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D is for diversity

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declarative programming
In computer science, declarative programming is a programming paradigm — a style of building the structure and elements of computer programs — that expresses the logic of a computation without describing its control flow.
See also imperative programming.
Source: Declarative programming
Diversity, Equity, and Inclusion (DEI)
Diversity, equity, and inclusion (usually abbreviated DEI) refers to organizational frameworks which seek to promote “the fair treatment and full participation of all people”, particularly groups “who have historically been underrepresented or subject to discrimination” on the basis of identity or disability.
These three notions (diversity, equity, and inclusion) together represent “three closely linked values” which organizations seek to institutionalize through DEI frameworks.
Some frameworks, primarily in Britain, substitute the notion of “equity” with equality: equality, diversity, inclusion (EDI). Other variations include diversity, equity, inclusion and belonging (DEIB), justice, equity, diversity, and inclusion (JEDI or EDIJ), or diversity, equity, inclusion and access (IDEA or DEAI).
Source: Diversity, equity, and inclusion
Document Object Model (DOM)
The Document Object Model (DOM) is a cross-platform and language-independent interface that treats an HTML or XML document as a tree structure wherein each node is an object representing a part of the document. The DOM represents a document with a logical tree.
Each branch of the tree ends in a node, and each node contains objects. DOM methods allow programmatic access to the tree; with them one can change the structure, style or content of a document. Nodes can have event handlers (also known as event listeners) attached to them. Once an event is triggered, the event handlers get executed.
Source: Document Object Model
Donʼt Repeat Yourself (DRY)
Donʼt repeat yourself (DRY) is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
But see AHA.
Source: Donʼt repeat yourself

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