A listing of State Representatives and State Senators. For more information see: http://www.cga.ct.gov/asp/menu/legdownload.asp

Context

I wanted to make this finance data about US representatives (very generous of OpenSecrets.org to provide that) available to all for easy data analysis & data science.

Content

This dataset contains the personal finance details of US representatives (Senate, House & the Executive) on the following topics from 2004 to 2016 (varying date ranges for different topics) : * Agreements * Assets * Compensation * Gifts * Honoraria * Income * Liability * Positions * Transactions * Travel

For each of these topics, it has exact amounts or amount ranges, details about the topic like asset type, asset income, industry, sector, etc. & candidate information including candidate name, party, chamber & state & district. There is also information about the members of the 113th, 114th & 115th congress along with congressional committees.

You can find the official Data Dictionary Data Dictionary & the User Guide

Acknowledgements

The source of this data is the Bulk data at https://www.opensecrets.org/

Documentation : https://www.opensecrets.org/open-data/bulk-data-documentation

Please follow the Terms Of Service for using this data : https://www.opensecrets.org/open-data/terms-of-service

Inspiration

I hope that we can analyze this data & understand more about the personal finance of US representatives to help us all going forward. Some questions to be answered : * Which candidates have the highest/lowest net worth? * What kind of investments & in which industry/sector do candidates that you are interested in have? * What are the trends that we in terms of income, investments, etc. for different chambers/parties?

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

This Data Set contains the number of Public Representation issued on CRM Management System from Councilor's and member of the public in which the issues are logged and then assigned to staff members and department for Resolution on behalf of Fingal County Council. This contains data from 2021-2024 inclusivePlease see new data set Elected Members Reps 2025-2027_FCC

These data were collected by Scott Frisch and Sean Kelly between 2000 and 2004 from the archived papers of former members of the United States Congress. In most cases, Frisch, Kelly, or both traveled to archives to collect the textual materials from which the data were generated. In most cases, the request data come from the committee request summaries (briefing books) compiled for the Democratic Members of the Committee on Ways and Means, the Democratic Steering and Policy Committee, and the Republican Executive Committee on Committees. In addition to Committee notebooks, copies of the letters that individual members wrote in support of their assignment requests were collected. The format of these letters typically consists of a lengthy justification of the member's qualifications and need for his or her first request, with perhaps a paragraph devoted to a list of additional committees to which assignment would be acceptable. In a few instances, when no briefing book was available, preferences were reconstructed from the request letters submitted by the members to their party committees. In these cases, the most recent letter was considered the definitive statement of the member's preferences. Data regarding subcommittee assignments are not included in these data, nor are temporary committee assignments, nor are assignments that were not made at the beginning of a Congressional term. Committee requests for temporary, joint committees, boards, select committees and the like are also not included. The total number of committee preference listings is 2,480. Included are the committee preferences of 1,163 first-term members, as well as committee transfer requests made by 1,317 incumbents. For Democrats, the data cover the 80th through the 103rd Congress (but not the 85th Congress, which has not been located), comprising the committee preferences of 1,366 members, 655 first-term Democrats, and 711 incumbent Democrats. For the Republicans, the data cover the 86th through the 102nd Congress, reflecting the committee preferences of 1,114 Republican members, including 452 first-term Republicans and 662 incumbent Republicans. Republican data prior to the 86th Congress could not be located and were apparently lost during an effort to consolidate the records of congressional leaders prior to the 86th Congress. Data for Republicans from the 103rd Congress have not been located.

Summary details for each election year for the House of Representatives elections since 1901. This data includes electoral system characteristics, seats in chamber, number of enrolled voters, ballots cast, rate of voter turnout and rate of informal voting for New South Wales.

Overview

This database of simulated arterial pulse waves is designed to be representative of a sample of pulse waves measured from healthy adults. It contains pulse waves for 4,374 virtual subjects, aged from 25-75 years old (in 10 year increments). The database contains a baseline set of pulse waves for each of the six age groups, created using cardiovascular properties (such as heart rate and arterial stiffness) which are representative of healthy subjects at each age group. It also contains 728 further virtual subjects at each age group, in which each of the cardiovascular properties are varied within normal ranges. This allows for extensive in silico analyses of haemodynamics and the performance of pulse wave analysis algorithms.

Data Description

The database contains the following waves:

• arterial flow velocity (U),
• luminal area (A),
• pressure (P), and
• photoplethysmogram (PPG).

These pulse waves are provided at a range of measurement sites, including:

• aorta (ascending and descending)
• carotid artery
• brachial artery
• radial artery
• finger
• femoral artery

The data are available in three formats: Matlab, CSV and WaveForm Database (WFDB) format. Further details of the formatting and contents of each file are available at: https://github.com/peterhcharlton/pwdb/wiki/Using-the-Pulse-Wave-Database

Accompanying Publication

The database is described in the following publication:

Charlton P.H., Mariscal Harana, J., Vennin, S., Li, Y., Chowienczyk, P. & Alastruey, J., “Modelling arterial pulse waves in healthy ageing: a database for in silico evaluation of haemodynamics and pulse wave indices,” [under review]

Please cite this publication when using the database.

Further Information

Further information on the Pulse Wave Database project can be found at: https://peterhcharlton.github.io/pwdb/

This dataset provides information about the number of properties, residents, and average property values for Reps Road cross streets in Cooksville, MD.

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

View Smart reps import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

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 about 128,000 adults in more than 120 economies. 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

National coverage

Analysis unit

Individual

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. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

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 Korea, Rep. is 1011.

Mode of data collection

Landline and mobile telephone

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.

Abstract

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.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Korea, Rep. was 1,001 individuals.

Mode of data collection

Landline and cellular telephone

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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

The Business Service Representatives data set houses information about business service representatives across the state. These representatives are able to help businesses with their workforce needs.

The 119th Congressional Districts dataset reflects boundaries from January 3rd, 2025 from the United States Census Bureau (USCB), and the attributes are updated every Sunday from the United States House of Representatives and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Information for each member of Congress is appended to the Census Congressional District shapefile using information from the Office of the Clerk, U.S. House of Representatives' website https://clerk.house.gov/xml/lists/MemberData.xml and its corresponding XML file. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. This dataset also includes 9 geographies for non-voting at large delegate districts, resident commissioner districts, and congressional districts that are not defined. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 3, 2025 through January 3, 2027. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529006

View Produce reps llc import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Abstract

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.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Egypt, Arab Rep. was 1,044 individuals.

Mode of data collection

Face-to-face [f2f]

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 over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

The FCA presents data on the appointed representatives population and financial services activity. This data is updated every 6 months. The appointed representatives regime plays a crucial role in how consumers interact with financial services. The FCA is focused on strengthening principal firm oversight of appointed representatives and its commitment to being a smarter data-led regulator shapes the work it does.

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

Prime Reps Llc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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.