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| Usage in English | |
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In English, the word datum is still used in the general sense of "an item given". In cartography, geography, nuclear magnetic resonance and technical drawing it is often used to refer to a single specific reference datum from which distances to all other data are measured. Any measurement or result is a datum, but data point is more usual,[1] albeit tautological. Both datums (see usage in datum article) and the originally Latin plural data are used as the plural of datum in English, but data is commonly treated as a mass noun and used with a verb in the singular form, especially in day-to-day usage. For example, This is all the data from the experiment. This usage is inconsistent with the rules of Latin grammar and traditional English (These are all the data from the experiment). Even when a very small quantity of data is referenced (One number, for example) the phrase piece of data is often used, as opposed to datum. The debate over appropriate usage is ongoing.[citation needed]
The IEEE Computer Society, allows usage of data as either a mass noun or plural based on author preference.[2] Other professional organizations and style guides[3] require that authors treat data as a plural noun. For example, the Air Force Flight Test Center specifically states that the word data is always plural, never singular.[4]
Data is most often used as a singular mass noun in educated everyday usage.[5][6] Some major newspapers such as The New York Times use it either in the singular or plural. In the New York Times the phrases "the survey data are still being analyzed" and "the first year for which data is available" have appeared within one day.[7] In scientific writing data is often treated as a plural, as in These data do not support the conclusions, but it is also used as a singular mass entity like information. British usage now widely accepts treating data as singular in standard English,[8] including everyday newspaper usage[9] at least in non-scientific use.[10] UK scientific publishing still prefers treating it as a plural.[11] Some UK university style guides recommend using data for both singular and plural use[12] and some recommend treating it only as a singular in connection with computers.[13]
[edit] Tags:Latin,English,Cartography,Geography,Nuclear Magnetic Resonance,Technical Drawing,Tautological,Mass Noun,Information, | |
| Meaning of data, information and knowledge | |
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The terms data, information and knowledge are frequently used for overlapping concepts. The main difference is in the level of abstraction being considered. Data is the lowest level of abstraction, information is the next level, and finally, knowledge is the highest level among all three.[citation needed] Data on its own carries no meaning. For data to become information, it must be interpreted and take on a meaning. For example, the height of Mt. Everest is generally considered as "data", a book on Mt. Everest geological characteristics may be considered as "information", and a report containing practical information on the best way to reach Mt. Everest's peak may be considered as "knowledge".
Information as a concept bears a diversity of meanings, from everyday usage to technical settings. 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 are symbols while information occurs when symbols are used to refer to something.[14]
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.[15]
Mechanical computing devices are classified according to the means by which they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a datum as a sequence of symbols drawn from a fixed alphabet. The most common digital computers use a binary alphabet, that is, an alphabet of two characters, typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet.
Some special forms of data are distinguished. A computer program is a collection of data, which can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata, that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books.
[edit] Tags:Computing,Abstraction,Numbers,Knowledge,Analog Computer,Digital Computer,Mean,Contents, | |
| See also | |
| 2>
Biological data
Data acquisition
Data analysis
Data cable
Data domain
Data element
Data farming
Data governance
Data integrity
Data maintenance
Data management
Data mining
Data modeling
Computer data processing
Data remanence
Data set
Data warehouse
Database
Datasheet
Environmental data rescue
Fieldwork
Metadata
Scientific data archiving
Statistics
Datastructure
[edit] Tags:Fieldwork,Statistics,Mode, | |
| References | |
| 2>
This article was originally based on material from the Free On-line Dictionary of Computing, which is licensed under the GFDL.
^ Matt Dye (2001). "Writing Reports". University of Bristol. http://www.bris.ac.uk/Depts/DeafStudiesTeaching/dissert/Writing%20Reports.htm.
^ "IEEE Computer Society Style Guide, DEF". IEEE Computer Society. http://www.computer.org/portal/web/publications/styleguidedef.
^ "WHO Style Guide". Geneva: World Health Organization. 2004. p. 43. http://whqlibdoc.who.int/hq/2004/WHO_IMD_PUB_04.1.pdf. [dead link]
^ The Author's Guide to Writing Air Force Flight Test Center Technical Reports. Air Force Flight Test Center.
^ New Oxford Dictionary of English, 1999
^ "...in educated everyday usage as represented by the Guardian newspaper, it is nowadays most often used as a singular." http://www.eisu2.bham.ac.uk/johnstf/revis006.htm
^
"When Serving the Lord, Ministers Are Often Found to Neglect Themselves". New York Times. 2009. http://www.nytimes.com/2009/01/10/us/10religion.html.
"Investment Tax Cuts Help Mostly the Rich". New York Times. 2009. http://www.nytimes.com/2009/01/10/business/10charts.html.
^ New Oxford Dictionary of English. 1999.
^ Tim Johns (1997). "Data: singular or plural?". http://www.eisu2.bham.ac.uk/johnstf/revis006.htm. "...in educated everyday usage as represented by The Guardian newspaper, it is nowadays most often used as a singular."
^ "Data". Compact Oxford Dictionary. http://www.askoxford.com/concise_oed/data?view=uk.
^ "Data: singular or plural?". Blair Wisconsin International University. http://www.eisu2.bham.ac.uk/johnstf/revis006.htm.
^ "Singular or plural". University of Nottingham Style Book. University of Nottingham. http://www.nottingham.ac.uk/public-affairs/uon-style-book/singular-plural.htm. [dead link]
^ "Computers and computer systems". OpenLearn. http://openlearn.open.ac.uk/mod/resource/view.php?id=182902. [dead link]
^
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.
[edit] Tags:/,Free On-line Dictionary Of Computing,Licensed,Gfdl,Dead Link,Palgrave Macmillan,Studies,Portal,Help, | |
| External links | |
| 2>
Look up data in Wiktionary, the free dictionary.
Data is a singular noun (a detailed assessment)
v
d
e
Statistics
Descriptive statistics
Continuous data
Location
Mean (Arithmetic, Geometric, Harmonic)
Median
Mode
Dispersion
Range
Standard deviation
Coefficient of variation
Percentile
Interquartile range
Shape
Variance
Skewness
Kurtosis
Moments
L-moments
Count data
Index of dispersion
Summary tables
Grouped data
Frequency distribution
Contingency table
Dependence
Pearson product-moment correlation
Rank correlation (Spearman's rho, Kendall's tau)
Partial correlation
Scatter plot
Statistical graphics
Bar chart
Biplot
Box plot
Control chart
Correlogram
Forest plot
Histogram
Q-Q plot
Run chart
Scatter plot
Stemplot
Radar chart
Data collection
Designing studies
Effect size
Standard error
Statistical power
Sample size determination
Survey methodology
Sampling
Stratified sampling
Opinion poll
Questionnaire
Controlled experiment
Design of experiments
Randomized experiment
Random assignment
Replication
Blocking
Factorial experiment
Optimal design
Uncontrolled studies
Natural experiment
Quasi-experiment
Observational study
Statistical inference
Statistical theory
Sampling distribution
Sufficient statistic
Meta-analysis
Bayesian inference
Bayesian probability
Prior
Posterior
Credible interval
Bayes factor
Bayesian estimator
Maximum posterior estimator
Frequentist inference
Confidence interval
Hypothesis testing
Likelihood-ratio
Specific tests
Z-test (normal)
Student's t-test
F-test
Pearson's chi-squared test
Wald test
Mann–Whitney U
Shapiro–Wilk
Signed-rank
Kolmogorov–Smirnov test
General estimation
Bias
Robustness
Efficiency
Maximum likelihood
Method of moments
Minimum distance
Density estimation
Correlation and regression analysis
Correlation
Pearson product-moment correlation
Partial correlation
Confounding variable
Coefficient of determination
Regression analysis
Errors and residuals
Regression model validation
Mixed effects models
Simultaneous equations models
Linear regression
Simple linear regression
Ordinary least squares
General linear model
Bayesian regression
Non-standard predictors
Nonlinear regression
Nonparametric
Semiparametric
Isotonic
Robust
Generalized linear model
Exponential families
Logistic (Bernoulli)
Binomial
Poisson
Partition of variance
Analysis of variance (ANOVA)
Analysis of covariance
Multivariate ANOVA
Degrees of freedom
Categorical, multivariate, time-series, or survival analysis
Categorical data
Cohen's kappa
Contingency table
Graphical model
Log-linear model
McNemar's test
Multivariate statistics
Multivariate regression
Principal components
Factor analysis
Cluster analysis
Copulas
Time series analysis
Decomposition (Trend, Stationary process)
ARMA model
ARIMA model
Vector autoregression
Spectral density estimation
Survival analysis
Survival function
Kaplan–Meier
Logrank test
Failure rate
Proportional hazards models
Accelerated failure time model
Applications
Biostatistics
Bioinformatics
Biometrics
Clinical trials & studies
Epidemiology
Medical statistics
Engineering statistics
Chemometrics
Methods engineering
Probabilistic design
Process & Quality control
Reliability
System identification
Social statistics
Actuarial science
Census
Crime statistics
Demography
Econometrics
National accounts
Official statistics
Population
Psychometrics
Spatial statistics
Cartography
Environmental statistics
Geographic information system
Geostatistics
Kriging
Category
Portal
Outline
Index
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Русинь Tags:Descriptive Statistics,Location,Arithmetic,Geometric,Harmonic,Median,Dispersion,Range,Standard Deviation,Coefficient Of Variation,Percentile,Interquartile Range,Shape,Variance,Skewness,Kurtosis,Moments,L-moments,Index Of Dispersion,Frequency Distribution,Contingency Table,Dependence,Pearson Product-moment Correlation,Rank Correlation,Spearman's Rho,Kendall's Tau,Partial Correlation,Scatter Plot,Statistical Graphics,Bar Chart,Biplot,Box Plot,Control Chart,Correlogram,Forest Plot,Histogram,Q-q Plot,Run Chart,Stemplot,Effect Size,Standard Error,Statistical Power,Sample Size Determination,Survey Methodology,Sampling,Stratified Sampling,Opinion Poll,Questionnaire,Design Of Experiments,Randomized Experiment,Random Assignment,Replication,Blocking,Factorial Experiment,Optimal Design,Natural Experiment,Quasi-experiment,Statistical Inference,Statistical Theory,Sampling Distribution,Sufficient Statistic,Meta-analysis,Bayesian Inference,Bayesian Probability,Prior,Posterior,Credible Interval,Bayes Factor,Bayesian Estimator,Maximum Posterior Estimator,Frequentist Inference,Confidence Interval,Hypothesis Testing,Likelihood-ratio,Z-test (normal),Student's T-test,F-test,Pearson's Chi-squared Test,Wald Test,Mann–whitney U,Shapiro–wilk,Signed-rank,Kolmogorov–smirnov Test,General Estimation,Bias,Robustness,Efficiency,Maximum Likelihood,Method Of Moments,Minimum Distance,Correlation,Regression Analysis,Confounding Variable,Coefficient Of Determination,Errors And Residuals,Regression Model Validation,Mixed Effects Models,Simultaneous Equations Models,Linear Regression,Simple Linear Regression,Ordinary Least Squares,General Linear Model, | |
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