Literature DB >> 26678260

Correction: Quantifying and Mapping Global Data Poverty.

Mathias Leidig, Richard M Teeuw.   

Abstract

Year:  2015        PMID: 26678260      PMCID: PMC4682997          DOI: 10.1371/journal.pone.0145591

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


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Table 3

Example scores of the Data Poverty Index (DPI) and relationships to the World Bank income classification.

Top ScoresBottom Scores
Country Score (max. 5) Country Score (max. 5)
1. Iceland0.17.. 83. China* 2.05
2. Norway0.36.. 109. Indonesia2.75
3. Finland0.39.. 114. Nigeria2.88
4. Estonia0.51.. 129. India3.16
5. Denmark0.52.. 142. Benin3.49
.. 8. U.S.A0.55148. Congo, Dem.3.67
.. 17. United Kingdom0.71149. Malawi3.72
.. 21. Germany0.76150. Yemen3.78
.. 23. Japan0.77151. Myanmar3.95
.. 39. Russia1.04152. Burkina Faso4.04
2014 World Bank income classification Data Poverty Index Range
Low-income countries4.04–2.62
Lower-middle income countries3.78–1.41
Upper-middle income countries3.32–0.97
High-income countries1.53–0.17

Scores: < 1.21, low data poverty; 1.21–2.42, below average data poverty; 2.42–3.62, above average data poverty; > 3.62, high data poverty. Remark: Only countries with a complete dataset have been considered.

* China Mainland, excluding Macao and Hong Kong.

Scores: < 1.21, low data poverty; 1.21–2.42, below average data poverty; 2.42–3.62, above average data poverty; > 3.62, high data poverty. Remark: Only countries with a complete dataset have been considered. * China Mainland, excluding Macao and Hong Kong.
  1 in total

1.  Quantifying and Mapping Global Data Poverty.

Authors:  Mathias Leidig; Richard M Teeuw
Journal:  PLoS One       Date:  2015-11-11       Impact factor: 3.240

  1 in total

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