Data

Units of information

"Scientific data" redirects here. For the journal, see Scientific Data (journal).

For data in computer science, see Data (computer science). For other uses, see Data (disambiguation) and Datum (disambiguation).

These are some of the different types of data: Geographical, Cultural, Scientific, Financial, Statistical, Meteorological, Natural, Transport Part of a series on

Epistemology

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Schools

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Concepts

* Action
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Domains

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Epistemologists

* Aristotle
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Related fields

* Epistemic cognition
* Epistemic logic
* Philosophy of perception
* Philosophy of science

* v
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* e

Data (/ˈdeɪtə/ DAY-tə, US also /ˈdætə/ DAT-ə) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data are usually organized into structures such as tables that provide additional context and meaning, and may themselves be used as data in larger structures. Data may be used as variables in a computational process.[1][2] Data may represent abstract ideas or concrete measurements.[3] Data are commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represent the raw facts and figures from which useful information can be extracted.

Data are collected using techniques such as measurement, observation, query, or analysis, and are typically represented as numbers or characters that 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 are 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 can be seen as 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 accumulate over time resulting from the synthesis of data into information, can then be described as knowledge. Data has been described as "the new oil of the digital economy".[4][5] 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.

Advances in computing technologies have led to the advent of big data, which 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) methods that allow for efficient applications of analytic methods to big data.

## Etymology and terminology

[edit]

Further information: Data (word)

The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give".[6] 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.[6]

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 the 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.[7]

## Meaning

[edit]

Adrien Auzout's "A TABLE of the Apertures of Object-Glasses" from a 1665 article in Philosophical Transactions

See also: DIKW pyramid

Data, information, knowledge, and wisdom are closely related concepts, but each has its role concerning the other, and each term has its meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion.[8] One can say that the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information contained in a data stream may be characterized by its Shannon entropy.

Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height of Mount Everest is a datum that communicates a precisely measured value. This measurement may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge.

Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract.[9] In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge.[10] 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 differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something.[11][12]

Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social service usage by citizens to scientific research. These patterns in the data are seen as information that can be used to enhance knowledge. These patterns may be interpreted as "truth" (though "truth" can be a subjective concept) and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. 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.[13]

Mechanical computing devices are classified according to how they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a piece of data 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, that 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.

## Data sources

[edit]

With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to "party" depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares".[14] This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences.[15] "First-party data" may be collected by a company directly from its customers.[16] The secure exchange of first-party data among companies can be done using data clean rooms.[17] "Second-party data" refers to data obtained from other organizations or partners, through purchase or other means and has been described as "another organization's first-party data".[18][19] "Third-party data" is data collected by other organizations and subsequently aggregated from different sources, websites, and platforms.[18]

Summary of data sources[18] Data source  | Owned by  | Accuracy  | Use case  | Privacy risk

|
|
|
|

First-party  | The business  | High  | Personalization, retargeting  | Low
Second-party  | Partner  | Moderate  | Partnership campaigns  | Moderate
Third-party  | External entity  | Low  | Broad targeting  | High

"No-party" data can sometimes refer to synthetic data that is generated based on patterns from original data.[17]

## Data documents

[edit]

Part of a series on

Library and information science

* Outline
* Glossary

Histories

Libraries \- Information

Focus

Archives management \- Collections management (Preservation) - Data management \- Information management (cataloguing) - Knowledge management \- Library management

Curation

Data \- Metadata \- Information \- Documents \- Artefacts \- Knowledge

Interdisciplinary fields

Archival science \- Communication studies \- Computer science \- Data science \- Documentation science \- Epistemology \- Library science \- Information science \- Science and technology studies

Areas

Academic \- Archival \- Legal \- Health \- Private \- Public \- School \- Special

* WikiProject
* Category

* v
* t
* e

Whenever data needs to be registered, data exists in the form of a data document. Kinds of data documents include:

* data repository
* data study
* data set
* software
* data paper
* database
* data handbook
* data journal

Some of these data documents (data repositories, data studies, data sets, and software) are indexed in Data Citation Indexes, while data papers are indexed in traditional bibliographic databases, e.g., Science Citation Index.

### Data collection

[edit]

Gathering data can be accomplished through a primary source (the researcher is the first person to obtain the data) or a secondary source (the researcher obtains the data that has already been collected by other sources, such as data disseminated in a scientific journal). Data analysis methodologies vary and include data triangulation and data percolation.[20] The latter offers an articulate method of collecting, classifying, and analyzing data using five possible angles of analysis (at least three) to maximize the research's objectivity and permit an understanding of the phenomena under investigation as complete as possible: qualitative and quantitative methods, literature reviews (including scholarly articles), interviews with experts, and computer simulation. The data is thereafter "percolated" using a series of pre-determined steps so as to extract the most relevant information.

## Data longevity and accessibility

[edit]

An important field in computer science, technology, and library science is the longevity of data. Scientific research generates huge amounts of data, especially in genomics and astronomy, but also in the medical sciences, e.g. in medical imaging. In the past, scientific data has been published in papers and books, stored in libraries, but more recently practically all data is stored on hard drives or optical discs. However, in contrast to paper, these storage devices may become unreadable after a few decades. Scientific publishers and libraries have been struggling with this problem for a few decades, and there is still no satisfactory solution for the long-term storage of data over centuries or even for eternity.

Data accessibility. Another problem is that much scientific data is never published or deposited in data repositories such as databases. In a recent survey, data was requested from 516 studies that were published between 2 and 22 years earlier, but less than one out of five of these studies were able or willing to provide the requested data. Overall, the likelihood of retrieving data dropped by 17% each year after publication.[21] Similarly, a survey of 100 datasets in Dryad found that more than half lacked the details to reproduce the research results from these studies.[22] This shows the dire situation of access to scientific data that is not published or does not have enough details to be reproduced.

A solution to the problem of reproducibility is the attempt to require FAIR data, that is, data that is Findable, Accessible, Interoperable, and Reusable. Data that fulfills these requirements can be used in subsequent research and thus advances science and technology.[23]

## In other fields

[edit]

Although data is also increasingly used in other fields, it has been suggested that their highly interpretive nature might be at odds with the ethos of data as "given". Peter Checkland introduced the term capta (from the Latin capere, "to take") to distinguish between an immense number of possible data and a sub-set of them, to which attention is oriented.[24] Johanna Drucker has argued that since the humanities affirm knowledge production as "situated, partial, and constitutive," using data may introduce assumptions that are counterproductive, for example, that phenomena are discrete or are observer-independent.[25] The term capta, which emphasizes the act of observation as constitutive, is offered as an alternative to data for visual representations in the humanities.

The term data-driven is a neologism applied to an activity which is primarily compelled by data over all other factors.[citation needed] Data-driven applications include data-driven programming and data-driven journalism.

## See also

[edit]

* Biological data
* Computer data processing
* Computer memory
* Dark data
* Data (computer science)
* Data acquisition
* Data analysis
* Data bank
* Data cable
* Data curation
* Data domain
* Data element
* Data farming
* Data governance
* Data integrity
* Data maintenance
* Data management
* Data mining
* Data modeling
* Data point
* Data preservation
* Data protection
* Data publication
* Data remanence
* Data science
* Data set
* Data structure
* Data visualization
* Data warehouse
* Database
* Datasheet
* Data-driven programming
* Data-driven journalism
* Data-driven testing
* Data-driven learning
* Data-driven science
* Data-driven control system
* Data-driven marketing
* Digital privacy
* Environmental data rescue
* Fieldwork
* Information engineering
* Machine learning
* Open data
* Scientific data archiving
* Secondary Data
* Statistics
* Digital data
* Data aggregation

## References

[edit]

1. ^ OECD Glossary of Statistical Terms. OECD. 2008. p. 119\. ISBN 978-92-64-025561.
2. ^ "Statistical Language - What are Data?". Australian Bureau of Statistics. 2013-07-13. Archived from the original on 2019-04-19. Retrieved 2020-03-09.
3. ^ "Data vs Information - Difference and Comparison | Diffen". www.diffen.com. Retrieved 2018-12-11.
4. ^ Toonders, Joris (July 23, 2014). "Data Is the New Oil of the Digital Economy". Wired. Archived from the original on Jun 27, 2024.
5. ^ "Data is the new oil". Spotless Data. Archived from the original on 2018-07-16.
6. ^ a b "data | Origin and meaning of data". Online Etymology Dictionary.
7. ^ American Psychological Association (2020). "6.11". Publication Manual of the American Psychological Association: the official guide to APA style. American Psychological Association. ISBN 9781433832161.
8. ^ "Joint Publication 2-0, Joint Intelligence" (PDF). Joint Chiefs of Staff, Joint Doctrine Publications. Department of Defense. 23 October 2013. pp. I-1. Archived from the original (PDF) on 18 July 2018. Retrieved July 17, 2018.
9. ^ Akash Mitra (2011). "Classifying data for successful modeling". Archived from the original on 2017-11-07. Retrieved 2017-11-05.
10. ^ Tuomi, Ilkka (2000). "Data is more than knowledge". Journal of Management Information Systems. 6 (3): 103–117\. doi:10.1080/07421222.1999.11518258.
11. ^ P. Beynon-Davies (2002). Information Systems: An introduction to informatics in organisations. Basingstoke, UK: Palgrave Macmillan. ISBN 0-333-96390-3.
12. ^ P. Beynon-Davies (2009). Business information systems. Basingstoke, UK: Palgrave. ISBN 978-0-230-20368-6.
13. ^ Sharon Daniel. The Database: An Aesthetics of Dignity.
14. ^ Liu, Stephanie (2020-07-30). "Straight From The Source: Collecting Zero-Party Data From Customers". Forrester. Retrieved 2025-01-14.
15. ^ Greenstein, Danielle (2019-08-19). "What is First-Party vs Third-Party Data: Definitions & Strategies". Lotame. Retrieved 2025-01-14.
16. ^ Studio, AdExchanger Content (2025-01-02). "The Dawn Of First-Party Data: Navigating The New Advertising Landscape". AdExchanger. Retrieved 2025-01-14.
17. ^ a b Bridgwater, Adrian. "Third-Party Data Is Now First-Class". Forbes. Retrieved 2025-01-14.
18. ^ a b c Fallows, Carley (2025-01-13). "Which Data Source Can You Trust for Better Marketing ROI?". Littlegate Publishing. Archived from the original on 2025-03-05. Retrieved 2025-01-14.
19. ^ Greenstein, Danielle (2024-03-15). "What is Second Party Data and How Can you Use it?". Lotame. Retrieved 2025-01-14.
20. ^ Mesly, Olivier (2015), Creating Models in Psychological Research, Springer Psychology : 126 pages. ISBN 978-3-319-15752-8
21. ^ Vines, Timothy H.; Albert, Arianne Y. K.; Andrew, Rose L.; Débarre, Florence; Bock, Dan G.; Franklin, Michelle T.; Gilbert, Kimberly J.; Moore, Jean-Sébastien; Renaut, Sébastien; Rennison, Diana J. (2014-01-06). "The availability of research data declines rapidly with article age". Current Biology. 24 (1): 94–97\. arXiv:1312.5670. Bibcode:2014CBio...24...94V. doi:10.1016/j.cub.2013.11.014. ISSN 1879-0445. PMID 24361065. S2CID 7799662.
22. ^ Roche, Dominique G.; Kruuk, Loeske E. B.; Lanfear, Robert; Binning, Sandra A. (2015). "Public Data Archiving in Ecology and Evolution: How Well Are We Doing?". PLOS Biology. 13 (11): e1002295. doi:10.1371/journal.pbio.1002295. ISSN 1545-7885. PMC 4640582. PMID 26556502.
23. ^ Eisenstein, Michael (April 2022). "In pursuit of data immortality". Nature. 604 (7904): 207–208\. Bibcode:2022Natur.604..207E. doi:10.1038/d41586-022-00929-3. ISSN 1476-4687. PMID 35379989. S2CID 247954952.
24. ^ 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.
25. ^ Johanna Drucker (2011). "Humanities Approaches to Graphical Display". Digital Humanities Quarterly. 005 (1).

## External links

[edit]

Look up data in Wiktionary, the free dictionary.

Wikimedia Commons has media related to Data.

Scholia has a topic profile for Data.

* Data is a singular noun (a detailed assessment)

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Data

* Acquisition
* Augmentation
* Analysis
* Anonymization
* Archaeology
* Big
* Cleansing
* Collection
* Compression
* Corruption
* Curation
* Deduplication
* Degradation
* De-identification
* Ecosystem
* Editing
* Engineering
* Erasure
* ETL/ELT
* Extract
* Transform
* Load
* Ethics
* Exhaust
* Exploration
* Farming
* Format management
* Fusion
* Governance
* Cooperatives
* Infrastructure
* Integration
* Integrity
* Library
* Lineage
* Loss
* Management
* Meta
* Migration
* Mining
* Philanthropy
* Pre-processing
* Preservation
* Processing
* Protection (privacy)
* Publishing
* Open data
* Recovery
* Reduction
* Redundancy
* Re-identification
* Remanence
* Rescue
* Retention
* Quality
* Science
* Scraping
* Scrubbing
* Security
* Sharing
* Stewardship
* Storage
* Structure
* Synchronization
* Topological data analysis
* Type
* Validation
* Warehouse
* Wrangling/munging

* v
* t
* e

Statistics

* Outline
* Index

Descriptive statistics

| Continuous data|

| Center|

* Mean
* Arithmetic
* Arithmetic-Geometric
* Contraharmonic
* Cubic
* Generalized/power
* Geometric
* Harmonic
* Heronian
* Heinz
* Lehmer
* Median
* Mode

|

Dispersion|

* Average absolute deviation
* Coefficient of variation
* Interquartile range
* Percentile
* Range
* Standard deviation
* Variance

Shape|

* Central limit theorem
* Moments
* Kurtosis
* L-moments
* Skewness

Count data|

* Index of dispersion

Summary tables|

* Contingency table
* Frequency distribution
* Grouped data

Dependence|

* Partial correlation
* Pearson product-moment correlation
* Rank correlation
* Kendall's τ
* Spearman's ρ
* Scatter plot

Graphics|

* Bar chart
* Biplot
* Box plot
* Control chart
* Correlogram
* Fan chart
* Forest plot
* Histogram
* Pie chart
* Q–Q plot
* Radar chart
* Run chart
* Scatter plot
* Stem-and-leaf display
* Violin plot

Data collection

| Study design|

* Effect size
* Missing data
* Optimal design
* Population
* Replication
* Sample size determination
* Statistic
* Statistical power

|

Survey methodology|

* Sampling
* Cluster
* Stratified
* Opinion poll
* Questionnaire
* Standard error

Controlled experiments|

* Blocking
* Factorial experiment
* Interaction
* Random assignment
* Randomized controlled trial
* Randomized experiment
* Scientific control

Adaptive designs|

* Adaptive clinical trial
* Stochastic approximation
* Up-and-down designs

Observational studies|

* Cohort study
* Cross-sectional study
* Natural experiment
* Quasi-experiment

Statistical inference

| Statistical theory|

* Population
* Statistic
* Probability distribution
* Sampling distribution
* Order statistic
* Empirical distribution
* Density estimation
* Statistical model
* Model specification
* Lp space
* Parameter
* location
* scale
* shape
* Parametric family
* Likelihood (monotone)
* Location–scale family
* Exponential family
* Completeness
* Sufficiency
* Statistical functional
* Bootstrap
* U
* V
* Optimal decision
* loss function
* Efficiency
* Statistical distance
* divergence
* Asymptotics
* Robustness

|

Frequentist inference|

| Point estimation|

* Estimating equations
* Maximum likelihood
* Method of moments
* M-estimator
* Minimum distance
* Unbiased estimators
* Mean-unbiased minimum-variance
* Rao–Blackwellization
* Lehmann–Scheffé theorem
* Median unbiased
* Plug-in

|

Interval estimation|

* Confidence interval
* Pivot
* Likelihood interval
* Prediction interval
* Tolerance interval
* Resampling
* Bootstrap
* Jackknife

Testing hypotheses|

* 1- & 2-tails
* Power
* Uniformly most powerful test
* Permutation test
* Randomization test
* Multiple comparisons

Parametric tests|

* Likelihood-ratio
* Score/Lagrange multiplier
* Wald

Specific tests|

* Z-test (normal)
* Student's t-test
* F-test

Goodness of fit|

* Chi-squared
* G-test
* Kolmogorov–Smirnov
* Anderson–Darling
* Lilliefors
* Jarque–Bera
* Normality (Shapiro–Wilk)
* Likelihood-ratio test
* Model selection
* Cross validation
* AIC
* BIC

Rank statistics|

* Sign
* Sample median
* Signed rank (Wilcoxon)
* Hodges–Lehmann estimator
* Rank sum (Mann–Whitney)
* Nonparametric anova
* 1-way (Kruskal–Wallis)
* 2-way (Friedman)
* Ordered alternative (Jonckheere–Terpstra)
* Van der Waerden test

Bayesian inference|

* Bayesian probability
* prior
* posterior
* Credible interval
* Bayes factor
* Bayesian estimator
* Maximum posterior estimator

* Correlation
* Regression analysis

| Correlation|

* Pearson product-moment
* Partial correlation
* Confounding variable
* Coefficient of determination

|

Regression analysis|

* Errors and residuals
* Regression validation
* Mixed effects models
* Simultaneous equations models
* Multivariate adaptive regression splines (MARS)

Linear regression|

* Simple linear regression
* Ordinary least squares
* General linear model
* Bayesian regression

Non-standard predictors|

* Nonlinear regression
* Nonparametric
* Semiparametric
* Isotonic
* Robust
* Homoscedasticity and Heteroscedasticity

Generalized linear model|

* Exponential families
* Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance|

* Analysis of variance (ANOVA, anova)
* Analysis of covariance
* Multivariate ANOVA
* Degrees of freedom

Categorical / multivariate / time-series / survival analysis

| Categorical|

* Cohen's kappa
* Contingency table
* Graphical model
* Log-linear model
* McNemar's test
* Cochran–Mantel–Haenszel statistics

|

Multivariate|

* Regression
* Manova
* Principal components
* Canonical correlation
* Discriminant analysis
* Cluster analysis
* Classification
* Structural equation model
* Factor analysis
* Multivariate distributions
* Elliptical distributions
* Normal

Time-series|

| General|

* Decomposition
* Trend
* Stationarity
* Seasonal adjustment
* Exponential smoothing
* Cointegration
* Structural break
* Granger causality

|

Specific tests|

* Dickey–Fuller
* Johansen
* Q-statistic (Ljung–Box)
* Durbin–Watson
* Breusch–Godfrey

Time domain|

* Autocorrelation (ACF)
* partial (PACF)
* Cross-correlation (XCF)
* ARMA model
* ARIMA model (Box–Jenkins)
* Autoregressive conditional heteroskedasticity (ARCH)
* Vector autoregression (VAR)

Frequency domain|

* Spectral density estimation
* Fourier analysis
* Least-squares spectral analysis
* Wavelet
* Whittle likelihood

Survival|

| Survival function|

* Kaplan–Meier estimator (product limit)
* Proportional hazards models
* Accelerated failure time (AFT) model
* First hitting time

|

Hazard function|

* Nelson–Aalen estimator

Test|

* Log-rank test

Applications

| Biostatistics|

* Bioinformatics
* 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
* Jurimetrics
* National accounts
* Official statistics
* Population statistics
* Psychometrics

Spatial statistics|

* Cartography
* Environmental statistics
* Geographic information system
* Geostatistics
* Kriging

* Category
* Mathematics portal
* Commons
* WikiProject

Authority control databases

National|

* Germany
* Czech Republic

Artists|

* MusicBrainz

Other|

* Yale LUX

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