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.

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.

Data has been collected from three questionnaires- Sense of Coherence-13 (SOC-13), Multidimensional Scale of Perceived Social Support and Beck Anxiety Inventory.

This dataset presents the distribution networks developed in our unpublished paper

Data on the perception of medical artificial intelligence in Germany is currently lacking. Two online surveys were launched in Germany in 2021 to assess the knowledge and perception of artificial intelligence in general and in medicine, including the management of data in medicine. A total of 1,001 and 1,000 adults participated in the surveys. The data collected and the questionnaires will be published.

This panel dataset includes every single candidate who ran in any general election or by-election for the Japanese House of Representatives from 1947 to 2014. The data set includes a total of 27,545 observations, i.e., candidate-elections, for 10,060 unique individuals, across 25 general elections. For each candidate, the dataset includes information on election outcomes, personal and occupational backgrounds, dynastic family ties, and cabinet appointments, among other variables.

This data set is captured from a robot workcell that is performing activities representative of several manufacturing operations. The workcell contains two, 6-degree-of-freedom robot manipulators where one robot is performing material handling operations (e.g., transport parts into and out of a specific work space) while the other robot is performing a simulated precision operation (e.g., the robot touching the center of a part with a tool tip that leaves a mark on the part). This precision operation is intended to represent a precise manufacturing operation (e.g., welding, machining). The goal of this data set is to provide robot level and process level measurements of the workcell operating in nominal parameters. There are no known equipment or process degradations in the workcell. The material handling robot will perform pick and place operations, including moving simulated parts from an input area to in-process work fixtures. Once parts are placed in/on the work fixtures, the second robot will interact with the part in a specified precise manner. In this specific instance, the second robot has a pen mounted to its tool flange and is drawing the NIST logo on a surface of the part. When the precision operation is completed, the material handling robot will then move the completed part to an output. This suite of data includes process data and performance data, including timestamps. Timestamps are recorded at predefined state changes and events on the PLC and robot controllers, respectively. Each robot controller and the PLC have their own internal clocks and, due to hardware limitations, the timestamps recorded on each device are relative to their own internal clocks. All timestamp data collected on the PLC is available for real-time calculations and is recorded. The timestamps collected on the robots are only available as recorded data for post-processing and analysis. The timestamps collected on the PLC correspond to 14 part state changes throughout the processing of a part. Timestamps are recorded when PLC-monitored triggers are activated by internal processing (PLC trigger origin) or after the PLC receives an input from a robot controller (robot trigger origin). Records generated from PLC-originated triggers include parts entering the work cell, assignment of robot tasks, and parts leaving the work cell. PLC-originating triggers are activated by either internal algorithms or sensors which are monitored directly in the PLC Inputs/Outputs (I/O). Records generated from a robot-originated trigger include when a robot begins operating on a part, when the task operation is complete, and when the robot has physically cleared the fixture area and is ready for a new task assignment. Robot-originating triggers are activated by PLC I/O. Process data collected in the workcell are the variable pieces of process information. This includes the input location (single option in the initial configuration presented in this paper), the output location (single option in the initial configuration presented in this paper), the work fixture location, the part number counted from startup, and the part type (task number for drawing robot). Additional information on the context of the workcell operations and the captured data can be found in the attached files, which includes a README.txt, along with several noted publications. Disclaimer: Certain commercial entities, equipment, or materials may be identified or referenced in this data, or its supporting materials, in order to illustrate a point or concept. Such identification or reference is not intended to imply recommendation or endorsement by NIST; nor does it imply that the entities, materials, equipment or data are necessarily the best available for the purpose. The user assumes any and all risk arising from use of this dataset.

ParlSpeech V2 contains complete full-text vectors of more than 6.3 million parliamentary speeches in the key legislative chambers of Austria, the Czech Republic, Germany, Denmark, the Netherlands, New Zealand, Spain, Sweden, and the United Kingdom, covering periods between 21 and 32 years. Meta-data include information on date, speaker, party, and partially agenda item under which a speech was held. The accompanying release note provides a more detailed guide to the data.

This dataset contains the tweet ids of 2,041,399 tweets from the Twitter accounts of members of the 115th U.S. Congress. They were collected between January 27, 2017 and January 2, 2019 from the Twitter API using Social Feed Manager. Some tweets may come before this time period. These tweet ids are broken up into 2 collections. Each collection was collected either from the GET statuses/user_timeline method of the Twitter REST API (retrieved on a weekly schedule). The collections are: Senators: senators.txt Representatives: representatives.txt There is a README.txt file for each collection containing additional documentation on how it was collected. There is also an accounts.csv file for each collection collected from the GET statuses/user_timeline method listing the Twitter accounts that were collected. The GET statuses/lookup method supports retrieving the complete tweet for a tweet id (known as hydrating). Tools such as Twarc or Hydrator can be used to hydrate tweets. Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. We intend to update this dataset periodically. Questions about this dataset can be sent to sfm@gwu.edu. George Washington University researchers should contact us for access to the tweets.

This dataset belongs to a three-part study on American representation conducted shortly before and after the 1958 congressional election (see also AMERICAN REPRESENTATION STUDY, 1958: CANDIDATES [ICPSR 7226] and AMERICAN REPRESENTATION STUDY, 1958: CANDIDATE AND CONSTITUENT, INCUMBENCY [ICPSR 7293]). The survey administered to the candidates was designed to elicit information on what they considered to be the most important issues of the campaign, their views on these issues, and their perceptions of the positions of their constituents. The candidates were also asked what influenced them, and what they felt influenced the outcome of the campaign. Derived measures calculate 85th Congress roll-call scores on social welfare, foreign involvement, and civil rights issues. Roll-call data and information on committee activities of the congressmen are also provided. The two combined candidate and constituent files (this collection and ICPSR 7293) contain the same candidate information as in ICPSR 7226 but are structured around the district as the unit of analysis. This data collection provides candidate and constituent information, organized by party identification of candidates, while ICPSR 7293 is organized by incumbency status of the candidates. In addition to the survey information on the candidates, this collection contains data on constituents taken from the 1956, 1958, and 1960 AMERICAN NATIONAL ELECTION STUDIES (ICPSR 7214, 7215, and 7216) for 114 of the 146 districts. Demographic information on candidates includes sex, race, year of birth, size of birthplace, highest graduate degree, prior occupations, public offices previously held, several indices of spatial mobility, religious preference, and ethnic background.

This dataset is the supplement data of our publication inCVPR 2025: Deep Change Monitoring: A Hyperbolic Representative Learning Framework and a Dataset for Long-term Fine-grained Tree Change Detection.

The data includes "Train set" and "Test set", the pictures of the same tree are saved in the same folder, and each picture is named after their date, e.g., "20230425.jpg" means the picture is collected on 25th, April, 2023.

The "labelling.xlsx" contains the reference status of each tree, the meaning of status ID is below:

0 : The leaves have fallen out

1 : The leaves are green

2 : The leaves are yellow

3 : It starts to grow leaves or leaves starts to fall

4 : blossom

This is a representative sequence set for cytochrome oxidase subunit 1 (CO1 or COI) combining all available eukaryotic CO1 sequences from GenBank and BOLD, clustered at 99% similarity.

TODO:

generate and add 7-level taxonomies for each sequence in this rep set.

Replication Data for: A Replication of "Representative Bureaucracy and the Willingness to Coproduce" Research on symbolic representation suggests that citizen-state interactions might benefit from public organizations’ representativeness. Recent experiments on symbolic gender representation provide contradictory findings regarding the influence on citizens’ co-production intentions. This study conducts a wide replication based on new data to reexamine the positive impact of symbolic gender representation identified by Riccucci, Van Ryzin, and Li (Public Administration Review 2016; 76(1): 121–130). The applied survey experiment closely resembles the original design aspects. The experiment is set in criminal justice policy, a policy field featuring co-production of core public services such as prisoner rehabilitation. The results do not confirm a positive effect of symbolic gender representation on willingness to co-produce. Instead, several arguments point to citizens’ perceptions of uncertainty related to the co-production context and procedures as a boundary condition for the effects of symbolic gender representation.

The accelerating growth in the number of protein sequences taxes both the computational and manual resources needed to analyze them. One approach to dealing with this problem is to minimize the number of proteins subjected to such analysis in a way that minimizes loss of information. To this end we have developed a set of Representative Proteomes (RPs), each selected from a Representative Proteome Group (RPG) containing similar proteomes calculated based on co-membership in UniRef50 clusters. A Representative Proteome is the proteome that can best represent all the proteomes in its group in terms of the majority of the sequence space and information. RPs at 75%, 55%, 35% and 15% co-membership threshold (CMT) are provided to allow users to decrease or increase the granularity of the sequence space based on their requirements. We find that a CMT of 55% (RP55) most closely follows standard taxonomic classifications. Further analysis of this set reveals that sequence space is reduced by more than 80% relative to UniProtKB, while retaining both sequence diversity (over 95% of InterPro domains) and annotation information (93% of experimentally characterized proteins). All sets can be browsed and are available for sequence similarity searches and download at http://www.proteininformationresource.org/rps, while the set of 637 RPs determined using a 55% CMT are also available for text searches. Potential applications include sequence similarity searches, protein classification and targeted protein annotation and characterization.

This publication provides information on valid votes cast and distribution of seats in relation to the election of the House of Representatives of the States General, 6 September 1989.

Data available for 6 September 1989.

Status of the figures: The data in this table are final.

Changes as of 16 May 2018: None, this table has been discontinued.

When will there be new figures? No longer applicable.

This is a representative sample from the dataset that was used to develop resolution-agnostic convolutional neural networks for tissue segmentation1 in whole-slide histopathology images.

The dataset is composed of two parts: development set and dissimilar set.

Sample images from the development set:

breast_hne_00.tif

breast_lymph_node_hne_00.tif

tongue_ae1ae3_00.tif

tongue_hne_00.tif

tongue_ki67_00.tif

Sample images from the dissimilar set:

brain_alcianblue_00.tif

cornea_grocott_00.tif

kidney_cab_00.tif

skin_perls_00.tif

uterus_vonkossa_00.tif

The California Citizens Redistricting Commission (Commissioner) was established pursuant to the procedures set forth by Proposition 11, the Voters First Act, and Proposition 20, the Voters First Act for Congress, the provisions of which are now found in Section 2 of Article XXI of the California Constitution and at Government Code Section 8252. These constitutional and statutory provisions set forth the Commission’s responsibilities with respect to drawing the boundary lines for the California Assembly, Senate, Board of Equalization and Congressional districts (the Maps).Resolution of August 15, 2011 certifying the statewide California Congressional districts were approved by the Commission in the manner required by Section 2 of Article XXI of the California Constitution; a copy of the statewide Congressional map; copies of the 53 individual Congressional districts; and a “disc” labeled crc_20110815_congress_certified_statewide.zip SHA-1: 1893c0695a42454a202f5b1ef433abff6b491db9 containing the unique data files for the Congressional districts from which the statewide and individual district maps are created.Commission Background:In accordance with the Voters FIRST Act (Act), the California State Auditor randomly selected the first eight members of the first Citizens Redistricting Commission (Commission) on November 18, 2010. These first eight commissioners—three who are Democrats, three who are Republican, and two who are either Decline-to-State or are registered with another party—were part of the 36 eligible applicants that remained in the sub-pools after the legislative leaders, exercised their authority to make strikes and eliminated the names of 24 applicants from the pool of 60 of the most qualified applicants identified on September 23, 2010 by the Auditor’s Applicant Review Panel (Panel). The Panel reviewed and evaluated the applicants based on criteria set forth by the Act approved by voters in November 2008; including relevant analytical skills, the ability to be impartial; and a demonstrated appreciation for California’s diverse demographics and geography.

© California Citizens Redistricting Commission

1. An underlying aim in conservation planning is to maximize the long-term persistence of biodiversity. To fulfill this aim, the ecological and evolutionary processes that sustain biodiversity must be preserved. One way to conserve such processes at the feature level (eg. species, ecosystem) is to preserve a sample of the feature (eg. individuals, areas) that is representative of the intrinsic or extrinsic physical attributes that underpin the process of interest. For example, by conserving a sample of populations with local adaptations---physical attributes associated with adaptation---that is representative of the range of adaptations found in the species, protected areas can maintain adaptive processes by ensuring these adaptations are not lost. Despite this, current reserve selection methods overwhelmingly focus on securing an adequate amount of area or habitat for each feature. Little attention has been directed towards capturing a representative sample of the variation within each feature.

2. To address this issue, we developed the raptr R package to help guide reserve selection. Users set "amount targets"---similar to conventional methods---to ensure that solutions secure a sufficient proportion of area or habitat for each feature. Additionally, users set "space targets" to secure a representative sample of variation in ecologically or evolutionarily relevant attributes (eg. environmental or genetic variation). We demonstrate the functionality of this package using simulations and two case studies. In these studies, we generated solutions using amount targets---similar to conventional methods---and compared them with solutions generated using amount and space targets.

3. Our results demonstrate that markedly different solutions emerge when targeting a representative sample of each feature. We show that using these targets is important for features that have multimodal distributions in the process-related attributes (eg. species with multimodal niches). We also found that solutions could conserve a far more representative sample with only a slight increase in reserve system size.

4. The raptr R package provides a toolkit for making prioritizations that secure an adequate and representative sample of variation within each feature. By using solutions that secure a representative sample of each feature, prioritizations may have a greater chance of achieving long-term biodiversity persistence.

This resource is a member of a series. 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 118th Congress is seated from January 2023 through December 2024. 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 August 31, 2022.