computer science

Machine-readable medium and data

Machine-readable medium and data is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Machine-readable medium and data rather than just read about it. In short: In communications and computing, a machine-readable medium (or computer-readable medium) is a medium capable of storing data in a format easily readable by a digital computer or a sensor. It contrasts with human-readable medium and data.

Key takeaways

Machine-readable medium and data belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Machine-readable medium and data to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Machine-readable medium and data from memory before moving on to harder problems.

Reference excerpt

In communications and computing, a machine-readable medium (or computer-readable medium) is a medium capable of storing data in a format easily readable by a digital computer or a sensor. It contrasts with human-readable medium and data. The result is called machine-readable data or computer-readable data, and the data itself can be described as having machine-readability.

Data Machine-readable data must be structured data. Attempts to create machine-readable data occurred as early as the 1960s. At the same time that seminal developments in machine-reading and natural-language processing were releasing (like Weizenbaum's ELIZA), people were anticipating the success of machine-readable functionality and attempting to create machine-readable documents. One such example was musicologist Nancy B. Reich's creation of a machine-readable catalog of composer William Jay Sydeman's works in 1966. In the United States, the OPEN Government Data Act of 14 January 2019 defines machine-readable data as "data in a format that can be easily processed by a computer without human intervention while ensuring no semantic meaning is lost." The law directs U.S. federal agencies to publish public data in such a manner, ensuring that "any public data asset of the agency is machine-readable". Machine-readable data may be classified into two groups: human-readable data that is marked up so that it can also be read by machines (e.g. microformats, RDFa, HTML), and data file formats intended principally for processing by machines (CSV, RDF, XML, JSON). These formats are only machine readable if the data contained within them is formally structured; exporting a CSV file from a badly structured spreadsheet does not meet the definition. Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for humans to access via computers, but its content is much harder to extract, transform, and process via computer programming logic if it is not machine-readable. Extensible Markup Language (XML) is designed to be both human- and machine-readable, and Extensible Stylesheet Language Transformations (XSLT) is used to improve the presentation of the data for human readability. For example, XSLT can be used to automatically render XML in Portable Document Format (PDF). Machine-readable data can be automatically transformed for human-readability but, generally speaking, the reverse is not true. For purposes of implementation of the Government Performance and Results Act (GPRA) Modernization Act, the Office of Management and Budget (OMB) defines "machine readable format" as follows: "Format in a standard computer language (not English text) that can be read automatically by a web browser or computer system. (e.g.; xml). Traditional word processing documents and portable document format (PDF) files are easily read by humans but typically are difficult for machines to interpret. Other formats such as extensible markup language (XML), (JSON), or spreadsheets with header columns that can be exported as comma separated values (CSV) are machine readable formats. As HTML is a structural markup language, discreetly labeling parts of the document, computers are able to gather document components to assemble tables of contents, outlines, literature search bibliographies, etc. It is possible to make traditional word processing documents and other formats machine readable but the documents must include enhanced structural elements."

Media Examples of machine-readable media include magnetic media such as magnetic disks, cards, tapes, and drums, punched cards and paper tapes, optical discs, barcodes and magnetic ink characters. Common machine-readable technologies include magnetic recording, processing waveforms, and barcodes. Optical character recognition (OCR) can be used to enable machines to read information available to humans. Any information retrievable by any form of energy can be machine-readable. Examples include:

Acoustics Chemical Photochemical Electrical Semiconductor used in volatile RAM microchips Floating-gate transistor used in non-volatile memory cards Radio transmission Magnetic storage Mechanical Tins And Swins Punched card Paper tape Music roll Music box cylinder or disk Grooves (See also: Audio Data) Phonograph cylinder Gramophone record DictaBelt (groove on plastic belt) Capacitance Electronic Disc Optics Optical storage Thermodynamic

Applications

Documents

Catalogs

Dictionaries

Passports

See also Paper data storage Symmetric Phase Recording Open data Linked data Human-readable medium and data Semantic Web Machine-readable postal marking

References

This article incorporates public domain material from Federal Standard 1037C. General Services Administration. Archived from the original on 2022-01-22.

Illustrations

Worked examples

Example 1 — a first encounter with Machine-readable medium and data

Start with the simplest possible case. Write down what Machine-readable medium and data claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Machine-readable medium and data before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Machine-readable medium and data ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Machine-readable medium and data

In research: Machine-readable medium and data appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Machine-readable medium and data in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Machine-readable medium and data is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computer storage stubs, Computing terminology, Optical character recognition, so understanding it makes those chapters shorter.
In everyday life: Look for Machine-readable medium and data outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Machine-readable medium and data in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Machine-readable medium and data means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Machine-readable medium and data out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Machine-readable medium and data in simple terms?

Why does Machine-readable medium and data matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Machine-readable medium and data?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Machine-readable medium and data.