Data collection in the context of Data

In its most common sense, methodology is the study of research methods. However, the term can also refer to the methods themselves or to the philosophical discussion of associated background assumptions. A method is a structured procedure for bringing about a certain goal, like acquiring knowledge or verifying knowledge claims. This normally involves various steps, like choosing a sample, collecting data from this sample, and interpreting the data. The study of methods concerns a detailed description and analysis of these processes. It includes evaluative aspects by comparing different methods. This way, it is assessed what advantages and disadvantages they have and for what research goals they may be used. These descriptions and evaluations depend on philosophical background assumptions. Examples are how to conceptualize the studied phenomena and what constitutes evidence for or against them. When understood in the widest sense, methodology also includes the discussion of these more abstract issues.

Methodologies are traditionally divided into quantitative and qualitative research. Quantitative research is the main methodology of the natural sciences. It uses precise numerical measurements. Its goal is usually to find universal laws used to make predictions about future events. The dominant methodology in the natural sciences is called the scientific method. It includes steps like observation and the formulation of a hypothesis. Further steps are to test the hypothesis using an experiment, to compare the measurements to the expected results, and to publish the findings.

In statistics, a unit of observation is the unit described by the data that one analyzes. A study may treat groups as a unit of observation with a country as the unit of analysis, drawing conclusions on group characteristics from data collected at the national level. For example, in a study of the demand for money, the unit of observation might be chosen as the individual, with different observations (data points) for a given point in time differing as to which individual they refer to; or the unit of observation might be the country, with different observations differing only in regard to the country they refer to.

Data processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer.

The Earth Observing System (EOS) is a program of NASA comprising a series of artificial satellite missions and scientific instruments in Earth orbit designed for long-term global observations of the land surface, biosphere, atmosphere, and oceans. Since the early 1970s, NASA has been developing its Earth Observing System, launching a series of Landsat satellites in the decade. Some of the first included passive microwave imaging in 1972 through the Nimbus 5 satellite. Following the launch of various satellite missions, the conception of the program began in the late 1980s and expanded rapidly through the 1990s. Since the inception of the program, it has continued to develop, including; land, sea, radiation and atmosphere. Collected in a system known as EOSDIS, NASA uses this data in order to study the progression and changes in the biosphere of Earth. The main focus of this data collection surrounds climatic science. The program is the centrepiece of NASA's Earth Science Enterprise.

Research stations are facilities where scientific investigation, collection, analysis and experimentation occurs. A research station is a facility that is built for the purpose of conducting scientific research. There are also many types of research stations including: biological field stations, space stations etc. Research station sites might include remote areas of the world, oceans, as well as outer space, such as the International Space Station. Biological research stations developed during a time of European colonization and imperialism where naturalists were employed to conduct observations on fauna and flora. Today, the discipline is represented by a number of organizations which span across multiple continents. Some examples include: the Organization of Biological Field Stations and the Organization for Tropical Studies.

Space stations were also developed over a number of decades through scientific analysis and writing, with the first design aspects of early space stations being introduced by Herman Potocnik in 1928. Since then, the construction and launch of space stations have been both national and international, collaborative efforts which have allowed different design philosophies to form key space stations such as the International Space Station (ISS). Similarly, stations in Antarctica are built to ensure that they are well insulated against the sub-zero temperatures of the exterior landscape with many redevelopments being required over the years to overcome issues associated with snowdrifts, accessibility and rusting.

Participant observation is one type of data collection method by practitioner-scholars typically used in qualitative research and ethnography. This type of methodology is employed in many disciplines, particularly anthropology (including cultural anthropology and ethnology), sociology (including sociology of culture and cultural criminology), communication studies, human geography, and social psychology. Its aim is to gain a close and intimate familiarity with a given group of individuals (such as a religious, occupational, youth group, or a particular community) and their practices through an intensive involvement with people in their cultural environment, usually over an extended period of time.

The concept "participant observation" was first coined in 1924 by Eduard C. Lindeman (1885-1953), an American pioneer in adult education influenced by John Dewey and Danish educator-philosopher N.F.S.Grundtvig, in his 1925 book Social Discovery: An Approach to the Study of Functional Groups. The method, however, originated earlier and was applied in the field research linked to European and American voyages of scientific exploration.

In politics, opposition research (also called oppo research) is the practice of collecting information on a political opponent or other adversary that can be used to discredit or otherwise weaken them. The information can include biographical, legal, criminal, medical, educational, or financial history or activities, as well as prior media coverage, or the voting record of a politician. Opposition research can also entail using trackers to follow an individual and record their activities or political speeches.

The research is usually conducted in the time period between announcement of intent to run and the actual election; however political parties maintain long-term databases that can cover several decades. The practice is both a tactical maneuver and a cost-saving measure. The term is frequently used to refer not just to the collection of information but also how it is utilized, as a component of negative campaigning.

Data reporting is the process of collecting and submitting data.

The effective management of any organization relies on accurate data. Inaccurate data reporting can lead to poor decision-making based on erroneous evidence. Data reporting is different from data analysis which transforms data and information into insights. Data reporting is the previous step that translates raw data into information. When data is not reported, the problem is known as underreporting; the opposite problem leads to false positives.

E-commerce (electronic commerce) refers to commercial activities including the electronic buying or selling products and services which are conducted on online platforms or over the Internet. E-commerce draws on technologies such as mobile commerce, electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. E-commerce is a part of the retail, the largest segment of the electronics industry and is in turn driven by the technological advances of the semiconductor industry.

Telemetry is the in situ collection of measurements or other data at remote points and their automatic transmission to receiving equipment (telecommunication) for monitoring. The word is derived from the Greek roots tele, 'far off', and metron, 'measure'. Systems that need external instructions and data to operate require the counterpart of telemetry: telecommand.

Although the term commonly refers to wireless data transfer mechanisms (e.g., using radio, ultrasonic, or infrared systems), it also encompasses data transferred over other media such as a telephone or computer network, optical link or other wired communications like power line carriers. Many modern telemetry systems take advantage of the low cost and ubiquity of GSM networks by using SMS to receive and transmit telemetry data.

Anthropometry (/ænθrəˈpɒmɪtrɪ/ , from Ancient Greek ἄνθρωπος (ánthrōpos) 'human' and μέτρον (métron) 'measure') refers to the measurement of the human individual. An early tool of physical anthropology, it has been used for identification, for the purposes of understanding human physical variation, in paleoanthropology and in various attempts to correlate physical with racial and psychological traits. Anthropometry involves the systematic measurement of the physical properties of the human body, primarily dimensional descriptors of body size and shape. Since commonly used methods and approaches in analysing living standards were not helpful enough, the anthropometric history became very useful for historians in answering questions that interested them.

Today, anthropometry plays an important role in industrial design, clothing design, ergonomics and architecture where statistical data about the distribution of body dimensions in the population are used to optimize products. Changes in lifestyles, nutrition, and ethnic composition of populations lead to changes in the distribution of body dimensions (e.g. the rise in obesity) and require regular updating of anthropometric data collections.

Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it.

The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection.

Naturalistic observation, sometimes referred to as fieldwork, is a valuable form of empirical data collection in research methodology across numerous fields of science (including ethology, anthropology, linguistics, social sciences, and psychology) in which data are collected as they occur in nature, without any manipulation by the observer. Examples range from watching an animal's eating patterns in the forest to observing the behavior of students in a school setting. During naturalistic observation, researchers take great care using unobtrusive methods to avoid interfering with the behavior they are observing. Naturalistic observation contrasts with analog observation in an artificial setting that is designed to be an analog of the natural situation, constrained so as to eliminate or control for effects of any variables other than those of interest. There is similarity to observational studies in which the independent variable of interest cannot be experimentally controlled for ethical or logistical reasons.

Naturalistic observation has both advantages and disadvantages as a research methodology. Observations are more credible because the behavior occurs in a real, typical scenario as opposed to an artificial one generated within a lab. Behavior that could never occur in controlled laboratory environment can lead to new insights. Naturalistic observation also allows for study of events that are deemed unethical to study experimentally, such as the impact of high school shootings on students attending the high school. However, because extraneous variables cannot be controlled as in a laboratory, it is difficult to replicate findings and demonstrate their reliability. In particular, if subjects know they are being observed they may behave differently than otherwise. It may be difficult to generalize findings of naturalistic studies beyond the observed situations.

Customer relationship management (CRM) is a strategic process that organizations use to manage, analyze, and improve their interactions with customers. By using data-driven insights, CRM often involves dedicated information systems that help store and analyze customer data, support communication, and coordinate sales, marketing, and service activities.

CRM systems compile data from a range of different communication channels, including a company's website, telephone (which many services come with a softphone), email, live chat, marketing materials and more recently, social media. They allow businesses to learn more about their target audiences and how to better cater to their needs, thus retaining customers and driving sales growth. CRM may be used with past, present or potential customers. The concepts, procedures, and rules that a corporation follows when communicating with its consumers are referred to as CRM. This complete connection covers direct contact with customers, such as sales and service-related operations, forecasting, and the analysis of consumer patterns and behaviours, from the perspective of the company.

Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support systems, including artificial intelligence (e.g., machine learning) and business intelligence. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.