Provide Data /Information In Standard Formats.
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Wikipedia
In the pursuit of knowledge, data (US: /ˈdætə/; UK: /ˈdeɪtə/) is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted. A datum is an individual state in a set of data. Data usually is organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, finance, and in virtually every other form of human organizational activity. Examples of data sets include stock prices, crime rates, unemployment rates, literacy rates, and census data.
Data represents the raw facts and figures which can be used in such a manner in order to capture the useful information out of it.
Data is collected using techniques such as measurement, observation, query, or analysis, and typically represented as numbers or characters which may be further processed. Field data are data that are collected in an uncontrolled in-situ environment. Experimental data are data that are generated in the course of a controlled scientific experiment. Data is analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed and obvious instrument or data entry errors are corrected.
Data has been described as “the new oil of the digital economy”. Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Data is the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including but not limited to, statistics. Thematically connected data presented in some relevant context can be viewed as information. Contextually connected pieces of information can then be described as data insights or intelligence. The stock of insights and intelligence that accumulates over time resulting from the synthesis of data into information, can then be described as knowledge.
Advances in computing technologies have led to the advent of ”Big Data”. Big Data usually refers to very large quantities of data, usually at the petabyte scale. Using traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. (Theoretically speaking, infinite data would yield infinite information, which would render extracting insights or intelligence impossible.) In response, the relatively new field of ”Data Science” uses machine learning (and other Artificial Intelligence (AI)) methods that allow for efficient applications of analytic methods to Big Data.
The Latin word data is the plural of ‘datum’, “(thing) given,” neuter past participle of dare “to give”. The first English use of the word “data” is from the 1640s. The word “data” was first used to mean “transmissible and storable computer information” in 1946. The expression “data processing” was first used in 1954.
When “data” is used more generally as a synonym for “information”, it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term “big data”. When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term, and simply recommend the form that best suits the target audience of the guide. For example, APA style as of the 7th edition requires “data” to be treated as a plural form.
Information is an abstract concept that refers to that which has the power to inform. At the most fundamental level information pertains to the interpretation of that which may be sensed. Any natural process that is not completely random, and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analog signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form. Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation.
Information is often processed iteratively: Data available at one step are processed into information to be interpreted and processed at the next step. For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information relevant to the phrase it is part of, each phrase conveys information relevant to the sentence it is part of, and so on until at the final step information is interpreted and becomes knowledge in a given domain. In a digital signal bits may be interpreted into the symbols, letters, numbers, or structures that convey the information available at the next level up. The key characteristic of information is that it is subject to interpretation and processing.
The concept of information is relevant in various contexts, including those of constraint, communication, control, data, form, education, knowledge, meaning, understanding, mental stimuli, pattern, perception, proposition, representation, and entropy.
The derivation of information from a signal or message may be thought of as the resolution of ambiguity or uncertainty that arises during the interpretation of patterns within the signal or message.
Information may be structured as data. Redundant data can be compressed up to an optimal size, which is the theoretical limit of compression.
The information available through a collection of data may be derived by analysis. For example, data may be collected from a single customer’s order at a restaurant. The information available from many orders may be analyzed, and then becomes knowledge that is put to use when the business subsequently is able to identify the most popular or least popular dish.
Information can be transmitted in time, via data storage, and space, via communication and telecommunication. Information is expressed either as the content of a message or through direct or indirect observation. That which is perceived can be construed as a message in its own right, and in that sense, all information is always conveyed as the content of a message.
Information can be encoded into various forms for transmission and interpretation (for example, information may be encoded into a sequence of signs, or transmitted via a signal). It can also be encrypted for safe storage and communication.
The uncertainty of an event is measured by its probability of occurrence. Uncertainty is inversely proportional to the probability of occurrence. Information theory takes advantage of this by concluding that more uncertain events require more information to resolve their uncertainty. The bit is a typical unit of information. It is ‘that which reduces uncertainty by half’. Other units such as the nat may be used. For example, the information encoded in one “fair” coin flip is log2(2/1) = 1 bit, and in two fair coin flips is log2(4/1) = 2 bits. A 2011 Science article estimated that 97% of technologically stored information was already in digital bits in 2007, and that the year 2002 was the beginning of the digital age for information storage (with digital storage capacity bypassing analog for the first time).
The English word “information” comes from Middle French enformacion/informacion/information ‘a criminal investigation’ and its etymon, Latin informatiō(n) ‘conception, teaching, creation’.
In English, “information” is an uncountable mass noun.
The English word “information” comes from Middle French enformacion/informacion/information ‘a criminal investigation’ and its etymon, Latin informatiō(n) ‘conception, teaching, creation’.
In English, “information” is an uncountable mass noun.
Minimum information standards are sets of guidelines and formats for reporting data derived by specific high-throughput methods. Their purpose is to ensure the data generated by these methods can be easily verified, analysed and interpreted by the wider scientific community. Ultimately, they facilitate the transfer of data from journal articles (unstructured data) into databases (structured data) in a form that enables data to be mined across multiple data sets. Minimal information standards are available for a vast variety of experiment types including microarray (MIAME), RNAseq (MINSEQE), metabolomics (MSI) and proteomics (MIAPE).
Minimum information standards typically have two parts. Firstly, there is a set of reporting requirements – typically presented as a table or a checklist. Secondly, there is a data format. Information about an experiment needs to be converted into the appropriate data format for it to be submitted to the relevant database. In the case of MIAME, the data format is provided in spreadsheet format (MAGE-TAB). Some of the communities that maintain minimum information standards also provide tools to help experimental researchers to annotate their data.
The individual minimum information standards are brought by the communities of cross-disciplinary specialists focused on the problematic of the specific method used in experimental biology. The standards then provide specifications what information about the experiments (metadata) is crucial and important to be reported together with the resultant data to make it comprehensive. The need for this standardization is largely driven by the development of high-throughput experimental methods that provide tremendous amounts of data. The development of minimum information standards of different methods is since 2008 being harmonized by “Minimum Information about a Biomedical or Biological Investigation” (MIBBI) project.
MIAPPE is an open, community driven project to harmonize data from plant phenotyping experiments. MIAPPE comprises both a conceptual checklist of metadata required to adequately describe a plant phenotyping experiment.
Published in 2009 these guidelines for the basis of requirements by many journals when submitting QPCR data, sadly they are not adhered to enough.
Minimum information about a microarray experiment (MIAME) is a standard created by the FGED Society for reporting microarray experiments.
MIAME is intended to specify all the information necessary to interpret the results of the experiment unambiguously and to potentially reproduce the experiment. While the standard defines the content required for compliant reports, it does not specify the format in which this data should be presented. MIAME describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. There are a number of file formats used to represent this data, as well as both public and subscription-based repositories for such experiments. Additionally, software exists to aid the preparation of MIAME-compliant reports.
MIAME revolves around six key components: raw data, normalized data, sample annotations, experimental design, array annotations, and data protocols.
Electrophysiology is a technology used to study the electrical properties of biological cells and tissues. Electrophysiology typically involves the measurements of voltage change or electric current flow on a wide variety of scales from single ion channel proteins to whole tissues. This document is a single module, as part of the Minimum Information about a Neuroscience investigation (MINI) family of reporting guideline documents, produced by community consultation and continually available for public comment. A MINI module represents the minimum information that should be reported about a dataset to facilitate computational access and analysis to allow a reader to interpret and critically evaluate the processes performed and the conclusions reached, and to support their experimental corroboration. In practice a MINI module comprises a checklist of information that should be provided (for example about the protocols employed) when a data set is described for publication. The full specification of the MINI module can be found here.