The other also contains two properties: an array of languages the person speaks and a Boolean indicating if the person is married or not. One of them contains two properties: name and age. These are the objects we will merge.īoth JSON objects will be very simple, representing possible information about a person. We will start our code by defining two JSON strings containing two different objects. The objective of this tutorial is to show a simple approach to the merging problem, which might be adapted for each specific need. Naturally, depending on the necessities of each application, the code below might need to be adapted. There won’t be any deep copy of the object properties.There won’t by any deep merge between nested objects.There won’t be properties with the same name in both JSONs.In our example, we will use the spread syntax to merge the objects.įor our tutorial, we will do some simplifications to our merging operation: Take also in consideration that there are a lot of approaches that can be taken in JavaScript to merge JSON objects: using a loop to iterate over the keys of the objects and assign them to a destination object, using the assign method, using a library, etc. It might also be useful for some JavaScript applications where we simply want to merge two JavaScript objects, without actually doing any deserialization / serialization of JSON strings. Note however that the merging operation is useful outside the scope of JSON processing. ![]() Nonetheless, we will do the merging operation over the parsed objects, on their JavaScript representation. We will assume that we are starting from two JSON strings and, at the end, we want to obtain a single JSON string with the merged objects. This tool checks multiple source code language syntaxes to determine the difference between two text lines, text comparison and merging, code similarity. In this tutorial we will learn how to merge two JSON objects using JavaScript. Benefits accrue when templates for metadata become central elements in an ecosystem of tools to manage online datasets-both because the templates serve as a community reference for what constitutes FAIR data, and because they embody that perspective in a form that can be distributed among a variety of software applications to assist with data stewardship and data sharing.In this tutorial we will learn how to merge two JSON objects using JavaScript. The other is the FAIRware Workbench, which evaluates the metadata of archived datasets for their adherence to community standards. One system is the CEDAR Workbench, which investigators use to author new metadata. We have explored this template-based approach in the context of two software systems. Scientific communities should be able to define their own machine-actionable templates for metadata that encode these “rich,” discipline-specific elements. Specifically, the FAIR principles require metadata to be “rich” and to adhere to “domain-relevant” community standards. ![]() It is challenging to determine whether datasets are findable, accessible, interoperable, and reusable (FAIR) because the FAIR Guiding Principles refer to highly idiosyncratic criteria regarding the metadata used to annotate datasets. An RDF graph prototype pipeline is presented, followed by a discussion of research implications and conclusion summarizing the results. The work is part of the NSF Harnessing the Data Revolution, Biology Guided Neural Networks (NSF/HDR-BGNN) project and the HDR Imageomics Institute. The paper provides contextual background, followed by the presentation of a four-phased approach involving: 1. The images and their associated metadata are being used for AI-related scientific research involving automated species identification, segmentation and trait extraction. The Diff Checker was created to help with debugging your versions of codes, find differences between files. This paper reports on an initiative targeting the development of a flexible metadata pipeline for a collection containing over 300,000 digital fish specimen images, harvested from multiple data repositories and fish collections. Despite this need, researchers seldom report their approaches for identifying metadata standards and protocols that support optimal flexibility. Flexible metadata pipelines are crucial for supporting the FAIR data principles.
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