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|---|---|---|---|---|---|---|---|---|---|---|---|---|
jdc_operational:000047 | 41 | 0 | e- card food voucher beneficiaries list | 1,750 | 1,789 | descriptive | descriptive | administrative | managing the ⟦e- card food voucher beneficiaries list⟧[20] | true | null | |
jdc_operational:000047 | 41 | 1 | e- card food voucher beneficiaries list | 137 | 176 | descriptive | descriptive | administrative | managing the ⟦e- card food voucher beneficiaries list⟧[21] | true | null | |
jdc_operational:000047 | 41 | 1 | household data | 484 | 498 | descriptive | vague | other | processing ⟦household data⟧[22], generating scores and ranks | false | null | |
jdc_operational:000047 | 41 | 1 | national data | 669 | 682 | vague | vague | other | analyzing ⟦national data⟧[23] and reporting findings | false | null | |
jdc_operational:000047 | 42 | 0 | NPTP database | 961 | 974 | named | named | database | based on the ⟦NPTP database⟧[24] and operations | true | null | |
jdc_operational:000047 | 51 | 0 | household surveys | 835 | 852 | descriptive | descriptive | survey | ⟦household surveys⟧[25] (monthly level of effort | true | null | |
jdc_operational:000047 | 5 | 0 | PAD DATA SHEET | 10 | 24 | named | named | other | ⟦PAD DATA SHEET⟧[26] | false | null | |
jdc_operational:000047 | 68 | 2 | 2004 HBS data | 404 | 417 | named | named | survey | using ⟦2004 HBS data⟧[27] to simulate NPTP impact | true | null | |
jdc_operational:000047 | 70 | 0 | Emergency National Poverty Targeting Program Project | 43 | 95 | named | named | other | ⟦Emergency National Poverty Targeting Program Project⟧[28] | false | null | |
jdc_operational:000048 | 10 | 0 | Human Development Index | 1,155 | 1,178 | named | named | indicator | The 2018 United Nations Development Programme (UNDP) ⟦Human Development Index⟧[29] ranks Chad | true | null | |
jdc_operational:000048 | 10 | 0 | 2018 Global Hunger Index | 1,723 | 1,747 | named | named | indicator | The ⟦2018 Global Hunger Index⟧[30] ranks Chad | true | null | |
jdc_operational:000048 | 10 | 1 | 2018 Global Hunger Index | 162 | 186 | named | named | indicator | The ⟦2018 Global Hunger Index⟧[1] ranks Chad second last of 119 countries. | true | null | |
jdc_operational:000048 | 10 | 1 | General Population and Housing Census | 1,538 | 1,575 | named | named | census | ⟦General Population and Housing Census⟧[2] (⟦RGPH 2⟧[3]) of 2009. | true | null | |
jdc_operational:000048 | 10 | 1 | RGPH 2 | 1,577 | 1,583 | named | named | census | ⟦General Population and Housing Census⟧[2] (⟦RGPH 2⟧[3]) of 2009. | true | null | |
jdc_operational:000048 | 15 | 1 | 2016–2017 agricultural season statistics | 1,428 | 1,468 | named | descriptive | report | ⟦2016–2017 agricultural season statistics⟧[4] from the Directorate of Agricultural Production and Statistics, Ministry of Agriculture. | true | null | |
jdc_operational:000048 | 18 | 0 | road traffic accidents data | 1,737 | 1,764 | descriptive | vague | other | an efficient system for collecting and analyzing ⟦road traffic accidents data⟧[5] in Chad | true | null | |
jdc_operational:000048 | 18 | 1 | road traffic accidents data | 154 | 181 | descriptive | vague | other | an efficient system for collecting and analyzing ⟦road traffic accidents data⟧[6] in Chad | true | null | |
jdc_operational:000048 | 19 | 1 | road data | 468 | 477 | descriptive | vague | other | use of smartphone for ⟦road data⟧[7] collection | true | null | |
jdc_operational:000048 | 21 | 0 | Chad Rural Mobility and Connectivity Project | 19 | 63 | named | named | other | The World Bank ⟦Chad Rural Mobility and Connectivity Project⟧[8] (P164747) | false | null | |
jdc_operational:000048 | 31 | 1 | road safety data | 407 | 423 | descriptive | vague | other | training of first responders through NGOs, gendarmes, and police officers in ⟦road safety data⟧[9] collection | true | null | |
jdc_operational:000048 | 32 | 1 | land registry systems | 361 | 382 | descriptive | descriptive | administrative | the lack of reliable ⟦land registry systems⟧[10] | true | null | |
jdc_operational:000048 | 33 | 0 | Gender Inequality Index | 770 | 793 | named | named | indicator | Chad is ranked in the bottom five countries (186/189) of the ⟦Gender Inequality Index⟧[11] | true | null | |
jdc_operational:000048 | 33 | 0 | Global Gender Gap Index | 826 | 849 | named | named | indicator | ranks last in Africa in the ⟦Global Gender Gap Index⟧[12] | true | null | |
jdc_operational:000048 | 33 | 1 | Demographic and health survey | 1,732 | 1,761 | descriptive | descriptive | survey | ⟦Demographic and health survey⟧[13] (DHS 2014–2015), sponsored by the Government and multiple donors. | true | null | |
jdc_operational:000048 | 33 | 1 | Global Gender Gap Report 2017 | 1,834 | 1,863 | named | named | report | according to the 2017 Global Gender Gap report, estimated earned income | true | null | |
jdc_operational:000048 | 36 | 2 | Gender, Institutions and Development Database 2015 | 459 | 509 | named | named | database | Source: OECD, ⟦Gender, Institutions and Development Database 2015⟧[15] (⟦GID-DB⟧[16]) | true | null | |
jdc_operational:000048 | 36 | 2 | GID-DB | 511 | 517 | named | named | database | Source: OECD, ⟦Gender, Institutions and Development Database 2015⟧[15] (⟦GID-DB⟧[16]) | true | null | |
jdc_operational:000048 | 39 | 1 | Census 2009 | 207 | 218 | named | named | census | Annual population will be calculated from the data of ⟦Census 2009⟧[17] | true | null | |
jdc_operational:000048 | 44 | 0 | inventory of rural roads | 1,230 | 1,254 | descriptive | descriptive | other | must be established the ⟦inventory of rural roads⟧[18] (about 15,000 km) by local communities | true | null | |
jdc_operational:000048 | 44 | 1 | inventory of rural roads | 153 | 177 | descriptive | descriptive | other | must be established the ⟦inventory of rural roads⟧[19] (about 15,000 km) by local communities | true | null | |
jdc_operational:000048 | 45 | 0 | active road safety data | 372 | 395 | descriptive | vague | other | first responders ... in ⟦active road safety data⟧[20] collection | true | null | |
jdc_operational:000048 | 45 | 1 | beneficiary survey | 122 | 140 | descriptive | descriptive | survey | Procurement plan and ⟦beneficiary survey⟧[21] | true | null | |
jdc_operational:000048 | 58 | 0 | stressor-response engineering data | 853 | 887 | descriptive | descriptive | other | IPSS utilizes ⟦stressor-response engineering data⟧[22] coupled with ⟦IPCC-approved climate change data⟧[23] | true | null | |
jdc_operational:000048 | 58 | 0 | IPCC-approved climate change data | 901 | 934 | named | descriptive | other | IPSS utilizes ⟦stressor-response engineering data⟧[22] coupled with ⟦IPCC-approved climate change data⟧[23] | true | null | |
jdc_operational:000048 | 59 | 0 | large-scale study assessing road networks throughout Sub-Saharan Africa | 1,296 | 1,367 | descriptive | descriptive | other | For additional application of the IPSS methodology, the ⟦large-scale study assessing road networks throughout Sub-Saharan Africa⟧[24] conducted by the World Bank should be referenced. | false | null | |
jdc_operational:000048 | 59 | 1 | large-scale study assessing road networks throughout Sub-Saharan Africa | 93 | 164 | descriptive | descriptive | other | For additional application of the IPSS methodology, the ⟦large-scale study assessing road networks throughout Sub-Saharan Africa⟧[25] conducted by the World Bank should be referenced. | false | null | |
jdc_operational:000048 | 60 | 0 | CMIP5 dataset | 531 | 544 | named | named | database | estimated, by the median model from the ⟦CMIP5 dataset⟧[26], to be US$654,084 in 2050 | true | null | |
jdc_operational:000048 | 61 | 0 | historic data | 197 | 210 | vague | vague | other | estimated difference in repair costs between the historically required maintenance costs and the maintenance costs predicted to be needed as a result of climate change | false | null | |
jdc_operational:000048 | 64 | 1 | Reported statistics on GBV cases in Chad | 1,373 | 1,413 | descriptive | descriptive | report | ⟦Reported statistics on GBV cases in Chad⟧[28], particularly in the Latin America and the Caribbean region and in the south | true | null | |
jdc_operational:000048 | 66 | 0 | mapping of the service providers available in Chad | 555 | 605 | descriptive | descriptive | other | The ⟦mapping of the service providers available in Chad⟧[29] showed that there are 141 humanitarian actors | false | null | |
jdc_operational:000048 | 71 | 1 | mapping of NGOs available in Chad | 925 | 958 | descriptive | descriptive | other | An initial ⟦mapping of NGOs available in Chad⟧[30] and focusing on GBV issues was performed during the July mission. | false | null | |
jdc_operational:000049 | 14 | 0 | global development indicators | 777 | 806 | descriptive | descriptive | indicator | ranked almost at the bottom of the ⟦global development indicators⟧[1] | true | null | |
jdc_operational:000049 | 14 | 0 | Global Climate Risk Index | 1,506 | 1,531 | named | named | indicator | The ⟦Global Climate Risk Index⟧[2] ranked the country 125 out of 171 between 1998 and 2018 | true | null | |
jdc_operational:000049 | 14 | 0 | Historical records | 1,802 | 1,820 | descriptive | descriptive | other | ⟦Historical records⟧[3] show a large year-to-year variability in precipitation | true | null | |
jdc_operational:000049 | 14 | 1 | FAO desert locust situation update | 1,101 | 1,135 | named | named | report | According to the most recent ⟦FAO desert locust situation update⟧[4] | true | null | |
jdc_operational:000049 | 15 | 1 | Human Development Index | 1,396 | 1,419 | named | named | indicator | the ⟦Human Development Index⟧[5] (HDI) 2019 | true | null | |
jdc_operational:000049 | 16 | 2 | Displacement Tracking Matrix | 305 | 333 | named | named | database | International Organization for Migration (IOM), ⟦Displacement Tracking Matrix⟧[6] (DTM), Round 6 | true | null | |
jdc_operational:000049 | 18 | 0 | intention surveys | 188 | 205 | descriptive | descriptive | survey | According to recent ⟦intention surveys⟧[7] by both the United Nations High Commission for Refugees and IOM | true | null | |
jdc_operational:000049 | 18 | 0 | IOM assessment | 1,452 | 1,466 | named | descriptive | other | An ⟦IOM assessment⟧[8] commissioned by the World Bank found | true | null | |
jdc_operational:000049 | 19 | 2 | Trader Survey in South Sudan | 663 | 691 | named | named | survey | Findings from a ⟦Trader Survey in South Sudan⟧[9] | false | null | |
jdc_operational:000049 | 1 | 0 | Displacement TrackingMatrix | 691 | 718 | named | named | other | |DTM|=|⟦Displacement TrackingMatrix⟧[10]| | false | null | |
jdc_operational:000049 | 22 | 0 | high frequency phone surveys | 960 | 988 | descriptive | descriptive | survey | The World Bank’s current effort on ⟦high frequency phone surveys⟧[11] will be used to monitor the impact | true | null | |
jdc_operational:000049 | 25 | 0 | population data | 1,202 | 1,217 | descriptive | vague | other | using humanitarian agencies’ latest ⟦population data⟧[12] | true | null | |
jdc_operational:000049 | 26 | 0 | IOM’s DTM projections | 338 | 359 | named | named | estimates | utilizing ⟦IOM’s DTM projections⟧[13] | true | null | |
jdc_operational:000049 | 26 | 0 | population data | 447 | 462 | vague | vague | other | as no ⟦population data⟧[14] are available | true | null | |
jdc_operational:000049 | 26 | 1 | 2008 census | 1,883 | 1,894 | descriptive | named | census | complementing ⟦2008 census⟧[15] with other data sources | true | null | |
jdc_operational:000049 | 26 | 1 | DTM | 1,933 | 1,936 | named | named | database | for example, ⟦DTM⟧[16] | true | null | |
jdc_operational:000049 | 26 | 2 | 2008 census | 140 | 151 | descriptive | named | census | complementing ⟦2008 census⟧[17] with other data sources | true | null | |
jdc_operational:000049 | 26 | 2 | DTM | 190 | 193 | named | named | database | for example, ⟦DTM⟧[18] | true | null | |
jdc_operational:000049 | 29 | 0 | flood and drought data | 906 | 928 | descriptive | descriptive | other | based on available ⟦flood and drought data⟧[19] | true | null | |
jdc_operational:000049 | 31 | 2 | UNOCHA Humanitarian Response Plan Monitoring Report | 362 | 413 | named | named | report | ⟦UNOCHA Humanitarian Response Plan Monitoring Report⟧[20] January–March 2019 | true | null | |
jdc_operational:000049 | 31 | 2 | Heads of Cooperation Estimate | 441 | 470 | named | named | estimates | ⟦Heads of Cooperation Estimate⟧[21]. November 2019 | true | null | |
jdc_operational:000049 | 43 | 0 | gender-disaggregated data | 437 | 462 | descriptive | vague | other | measured through ⟦gender-disaggregated data⟧[22] in the M&E framework | true | null | |
jdc_operational:000049 | 65 | 0 | georeferenced monitoring system | 1,487 | 1,518 | descriptive | descriptive | system | a strong M&E mechanism with ⟦georeferenced monitoring system⟧[23] | true | null | |
jdc_operational:000049 | 68 | 0 | World Bank sanctioned list | 1,396 | 1,422 | named | named | other | such as ⟦World Bank sanctioned list⟧[24] | false | null | |
jdc_operational:000049 | 68 | 1 | external databases | 183 | 201 | descriptive | descriptive | database | the World Bank and other ⟦external databases⟧[25] are included | true | null | |
jdc_operational:000049 | 75 | 0 | data sets | 1,295 | 1,304 | vague | vague | other | determined at appraisal using the latest ⟦data sets⟧[26] | true | null | |
jdc_operational:000049 | 75 | 0 | county-level population projection | 1,455 | 1,489 | descriptive | descriptive | estimates | using the latest ⟦county-level population projection⟧[27] used by all humanitarian agencies | true | null | |
jdc_operational:000049 | 76 | 0 | IOM’s DTM projections | 1,165 | 1,186 | named | named | estimates | utilizing ⟦IOM’s DTM projections⟧[28] | true | null | |
jdc_operational:000049 | 76 | 0 | population data | 1,297 | 1,312 | vague | vague | other | as there are no ⟦population data⟧[29] available | true | null | |
jdc_operational:000049 | 76 | 1 | 2008 census | 1,367 | 1,378 | descriptive | descriptive | census | calculated based on a headcount or by complementing ⟦2008 census⟧ with other data sources | true | null | |
jdc_operational:000049 | 76 | 1 | DTM | 1,417 | 1,420 | named | named | system | complementing 2008 census with other data sources (for example, ⟦DTM⟧) | true | null | |
jdc_operational:000049 | 83 | 2 | Village Assessment Survey County Profiles | 822 | 863 | named | named | database | This information can be accessed at ⟦Village Assessment Survey County Profiles⟧ | true | null | |
jdc_operational:000049 | 86 | 1 | World Bank staff estimates | 343 | 369 | descriptive | named | estimates | _Source:_ Based on ⟦World Bank staff estimates⟧. | true | null | |
jdc_operational:000049 | 90 | 0 | Aggregated data sets based on global models | 766 | 809 | descriptive | descriptive | estimates | ⟦Aggregated data sets based on global models⟧ are available for South Sudan | true | null | |
jdc_operational:000049 | 90 | 1 | Emergency Events Database | 667 | 692 | named | named | database | “⟦Emergency Events Database⟧, Centre for Research on the Epidemiology of Disasters (CRED).” | true | null | |
jdc_operational:000049 | 90 | 1 | Global Risk Data Platform | 887 | 912 | named | named | system | data are based on global observations. cf. ⟦Global Risk Data Platform⟧ | true | null | |
jdc_operational:000049 | 90 | 1 | FATHOM | 992 | 998 | named | named | estimates | or ⟦FATHOM⟧. | true | null | |
jdc_operational:000049 | 91 | 1 | hazard and exposure data | 569 | 593 | descriptive | descriptive | geospatial | UNDP in coordination with other disaster relief organizations is gathering ⟦hazard and exposure data⟧ to produce a comprehensive hazard risk profile for South Sudan | true | null | |
jdc_operational:000049 | 91 | 1 | flood and drought hazard data | 1,568 | 1,597 | descriptive | descriptive | geospatial | a hazard screening will be performed based on available ⟦flood and drought hazard data⟧ | true | null | |
jdc_operational:000049 | 92 | 0 | state-level hazard and risk maps | 308 | 340 | descriptive | descriptive | geospatial | These mapping activities use ⟦state-level hazard and risk maps⟧ as a starting point | true | null | |
jdc_operational:000049 | 92 | 0 | Fathom Global flood model | 1,437 | 1,462 | named | named | estimates | on the basis of the ⟦Fathom Global flood model⟧ (2019) | true | null | |
jdc_operational:000049 | 92 | 0 | population and settlement exposure data | 1,484 | 1,523 | descriptive | descriptive | geospatial | and available ⟦population and settlement exposure data⟧ | true | null | |
jdc_operational:000050 | 11 | 2 | Poverty Maps of Uganda | 488 | 510 | named | named | database | ⟦Poverty Maps of Uganda⟧, Uganda Bureau of Statistics | true | null | |
jdc_operational:000050 | 12 | 1 | Logistics Performance Index 2018 | 320 | 352 | named | named | indicator | Uganda is ranked 102 in the ⟦Logistics Performance Index 2018⟧ | true | null | |
jdc_operational:000050 | 13 | 2 | Uganda Refugee and Host Communities 2018 Household Survey | 313 | 370 | named | named | survey | Results from the ⟦Uganda Refugee and Host Communities 2018 Household Survey⟧ | true | null | |
jdc_operational:000050 | 14 | 0 | 2018 Gender Inequality Index | 500 | 528 | named | named | indicator | in the ⟦2018 Gender Inequality Index⟧ | true | null | |
jdc_operational:000050 | 14 | 0 | Uganda Demographic and Health Survey | 631 | 667 | named | named | survey | According to the ⟦Uganda Demographic and Health Survey⟧ (UDHS) | true | null | |
jdc_operational:000050 | 14 | 1 | quantitative data collection | 1,468 | 1,496 | vague | descriptive | other | A ⟦quantitative data collection⟧ in 18 refugee settlements across Uganda reveals | false | null | |
jdc_operational:000050 | 14 | 2 | Uganda Demographic and Health Survey | 433 | 469 | named | named | survey | ⟦Uganda Demographic and Health Survey⟧ (2016), Uganda Bureau of Statistics (UBOS) and ICF | true | null | |
jdc_operational:000050 | 14 | 2 | Uganda Violence Against Children Survey | 529 | 568 | named | named | survey | ⟦Uganda Violence Against Children Survey⟧, Ministry of Gender, Labour and Social Development (2015) | true | null | |
jdc_operational:000050 | 14 | 2 | Uganda Refugee and Host Communities 2018 Household Survey | 1,483 | 1,540 | named | named | survey | Results from the ⟦Uganda Refugee and Host Communities 2018 Household Survey⟧ | true | null | |
jdc_operational:000050 | 14 | 3 | Uganda Refugee and Host Communities 2018 Household Survey | 64 | 121 | named | named | survey | Results from the ⟦Uganda Refugee and Host Communities 2018 Household Survey⟧ | true | null | |
jdc_operational:000050 | 17 | 0 | 2016 UDHS | 394 | 403 | named | named | survey | Fertility and mortality rates, from the ⟦2016 UDHS⟧, forecasted that 2 percent of women in Uganda would die from maternal causes at existing rates | true | null | |
jdc_operational:000050 | 17 | 0 | UDHS | 761 | 765 | named | named | survey | As per the ⟦UDHS⟧, 40.9 percent of women between 35-49 years old reported | true | null | |
jdc_operational:000050 | 17 | 1 | Accident Database Management System | 971 | 1,006 | named | named | system | Uganda currently does not have a functional ⟦Accident Database Management System⟧ | true | null | |
jdc_operational:000050 | 17 | 2 | road crash database system | 899 | 925 | descriptive | descriptive | system | A ⟦road crash database system⟧ was partially developed under the Transport Sector Development Project | true | null | |
jdc_operational:000050 | 18 | 2 | UNRA Diagnostic Study | 552 | 573 | named | named | other | ⟦UNRA Diagnostic Study⟧ funded by the World Bank – DFID multi-donor trust fund | false | null | |
jdc_operational:000050 | 19 | 0 | baseline surveys for monitoring of air quality | 682 | 728 | descriptive | descriptive | survey | Results of ⟦baseline surveys for monitoring of air quality⟧ conducted at eight locations along the KYM road indicate | true | null | |
jdc_operational:000050 | 25 | 0 | Crash data | 567 | 577 | descriptive | vague | other | Crash data entered into system, publicly reported, and used in decision making | true | null |
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