How Information is Generated: A Look at Different Modes and Processes

Every second of every day, new information comes into existence. A researcher records a reading, a student solves a problem, a satellite captures an image of a distant galaxy. This constant flow does not happen by accident. Information is generated through identifiable methods, and understanding these methods helps us see how knowledge itself is built. For students of library and information science, knowing how information originates is the first step to understanding how it is later organised, stored, and shared. This post breaks down the main modes through which information is generated, with concrete examples drawn from astronomy, biology, physics, and everyday life.

What does generation of information mean? 🔗

Generation of information refers to the process through which new knowledge is created, recorded, or made available for use. It is not a one-time event but a continuous activity that takes place across science, technology, business, and ordinary daily life. A chemist running a reaction, an economist studying market data, and a farmer noticing changes in the weather are all generating information in their own way.

What makes this idea useful is that the process follows recognisable patterns, called modes. The commonly studied modes include observation, thought process, experimentation, processing of data, events, evolution, and even dreams. These modes rarely act in isolation. More often, two or three combine to produce a single piece of meaningful knowledge. Looking at each mode separately makes it easier to see how they eventually fit together.

Observation as a mode of information generation 🔗

Observation is the most basic and oldest mode. It involves carefully and systematically gathering information by watching phenomena, events, or objects using our senses, often aided by instruments. Observation gives us raw facts about the world before any analysis begins. In fact, the entire scientific method usually begins with careful observation of a specific occurrence, which then leads to questions worth investigating.

Observation in astronomy 🔗

Astronomy is perhaps the clearest example of a field built almost entirely on observation. For most of its history, astronomers could not experiment on stars or planets. They could only watch and record. The Danish astronomer Tycho Brahe spent years recording the precise positions of planets with remarkable accuracy. Johannes Kepler later used those same observations to formulate his laws of planetary motion, showing how raw observational data can become organised knowledge in the hands of another thinker.

This pattern continues today. Telescopes, space probes, and instruments collect enormous amounts of data about objects that no scientist can ever touch. The information generated through this observation becomes the foundation for theories about the origin and structure of the universe.

Observation in biology and medicine 🔗

Observation has also driven knowledge in biology and medicine for centuries. Early anatomical knowledge was generated by carefully observing the structure of the human body. In modern medicine, doctors observe symptoms, study tissue samples, and watch how patients respond to treatment. Naturalists like Charles Darwin built entire theories by patiently observing variations among living creatures across different regions. In all these cases, the careful act of watching and recording is what produces new, reliable information.

The thought process and reasoning 🔗

Sometimes information is not gathered directly from the outside world. Instead, it is generated inside the mind by connecting existing pieces of knowledge in new ways. This mode is called the thought process, and it includes reasoning, analysis, judgement, and reflection. Through thinking, we draw conclusions, identify patterns, and create explanations that were not obvious from the raw facts alone.

Newton’s gravitation: observation meets reasoning 🔗

Isaac Newton’s law of universal gravitation is a perfect example of how observation and reasoning work together. Newton did not simply watch an apple fall and instantly produce a law. He took existing observations, including Kepler’s laws describing planetary motion, and reasoned that the same force pulling objects to the ground might also govern the movement of the Moon and the planets. Newton’s law is described as a general physical law that he derived from empirical observations through inductive reasoning. His real breakthrough came from thinking through the problem mathematically.

The result was published in 1687 in his work the Principia, a book often described as the fundamental work for the whole of modern science. By combining careful observation with disciplined reasoning and mathematical calculation, Newton produced a theory that united the motion of falling objects on Earth with the orbits of distant planets. This shows that the most powerful information often emerges when the thought process organises what observation has supplied. Newton himself was clear that he was reasoning from appearances and admitted he could not explain the underlying cause of gravity, choosing to describe how it behaved rather than why.

Reasoning beyond the physical world 🔗

The thought process generates information far beyond physics. In philosophy, René Descartes arrived at his famous conclusion about existence purely through introspective reasoning. In computer science, the design of algorithms depends heavily on logical thinking rather than physical observation. Entrepreneurs use reflection and analysis to identify gaps in a market and develop new business ideas. In each case, the mind takes known facts and produces something new, demonstrating that thinking is a genuine source of information, not merely a tool for processing it.

Experimentation as a powerful mode 🔗

Experimentation is one of the most productive modes of information generation, especially in scientific and social science research. It involves conducting controlled tests to observe the effect of specific variables. Unlike pure observation, experimentation lets the researcher actively change conditions and measure what happens. This is the heart of the empirical method for acquiring knowledge through observation, hypothesis testing, and experimental validation.

The typical flow of experimentation begins with an observation or question, moves to a hypothesis, and then tests that hypothesis under controlled conditions. The scientific method is best understood as a process with sequential steps designed to produce explainable outcomes that expand our knowledge base. A hypothesis must be testable and capable of being proven false, otherwise it cannot be meaningfully examined.

The sheer volume of information produced this way is striking. Across scientific disciplines such as physics, chemistry, biology, and engineering, millions of research articles are added to databases every year, and a large share of them come from experimentation. The outcomes usually appear as research papers, short communications, and patents. It is worth remembering that experimentation never stands alone. Every experiment depends on observation to record results and on the thought process to interpret them.

Processing of data 🔗

Once raw facts have been gathered, they often need to be processed before they become useful information. Processing of data involves organising, analysing, and interpreting collected facts to reveal patterns and meaning. A census produces enormous tables of numbers, but those numbers only become useful information about population trends after careful processing.

This mode has become enormously important in the age of computers. Weather forecasting, economic planning, and public health monitoring all depend on processing huge quantities of data. The data itself may have come from observation or experimentation, but the act of analysing it generates fresh information that was hidden in the original numbers. As the scientific method reminds us, the analysis of data obtained can support a working hypothesis or disprove it, which then leads to new questions and further inquiry.

Information from events, evolution, and dreams 🔗

Not all information comes from deliberate scientific work. Several other modes contribute to the constant generation of knowledge.

Events 🔗

Significant events generate large amounts of information. A natural disaster, an election, a pandemic, or a major scientific discovery all produce records, reports, analyses, and data. Much of journalism and history is built on capturing the information that events create. When astronomers tracked a comet or observed a rare eclipse, the event itself became a source of new knowledge.

Evolution 🔗

Knowledge also evolves over time. Information generated in one era forms the base on which later thinkers build. Newton relied on Kepler, Kepler relied on Brahe, and modern physicists later refined and even superseded Newton with the help of new theories. This gradual layering, where each generation adds to and corrects the work of the previous one, is itself a mode of information generation. The way Newton bridged terrestrial and celestial motion into a single set of laws only became possible because earlier astronomers had supplied the observations and models he needed.

Dreams 🔗

Even dreams have occasionally produced new information. The chemist August Kekulé is famously said to have understood the ring structure of the benzene molecule after a dream of a snake biting its own tail. While dreams are not a reliable method, they remind us that the human mind can generate insights in unexpected ways, which are then tested and confirmed through more rigorous modes.

Why these modes rarely work alone 🔗

The most important lesson is that these modes are deeply connected. Observation supplies raw facts. The thought process organises and interprets them. Experimentation tests ideas under controlled conditions. Processing of data extracts hidden patterns. Events and evolution feed fresh material into the cycle. Newton’s gravitation needed observation, reasoning, and mathematics all at once. A modern medical breakthrough may need observation of symptoms, experimentation through clinical trials, and processing of statistical data before it counts as reliable knowledge.

For anyone studying information science, recognising these modes is genuinely practical. It explains why information appears in so many forms, from research papers and patents to government reports and statistical tables. Understanding where information comes from makes it far easier to evaluate how trustworthy it is and how it should be managed once it enters the vast and growing record of human knowledge.

What do you think? Which mode of information generation do you rely on most in your own learning, and can you recall a moment when reasoning helped you make sense of something you had only observed before?

References

1. https://www.ebsco.com/research-starters/science/scientific-method
2. https://en.wikipedia.org/wiki/Newton%27s_law_of_universal_gravitation
3. https://www.britannica.com/topic/Principia
4. https://www.encyclopedia.com/science/encyclopedias-almanacs-transcripts-and-maps/newtons-law-universal-gravitation
5. https://en.wikipedia.org/wiki/Scientific_method
6. https://extension.unr.edu/publication.aspx?PubID=4239
7. https://pmc.ncbi.nlm.nih.gov/articles/PMC7965632/
8. https://www.loc.gov/static/collections/finding-our-place-in-the-cosmos-with-carl-sagan/articles-and-essays/modeling-the-cosmos/physical-astronomy-for-the-mechanistic-universe.html