SDG Metadata/7-1-1 — French @ Hosted Weblate

Translation status

32	Strings	18%	Browse Translate
2,521	Words	1%
18,504	Characters	1%

Strings status

Strings Words Characters
32 2,521 18,504	All strings	Browse Translate Zen
6 42 328	Translated strings	Browse Translate Zen
6 42 328	Strings waiting for review	Browse Translate Zen
26 2,479 18,176	Unfinished strings	Browse Translate Zen
7 419 2,872	Untranslated strings	Browse Translate Zen
19 2,060 15,304	Strings marked for edit	Browse Translate Zen
26 2,479 18,176	Unfinished strings without suggestions	Browse Translate Zen
22 2,262 16,671	Strings with any failing checks	Browse Translate Zen
1 2 18	Translated strings with any failing checks	Browse Translate Zen
4 203 1,377	Failing check: Inconsistent	Browse Translate Zen
18 2,059 15,294	Failing check: Mismatching line breaks	Browse Translate Zen
18 2,059 15,294	Failing check: XML markup	Browse Translate Zen

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	brockfanning
Translation license	MIT License
Translation process	Translations can be made directly. Translation suggestions are turned off. Translations are reviewed by dedicated reviewers. 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 #553 from weblate/weblate-sdg-metadata-1-1-1a `d3a3ca3e2bb` brockfanning authored a month ago
Last commit in Weblate	Merge pull request #553 from weblate/weblate-sdg-metadata-1-1-1a `d3a3ca3e2bb` brockfanning authored a month ago
Weblate repository	`https://hosted.weblate.org/git/sdg-metadata/1-1-1a/`
File mask	`translations-metadata/*/7-1-1.yml`
Monolingual base language file	`translations-metadata/en/7-1-1.yml`
Translation file	Download `translations-metadata/fr/7-1-1.yml`
Last change	Aug. 19, 2023, 4:52 p.m.
Last change made by	None
Language	French
Language code	fr
Text direction	Left to right
Number of speakers	295,510,923
Number of plurals	3
Plural type	One/many/other
Plurals	One	0, 1
	Many	1000000, 2000000
	Other	2, 3, 4, 5, 6, 7, 8, 9, 10, 11, …
Plural formula	`(n == 0 \|\| n == 1) ? 0 : ((n != 0 && n % 1000000 == 0) ? 1 : 2)`

10 hours ago

String statistics

	Strings percent	Hosted strings	Words percent	Hosted words	Characters percent	Hosted characters
Total		32		2,521		18,504
Approved	0%	0	0%	0	0%	0
Waiting for review	18%	6	1%	42	1%	328
Translated	18%	6	1%	42	1%	328
Needs editing	59%	19	81%	2,060	82%	15,304
Read-only	0%	0	0%	0	0%	0
Failing checks	68%	22	89%	2,262	90%	16,671
Strings with suggestions	0%	0	0%	0	0%	0
Untranslated strings	21%	7	16%	419	15%	2,872

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2,521

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18%

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None Resource updated SDG Metadata 7-1-1 French	The “`translations-metadata/en/7-1-1.yml`” file was changed. a year ago
None String updated in the repository SDG Metadata 7-1-1 French	English <p><strong>Data availability:</strong></p> <p>Data have been collected from 1990 through the latest year on an annual basis. </p> <p><strong>Time series:</strong></p> <p>Data for countries have been compiled from 1990 to the latest year, but there are gaps in accurate data availability.</p> <p><strong>Disaggregation:</strong></p> <p>Electricity access rates are disaggregated by geographic location into total, urban and rural rates. Countries that are classified as “High Income” are assumed to reach universal access from the first year it was added to the category. Disaggregation of access to electricity by rural or urban place of residence is available at country, regional and global levels.</p> French 0 characters edited Current translation Needs editing <h1> Disponibilité des données </h1> <h2> Description :</h2> <p> Des données sont actuellement collectées pour 140 pays de 1990 à 2018, à l'exclusion des «pays à revenu élevé» ou «des pays développés» selon la classification des Nations Unies. </p> <h2> Séries chronologiques :</h2> <p> Les données pour les pays ont été compilées pour la période 1990-2018, bien qu'il y ait des lacunes dans la disponibilité des données exactes. </p> <h2> <strong>Désagrégation </strong> : </h2> <p> Les tarifs d'accès à l'électricité sont ventilés par emplacement géographique en tarifs totaux, urbains et ruraux. Les pays classés comme «développés» ou «à revenu élevé» sont supposés avoir 100 pour cent dès la première année où ils ont été ajoutés à la catégorie. La ventilation de l'accès à l'électricité par lieu de résidence rural ou urbain est possible pour tous les pays. </p> a year ago
None Source string changed SDG Metadata 7-1-1 French	English 103 characters edited <p><strong>Data availability:</strong></p> <p>Data have been collected from 1990 tohrough the latest year~~, excluding “Developed” countries classified by the United Nation~~ on an annual basis. </p> <p><strong>Time series:</strong></p> <p>Data for countries have been compiled from 1990 to the latest year, but there are gaps in accurate data availability.</p> <p><strong>Disaggregation:</strong></p> <p>Electricity access rates are disaggregated by geographic location into total, urban and rural rates. Countries that are classified as ~~“Developed” or~~ “High Income” are assumed to reach universal access from the first year it was added to the category. Disaggregation of access to electricity by rural or urban place of residence is available at country, regional and global levels.</p> a year ago
None String updated in the repository SDG Metadata 7-1-1 French	English <p>Not applicable</p> French 0 characters edited Current translation Empty a year ago
None String updated in the repository SDG Metadata 7-1-1 French	English <p>To estimate values, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701999/pdf/ehp.1205987.pdf">a multilevel nonparametric modelling approach</a>—developed by the World Health Organization to estimate clean fuel usage—was adapted to predict electricity access and used to fill in the missing data points for the time period from 1990 onwards. Where data is available, access estimates are weighted by population. Multilevel nonparametric modelling considers the hierarchical structure of data (country and regional levels), using the regional classification of the United Nations. </p> <p>The model is applied for all countries with at least one data point. In order to use as much real data as possible, results based on real survey data are reported in their original form for all years available. The statistical model is used to fill in data only for years where they are missing and to conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The difference between real data points and estimated values is clearly identified in the database. </p> <p>Countries classified as “High Income” based on the World Bank Country and Lending Groups are assumed to reach universal access from the first year the country joined the category. </p> <p>In the present report, to avoid having electrification trends from 1990 to 2010 overshadow electrification efforts since 2010, the model was run twice: </p> <ul> <li>With survey data and assumptions from 1990 to the latest year for model estimates from 1990 to the latest year</li> <li>With survey data and assumptions from 2010 to the latest year for model estimates from 2010 to the latest year </li> </ul> <p>Given the low frequency and the regional distribution of some surveys, several countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modelling approach was adopted to fill in the missing data points. This modelling approach allowed the estimation of electrification rates for 219 countries over the time periods. The SE4ALL Global Tracking Framework Report (2013) referenced below provides more details on the suggested methodology for tracking access to energy (Chapter 2, Section 1, page 82-87).</p> French 0 characters edited Current translation Needs editing <h1> Méthodologie </h1> <h2> Méthode de calcul :</h2> <p> La base de données mondiale sur l'électrification de la Banque mondiale compile des données d'enquêtes nationales représentatives auprès des ménages ainsi que des données de recensement de 1990 à 2018. Elle intègre également des données de la base de données socio-économique pour l'Amérique latine et les Caraïbes, le Moyen-Orient et Base de données sur la pauvreté en Afrique du Nord et base de données sur la pauvreté en Europe et en Asie centrale, toutes basées sur des enquêtes similaires. Au moment de cette analyse, la base de données mondiale sur l'électrification contenait 1 215 enquêtes provenant de 140 pays, à l'exclusion des enquêtes des pays à revenu élevé selon la classification des Nations Unies. </p> <p> Pour estimer les valeurs, une approche de modélisation non paramétrique à plusieurs niveaux (développée par l'Organisation mondiale de la santé pour estimer la consommation de carburant propre) a été adaptée pour prédire l'accès à l'électricité et utilisée pour remplir les points de données manquants. pour la période comprise entre 1990 et 2018. Lorsque les données sont disponibles, les estimations d'accès sont pondérées en fonction de la population. La modélisation non paramétrique à plusieurs niveaux considère la structure hiérarchique des données (niveaux national et régional), en utilisant la classification régionale des Nations Unies. </p> <p> Le modèle est appliqué à tous les pays avec au moins un point de données. Afin d'utiliser autant de données réelles que possible, les résultats basés sur des données d'enquête réelles sont rapportés dans leur forme originale pour toutes les années disponibles. Le modèle statistique est utilisé pour remplir des données uniquement pour les années où elles sont manquantes et pour mener des analyses mondiales et régionales. En l'absence de données d'enquête pour une année donnée, des informations sur les tendances régionales ont été empruntées. La différence entre les points de données réels et les valeurs estimées est clairement identifiée dans la base de données.</p> <p> Les pays considérés «développés » par les Nations Unies et classés comme «à revenu élevé» sont supposés avoir des taux d'électrification de 100 pour cent dès la première année où le pays a rejoint la catégorie.</p> <p> Dans le présent rapport, pour éviter que les tendances de l'électrification de 1990 à 2010 éclipsent les efforts d'électrification depuis 2010, le modèle a été utilisé deux fois :</p> <ul> <li> Avec données d'enquête + hypothèses de 1990-2018 pour les estimations modèles de 1990-2018 </li> <li> Avec données d'enquête + hypothèses de 2010-2018 pour les estimations modèles de 2010-2018 </li> </ul> <p> Compte tenu de la faible fréquence et de la distribution régionale de certaines enquêtes, plusieurs pays présentent des lacunes dans les données disponibles. Pour développer l'évolution historique et le point de départ des taux d'électrification, une approche de modélisation simple a été adoptée pour compléter les points de données manquants. Cette approche de modélisation a permis d'estimer les taux d'électrification pour 212 pays sur ces périodes. Le rapport sur le cadre mondial de suivi SE4ALL (2013) référencé ci-dessous fournit plus de détails sur la méthodologie suggérée pour suivre l'accès à l'énergie (chapitre 2, section 1, pages 82-87). </p> a year ago
None Source string changed SDG Metadata 7-1-1 French	English 63 characters edited <p>To estimate values, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701999/pdf/ehp.1205987.pdf">a multilevel nonparametric modelling approach</a>—developed by the World Health Organization to estimate clean fuel usage—was adapted to predict electricity access and used to fill in the missing data points for the time period from 1990 onwards. Where data is available, access estimates are weighted by population. Multilevel nonparametric modelling considers the hierarchical structure of data (country and regional levels), using the regional classification of the United Nations. </p> <p>The model is applied for all countries with at least one data point. In order to use as much real data as possible, results based on real survey data are reported in their original form for all years available. The statistical model is used to fill in data only for years where they are missing and to conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The difference between real data points and estimated values is clearly identified in the database. </p> <p>Countries ~~considered “Developed” by the United Nations and~~ classified as “High Income” based on the World Bank Country and Lending Groups are assumed to reach universal access from the first year the country joined the category. </p> <p>In the present report, to avoid having electrification trends from 1990 to 2010 overshadow electrification efforts since 2010, the model was run twice: </p> <ul> <li>With survey data and assumptions from 1990 to the latest year for model estimates from 1990 to the latest year</li> <li>With survey data and assumptions from 2010 to the latest year for model estimates from 2010 to the latest year </li> </ul> <p>Given the low frequency and the regional distribution of some surveys, several countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modelling approach was adopted to fill in the missing data points. This modelling approach allowed the estimation of electrification rates for 219 countries over the time periods. The SE4ALL Global Tracking Framework Report (2013) referenced below provides more details on the suggested methodology for tracking access to energy (Chapter 2, Section 1, page 82-87).</p> a year ago
None String updated in the repository SDG Metadata 7-1-1 French	English <p>Not applicable</p> French 0 characters edited Current translation Translated <p>Sans objet</p> a year ago
None String updated in the repository SDG Metadata 7-1-1 French	English 2023-03-31 French 0 characters edited Current translation Needs editing <p>2022-03-31</p> a year ago
None Source string changed SDG Metadata 7-1-1 French	English 1 character edited 20223-03-31 a year ago
deblsyl Translation changed SDG Metadata 7-1-1 French	English <p>Not applicable</p> French 55 characters edited Current translation Translated <h1> Indicateurs connexes en date de février 2020</h1> <p> ODD7p>Sans objet</p> 2 years ago

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26	Unfinished strings, converted files enriched with comments; suitable for offline translation	Android String Resource	CSV	JSON	JSON nested structure file	gettext PO	iOS strings	TBX	TMX	XLIFF 1.1 with gettext extensions	XLIFF 1.1	XLSX

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