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The Portable Format for Analytics (PFA) is a JSON-based predictive model interchange format conceived and developed by Jim Pivarski. [citation needed] PFA provides a way for analytic applications to describe and exchange predictive models produced by analytics and machine learning algorithms.
Web Data Connectors for CSV, JSON, Excel, MATLAB. Rug Plots, Split Heatmap Plot. Validation Reports using NIST data. New Apps for Quantile Regression, 2D Correlation, Isosurface Plot, etc. 2018/10/26 Origin 2019.
Transformation of data: converting values to other formats, normalizing and denormalizing. Parsing data from web sites: OpenRefine has a URL fetch feature and jsoup HTML parser and DOM engine. [9] Adding data to dataset by fetching it from web services (i.e. returning JSON). [10] For example, can be used for geocoding addresses to geographic ...
Trino is an open-source distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources. [1] Trino can query data lakes that contain a variety of file formats such as simple row-oriented CSV and JSON data files to more performant open column-oriented data file formats like ORC or Parquet [2] [3] residing on different storage systems like ...
An included CiteProc option allows pandoc to use bibliographic data from reference management software in any of five formats: BibTeX, BibLaTeX, CSL JSON or CSL YAML, or RIS. [7] The information is automatically transformed into a citation in various styles (such as APA, Chicago, or MLA) using an implementation of the Citation Style Language. [7]
table convert to create mediawiki table from excel/csv and others (json to excel for ex) FAQ ... By using this site, ...
Support for JSON and plain-text transformation was added in later updates to the XSLT 1.0 specification. As of August 2022, the most recent stable version of the language is XSLT 3.0, which achieved Recommendation status in June 2017. XSLT 3.0 implementations support Java, .NET, C/C++, Python, PHP and NodeJS.
JSON-LD is designed around the concept of a "context" to provide additional mappings from JSON to an RDF model. The context links object properties in a JSON document to concepts in an ontology. In order to map the JSON-LD syntax to RDF, JSON-LD allows values to be coerced to a specified type or to be tagged with a language.