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Extensive data warehouse system for the analysis and integration of biological datasets written in Java and JavaScript Cross-platform: LGPL: University of Cambridge: LabKey Server: Software platform, allows organizations to integrate, analyze, and share complex biomedical data Linux, macOS, Windows: Apache: LabKey Software Foundation LAMMPS
Edit as needed. Remove the parts you don't want. Keep only tables for example. Then export to MediaWiki. Tables can be further edited in LibreOffice Calc. See: Commons:Convert tables and charts to wiki code or image files. And: Help:Table and the section on spreadsheets and the Visual Editor.
To do so with multiple columns click the top left non-column-header cell, and then shift-click the bottom right cell. When you click on "ascending" or "descending" in the data menu the table will be sorted alphabetically. That is the default. Paste that sorted table (or just the selected columns of interest) directly into the visual editor.
All data tables need a table caption that succinctly describes what the table is about. [ WCAG 2 ] It plays the role of a table heading, and is recommended as a best practice. [ 2 ] You would usually need some kind of heading or description introducing a new table anyway, and this is what the caption feature exists for.
gretl is an example of an open-source statistical package. ADaMSoft – a generalized statistical software with data mining algorithms and methods for data management; ADMB – a software suite for non-linear statistical modeling based on C++ which uses automatic differentiation; Chronux – for neurobiological time series data; DAP – free ...
Ooms, Marius (2009). "Trends in Applied Econometrics Software Development 1985–2008: An Analysis of Journal of Applied Econometrics Research Articles, Software Reviews, Data and Code". Palgrave Handbook of Econometrics. Vol. 2: Applied Econometrics. Palgrave Macmillan. pp. 1321– 1348. ISBN 978-1-4039-1800-0. Renfro, Charles G. (2004).
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Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. [3]