Language Approved Translated Unfinished Unfinished words Unfinished characters Untranslated Checks Suggestions Comments
English MIT 0% 0 0 0 0 26 0 0
Arabic MIT 0% 0% 32 4,020 30,263 32 6 0 0
French MIT 0% 50% 16 1,946 15,932 0 17 0 0
Portuguese MIT 0% 0% 32 4,020 30,263 32 0 0 0
Russian MIT 0% 12% 28 3,862 29,161 11 21 0 0
Spanish MIT 0% 12% 28 3,862 29,161 11 20 0 0
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Overview

Project website github.com/worldbank/sdg-metadata
Instructions for translators

This project is limited to Russian translation only, for now.

More detailed instructions to come.
Project maintainers User avatar brockfanning
Translation license MIT License
Translation process
  • Translations can be made directly.
  • Translation suggestions are turned off.
  • Any authenticated user can contribute.
  • The translation uses monolingual files.
  • The translation base language can not be edited.
Source code repository https://github.com/worldbank/sdg-metadata
Repository branch master
Last remote commit Merge pull request #543 from worldbank/harvest-20230803 1bee05e3537
User avatar brockfanning authored a month ago
Last commit in Weblate Translated using Weblate (French) feda7753c00
User avatar deblsyl authored a year ago
Weblate repository https://hosted.weblate.org/git/sdg-metadata/1-1-1a/
File mask translations-metadata/*/2-2-1.yml
Monolingual base language file translations-metadata/en/2-2-1.yml

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 192 24,120 181,578
Source 32 4,020 30,263
Approved 0% 0 0% 0 0% 0
Waiting for review 12% 24 9% 2,390 9% 16,535
Translated 29% 56 26% 6,410 25% 46,798
Needs editing 26% 50 24% 5,914 26% 48,300
Read-only 16% 32 16% 4,020 16% 30,263
Failing checks 46% 90 51% 12,458 52% 95,223
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 44% 86 48% 11,796 47% 86,480
Last 12 months Previous

Activity in last 12 months

104 changes 0 changes 14 changes 0 changes 5 changes 0 changes 4 changes 0 changes 32 changes 0 changes 104 changes 0 changes 13 changes 0 changes 9 changes 0 changes 11 changes 1 change 0 changes 0 changes 0 changes 0 changes 1 change 221 changes
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Quick numbers

24,120
Hosted words
192
Hosted strings
29%
Translated
0
Contributors
and previous 30 days

Trends of last 30 days

+67%
Hosted words
+100%
+34%
Hosted strings
+100%
−5%
Translated
+34%
—
Contributors
—
User avatar None

New strings to translate

SDG Metadata / 2-2-1 — Russian

24 new strings to translate appeared to the translation. a month ago
User avatar None

New strings to translate

SDG Metadata / 2-2-1 — Portuguese

27 new strings to translate appeared to the translation. a month ago
User avatar None

New strings to translate

SDG Metadata / 2-2-1 — French

16 new strings to translate appeared to the translation. a month ago
User avatar None

New strings to translate

SDG Metadata / 2-2-1 — Spanish

24 new strings to translate appeared to the translation. a month ago
User avatar None

New strings to translate

SDG Metadata / 2-2-1 — Arabic

27 new strings to translate appeared to the translation. a month ago
User avatar None

Resource update

SDG Metadata / 2-2-1 — Russian

The "translations-metadata/en/2-2-1.yml" file was changed. a month ago
User avatar None

String updated in the repository

SDG Metadata / 2-2-1 — Russian

<p><strong>URL:</strong></p>
<p>data.unicef.org/nutrition/malnutrition.html;</p>
<p>https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;</p>
<p><strong>References:</strong></p>
<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/15542535/">https://pubmed.ncbi.nlm.nih.gov/15542535/</a>&gt;</p>
<p>de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health pro&#xAD;grammes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 &lt;<a href="https://www.who.int/childgrowth/publications/Comparison_implications.pdf">https://www.who.int/childgrowth/publications/Comparison_implications.pdf</a>&gt;</p>
<p>McLain A, Frongillo E, Feng J, Borghi E (2018). Prediction intervals for penalized longitudinal models with multi-source summary measures: an application to childhood malnutrition. Stat Med; 38(6):1002-1012; doi: 10.1002/sim.8024. Epub 2018 Nov 14. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">https://pubmed.ncbi.nlm.nih.gov/30430613/</a>&gt;</p>
<p>United Nations Children&#x2019;s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank (2019). Meeting report on Technical Consultation on a Country-level model for SDG2.2. December 2019.</p>
<p>UNICEF-WHO-World Bank (2020). Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight &lt;<a href="https://data.unicef.org/resources/jme-2021-country-consultations/">https://data.unicef.org/resources/jme-2021-country-consultations/</a>&gt;</p>
<p>WHO (2006). WHO Multicentre Growth Reference Study (MGRS) &lt;<a href="https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study">https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study</a>&gt;</p>
<p>World Health Organization and United Nations Children&#x2019;s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: </p>
<p>World Health Organization and the United Nations Children&#x2019;s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. &lt;<a href="https://www.who.int/nutrition/publications/anthropometry-data-quality-report">https://www.who.int/nutrition/publications/anthropometry-data-quality-report</a>&gt;</p>
<p>WHO. WHO Anthro Survey Analyser (2019). Available at <a href="https://www.who.int/tools/child-growth-standards/software">https://www.who.int/tools/child-growth-standards/software</a>. </p>
<p>Yang H and de Onis M (2008). <a href="http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a>. BMC Pediatrics 2008, 8:19 (05 May 2008) &lt;<a href="http://www.biomedcentral.com/1471-2431/8/19">http://www.biomedcentral.com/1471-2431/8/19</a>&gt;.</p>
<h1> Ссылки </h1>
<h2> URL: </h2>
<p> <a href="http://data.unicef.org/nutrition/malnutrition.html"> http://data.unicef.org/nutrition/malnutrition.html </a> </p>
<p> <a href="http://www.who.int/nutgrowthdb/estimates2014/en/"> http://www.who.int/nutgrowthdb/estimates2014/en/ </a> </p>
<p> <a href="http://datatopics.worldbank.org/child-malnutrition"> http://datatopics.worldbank.org/child-malnutrition </a> </p>
<h2> Ссылки: </h2>
<p> Детский фонд Организации Объединенных Наций, Всемирная организация здравоохранения, Всемирный банк (2012). Совместные оценки детского недоедания ЮНИСЕФ-ВОЗ и Всемирного банка. (ЮНИСЕФ, Нью-Йорк; ВОЗ, Женева; Всемирный банк, Вашингтон, округ Колумбия; 2012 г.). </p>
<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70.</p>
<p>Yang H and de Onis M. <a href="http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a> BMC Pediatrics 2008, 8:19 (05 May 2008) (<a href="http://www.biomedcentral.com/1471-2431/8/19">http://www.biomedcentral.com/1471-2431/8/19</a>).</p>
<p> Всемирная организация здравоохранения (2008 г.). <em> Учебный курс по оценке роста детей </em>. Женева, ВОЗ, 2008 г.</p>
<p> Международный журнал по эпидемиологии 2004г.;33:1260-70</p>
<p> Международный журнал по эпидемиологии 2003г.;32:518-26</p>
a month ago
User avatar None

Source string changed

SDG Metadata / 2-2-1 — Russian

<p><strong>URL:</strong></p>
<p>data.unicef.org/nutrition/malnutrition.html; </p>
<p>https://www.who.int/nutgrowthdb/estimates2014/en/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;</p>
<p><strong>References:</strong></p>
<p>United Nations Children&#x2019;s Fund, World Health Organization, The World Bank (2012). UNICEFWHO-World Bank Joint Child Malnutrition Estimates.de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/15542535/">https://pubmed.ncbi.nlm.nih.gov/15542535/</a>&gt;</p>
<p>de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health pro&#xAD;grammes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 &lt;<a href="https://www.who.int/childgrowth/publications/Comparison_implications.pdf">https://www.who.int/childgrowth/publications/Comparison_implications.pdf</a>&gt;</p>
<p>McLain A, Frongillo E, Feng J, Borghi E (2018). Prediction intervals for penalized longitudinal models with multi-source summary measures: an application to childhood malnutrition. Stat Med; 38(6):1002-1012; doi: 10.1002/sim.8024. Epub 2018 Nov 14. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">https://pubmed.ncbi.nlm.nih.gov/30430613/</a>&gt;</p>
<p>United Nations Children&#x2019;s Fund (UNICEF), New York; WHO, Geneva; The World Bank, Washington, DC; 2012). </p>
<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70.</p>
<p>Yang H and de Onis M. <a href="http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a> <br>BMC Pediatrics 2008, 8:19 (05 May 2008) <br>(http://www.biomedcentral.com/1471-2431/8/19).</p>
<p>World Health Organization (2008). Training Course on Child Growth Assessment. Geneva, WHO, 2008.</p>
<p>International Journal of Epidemiology 2004;33:1260-70</p>
<p>International Journal of Epidemiology 2003;32:518-26</p>
<p>http://www.biomedcentral.com/1471-2431/8/19World Health Organization, International Bank for Reconstruction and Development/The World Bank (2019). Meeting report on Technical Consultation on a Country-level model for SDG2.2. December 2019.</p>
<p>UNICEF-WHO-World Bank (2020). Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight &lt;<a href="https://data.unicef.org/resources/jme-2021-country-consultations/">https://data.unicef.org/resources/jme-2021-country-consultations/</a>&gt;</p>
<p>WHO (2006). WHO Multicentre Growth Reference Study (MGRS) &lt;<a href="https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study">https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study</a>&gt;</p>
<p>World Health Organization and United Nations Children&#x2019;s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: </p>
<p>World Health Organization and the United Nations Children&#x2019;s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. &lt;<a href="https://www.who.int/nutrition/publications/anthropometry-data-quality-report">https://www.who.int/nutrition/publications/anthropometry-data-quality-report</a>&gt;</p>
<p>WHO. WHO Anthro Survey Analyser (2019). Available at <a href="https://www.who.int/tools/child-growth-standards/software">https://www.who.int/tools/child-growth-standards/software</a>. </p>
<p>Yang H and de Onis M (2008). <a href="http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a>. BMC Pediatrics 2008, 8:19 (05 May 2008) &lt;<a href="http://www.biomedcentral.com/1471-2431/8/19">http://www.biomedcentral.com/1471-2431/8/19</a>&gt;.</p>
a month ago
User avatar None

String updated in the repository

SDG Metadata / 2-2-1 — Russian

<p><strong>Sources of discrepancies:</strong></p>
<p>For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in <em>Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old</em> (<a href="https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) to undertake survey analysis and harmonize with the global standard analysis methods. </p>
<p>For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. Discrepancies between results from standardised methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (<a href="http://www.biomedcentral.com/1471-2431/8/19">Yang and de Onis, 2008</a>). In addition, when surveys do not cover the age interval 0-59 month, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.</p>
<p>The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">McLain et al. 2018</a>), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).</p>
<h2> Источники расхождений: </h2>
<p> Стандартный подход к анализу для создания совместного набора данных нацелен на максимальную сопоставимость оценок по странам. Для включения оценок обследования в набор данных публикации "Совместные оценки недоедания" (JME) межучрежденческая группа применяет критерии оценки качества обследования. Если документации недостаточно, данные обследования не включаются до тех пор, пока информация не станет доступной. Когда доступны исходные данные и возникает вопрос о подходе к анализу, повторный анализ данных выполняется по стандартной методологии. Расхождения между результатами стандартизированного подхода и результатами в представленном отчете могут возникать по разным причинам, например, из-за использования разных стандартов для расчета z-оценки, вменения дня рождения в случае его отсутствия, использования округленного возраста в месяцах, использования разных систем отметок для исключения данных. Для обследований, основанных на предыдущих справочных материалах Национального центра медицинской статистики / Всемирной организации здравоохранения (NCHS / ВОЗ) и по которым исходные данные недоступны, применяется метод преобразования z-оценок на основе Стандартов роста детей ВОЗ (Yang and de Onis, 2008). Кроме того, если обследования не охватывают возрастной интервал от 0 до 5 лет или являются репрезентативными только для сельской местности, то выполняется корректировка на основе других обследований для той же страны. Любая корректировка или преобразование ясно и понятно объясняется в аннотированном совместном наборе данных.</p>
a month ago
User avatar None

Source string changed

SDG Metadata / 2-2-1 — Russian

<p><strong>Sources of discrepancies:</strong></p>
<p>The standard analysis approach to construct the joint data set aims for a maximum comparability of country estimates. For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in <em>Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old</em> (<a href="https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) to undertake survey analysis and harmonize with the global standard analysis methods. </p>
<p>For the inclusion of survey estimates into the JME databaset, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. When raw data are available, and there is a question about the analysis approach, data re-analysis is performed following the standard methodology. Discrepancies between results from standardised approachmethodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (<a href="http://www.biomedcentral.com/1471-2431/8/19">Yang and de Onis, 2008</a>). In addition, when surveys do not cover the age interval 0-&lt;5 years59 month, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.</p>
<p>The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">McLain et al. 2018</a>), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).</p>
a month ago
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