamic.tif), field plots used for AGB estimation come from Sabrinado Couto de Miranda from University of Goiás State (UEG), Brazil, and her team.</p> <p><br> 4) Landscape metric interpolation raster file (SHDI.tif; SHEI.tif; AREA_CV.tif; CIRCLE_MN.tif; SHAPE_MN.tif) with a resolution of 10m, land cover map Map come from Biomas team for landscape metric calculation.</p>
Improving neuropsychiatric symptoms following stroke using virtual reality: A case report
<p>Post-stroke cognitive impairment occurs frequently in patients with stroke, with a 20% to 80% prevalence. Anxiety is common after stroke, and is associated with a poorer quality of life. The use of standard relaxation techniques in treating anxiety in patients undergoing post-stroke rehabilitation have shown some positive effects, whereas virtual reality seems to have a role in the treatment of anxiety disorders, especially when associated to neurological damage. A 50-year-old woman, smokers, affected by hypertension and right ischemic stroke in the chronic phase (i.e., after 12 months by cerebrovascular event), came to our observation for a severe anxiety state and a mild cognitive deficit, mainly involving attention and visuo-executive processes, besides a mild left hemiparesis. Anxiety in a patient with ischemic stroke. Standard relaxation techniques alone in a common clinical setting or the same psychological approach in an immersive virtual environment (i.e., Computer Assisted Rehabilitation Environment – CAREN). The patient's cognitive and psychological profile, with regard to attention processes, mood, anxiety, and coping strategies, were evaluated before and after the 2 different trainings. A significant improvement in the functional and behavioral outcomes were observed only at the end of the combined approach.The immersive virtual reality environment CAREN might be useful to improve cognitive and psychological status, with regard to anxiety symptoms, in post-stroke individuals.</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.