An extension to python markdown that takes metadata embedded as YAML in a page of markdown and render it as JSON-LD in the HTML created by MkDocs.

The metadata that we use for OCX is a profile of schema.org / LRMI,  OERSchema and few bits that we have added because we couldn’t find them elsewhere. Here’s what (mostly) schema.org metadata looks like in YAML:

"@context":
    - "http://schema.org"
    - "oer": "http://oerschema.org/"
    - "ocx": "https://github.com/K12OCX/k12ocx-specs/"
"@id": "#Lesson1"
"@type":
    - oer:Lesson
    - CreativeWork
learningResourceType: LessonPlan
hasPart:
  "@id": "#activity1-1"
author:
    "@type": Person
    name: Phil Barker

I’ve been experimenting with ways of putting JSON-LD schema.org metadata into HTML created by MkDocs. The result is a python-markdown plugin that will (hopefully) find blocks of YAML in markdown and insert then into the HTML that is generated.

HyperGraphQL is a GraphQL interface for querying and serving linked data on the Web. It is designed to support federated querying and exposing data from multiple linked data services using GraphQL query language and schemas. The basic response format is JSON-LD, which extends the standard JSON with the JSON-LD context enabling semantic disambiguation of the contained data.

The Annotations API is an extension to the Europeana REST API which allows you to create, retrieve and manage annotations on Europeana objects. Annotations are user-contributed or system-generated enhancements, additions or corrections to (or a selection of) metadata or media. We adopted the Web Annotation Data Model as a base model for the representation of annotations and as a format for exchanging annotations between client applications and the API, but also the Web Annotation Protocol as base HTTP protocol for the API.

{
  "@context": “http://www.w3.org/ns/anno.jsonld”
  "id": "http://data.europeana.eu/annotations/1",
  "type": "Annotation",
  "created": "2015-03-10T14:08:07Z",
  "creator": {
    "type": "Person",
    "name": "John Smith"
  },
  "generated": "2015-04-01T09:00:00Z",
  "generator": {
      "type": "Software",
      "name": "HistoryPin",
      "homepage": "https://www.historypin.org/"
  },
  "motivation": "tagging",
  "bodyValue": "MyBeautifulTag",
  "target": "http://data.europeana.eu/item/92062/BibliographicResource_1000126189360"
}

How to get easy-to-read JSON trees with this free Chrome Extension (or Firefox Plugin) Fatos Morina JSON is a very popular file format. Sometimes we may have a JSON object inside a browser tab that we need to read and this can be difficult.We may need to go and search for an online tool that turns it into an easy-to-read format so we can understand it.Now, here is a Chrome and Firefox extension that does the formatting and makes your JSONs instantly pretty inside your browser, without having to perform many unnecessary steps.It comes with support for JSON and JSONP and highlights the syntax so that you can differentiate different attributes and values accordingly. It also comes with the option to collapse nodes, clickable URLs that you can open in new tabs, and you see the raw, unformatted JSON.It works with any JSON page, regardless of the URL you opened. It also works with local files, after you enable it in chrome://extensions. You can inspect the JSON by typing json into the console.You can install the extension by going here for Chrome and here for Firefox and then test it, for example, by visiting this API response.This is what it looks like, before formatting:Now, take a look at the beautiful JSON response you get with JSON Formatter:Here is a pro tip: Hold down CTRL (or CMD on Mac) while collapsing a tree, if you want to collapse all its siblings too.It is an open-source project, so you can view its source code on GitHub.Thanks for reading. Fatos Morina Experienced and passionate Software Engineer, specializing in Machine Learning

How does it work? Thanks to JSON-LD contexts and JavaScript Proxies we can treat Linked Data graphs as local objects The await keyword allows waiting for remote HTTP requests

upload edited CSV and convert back to JSON

import { createRequire } from "module"; const require = createRequire(import.meta.url); const data = require("./data.json");

Thanks to Rack Middleware and Rails 3 you can output pretty JSON for every request without changing any controller of your app. I have written such middleware snippet and I get nicely printed JSON in browser and curl output.

XPath for JSON

Jsonc is a simplified json format which allows comments and unquoted values delimited by whitespace. A jsonc formatted file can be transformed to a json file. Comments will be stripped out and quotes added.

You can use jsonb_agg and jsonb_array_elements_text to flatten or unnest an array of arrays in PostgreSQL: SELECT jsonb_array_elements_text(jsonb_agg(array_of_arrays)) FROM x;

jsonb_pretty

That's going to be extremely ugly. Nothing about this makes sense. Your JSON schema should just have one object that has {"is_enabled":true}, or something like this {"name":"change","is_enable":true}.

And this has some immediate benefits: more efficiency, significantly faster to process, supports indexing (which can be a significant advantage, as we'll see later), simpler schema designs (replacing entity-attribute-value (EAV) tables with jsonb columns, which can be queried, indexed and joined, allowing for performance improvements up until 1000X!)

Besides efficiency, there are extra ways in which you can benefit from storing JSON in binary form. One such enhancement is the GIN (Generalized Inverted Index) indexes and a new brand of operators that come with them.

SELECT jsonb_array_elements_text(data->'genres') AS genre FROM books WHERE book_id = 1; That will expand the JSON array into a column:

This is an important one, as it will enable us to use the aggregate functions that we are familiar when dealing with relational databases, but in the otherwise counter-intuitive environment of JSON data.

As stated in the title, I am in a situation where I need to return a count of occurrences within an array, that is within a jsonb column.

SELECT DISTINCT ON (1) t.id, t.name, d.last FROM tbl t LEFT JOIN LATERAL json_array_elements_text(data) WITH ORDINALITY d(last, rn) ON d.last <> t.name ORDER BY d.rn DESC;

jsonb_each_text

select t.* from my_table t, jsonb_each(my_col) as value1(key1, value1), jsonb_each(value1) as value2(key2, value2) where jsonb_typeof(my_col) = 'object' and jsonb_typeof(value1) = 'object' and value2->>'Param3' = '6';

json_array_elements_text ( json ) → setof text jsonb_array_elements_text ( jsonb ) → setof text Expands the top-level JSON array into a set of text values. select * from json_array_elements_text('["foo", "bar"]') → value ----------- foo bar

Use the -> operator instead of ->> in the reference to children. The way you have it, you'd first cast json / jsonb to text and then back to json.

Variables. Multiple lines. No more escaped quotation marks. Comments.

{'good': ['API objects', 'structured data', 'data sources', 'filtering and operations'],'improvement areas for v2': ['AWS deployment images too small', 'not setting up the data sources in aws that it talked up','not enough numpy','no deep learning yo','drop or shorten up the kitchen analogy, we get what distributed computing is'],'realization: "out of the box functionality won't be much help for real life... have to get weird w delayed objects"}

Although XML has become a lingua franca for publishing and data interchange, its usage has decreased among information technology professionals, who now tend to prefer JSON for data interchange, especially in situations where the data structure is straightforward.

JSON.parse() does not allow single quotes

JSON.parse() does not allow trailing commas

I have created a thin wrapper around fetch() with many improvements if you are using a purely json REST API:

Conclusions Draw your own. No? Fine. Good Enough: JSON? Protobuf Cap’n Proto Flatbuffers CBOR msgpack Avoid: YAML XML Thrift? BSON

Over the years, Google has gone from recommending uploading a text file, to parsing RDFa with a slightly modified Microformats vocabulary, to going all-in on Microdata, to then replacing Microdata with JSON-LD and the new Schema.org vocabulary. In the mean time, the Microformats hReview vocabulary hasn't changed, and has continued to be parsed by Google since it is so widely deployed. It would seem there is some advantage to using a format that was developed externally from Google, since they are unable to simply turn their backs on it and replace it with a new format whenever they want. For this reason, I'm sticking with publishing the Microformats 1 hReview markup for my reviews.

Note that the point of JSON-LD is that it's just JSON, in this case using the schema.org context to interpret the values.

Listens to changes in a PostgreSQL Database and broadcasts them over WebSocket. It works like this:the Phoenix server listens to PostgreSQL's replication functionality (using Postgres' logical decoding).it converts the byte stream into JSON.it then broadcasts over WebSocket.

JSON parsing is always pain in ass. If the input is not as expected it throws an error and crashes what you are doing. You can use the following tiny function to safely parse your input. It always turns an object even if the input is not valid or is already an object which is better for most cases.

I'll change at some point the format of the JSON so that the link can also be directly stored. That way it would be mandatory/useful to provide it with the JSON.

JSON-RPC is “level 0” of the Richardson Maturity Model – a model that describes the qualities of a REST design.

there was a push for standardisation of “SOAP” (simple object access protocol) APIs

Most APIs you use, or build will be “REST-ish”.

[

,

It is an unordered set of name/value pairs.

Square brackets hold arrays and values are separated by ,(comma).

JSON syntax is basically considered as a subset of JavaScript syntax;

name/value pairs.

As you can see the CSV parser is 20x faster and performs 75% fewer allocations.

in XML the ordering of elements is significant. Whereas in JSON the ordering of the key-value pairs inside objects is meaningless and undefined

XML is a document markup language; JSON is a structured data format, and to compare the two is to compare apples and oranges

A particular strength of JSON is its support for nested data structures

"favorite_movies": [ "Diehard", "Shrek" ]

JSON’s origins as a subset of JavaScript can be seen with how easily it represents key/value object data. XML, on the other hand, optimizes for document tree structures, by cleanly separating node data (attributes) from child data (elements)

The advantages of XML over JSON for trees becomes more pronounced when we introduce different node types. Assume we wanted to introduce departments into the org chart above. In XML, we can just use an element with a new tag name

JSON is well-suited for representing lists of objects with complex properties. JSON’s key/value object syntax makes it easy. By contrast, XML’s attribute syntax only works for simple data types. Using child elements to represent complex properties can lead to inconsistencies or unnecessary verbosity.

Central Intelligence Agency (CIA)

Defendant Edward Snowden

A utility function to safely escape JSON for embedding in a <script> tag function safeStringify(obj)

Components.js is a dependency injection framework for JavaScript applications. Instead of hard-wiring software components together, Components.js allows these components to be instantiated and wired together declaratively using semantic configuration files. The advantage of these semantic configuration files is that software components can be uniquely and globally identified using URIs. Configurations can be written in any RDF serialization, such as JSON-LD. This software is aimed for developers who want to build modular and easily configurable and rewireable JavaScript applications.

If you want to make the Semantic Web a reality, stop making the case for it and spend your time doing something more useful, like actually making machines smarter or helping people publish data in a way that’s useful to them.

RDF Translator is a multi-format conversion tool for structured markup. It provides translations between data formats ranging from RDF/XML to RDFa or Microdata. The service allows for conversions triggered either by URI or by direct text input. Furthermore it comes with a straightforward REST API for developers.

1 down vote unaccept It's necessary that the term match the IRI you use for the property. For example, schema.org defines name as http://schema.org/name. In your example, you have http://www.schema.org/name. There are also several places where values which should be IRIs (URLs) are treated as text, for this you want to use something like "http://schema.org/image": {"@id": "/static/track_images_200/lr1734_2009720_1372375126.jpg"} Part of term selection looks to be sure that a value matches the appropriate @type definition within the context. For example, image is set to {"@type": "@id"}, so it will only match things that look like that.

{ "@context": "https://schema.org/docs/jsonldcontext.json", "@type": ["Article", "NewsArticle", "TechArticle", "ScholarlyArticle"], "author": { "@embed": "@always" }, "creator": { "@embed": "@always" } }

<script type="application/ld+json"> { "@context": "http://schema.org", "@type" : "WebPage", "name" : "The Name of the Webpage", "author" : { "@type" : "Person", "@id": "#tim", "name" : "Tim" }, "creator": { "@id": "#tim" }, "copyrightHolder": { "@id": "#tim" } } </script>

Pitfalls of Framed JSON-LD The choice to provide framed JSON-LD does have some downsides, though. Any entity within the graph that is repeated is only fully referenced in the flattened, framed JSON-LD. This behavior is most evident when viewing an autobiographic work, such as "The autobiography of Benjamin Franklin, 1706-1757". In the example below, you can see that the entity representing Benjamin Franklin is both a schema:creator and a schema:about. However, Benjamin Franklin’s full details only appear in one place in the framed JSON-LD. This is problematic for someone using the data in an object-oriented manner rather than treating it as a graph.