100+ datasets found
  1. .rep.br TLD Whois Database | Whois Data Center

    • whoisdatacenter.com
    csv
    Updated Dec 1, 2023
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    AllHeart Web Inc (2023). .rep.br TLD Whois Database | Whois Data Center [Dataset]. https://whoisdatacenter.com/tld/.rep.br/
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    csvAvailable download formats
    Dataset updated
    Dec 1, 2023
    Dataset provided by
    AllHeart Web
    Authors
    AllHeart Web Inc
    License

    https://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/

    Time period covered
    Jul 14, 2025 - Dec 31, 2025
    Description

    .REP.BR Whois Database, discover comprehensive ownership details, registration dates, and more for .REP.BR TLD with Whois Data Center.

  2. Appointed Representative Management Information - Operational Data Store

    • catalog.data.gov
    Updated Jul 4, 2025
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    Social Security Administration (2025). Appointed Representative Management Information - Operational Data Store [Dataset]. https://catalog.data.gov/dataset/appointed-representative-management-information-operational-data-store
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    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Social Security Administrationhttp://ssa.gov/
    Description

    Stores information about appointed representatives used for reporting purposes.

  3. Z

    Mycobacterium representative kraken2 database

    • data.niaid.nih.gov
    • explore.openaire.eu
    Updated Feb 15, 2024
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    Hall, Michael B. (2024). Mycobacterium representative kraken2 database [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8339821
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    Dataset updated
    Feb 15, 2024
    Dataset authored and provided by
    Hall, Michael B.
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    A kraken2 database built from the a representative Mycobacterium set of genomes. This archive contains the three files required by kraken2, hash.k2d, opts.k2d, and taxo.k2d, along with inspect.txt, which is obtained by running kraken2-inspect on the database.

    The genomes for this database were downloaded wiuth genome_updater.sh (v0.6.3; https://github.com/pirovc/genome_updater) with one RefSeq genome from each species in the Mycobacteriaceae family, plus one RefSeq genome from each species in the following genera: Klebsiella, Escherichia, Salmonella, Enterobacter, Streptococcus, Staphylococcus, Pseudomonas, Xanthomonas, and Bifidobacterium.

    genome_updater.sh -A "species:1" -m -a -M "gtdb" -f "genomic.fna.gz" -g "bacteria" -d "refseq" -T "f_Mycobacteriaceae,g_Klebsiella,g_Escherichia,g_Enterobacter,g_Salmonella,g_Streptococcus,g_Staphylococcus,g_Pseudomonas,g_Xanthomonas,g_Bifidobacterium" -o GTDB_Mycobacterium/

    The python script prepare_kraken_fasta.py was then used to prepare the assemblies for use in kraken with the following command

    python prepare_kraken_fasta.py -r -x GCF_932530395.1,GCF_017190695.1,GCF_020735285.1,GCA_014701265.1,GCF_000013925.1,GCF_016756075.1,GCF_010727125.1,GCF_001307545.1 -o Mycobacterium.fna -s assembly_summary.txt GTDB_Mycobacterium/

    The database was then built with kraken2 using the following commands

    kraken2-build --download-taxonomy --db db/ kraken2-build --add-to-library Mycobacterium.fna --db db/ kraken2-build --build --db db/ --threads 16

  4. i

    Global Financial Inclusion (Global Findex) Database 2017 - Korea, Rep.

    • catalog.ihsn.org
    • microdata.worldbank.org
    Updated Mar 29, 2019
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    Development Research Group, Finance and Private Sector Development Unit (2019). Global Financial Inclusion (Global Findex) Database 2017 - Korea, Rep. [Dataset]. https://catalog.ihsn.org/index.php/catalog/7932
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    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2017
    Area covered
    South Korea
    Description

    Abstract

    Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

    By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

    Geographic coverage

    National coverage

    Universe

    The target population is the civilian, non-institutionalized population 15 years and above.

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see Table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

    In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

    Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

    In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

    The sample size was 1000.

    Mode of data collection

    Other [oth]

    Research instrument

    The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

    Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

  5. f

    DataSheet_1_RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated...

    • frontiersin.figshare.com
    docx
    Updated Jun 6, 2023
    + more versions
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    Yanfang Zhang; Tianjian Chen; Huikun Zeng; Xiujia Yang; Qingxian Xu; Yanxia Zhang; Yuan Chen; Minhui Wang; Yan Zhu; Chunhong Lan; Qilong Wang; Haipei Tang; Yan Zhang; Chengrui Wang; Wenxi Xie; Cuiyu Ma; Junjie Guan; Shixin Guo; Sen Chen; Wei Yang; Lai Wei; Jian Ren; Xueqing Yu; Zhenhai Zhang (2023). DataSheet_1_RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database.docx [Dataset]. http://doi.org/10.3389/fimmu.2021.717496.s001
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    docxAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Yanfang Zhang; Tianjian Chen; Huikun Zeng; Xiujia Yang; Qingxian Xu; Yanxia Zhang; Yuan Chen; Minhui Wang; Yan Zhu; Chunhong Lan; Qilong Wang; Haipei Tang; Yan Zhang; Chengrui Wang; Wenxi Xie; Cuiyu Ma; Junjie Guan; Shixin Guo; Sen Chen; Wei Yang; Lai Wei; Jian Ren; Xueqing Yu; Zhenhai Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The antibody repertoire is a critical component of the adaptive immune system and is believed to reflect an individual’s immune history and current immune status. Delineating the antibody repertoire has advanced our understanding of humoral immunity, facilitated antibody discovery, and showed great potential for improving the diagnosis and treatment of disease. However, no tool to date has effectively integrated big Rep-seq data and prior knowledge of functional antibodies to elucidate the remarkably diverse antibody repertoire. We developed a Rep-seq dataset Analysis Platform with an Integrated antibody Database (RAPID; https://rapid.zzhlab.org/), a free and web-based tool that allows researchers to process and analyse Rep-seq datasets. RAPID consolidates 521 WHO-recognized therapeutic antibodies, 88,059 antigen- or disease-specific antibodies, and 306 million clones extracted from 2,449 human IGH Rep-seq datasets generated from individuals with 29 different health conditions. RAPID also integrates a standardized Rep-seq dataset analysis pipeline to enable users to upload and analyse their datasets. In the process, users can also select set of existing repertoires for comparison. RAPID automatically annotates clones based on integrated therapeutic and known antibodies, and users can easily query antibodies or repertoires based on sequence or optional keywords. With its powerful analysis functions and rich set of antibody and antibody repertoire information, RAPID will benefit researchers in adaptive immune studies.

  6. w

    Korea, Rep. - Global Financial Inclusion (Global Findex) Database 2017

    • wbwaterdata.org
    Updated Mar 16, 2020
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    (2020). Korea, Rep. - Global Financial Inclusion (Global Findex) Database 2017 [Dataset]. https://wbwaterdata.org/dataset/korea-rep-global-financial-inclusion-global-findex-database-2017
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    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

  7. w

    Iran, Islamic Rep. - Global Financial Inclusion (Global Findex) Database...

    • wbwaterdata.org
    • datacatalog.worldbank.org
    Updated Mar 16, 2020
    + more versions
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    (2020). Iran, Islamic Rep. - Global Financial Inclusion (Global Findex) Database 2017 [Dataset]. https://wbwaterdata.org/dataset/iran-islamic-rep-global-financial-inclusion-global-findex-database-2017
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    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Iran
    Description

    Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

  8. w

    Egypt, Arab Rep. - Global Financial Inclusion (Global Findex) Database 2011...

    • wbwaterdata.org
    Updated Mar 16, 2020
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    (2020). Egypt, Arab Rep. - Global Financial Inclusion (Global Findex) Database 2011 - Dataset - waterdata [Dataset]. https://wbwaterdata.org/dataset/egypt-arab-rep-global-financial-inclusion-global-findex-database-2011
    Explore at:
    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Egypt
    Description

    Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies. The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

  9. Electronic Representative Payee Accounting

    • catalog.data.gov
    • data.amerigeoss.org
    • +1more
    Updated Jul 4, 2025
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    Social Security Administration (2025). Electronic Representative Payee Accounting [Dataset]. https://catalog.data.gov/dataset/electronic-representative-payee-accounting
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    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Social Security Administrationhttp://ssa.gov/
    Description

    Contains data for the Representative Payee Accounting process.

  10. T

    Fmr Fed. Rep. of Germany Imports from Togo of Organic chemicals

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Nov 12, 2022
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    TRADING ECONOMICS (2022). Fmr Fed. Rep. of Germany Imports from Togo of Organic chemicals [Dataset]. https://tradingeconomics.com/fmr-fed-rep-germany/imports/togo/organic-chemicals
    Explore at:
    excel, json, xml, csvAvailable download formats
    Dataset updated
    Nov 12, 2022
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1990 - Dec 31, 2025
    Area covered
    Germany
    Description

    Fmr Fed. Rep. of Germany Imports from Togo of Organic chemicals was US$55 during 2023, according to the United Nations COMTRADE database on international trade. Fmr Fed. Rep. of Germany Imports from Togo of Organic chemicals - data, historical chart and statistics - was last updated on June of 2025.

  11. i

    Global Financial Inclusion (Global Findex) Database 2021 - Congo, Dem. Rep.

    • catalog.ihsn.org
    • microdata.worldbank.org
    Updated Jun 9, 2023
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    Development Research Group, Finance and Private Sector Development Unit (2023). Global Financial Inclusion (Global Findex) Database 2021 - Congo, Dem. Rep. [Dataset]. https://catalog.ihsn.org/catalog/11345
    Explore at:
    Dataset updated
    Jun 9, 2023
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2022
    Area covered
    Democratic Republic of the Congo
    Description

    Abstract

    The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

    The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

    The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

    Geographic coverage

    Parts of Province Orientale, Nord Kivu, Sud Kivu, Katanga were excluded due to insecurity. Geographic exclusions represent 19% of the population.

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

    In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

    In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

    The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

    For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

    Sample size for Congo, Dem. Rep. is 1000.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Questionnaires are available on the website.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

  12. o

    Computational data of Orthorhombic ReP from Density Functional Theory...

    • oqmd.org
    + more versions
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    The Open Quantum Materials Database, Computational data of Orthorhombic ReP from Density Functional Theory calculations [Dataset]. https://www.oqmd.org/materials/entry/1108269
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    Dataset authored and provided by
    The Open Quantum Materials Database
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Name, Bandgap, Stability, Crystal volume, Formation energy, Symmetry spacegroup, Number of atoms in unit cell
    Measurement technique
    Computational, Density Functional Theory
    Description

    Data obtained from computational DFT calculations on Orthorhombic ReP is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

  13. T

    Fmr Fed. Rep. of Germany Imports from Iraq of Aircraft, spacecraft

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Nov 10, 2022
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    TRADING ECONOMICS (2022). Fmr Fed. Rep. of Germany Imports from Iraq of Aircraft, spacecraft [Dataset]. https://tradingeconomics.com/fmr-fed-rep-germany/imports/iraq/aircraft-spacecraft
    Explore at:
    csv, json, xml, excelAvailable download formats
    Dataset updated
    Nov 10, 2022
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1990 - Dec 31, 2025
    Area covered
    Germany
    Description

    Fmr Fed. Rep. of Germany Imports from Iraq of Aircraft, spacecraft was US$1.34 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Fmr Fed. Rep. of Germany Imports from Iraq of Aircraft, spacecraft - data, historical chart and statistics - was last updated on June of 2025.

  14. Electronic Representative Payee System

    • catalog.data.gov
    • s.cnmilf.com
    • +1more
    Updated Jul 4, 2025
    + more versions
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    Social Security Administration (2025). Electronic Representative Payee System [Dataset]. https://catalog.data.gov/dataset/electronic-representative-payee-system
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    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Social Security Administrationhttp://ssa.gov/
    Description

    Contains data for the Representative Payee application and selection process and the Representative Payee misuse process.

  15. w

    Congo, Dem. Rep. - Global Financial Inclusion (Global Findex) Database 2011...

    • wbwaterdata.org
    Updated Mar 16, 2020
    + more versions
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    (2020). Congo, Dem. Rep. - Global Financial Inclusion (Global Findex) Database 2011 - Dataset - waterdata [Dataset]. https://wbwaterdata.org/dataset/congo-dem-rep-global-financial-inclusion-global-findex-database-2011
    Explore at:
    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies. The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

  16. d

    Yemen, Rep. - Global Financial Inclusion (Global Findex) Database 2011 -...

    • waterdata3.staging.derilinx.com
    Updated Mar 16, 2020
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    (2020). Yemen, Rep. - Global Financial Inclusion (Global Findex) Database 2011 - Dataset - waterdata [Dataset]. https://waterdata3.staging.derilinx.com/dataset/yemen-rep-global-financial-inclusion-global-findex-database-2011
    Explore at:
    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Yemen
    Description

    Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies. The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

  17. China Export: Africa: Central African Rep

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China Export: Africa: Central African Rep [Dataset]. https://www.ceicdata.com/en/china/usd-trade-by-country/export-africa-central-african-rep
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2024 - Dec 1, 2024
    Area covered
    China
    Variables measured
    Merchandise Trade
    Description

    China Export: Africa: Central African Rep data was reported at 10.583 USD mn in Mar 2025. This records an increase from the previous number of 2.379 USD mn for Feb 2025. China Export: Africa: Central African Rep data is updated monthly, averaging 0.887 USD mn from Jan 2001 (Median) to Mar 2025, with 291 observations. The data reached an all-time high of 17.282 USD mn in Mar 2023 and a record low of 0.000 USD mn in Aug 2006. China Export: Africa: Central African Rep data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JA: USD: Trade by Country.

  18. zol: prepTG Databases for ESKAPE Pathogens

    • zenodo.org
    • data.niaid.nih.gov
    application/gzip
    Updated Oct 26, 2023
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    Rauf Salamzade; Rauf Salamzade; Lindsay Kalan; Lindsay Kalan (2023). zol: prepTG Databases for ESKAPE Pathogens [Dataset]. http://doi.org/10.5281/zenodo.10042148
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    application/gzipAvailable download formats
    Dataset updated
    Oct 26, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Rauf Salamzade; Rauf Salamzade; Lindsay Kalan; Lindsay Kalan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Each of the tar.gz compressed directories corresponds to prepTG databases (for the zol suite) featuring distinct, representative genomes for one of the six genera containing ESKAPE pathogens. Representative genomes for each genus/taxon were selected using skDER v1.0.7 in greedy mode with 99% ANI and 90% AF cutoffs.

    The compressed folders also contain an extra file, corresponding to a species tree of the representative genomes constructed using GToTree with Universal markers (ribosomal proteins) from Hug et al. 2016 and in best-hits mode. Note, GToTree was modified to always use -super5 mode for SCG alignments for computational efficiency. Also, note, because genomes can be dropped by GToTree prior to phylogeny inference (e.g. if they lack enough SCGs), not all genomes in the database might be represented in the phylogenies.

  19. w

    Korea, Rep. - Global Financial Inclusion (Global Findex) Database 2014 -...

    • wbwaterdata.org
    Updated Mar 16, 2020
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    (2020). Korea, Rep. - Global Financial Inclusion (Global Findex) Database 2014 - Dataset - waterdata [Dataset]. https://wbwaterdata.org/dataset/korea-rep-global-financial-inclusion-global-findex-database-2014
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    Dataset updated
    Mar 16, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

  20. China Import: Africa: Central African Rep

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China Import: Africa: Central African Rep [Dataset]. https://www.ceicdata.com/en/china/usd-trade-by-country/import-africa-central-african-rep
    Explore at:
    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2024 - Dec 1, 2024
    Area covered
    China
    Variables measured
    Merchandise Trade
    Description

    China Import: Africa: Central African Rep data was reported at 3.785 USD mn in Mar 2025. This records an increase from the previous number of 1.215 USD mn for Feb 2025. China Import: Africa: Central African Rep data is updated monthly, averaging 1.902 USD mn from Jan 2001 (Median) to Mar 2025, with 291 observations. The data reached an all-time high of 7.672 USD mn in Feb 2018 and a record low of 0.000 USD mn in Feb 2004. China Import: Africa: Central African Rep data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JA: USD: Trade by Country.

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AllHeart Web Inc (2023). .rep.br TLD Whois Database | Whois Data Center [Dataset]. https://whoisdatacenter.com/tld/.rep.br/
Organization logo

.rep.br TLD Whois Database | Whois Data Center

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csvAvailable download formats
Dataset updated
Dec 1, 2023
Dataset provided by
AllHeart Web
Authors
AllHeart Web Inc
License

https://whoisdatacenter.com/terms-of-use/https://whoisdatacenter.com/terms-of-use/

Time period covered
Jul 14, 2025 - Dec 31, 2025
Description

.REP.BR Whois Database, discover comprehensive ownership details, registration dates, and more for .REP.BR TLD with Whois Data Center.

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