Dontopedia

Generate Test Dataset

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Generate Test Dataset has 10 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

10 facts·6 predicates·2 sources·2 in dispute

Mostly:introduces(3), corrupts column(2), purpose(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (6)

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containsSectionContains Section(1)

describesDescribes(1)

generatedByGenerated by(1)

hasStepHas Step(1)

includesIncludes(1)

purposePurpose(1)

Other facts (9)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

9 facts
PredicateValueRef
IntroducesUnknown Names[2]
IntroducesNan Values[2]
IntroducesNan Ages[2]
Corrupts ColumnName Column[2]
Corrupts ColumnAge Column[2]
PurposePerformance Evaluation[1]
Rdf:typeProcess[2]
ProducesTest Dataset[2]
Replaces Values WithUnknown String[2]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

purposebeam/b00c301c-c592-4cd6-ad07-b1de426fb5c4
ex:performance-evaluation
typebeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:Process
labelbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
Generate Test Dataset
producesbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:test-dataset
introducesbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:unknown-names
introducesbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:nan-values
replacesValuesWithbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:unknown-string
introducesbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:nan-ages
corruptsColumnbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:name-column
corruptsColumnbeam/abbe86bc-57a3-4347-aab0-645abb0507b7
ex:age-column

References (2)

2 references
  1. ctx:claims/beam/b00c301c-c592-4cd6-ad07-b1de426fb5c4
    • full textbeam-chunk
      text/plain970 Bdoc:beam/b00c301c-c592-4cd6-ad07-b1de426fb5c4
      Show excerpt
      This approach should help you identify the most common date formats in your dataset and pinpoint the root cause of the inconsistencies. [Turn 4500] User: I want to evaluate the performance of OpenRefine in cleaning metadata. Can you help m
  2. ctx:claims/beam/abbe86bc-57a3-4347-aab0-645abb0507b7
    • full textbeam-chunk
      text/plain1 KBdoc:beam/abbe86bc-57a3-4347-aab0-645abb0507b7
      Show excerpt
      # Define a function to compare the two datasets def compare_cleaning(openrefine, manual): # Calculate the number of matching entries matches = 0 for index, row in openrefine.iterrows(): if row.equals(manual.loc[index]):

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