EXPLAIN DATA STORAGE, RETRIEVAL AND DATA SHARING FINANCING


EXPLAIN DATA STORAGE, RETRIEVAL AND DATA SHARING FINANCING
INTRODUCTION
Data is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. 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.
Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the “processed data” from one stage may be considered the “raw data” of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.
The word “data” used to be considered the plural of “datum”, and still is by some English speakers. Nowadays, though, “data” is most commonly used in the singular, as a mass noun (like “information”, “sand” or “rain”). Data is distinct pieces of information, usually formatted in a special way. All software is divided into two general categories: data and programs. Programs are collections of instructions for manipulating data.
Data can exist in a variety of forms — as numbers or text on pieces of paper, as bits and bytes stored in electronic memory, or as facts stored in a person’s mind.
Strictly speaking, data is the plural of datum, a single piece of information. In practice, however, people use data as both the singular and plural form of the word.
DATA STORAGE
Data storage can refer to:
• Computer data storage; memory, components, devices and media that retain digital computer data used for computing for some interval of time.
• Any data storage device; that records (stores) or retrieves (reads) information (data) from any medium, including the medium itself.
Data storage is a general term for archiving data in electromagnetic or other forms for use by a computer or device. Different types of data storage play different roles in a computing environment. In addition to forms of hard data storage, there are now new options for remote data storage, such as cloud computing, that can revolutionize the ways that users access data.

DATA RETRIEVAL
Data retrieval means obtaining data from a database management system such as ODBMS. In this case, it is considered that data is represented in a structured way, and there is no ambiguity in data.
In order to retrieve the desired data the user present a set of criteria by a query. Then the Database Management System (DBMS), software for managing databases, selects the demanded data from the database. The retrieved data may be stored in a file, printed, or viewed on the screen.
A query language, such as Structured Query Language (SQL), is used to prepare the queries. SQL is an American National Standards Institute (ANSI) standardized query language developed specifically to write database queries. Each DBMS may have its own language, but most relational DBMSs also support SQL.
DATA SHARING FINANCING
Data sharing is the practice of making data used for scholarly research available to other investigators. Replication has a long history in science. The motto of The Royal Society is ‘Nullius in verba’, translated “Take no man’s word for it.” Many funding agencies, institutions, and publication venues have policies regarding data sharing because transparency and openness are considered by many to be part of the scientific method.
A number of funding agencies and science journals require authors of peer-reviewed papers to share any supplemental information (raw data, statistical methods or source code) necessary to understand, develop or reproduce published research. A great deal of scientific research is not subject to data sharing requirements, and many of these policies have liberal exceptions. In the absence of any binding requirement, data sharing is at the discretion of the scientists themselves. In addition, in certain situations agencies and institutions prohibit or severely limit data sharing to protect proprietary interests, national security, and subject/patient/victim confidentiality. Data sharing may also be restricted to protect institutions and scientists from use of data for political purposes.
Data and methods may be requested from an author years after publication. In order to encourage data sharing and prevent the loss or corruption of data, a number of funding agencies and journals established policies on data archiving. Access to publicly archived data is a recent development in the history of science made possible by technological advances in communications and information technology. Despite policies on data sharing and archiving, data withholding still happens. Authors may fail to archive data or they only archive a portion of the data. Failure to archive data alone is not data withholding. When a researcher requests additional information, an author sometimes refuses to provide it. When authors withhold data like this, they run the risk of losing the trust of the science community.
CONCLUSION
Data, information and knowledge are closely related concepts, but each has its own role in relation to the other. Data is collected and analyzed to create information suitable for making decisions,[3] while knowledge is derived from extensive amounts of experience dealing with information on a subject. For example, the height of Mt. Everest is generally considered data. This data may be included in a book along with other data on Mt. Everest to describe the mountain in a manner useful for those who wish to make a decision about the best method to climb it. Using an understanding based on experience climbing mountains to advise persons on the way to reach Mt. Everest’s peak may be seen as “knowledge”.
That is to say, data is the least abstract, information the next least, and knowledge the most.[4] Data becomes information by interpretation; e.g., the height of Mt. Everest is generally considered “data”, a book on Mt. Everest geological characteristics may be considered “information”, and a report containing practical information on the best way to reach Mt. Everest’s peak may be considered “knowledge”.
‘Information’ bears a diversity of meanings that ranges from everyday to technical. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation.
Beynon-Davies uses the concept of a sign to distinguish between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something.[5][6]
It is people and computers who collect data and impose patterns on it. These patterns are seen as information which can be used to enhance knowledge. These patterns can be interpreted as truth, and are authorized as aesthetic and ethical criteria. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken

REFERNCES
• The pronunciation /ˈdeɪtə/ DAY-tə is widespread throughout most varieties of English. The pronunciation/ˈdætə/ DA-tə is chiefly Irish and North American. The pronunciation /ˈdɑːtə/ DAH-tə is chiefly Australian, New Zealand and South African. Each pronunciation may be realized differently depending on the dialect of the speaker.
• • Hickey, Walt (2014-06-17). “Elitist, Superfluous, Or Popular? We Polled Americans on the Oxford Comma”. FiveThirtyEight. Retrieved 2015-05-04.
• • “Joint Publication 2-0, Joint Intelligence” (PDF). Defense Technical Information Center (DTIC). Department of Defense. 22 June 2007. pp. GL–11. Retrieved February 22, 2013.
• • Akash Mitra (2011). “Classifying data for successful modeling”.
• • P. Beynon-Davies (2002). Information Systems: An introduction to informatics in organisations. Basingstoke, UK: Palgrave Macmillan. ISBN 0-333-96390-3.
• • P. Beynon-Davies (2009). Business information systems. Basingstoke, UK: Palgrave. ISBN 978-0-230-20368-6.
• • Sharon Daniel. The Database: An Aesthetics of Dignity.
• • P. Checkland and S. Holwell (1998). Information, Systems, and Information Systems: Making Sense of the Field. Chichester, West Sussex: John Wiley & Sons. pp. 86–89. ISBN 0-471-95820-4.
• Johanna Drucker (2011). “Humanities Approaches to Graphical Display”.

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