Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

The Crowdsourced Replication Initiative (CRI) involved 204 researchers who volunteered to engage in a replication of a well-known study on immigration and social policy preferences. In this project, the participants were surveyed four times between August 20th, 2018 and January 20th, 2019. Survey questions with identifying features have been removed to protect participant anonymity and the data are available in the file cri_survey_long_public with labels or *_nolabs, without. The survey included both objective criteria, such as experience with methods and the substantive topic of the replication, and subjective criteria, such as the participants own beliefs about the hypothesis and immigration in general. In addition, they were asked questions about their time commitment, constraints they faced and some other feedback about the process of crowdsourcing. As of 2024, we provide data on the participants’ reviews of the other teams’ models. These review scores were initially not directly useable due to some problems with the 4th wave of the participant survey. The participants were given model descriptions that did not always match with the models they should have reflected. However, we have now used these paragraphs to match descriptions. We were able to match roughly 95% of all models. The new data file peer_model_dyad allows users to analyze data that are in participant-model dyad format. These data are linkable to both the participant survey here, and the CRI model specification and results data on Github (https://github.com/nbreznau/CRI). Because of matching and uneven numbers of models per team, there are some participants whose rankings apply to dozens of models and others only a few. The variable descriptions for these data are in the peer_model_dyad_codebook file. We also now provide dyadic data that matches each participant with each model specification produced by their team in df_dyad. These data contain all model specifications and the AME (Average Marginal Effect) produced by that model.

Over the last two observations, the revenue is forecast to significantly increase in all regions. From the selected regions, the ranking by revenue in the data center market is forecast to be led by Central & Western Europe with ***** billion euro. In contrast, the ranking is trailed by Eastern Europe with **** billion euro, recording a difference of ***** billion euro to Central & Western Europe. The Statista Market Insights cover a broad range of additional markets.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Ethnicity for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01B.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Ethnicity Hispanic Equally Hispanic/non-Hispanic Non-Hispanic Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the...

Controlled source audio-frequency magnetotellurics (CSAMT) data were collected in the Big Chino Valley and Paulden areas, Yavapai County, Arizona, to better understand the hydrogeology of the area. CSAMT data provide vertical cross-section (profile) data about the resistivity of the subsurface, which may be related to lithologic boundaries and (or) grain-size distribution in the subsurface. CSAMT involves transmitting a current at various frequencies in one location, and measuring resistivity differences between electrodes spaced along a receiver line several kilometers from the transmitter. Data were collected using a GGT-30 transmitter and GDP32-II receiver (Zonge international. Inc.). Data processing and inversions were carried out using Zonge International, Inc. software. As a processing step, measured resistivity values must be "inverted", or converted to 2-d vertical resitivity profiles. The inversion step involves identifying a subsurface resistivity model that best matches the data, while accounting for the measurement precision of the data. The primary data format is a .kmz file that can be viewed in Google Earth or other geospatial browsers. The .kmz file can be unzipped to view the source images. Subsurface resistivity cross-sections are shown at their respective geographic locations, elevated above the land surface. The vertical scale for each cross-section is approximately identical. The .kmz file can be downloaded from this page. In addition, raw data, station location data, and inversion data are provided. These files are useful for reprocessing the inversions and testing alternative inversion schemes. They can be downloaded from the child pages linked below. Raw Data: Text files output by the data-collection instrument (GDP32-II, Zonge International, Inc.); averaged values are used in the inversion. These data may be useful for testing alternative inversion schemes. Station Data: Text files with the receiver locations for each line. data were collected using a handheld GPS. Inversion Data: Text files of inverted resistivity values, starting model values, and corresponding x, y, z coordinates These files allow the user to recreate the inversions provded in the accompanying kmz file.

These data were compiled to visualize the extent of Lake Powell at various elevation levels. These data represent water surface elevations for Lake Powell at levels critical to the operation of Glen Canyon Dam, at 5 foot intervals from the "Equalization Tier" ("Full Pool") to "Dead Pool", and at maximum and minimum elevations each water year throughout Glen Canyon Dam's operating history. These data were created for Lake Powell in Arizona and Utah. These data were created by the U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring & Research Center by reclassifying "Modified topobathymetric elevation data for Lake Powell" (Jones and Root, 2021) at discrete elevation levels and converting them into vector format. These data can be used to visualize locations or resources in Lake Powell at various elevation levels as it continues to change.

This file contains the evolution of the population of the municipalities of the Region of Murcia from the financial year 2008 until the last update of the data of the Municipal Register. These data have been provided by the Regional Statistical Center of Murcia.

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014.

Data from: The importance of partner inclusion criteria for understanding drivers of social variation among individuals: Data from blue monkeys

https://doi.org/10.5061/dryad.b2rbnzsns

Description of the data and file structure

 There are three data files in this dataset. Each file summarizes the social ties and sociodemographic, demographic, and individual characteristics of adult female blue monkeys (*Cercopithecus mitis stuhlmanni*) living in multiple groups in the Kakamega Forest, western Kenya, collected over 13 years through focal follow behavioral observations (see Methods in accompanying manuscript). Two individuals are considered to have a social tie (and thus be social partners) if they lived in the same group for at least three-quarters of a year, and were observed to interact with friendly social contact (i.e., grooming each other in either direction or sitting in contact). The strength of ties is defined as the proportio...

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These data are bathymetry (river bottom elevation) and depth-averaged velocities generated from the September 17–18, 2020, survey of the Kentucky Dam tailwater from just downstream from Kentucky Dam to approximately 1,500 feet upstream from the I-24 bridge (about 1 mile total length). Bathymetry and velocity data were collected using an acoustic Doppler current profiler (ADCP) with an integrated global navigation satellite system (GNSS) smart antenna with submeter accuracy. The ADCP and GNSS antenna were mounted on a marine survey vessel, and data were collected as the survey vessel traversed the tailwater along planned survey lines. There was typically one reciprocal pair (two passes) of data collected per line. There was a total of 53 survey lines equally spaced 100 feet apart and oriented approximately perpendicular to the primary flow direction. Data collection software integrated and stored the depth, velocity, and position data from the ADCP and GNSS antenna in real time. Data processing required computer software to extract the bathymetric data from the raw data files and to summarize and map the information. Water-surface elevations were measured at each planned line throughout the survey area with a survey-grade integrated GNSS system with real-time kinematic (RTK) observations in order to convert measured bathymetric depths to elevations referenced to NAVD 88. RTK observations were made using the Kentucky Continually Operating Reference System (KYCORS) network operated by the Kentucky Transportation Cabinet. Data were processed using the Velocity Mapping Toolbox (Parsons and others, 2013) to derive temporally- and spatially-averaged water velocity values. The surveys were conducted during steady discharge conditions from the hydropower turbines at Kentucky Dam. These data were collected to understand flow patterns in the Kentucky Dam tailwater during different discharge conditions from the hydropower turbines at Kentucky Dam and may be used to assist in invasive carp capture and control programs.

This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data about Veteran Status, 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 percentage of the civilian population over the age of 18 that are Veterans.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: 2018-2022ACS Table(s): DP02Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 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. 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:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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 Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

The anion data for the East River Watershed, Colorado, consists of fluoride, chloride, sulfate, nitrate, and phosphate concentrations collected at multiple, long-term monitoring sites that include stream, groundwater, and spring sampling locations. These locations represent important and/or unique end-member locations for which solute concentrations can be diagnostic of the connection between terrestrial and aquatic systems. Such locations include drainages underlined entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of solute concentrations from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on the riverine export of multiple anionic species. Such data may be combined with stream gauging stations co-located at each monitoring site to directly quantify the seasonal and annual mass flux of these anionic species out of the watershed. This data package contains (1) a zip file (anion_data_2014-2024.zip) containing a total of 381 files: 380 data files of anion data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v6_20250515_flmd.csv) file that lists each file contained in the dataset with associated metadata; and (3) a data dictionary (v6_20250515_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 46 locations containing anion data.Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated anion data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, and (5) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format.Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv).Update on 2022-12-20: Updates were made to both the data files and reporting format specific files. Conversion issues affecting ER-PLM locations for anion data was resolved for the data files. Additionally, the flmd and dd files were updated to reflect the updated versions of these files. Available data was added up until 2022-03-14.Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-05-19. The file level metadata and data dictionary files were updated to reflect the additional data added.Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-09-11. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added.Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added.

These data were used to examine grammatical structures and patterns within a set of geospatial glossary definitions. Objectives of our study were to analyze the semantic structure of input definitions, use this information to build triple structures of RDF graph data, upload our lexicon to a knowledge graph software, and perform SPARQL queries on the data. Upon completion of this study, SPARQL queries were proven to effectively convey graph triples which displayed semantic significance. These data represent and characterize the lexicon of our input text which are used to form graph triples. These data were collected in 2024 by passing text through multiple Python programs utilizing spaCy (a natural language processing library) and its pre-trained English transformer pipeline. Before data was processed by the Python programs, input definitions were first rewritten as natural language and formatted as tabular data. Passages were then tokenized and characterized by their part-of-spee ...

To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, and migration from the March Annual Demographic Supplement and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. These data come from Summary File 1 (SF-1). The geographic coverage for SF-1 includes the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, census tracts, block groups and blocks, among others. The data in this particular RGIS Clearinghouse table is for Cibola County and all census block groups in the county. Table DC10_00863 shows median age for all persons (both sexes), for males and for females. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

Terms of UseData Limitations and DisclaimerThe user’s use of and/or reliance on the information contained in the Document shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.This is an ongoing data development project. Attempts have been made to contact all PWS systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some PWS service areas included in this datalayer have not been verified by the PWS or the municipality involved, but since many of those areas are based on information published online by the municipality, the PWS, or in a publicly available report, they are included in the estimated PWS service area datalayer.Please note: All PWS service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a PWS service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the PWS – please contact the relevant PWS. Not all service areas have been confirmed by the systems.Please use the following citation to reference these data:MassDEP, Water Utility Resilience Program. 2025. Community and Non-Transient Non-Community Public Water System Service Area (PubV2025_3).IMPORTANT NOTICE: This MassDEP Estimated Water Service datalayer may not be complete, may contain errors, omissions, and other inaccuracies and the data are subject to change. This version is published through MassGIS. We want to learn about the data uses. If you use this dataset, please notify staff in the Water Utility Resilience Program (WURP@mass.gov).This GIS datalayer represents approximate service areas for Public Water Systems (PWS) in Massachusetts. In 2017, as part of its “Enhancing Resilience and Emergency Preparedness of Water Utilities through Improved Mapping” (Critical Infrastructure Mapping Project ), the MassDEP Water Utility Resilience Program (WURP) began to uniformly map drinking water service areas throughout Massachusetts using information collected from various sources. Along with confirming existing public water system (PWS) service area information, the project collected and verified estimated service area delineations for PWSs not previously delineated and will continue to update the information contained in the datalayers. As of the date of publication, WURP has delineated Community (COM) and Non-Transient Non-Community (NTNC) service areas. Transient non-community (TNCs) are not part of this mapping project.Layers and Tables:The MassDEP Estimated Public Water System Service Area data comprises two polygon feature classes and a supporting table. Some data fields are populated from the MassDEP Drinking Water Program’s Water Quality Testing System (WQTS) and Annual Statistical Reports (ASR).The Community Water Service Areas feature class (PWS_WATER_SERVICE_AREA_COMM_POLY) includes polygon features that represent the approximate service areas for PWS classified as Community systems.The NTNC Water Service Areas feature class (PWS_WATER_SERVICE_AREA_NTNC_POLY) includes polygon features that represent the approximate service areas for PWS classified as Non-Transient Non-Community systems.The Unlocated Sites List table (PWS_WATER_SERVICE_AREA_USL) contains a list of known, unmapped active Community and NTNC PWS services areas at the time of publication.ProductionData UniversePublic Water Systems in Massachusetts are permitted and regulated through the MassDEP Drinking Water Program. The WURP has mapped service areas for all active and inactive municipal and non-municipal Community PWSs in MassDEP’s Water Quality Testing Database (WQTS). Community PWS refers to a public water system that serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents.All active and inactive NTNC PWS were also mapped using information contained in WQTS. An NTNC or Non-transient Non-community Water System refers to a public water system that is not a community water system and that has at least 15 service connections or regularly serves at least 25 of the same persons or more approximately four or more hours per day, four or more days per week, more than six months or 180 days per year, such as a workplace providing water to its employees.These data may include declassified PWSs. Staff will work to rectify the status/water services to properties previously served by declassified PWSs and remove or incorporate these service areas as needed.Maps of service areas for these systems were collected from various online and MassDEP sources to create service areas digitally in GIS. Every PWS is assigned a unique PWSID by MassDEP that incorporates the municipal ID of the municipality it serves (or the largest municipality it serves if it serves multiple municipalities). Some municipalities contain more than one PWS, but each PWS has a unique PWSID. The Estimated PWS Service Area datalayer, therefore, contains polygons with a unique PWSID for each PWS service area.A service area for a community PWS may serve all of one municipality (e.g. Watertown Water Department), multiple municipalities (e.g. Abington-Rockland Joint Water Works), all or portions of two or more municipalities (e.g. Provincetown Water Dept which serves all of Provincetown and a portion of Truro), or a portion of a municipality (e.g. Hyannis Water System, which is one of four PWSs in the town of Barnstable).Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder. The location of these circles are estimates based on the general location of the source wells or the general estimated location of the service area - these do not represent the actual service area.Service areas were mapped initially from 2017 to 2022 and reflect varying years for which service is implemented for that service area boundary. WURP maintains the dataset quarterly with annual data updates; however, the dataset may not include all current active PWSs. A list of unmapped PWS systems is included in the USL table PWS_WATER_SERVICE_AREA_USL available for download with the dataset. Some PWSs that are not mapped may have come online after this iteration of the mapping project; these will be reconciled and mapped during the next phase of the WURP project. PWS IDs that represent regional or joint boards with (e.g. Tri Town Water Board, Randolph/Holbrook Water Board, Upper Cape Regional Water Cooperative) will not be mapped because their individual municipal service areas are included in this datalayer.PWSs that do not have corresponding sources, may be part of consecutive systems, may have been incorporated into another PWSs, reclassified as a different type of PWS, or otherwise taken offline. PWSs that have been incorporated, reclassified, or taken offline will be reconciled during the next data update.Methodologies and Data SourcesSeveral methodologies were used to create service area boundaries using various sources, including data received from the systems in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and PWS websites. When provided with water line data rather than generalized areas, 300-foot buffers were created around the water lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas.Verification ProcessSmall-scale PDF file maps with roads and other infrastructure were sent to every PWS for corrections or verifications. For small systems, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the PWS and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all or nearly all of the municipality is served by the PWS.Sources of information for estimated drinking water service areasThe following information was used to develop estimated drinking water service areas:EOEEA Water Assets Project (2005) water lines (these were buffered to create service areas)Horsely Witten Report 2008Municipal Master Plans, Open Space Plans, Facilities Plans, Water Supply System Webpages, reports and online interactive mapsGIS data received from PWSDetailed infrastructure mapping completed through the MassDEP WURP Critical Infrastructure InitiativeIn the absence of other service area information, for municipalities served by a town-wide water system serving at least 97% of the population, the municipality’s boundary was used. Determinations of which municipalities are 97% or more served by the PWS were made based on the Percent Water Service Map created in 2018 by MassDEP based on various sources of information including but not limited to:The Winter population served submitted by the PWS in the ASR submittalThe number of services from WQTS as a percent of developed parcelsTaken directly from a Master Plan, Water Department Website, Open Space Plan, etc. found onlineCalculated using information from the town on the population servedMassDEP staff estimateHorsely Witten Report 2008Calculation based on Water System Areas Mapped through MassDEP WURP Critical Infrastructure Initiative, 2017-2022Information found in publicly available PWS planning documents submitted to MassDEP or as part of infrastructure planningMaintenanceThe

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. These data represent the results of data collection/processing for a specific Department of Natural Resources - Maryland Geological Survey activity and indicate general existing conditions. As such - they are only valid for the intended use - content - time - and accuracy specification. The user is responsible for the results of any application of the data for other than their intended purpose. The Department of Natural Resources - Maryland Geological Survey makes no warranty - expressed or implied - as to the use or appropriateness of the data - and there are no warranties of merchantability or fitness for a particular purpose of use. The Maryland Geological Survey makes no representation to the accuracy or completeness of the data and may not be held liable for human error or defect. Data should not be used at a scale greater than that. By using the data - you signify that you have read the use constraints and accept its terms. Acknowledgment of the Maryland Geological Survey and credit to the originator(s)/author(s) are expected in products derived from this data. Bathymetric data reproduced from NOAA bathymetric database at http://maps.ngdc.noaa.gov/ Last Updated: Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/Elevation/MD_Bathymetry/MapServer/4 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States as of the initial date of reporting for each weekly metric. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

• County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
• Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA.
• For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.
• For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

• Time period: data for the previous MMWR week (Sunday-Saturday) will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction
• New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week).
• New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data]
• New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data]
• COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data]
• COVID-19 Inpatient Bed Occupancy percent change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data]
• COVID-19 ICU Bed Occupancy percent change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past week, compared with the prior week, in the in the entire jurisdiction.
• For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.

Notes: June 15, 2023: Due to incomplete or missing hospital data received for the June 4, 2023, through June 10, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and AS and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 15, 2023.

July 10, 2023: Due to incomplete or missing hospital data received for the June 25, 2023, through July 1, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and AS and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on July 10, 2023.

July 17, 2023: Due to incomplete or missing hospital data received for the July 2, 2023, through July 8, 2023, reporting

Data Set Overview In total 2304 images were returned on encounter night between 20:54 and 00:03 UT. Of these images, a total of 2017 are present in the data set submitted to IHW. Images taken in photometer mode have not been submitted. These data were obtained by using the spin of the spacecraft to scan the sky while the CCD remained unclocked. They therefore have one dimensional spatial information but each pixel contains the integrated intensity from some portion (depending upon the exposure time) of an annulus on the sky. These data would be useful for this purpose (particularly when taken through the narrowband filters because of the significantly higher exposure time) were it not for the stray light entering the optics of the camera when HMC was on the sunward side of the spacecraft. No effort has been made to reduce this data and its scientific usefulness is assumed to be negligible. The last three image sets returned in multidetector mode (MDM) immediately prior to the power disturbance which terminated operations before closest approach are also excluded. Image set 3504 does contain useful data but is corrupted and requires manual reduction. This task has not been completed at this time. Image sets 3505 and 3506 are also corrupted and probably do not contain useful image data. Seven images taken at the beginning of the encounter sequence (image ids 674 to 680) were not correctly converted by the telemetry conversion routine. These images are not currently in the HMC database system and are therefore not included in the IHW data set. The similarity between these data and the subsequent data probably ensures that, for scientific evaluation of HMC data, their omission is of little or no importance. One image (3142) has been omitted because it does not have an associated header. Available data The total numbers of images taken in each superpixel format (SPF) in the IHW data set are shown in Ta truncated!, Please [truncated!, Please see actual data for full text]