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Data

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Data editing

Activity aimed at detecting and correcting errors, logical inconsistencies and suspicious data. Editing techniques refer to a range of procedures and processes used for detecting and handling errors in data, also aiming at avoiding their future repetition. An "edit" is the correction of an error in data. An "editing rule" is the specification of the conditions under which edits are made.

Some examples of types of edits include:

  • Logical edits - ensures that two or more data items do not have contradictory values.
  • Consistency edits - ensures that precise and correct arithmetic relationships exists between two or more data items.
  • Range edits - identifies whether or not a data item value falls inside a determined acceptable range.
  • Variance edits - looks for suspiciously high variances at the output edit stage.

 

Source: SDMX (2009).

Data reference

A metadata concept that defines the reference source for data. 

Data revision

Data revision is defined as any change to a data value. It involves policies and practices for how data should be adjusted and disseminated as well as how to ensure transparency.

Dataset release

A dataset release is a part of a dataset that has a specific reference year and state. The concept of dataset release is applicable to systems that publish data through a predefined release calendar. Dataset release can revise already published data belonging to a previous release and can be in a preliminary or final state. They do not apply to systems where data collection and publication is a continuous process and the data is not revised.

Examples:
FAOSTAT Production Crops - Release 2009

Dataset

Any organised collection of data. Within data.fao.org, a dataset can be understood as a collection of similar data, sharing a structure, which covers a fixed period of time. A data set is any permanently stored collection of information usually containing either case level data, aggregation of case level data, or statistical manipulations of either the case level or aggregated survey data, for multiple survey instances. A logical collection of values or database objects relating to a single subject. Source: SDMX (2009).

Examples:
FAOSTAT Production Crops, GLIMS Livestock Production

Data validation

Process of monitoring the results of data compilation and ensuring the quality of the statistical results. Data validation describes methods and processes for assessing statistical data, and how the results of the assessments are monitored and made available to improve statistical processes. Source: SDMX (2009).

Dimension member

A dimension member is a possible value of a dimension.

Examples:
Dimension: CROPS (contains crop species) Dimension member: WHEAT

Dimension

A dimension is a data element that categorizes each item in a dataset into non-overlapping regions. A dimension provides the means to break data down into smaller parts in order to better understand it.

Characteristics:
  • Dimensions provide structured labeling information to otherwise unordered numeric measures.
  • It is similar to a categorical variable in statistics.
  • Dimension members are the possible values of the dimension.
  • There are three types of dimensions: Conformed dimensions, shared dimensions, and private dimensions
Examples:
Customer, date and product when applied as dimensions to a sales receipt.

Documentation

Documentation includes information about methodologies and quality assessments.

Documents

Textual information including reports, articles, papers, yearbooks, brochures, booklets, and so on.

FAOSTAT

A website that provides time-series and cross sectional data that relates to food and agriculture for about 200 countries.

Frequency of dissemination

Defines the frequency with which data is distributed.

Hierarchy

Classification structure arranged in levels of detail from the broadest to the most detailed level. Each level of the classification is defined in terms of the categories at the next lower level of the classification. Source: SDMX (2009).

Imputation

Procedure for entering a value for a specific data item where the response is missing or unusable. Source: SDMX (2009).

Landing pages

A landing page is a main article that displays information specific to a topic, place, data set, or dimension.

Linked Open Data (LOD)

A term coined by Tim Berners-Lee to describe the semantic web. It refers to a web publishing method that focuses on how data can be linked to other pieces of data, independent of its physical location.

Maps

Spatial representation of data.

Examples:
Regions, boundaries, cities

Measure

The phenomenon or phenomena to be measured in a dataset. Source: SDMX (2009).

Additional information:
A measure is a property on which calculations can be made using pre-computed aggregates. A measure can be shared across many different datasets.
Examples:
Sum, count, average, minimum, maximum

Metadata

Metadata is traditionally defined as "data about data". The need to define more information about data relies on the different purposes that metadata meets. Generally speaking, metadata should help to:

  • Retrieve a resource from a catalog.
  • Explain how the resource has been produced and all the concepts that are referred by it.
  • Describe the quality and limits of the resource.
  • Describe copyrights and terms of use.

 

Due to the complexity of functions, several attempts exist to classify metadata into different types. The first distinction can be made between "structural" metadata and "reference" metadata. Structural metadata refers to concepts used in the identification of a resource. Reference metadata, on the other hand, describes, explains, and qualifies resources more generally. A very popular type of metadata that is emerging within social networks is the social metadata added by users, such as, tags, ratings, votes, comments, and so on. Social metadata can help other users browse data and classify it into categories, find current topics, and help assess data quality.

Metadata model

Within a complex information system in which data passes through several steps of processing before being disseminated, the quantity of metadata produced can be overwhelming, difficult to manage for data owners and hard to interpret for users. On the other hand, a too simplistic approach cannot be sufficient to provide all the information needed by the user. The main solution to handle this issue is to define a proper metadata model with various levels of information organization, to which specific metadata concepts can be attached.

Metadata update

The date on which the metadata element was inserted or modified in the database. Source: SDMX (2009).

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