This page contains a list of all tags we use in the repository.
Both data sets and tasks may have a list of tags associated to them. Follow the link to an individual tag to see all entries that have that tag.
|artificial||This tag is for data sets that have been created artificially (synthetic data), or for data generator programs.|
|bibliographic||This tag is for data sets that contain bibliographic records (about publications, authors, venues, and so on).|
|census||This tag is for data sets that contain records from censuses.|
|confidential||This tag is for data sets that are confidential and need to be requested from the provider of the data set.|
|consumer||This tag is for data sets that contain records about consumer products, such as for example from online shopping Web sites.|
|external||This tag is for data sets that are not hosted on dlrep but are in an external repository.|
|generator||This tag is for 'data sets' that are downloadable programs, online demos, or Web sites, that allow the generation of artificial or synthetc data rather than being a materialised data set.|
|health||This tag is for data sets that originate in the health or biomedical domain.|
|historical||This tag is for historical data sets, such as from registries (birth, marriage and death) or censuses.|
|local||This tag is for data sets that are hosted locally on dlrep.|
|personal||This tag is for data sets that contain personal information about people, such as names, addresses, dates of birth, telephone numbers, etc.|
|public||This tag is for data sets that are publicly available and either hosted locally on dlrep or in an external repository.|
|similarity||This tag is for data sets that only contain similarity or comparson patterns (such as of compared record pair), but not the actual attribute (field) values of records. These similarities are also known as weight vectors, where there is one vector per compared record pair. The content of such vectors can be binary (agreement or disagreement on an attribute value) or numerical (approximate similarities between attribute values).|