Search Results for "cross platform sync"
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Showing 5 open source projects for "cross platform sync"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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1
LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
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StatAnt
The package for statistical data analysis
Aspire to perfection. The high performance open source package for statistical data analysis StatAnt is a standalone cross-platform application. Completely free. No donation required. StatAnt comes with absolutely no warranty. The software is not certified for use in healthcare. Please do not use this software for purposes related to diagnosing diseases or making treatment decisions. Rating: Everyone (All ages). NOTICE FOR EU RESIDENTS: This software is NOT compliant with EU regulations, including GDPR and AI Act. ... -
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AlphaPlot
Interactive scientific graphing and data analysis software.
Alpha Plot can generate different types of 2D and 3D plots (such as line, scatter, bar, pie, and surface plots) from data that is either imported from ASCII files, entered by hand, or calculated using formulas. The data is held in spreadsheets which are referred to as tables with column-based data (typically X and Y values for 2D plots) or matrices (for 3D plots). The spreadsheets as well as graphs and note windows are gathered in a project and can be organized using folders. The built-in... -
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SCalc
Calculates the transportation cost seasonality indexes
Calculates the transportation cost seasonality indexes on the basis of the measurement approach by G. L. Lo Magno, M. Ferrante and S. De Cantis. -
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5
tcsi
Stata command for evaluating seasonality
tcsi is a Stata command for evaluating seasonality according to the transportation cost approach by G. L. Lo Magno, M. Ferrante and S. De Cantis.
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