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The nebulous JSON 'number' type is strictly defined in Ion to be one of int: Signed integers of arbitrary size; float: 64-bit IEEE binary-encoded floating point numbers; decimal: Decimal-encoded real numbers of arbitrary precision; Ion adds these types: timestamp: Date/time/time zone moments of arbitrary precision
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 ]
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.
---- If we'll be converting to a JSON object, we'll want to sort the keys so that the-- end result is deterministic.--local string_keys = {} local number_keys = {} local number_keys_must_be_strings = false local maximum_number_key for key in pairs (T) do if type (key) == 'string' then table.insert (string_keys, key) elseif type (key) == 'number ...
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 validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
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.