This layer shows education level for adults (25+) by race by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent of adults age 25+ who have a bachelor's degree or higher as their highest education level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B15002, C15002B, C15002C, C15002D, C15002E, C15002F, C15002G, C15002H, C15002I (Not all lines of these ACS tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Raw data from table 1

The Galileo Probe Energetic Particles Investigation (EPI) Raw Data Set contains two tables of the EPI raw data values sorted by sampling time. The counter table contains the raw counter values as measured and the countrate table contains the countrates as derived from counter values, but without any correction. The tables are split into omnidirectional and sectorized data. The distance to Jupiter is given in Jupiter radii, Rj, and was derived from the Probe trajectory data. The time of probe entry is taken to be 1995-12-07T22:04:44Z when the probe reached an altitude of 450 km above the 1 bar pressure level.

Raw data for tables and figures

Raw data table

15 sheets of data, each sheet representing a table from the database that stores the information. The order of the sheets is based on the sequence of reporting forms for the Annual Report.

Raw data was used to make table 1 and table 2

This database includes the raw thermal, deformation, and degassing data tables for the 47 most active volcanoes in Latin America. These data were collected from the time-span of 1 January 2000 to 1 June 2017. Thermal data are measured as °C Above background from the ASTER instrument and Volcanic Radiative Power (VRP) from the MODIS instrument. Degassing data are measured in kt SO2 as both passive and active degassing from the OMI, TOMS, and IASI instruments. Deformation data are measured as Line of Sight (LOS) and Vertical displacement in cm as well as volume change in m3 from the ERS, RSAT, ALOS, Envisat, CSK, Sentinel, TSX, and TDX instruments. This is the full database of all raw data used to generate the time-series in the publication: Thermal, deformation, and degassing remote sensing time-series (A.D. 2000-2017) at the 47 most active volcanoes in Latin America: Implications for Volcanic Systems.

This dataset is about: (Table 08a) Raw data of reflectance spectrophotometer (RSC) from different holes of IODP Site 342-U1406. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.872548 for more information.

This metadata record contains a shapefile of site locations and 3 tables used to assess wetland elevation change over time and the long-term effects of sea-level rise to vulnerable tidal wetlands along the Edgewood peninsula of Aberdeen Proving Ground, Maryland. Included in the dataset are raw pin measurements of wetland sediment elevation and water levels measured for selected sites along Monks Creek and Swaderick Creek, in addition to water levels at the Marina along Bush River collected in 2018-2019.

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests.

Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881. • Kurbucz, M. T., Katona, A. I., Lantos, Z., & Kosztyán, Z. T. (2021). The role of societal aspects in the formation of official COVID-19 reports: A data-driven analysis. International journal of environmental research and public health, 18(4), 1505. • Kurbucz, M. T. (2022). Modeling the social determinants of official COVID-19 reports in the early stages of the pandemic. Journal of Applied Social Science, 16(1), 356-363.

Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.

Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.

Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.

raw correlation Au+Au 20-60%, 3<p_{\text{T}}^{(t)}<4 GeV/c, slice 2

Supplementary Table S1 (all raw data) of "Filtration extraction method using microfluidic channel for measuring environmental DNA ". Each data of the validation experiment; Experiment 1-4 was located in different sheets..

The new material introduced by the ITTF in 2014 for table tennis balls has attracted significant attention from players and coaches. Changes in both material selection and production procedures are likely to have affected the static performance of the ball. However, the raw data regarding the elasticity and hardness of these new material balls, encompassing various brands and structures, often lacks practical information crucial for players’ rapid adaptation and daily training. The static properties tested in this study were provided by the ITTF, covering both hardness and elasticity. Based on computed variables, this study revealed that the hardness of seam balls at the equator was not consistently higher than that at the pole. Additionally, the study confirmed that the hardness and bounce height of new material balls exceeded those of celluloid. Furthermore, correlation analysis was conducted to examine the relationship between these two properties, revealing a significant correlation between the hardness of seamless balls and their elasticity. This study provides an analysis of the static performance of various types of new material balls, aiding players and coaches in better understanding official event balls and offering a theoretical foundation for the formulation of diverse training and game strategies.

This metadata document describes the data contained in the "rawData" folder of this data package. This data package contains all data collected by the Argos System from 32 satellite transmitters attached to Whimbrels on their breeding range in arctic and western Alaska, 2006-2010. Five data files are included in the "rawData" folder of this data package. Two data files (with identical content) contain the raw Argos DIAG (Diagnostic) data, one in the legacy verbose ASCII format and one in a tabular Comma Separate Value (CSV) format. Two other data files (with identical content) contain the raw Argos DS (Dispose) data, one in the legacy verbose ASCII format and one in a tabular CSV format. The fifth file, "deploymentAttributes", contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter. This table is identical to the "deploymentAttributes" table in the "processedData" folder of this data package.

39 sheets of data, each sheet representing a table from the database that stores the information. The order of the sheets is based on the sequence of reporting forms for the Financial Transactions Report.

The file includes Tables S4 to S6 and some raw data that needs to be made public.

This table contains 92 series, with data for years 1977 - 1985 (not all combinations necessarily have data for all years).This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Price index (92 items: Total raw materials; Vegetable products; Total, fresh fruits; Fresh fruit, tropical; ...).

Supplementary and additional Datasets (raw and preprocessed) in relation to future work of the paper Towards Detecting Inauthentic Coordination in Twitter Likes Data by Laura Jahn and Rasmus K. Rendsvig The supplementary data contains liking and retweeting user data and tweet IDs, supplemented with e.g. Botometer's botscores and later lookups regarding existence. A README facilities ## Repository Structure: - [1] Data from Danish Twitter on National Election - [2] Data from German Twitter - [3] Supplementary data to paper_Towards Detecting Inauthentic Coordination in Twitter Likes Data ## Folder content - [1] - Raw Data: Raw data of liking and retweeting users (you might come across #fv22 in file naming: the hashtag #fv22 is an election hashtag about the Danish National Election) - Preprocessed Data: - Binary like-user and retweet-user matrices - Botscores: Botometer v4 and lite scores for all likers and retweeters, also conveniently summarized in feature-frame tables - Clusters: Bins of perfectly correlated users - Later User and Tweets Lookups: Later (January, February 2023) lookup of previously collected users and tweets they likes/retweeted - Likers Retweeters Pagination: Later (January, February 2023) lookup of likers and retweeters using new pagination parameter - [2] - Raw Data: Raw data of liking and retweeting users (you might come across #bundestag in file naming: the hashtag #bundestag is a German political hashtag) - Preprocessed Data: Binary like-user and retweet-user matrices - [3] - Additional dataset dkpol July - Raw Data: Raw data of liking and retweeting users - Preprocessed Data: - Binary like-user and retweet-user matrices - Supp data to data used in paper_Towards Detecting Inauthentic Coordination in Twitter Likes Data - Botscores: Botometer v4 and lite scores for all likers and retweeters, also conveniently summarized in feature-frame tables - Later User and Tweets Lookups: Later (January, February 2023) lookup of previously collected users and tweets they likes/retweeted - Likers Retweeters Pagination: Later (January, February 2023) lookup of likers and retweeters using new pagination parameter

This dataset is about: (Table 09a) Raw data of ln(Ca/K) from different holes of IODP Site 342-U1406. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.872548 for more information.