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. Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives. 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 115th Congress is seated from January 2017 to 2019. 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 1, 2016, and were in effect during the November 2016 election.

This dataset includes the retweets posted on Twitter by accounts associated with members of the US Congress during the 115th Congress (2017-2018). The list of accounts combines two sources: Justin Littman's list (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UIVHQR)The United States project list (https://github.com/unitedstates/congress-legislators)Tweets were collected using Twitter's Search API through the twitter_user_collector Python script (https://github.com/casmlab/twitter_user_collector).We filtered all tweets posted during the 115th Congress, leaving only those that have an associated attribute "retweeted_status", which indicates that the given CM's tweet is a retweet of another tweet. These retweets number 209,856 during the 115th Congress, made by 38,131 unique Twitter accounts.We preserved and renamed metadata these tweets provided through Twitter's API, including the fields 'tweet_id_str', 'full_text', 'user_id_str', 'user_screen_name', 'user_followers_count', 'created_at', 'retweet_count', 'retweeted_status', and 'year' (extracted from 'created_at').Beyond that tweet metadata provided through Twitter’s API, we collected additional demographic metadata for as many CMs as possible of those featured in our Tweet collection by using The United States Project's crowdsourced list of current legislators’ official Twitter handles, and associated metadata fields identifying a legislator’s unique bioguide ID ('bioguide' field), name (‘name’ field), chamber (‘chamber’ field), party (‘party’ field), state represented (‘state’ field), gender (‘gender’ field), and birthday (‘birthday’ field). For those CMs not included in The United States Project, we manually searched for information to fill each of these metadata fields.Based on which state each of these CMs represents, we assigned each CM a region (‘region’ field) based on those U.S. regional divisions outlined by Karl and Koss in their 1984 paper (https://repository.library.noaa.gov/view/noaa/10238) and which is also used by the U.S. National Centers for Environmental Information. For those states not captured by Karl and Koss’ regions, we made determinations ourselves and assigned them according to climatological and cultural contexts. In doing so, we developed an additional regional label, “Islands”. Those states or territories that we independently assigned include American Samoa, Virgin Islands, Puerto Rico, Hawaii, District of Columbia, and Alaska.We determined age (‘age’ field) at the time of dataset creation (Jan. 10, 2020) according to CMs’ reported birthdays. We then grouped these ages into those age buckets 30-39, 40-49, 50-59, 60-69, 70-79, 80-89 (‘age_bucket’ field).The OpenICPSR dataset features tweets by 520 CMs with this associated metadata.Finally, we include fields which describe the original tweet that the CM retweeted and the user who posted it. We include that original poster’s Twitter user ID ('rt_user_id' field), Twitter screen name ('rt_screen_name' field), number of Twitter followers ('rt_followers_count' field), and user bio ('rt_bio' field). We extracted these fields from the JSON value included in the Twitter API's 'retweeted_status' field.

The 2016 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File /Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives. 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 115th Congress is seated from January 2017 to 2019. The cartographic boundary files 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 are provided to the Census Bureau through the Redistricting Data Program (RDP).

This statistic shows the percentage of religiously affiliated members of the ***** United States Congress and the general public, by religion. *** percent of ***** U.S. Congress stated themselves as unaffiliated to a religion in comparison to ** percent of the general public.

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. The 115th Congressional District Data Profiles are available for the nation, all 50 states, the District of Columbia, and Puerto Rico (at large). Data profiles contain broad social, economic, housing, and demographic information. The data are presented as population counts for over 1,000 distinct variables.

NOTE: For information on confidentiality protection, nonsampling error, and definitions, see https://www2.census.gov/programs-surveys/decennial/rdo/technical-documentation/CD115_TechnicalDocumentation.pdf..Source: U.S. Census Bureau, 2010 Census..NOTE: For information on the codes that appear in this table see http://www.census.gov/prod/cen2010/doc/cd113.pdf, Appendix F.

US Census American Community Survey (ACS) 2016, 5-year estimates of the key demographic characteristics of Congressional Districts (115th US Congress) geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2016 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Congressional Districts data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain district boundaries for the 118th congress with associated Census and American Community Survey demographic data.

This hosted feature layer has been published in RI State Plane Feet NAD 83. This dataset is a modified version of the source data that are produced and distributed by the U.S. Census Bureau. Congressional Districts associated with Rhode Island were selected from the source national-scale dataset and clipped to the Rhode Island coastline for cartographic purposes. 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. Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives. 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 115th Congress is seated from January 2017 to 2019. 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 1, 2016, and were in effect during the November 2016 election.

This graph shows the average hours in session per day in U.S. Congress from 1981 to 2016. During the 115th Congress of 2017-18, the Senate was in session for around 5.7 hours per day.

US Census American Community Survey (ACS) 2017, 5-year estimates of the key demographic characteristics of Congressional Districts (115th US Congress) geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

The 118th U.S. Congress was sworn in in January 2023. There are *** total members of the U.S. Congress. This congress consisted of *** Protestants. Additionally, the 118th Congress consisted of three Muslim members.

Us House Congressional Representatives serving Macon-Bibb County.Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats 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 boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

General election results and geospatial data for the Senate and House of Representatives for the 27th-115th U.S. Congresses. This dataset is a continuation of the Mapping Congress dataset which contains the general election results and geospatial data for the Senate and House of Representatives for the 22nd-52nd U.S. Congresses. In collaboration with members of the Richmond Digital Scholarship Lab, Robert K. Nelson and Justin Madron, LaDale Winling has made available the newest version of this collection via the 'Electing the House of Representatives' website. This dataset was updated on 20200227 with additional data points in the MasterHouseFile.xlsx file. The original dataset can be found at the following DOI, doi:10.7294/W45Q4T11.

The 2017 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives. 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 115th Congress is seated from January 2017 to 2019. The cartographic boundary files 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 1, 2016, and were in effect during the November 2016 election.

This map shows Congressional District boundaries for the United States. The map is set to middle Georgia.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats 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 boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

Supporting/supplementary datasets for contribution to the Complex Networks 2018 special issue of Applied Network Science.control.csv provides a list of the party (by party_code) in control of the House, Senate, and Presidency for each Congress from the 74th to the 115th (inclusive)roles.csv provides a list of the roles (S = speaker, D = Democrat leader, R = Republican leader) filled by members of the House and Senate for each Congress from the 74th to the 115th (inclusive). The list includes icpsr, state date, and end date, for each member.READ FIRST.txt provides additional clarifying material regarding roles.csv.

US Census American Community Survey (ACS) 2016, 5-year estimates of the key economic characteristics of Congressional Districts (115th US Congress) geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2016 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census American Community Survey (ACS) 2017, 5-year estimates of the key housing characteristics of Congressional Districts (115th US Congress) geographic level in Orange County, California. The data contains 406 fields for the variable groups H01: Housing occupancy (universe: total housing units, table X25, 3 fields); H02: Units in structure (universe: total housing units, table X25, 11 fields); H03: Population in occupied housing units by tenure by units in structure (universe: total population in occupied housing units, table X25, 13 fields); H04: Year structure built (universe: total housing units, table X25, 15 fields); H05: Rooms (universe: total housing units, table X25, 18 fields); H06: Bedrooms (universe: total housing units, table X25, 21 fields); H07: Housing tenure by race of householder (universe: occupied housing units, table X25, 51 fields); H08: Total population in occupied housing units by tenure (universe: total population in occupied housing units, table X25, 3 fields); H09: Vacancy status (universe: vacant housing units, table X25, 8 fields); H10: Occupied housing units by race of householder (universe: occupied housing units, table X25, 8 fields); H11: Year householder moved into unit (universe: occupied housing units, table X25, 18 fields); H12: Vehicles available (universe: occupied housing units, table X25, 18 fields); H13: Housing heating fuel (universe: occupied housing units, table X25, 10 fields); H14: Selected housing characteristics (universe: occupied housing units, table X25, 9 fields); H15: Occupants per room (universe: occupied housing units, table X25, 13 fields); H16: Housing value (universe: owner-occupied units, table X25, 32 fields); H17: Price asked for vacant for sale only, and sold not occupied housing units (universe: vacant for sale only, and sold not occupied housing units, table X25, 28 fields); H18: Mortgage status (universe: owner-occupied units, table X25, 10 fields); H19: Selected monthly owner costs, SMOC (universe: owner-occupied housing units with or without a mortgage, table X25, 45 fields); H20: Selected monthly owner costs as a percentage of household income, SMOCAPI (universe: owner-occupied housing units with or without a mortgage, table X25, 26 fields); H21: Contract rent distribution and rent asked distribution in dollars (universe: renter-occupied housing units paying cash rent and vacant, for rent, and rented not occupied housing units, table X25, 7 fields); H22: Gross rent (universe: occupied units paying rent, table X25, 28 fields), and; X23: Gross rent as percentage of household income (universe: occupied units paying rent, table X25, 11 fields). The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

2017 Census Tigerline 115th Congressional District boundaries joined with 2017 ACS 5yr Table DP04 Selected Housing Characteristics to create 115th Congressional District data profiles