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A SHACL validation engine takes as input a graph to be validated (called data graph) and a graph containing SHACL shapes declarations (called shapes graph) and produces a validation report, also expressed as a graph. All these graphs can be represented in any Resource Description Framework (RDF) serialization formats including JSON-LD or Turtle.
Users can download the raw PAE data for all residue pairs in a custom JSON format for further analysis or visualization using a programming language such as Python. The format of the JSON file is as follows:
JSON Schema specifies a JSON-based format to define the structure of JSON data for validation, documentation, and interaction control. It provides a contract for the JSON data required by a given application and how that data can be modified. [29] JSON Schema is based on the concepts from XML Schema (XSD) but is JSON-based. As in XSD, the same ...
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.
Ion is a data serialization language developed by Amazon. It may be represented by either a human-readable text form or a compact binary form. The text form is a superset of JSON; thus, any valid JSON document is also a valid Ion document.
It was proposed at the 2012 RDF Validation Workshop [3] as a high-level, concise language for RDF validation. The shapes can be defined in a human-friendly compact syntax called ShExC or using any RDF serialization formats like JSON-LD or Turtle. ShEx expressions can be used both to describe RDF and to automatically check the conformance of RDF ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...