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.

In 2024, Germany ranked first by revenue in the data center market among the 27 countries presented in the ranking. Germany's revenue amounted to ************* U.S. dollars, while France and Italy, the second and third countries, had records amounting to ************* U.S. dollars and ************ U.S. dollars, respectively.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Data Center.

This dataset provides data at the county level for the contiguous United States. It includes daily Global Horizontal Irradiance (GHI) data from 1991-2012 provided by the Environmental Remote Sensing group at the Rollins School of Public Health at Emory University. Please refer to the metadata attachment for more information.

These data are used by the CDC's National Environmental Public Health Tracking Network to generate sunlight and ultraviolet (UV) measures. Learn more about sunlight and UV on the Tracking Network's website: https://ephtracking.cdc.gov/showUVLanding.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

Problems or Questions? Email trackingsupport@cdc.gov.

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 version 3.1.1.2020f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.1.0.2019f to include 2020 and so spans 1931-2020. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20210101_v3-1-1-2020f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

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.

Ce paquet de données fait partie du projet collaboratif Marna Wet Lab de l'Institut Hakai et du Martone Lab (Université de la Colombie-Britannique, UBC) qui étudie les effets environnementaux sur les algues coralliennes. Le programme de recherche expérimentale Marna Wet Lab de l'Institut Hakai utilise des expériences de laboratoire pour évaluer les réponses des organismes marins à des conditions environnementales océaniques simulées actuelles et futures. L'objectif principal de la recherche expérimentale du Hakai Wet Lab est d'étudier les mécanismes de vulnérabilité et de résilience de diverses espèces et communautés marines dans des conditions environnementales futures statiques ou dynamiques, et de comprendre comment les organismes réagissent phénotypiquement, physiologiquement et/ou génomiquement au stress thermique et à l'acidification.

Les algues corallines constituent un groupe diversifié d'algues rouges calcifiantes qui peuplent un large éventail d'environnements marins dans le monde entier où elles fournissent un soutien structurel aux récifs, créent un habitat et des ressources alimentaires pour les invertébrés et soutiennent la biodiversité en favorisant le recrutement de larves et de varech. L'une des caractéristiques uniques de ce groupe d'algues rouges est qu'elles déposent du carbonate de calcium dans leurs parois cellulaires végétatives, créant ainsi une structure de thalle rigide essentielle au soutien et à l'habitat. Bien que la calcification soit un processus clé pour la physiologie et l'écologie des algues coralliennes, on en sait peu sur les mécanismes moléculaires, physiologiques et cellulaires qui la soutiennent et sur la manière dont ceux-ci peuvent être affectés par les changements climatiques. Des travaux sur d'autres espèces tropicales d'algues coralliennes ont toutefois suggéré que les algues calcifiantes pourraient être particulièrement sensibles au stress thermique et à l'acidification, bien que les réponses des espèces tempérées soient largement sous-étudiées.

Des travaux sont en cours dans le laboratoire du Dr Patrick Martone pour identifier des gènes de calcification putatifs à l'aide de transcriptomes spécifiques aux tissus (calcifiés ou non calcifiés) (RNASeq) chez l'algue coralline articulée, Calliarthron tuberculosis. L'objectif de ce travail est de comprendre les fondements moléculaires du mécanisme de calcification en mettant en évidence les principaux gènes de calcification et en développant des amorces qPCR spécifiques aux gènes pour étudier l'expression des gènes. Sur la base de ces travaux fondamentaux, nous proposons d'explorer les effets interactifs du pH et de la température sur une population de Calvert de Calliarthron en nous concentrant sur la calcification, l'expression génique, la croissance et les réponses physiologiques au stress par le biais d'une expérience en mésocosme de plusieurs semaines.

Ce paquet de données comprend une partie des données de cette expérience concernant la température du mésocosme et la chimie des carbonates et les protocoles associés, le traitement et l'analyse de ces données collectées par l'équipe du Marna Wet Lab. Des données expérimentales supplémentaires sont détenues par nos collaborateurs Emma Jourdain et Patrick Martone (UBC).

Compte tenu des efforts nécessaires pour obtenir ces données et créer des packages de données, nous demandons à tous les utilisateurs de données de respecter les termes de la licence CC-BY, de citer les fournisseurs de données et de suivre les directives d'utilisation équitable : 1) respecter les fournisseurs de données et fournir des commentaires utiles sur la qualité des données, et 2) communiquer et/ou collaborer avec les chercheurs et collaborateurs du Hakai Marna Wet Lab si vous envisagez d'utiliser cet ensemble de données pour des manuscrits ou d'autres formes de reporting.

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 ...

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

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.

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.

This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].

This map symbolizes the relative population counts for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2018 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate more population.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2014-2018 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.

These data were automated to provide an accurate high-resolution historical shoreline of Wequetequock Cove and Fishers Island Sound, Connecticut and Rhode Island suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpre...

This project has received funding from the H2020 innovation and research program of the European Commission under the Marie Sklodowska-Curie grant no: 846077, entitled “Quality of Service for the Internet Things in Smart Cities via Predictive Networks".

Each file consists of time series data which are the number of bits at each sampling.

• Purpose of the data: These data are provided to aid researchers who aim to develop methods to process IoT sensor traffic data.
• Time and date of creation and availability: These data were collected as part of the QoSIoTSmartCities project in the years 2019 – 2021 and made available on this site.
• Data file size: The data file size appears next to the data file.
• Data Units: All of the units of these data are “bits”

The data produced in the project is useable by third parties with the disclaimer that the Coordinator, the funding agency, and the host institution bear no responsibility whatsoever, legal or otherwise, that result from the re-use of these data sets.

Data Access Policy Management Market Outlook

According to our latest research, the global Data Access Policy Management market size in 2024 stands at USD 2.3 billion, reflecting the growing prioritization of data security and compliance across industries. The market is experiencing robust expansion, with a projected CAGR of 13.2% from 2025 to 2033. By 2033, the market is forecasted to reach an impressive USD 6.7 billion. This growth is primarily driven by increasing regulatory requirements, the rapid adoption of cloud technologies, and the ever-expanding digital footprint of organizations worldwide. As per our latest research, organizations are investing heavily in advanced data access policy management solutions to ensure secure, compliant, and efficient access to critical data assets.

A key growth factor for the Data Access Policy Management market is the intensifying regulatory landscape. With the introduction and enforcement of data protection regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA), organizations are under immense pressure to manage and monitor data access efficiently. These regulations mandate strict controls over who can access sensitive data, how access is granted, and how access activities are audited. Non-compliance can result in severe financial penalties and reputational damage, prompting organizations across sectors to invest in comprehensive data access policy management solutions. The demand for automated policy enforcement, real-time monitoring, and detailed audit trails is higher than ever, spurring innovation and adoption in this market.

Another significant driver is the accelerated adoption of cloud computing and hybrid IT environments. As organizations migrate their workloads to public and private clouds, the complexity of managing data access policies across diverse platforms increases exponentially. Traditional access management approaches often fall short in these dynamic environments, necessitating more sophisticated, centralized solutions that can enforce consistent policies regardless of where data resides. The need to support remote workforces and facilitate secure collaboration further amplifies the demand for robust data access policy management tools. These solutions not only help organizations maintain control over their data but also enhance operational agility by enabling secure, role-based access to information assets.

Furthermore, the proliferation of digital transformation initiatives is fueling market growth. Enterprises are leveraging big data, artificial intelligence, and Internet of Things (IoT) technologies to gain competitive advantage, resulting in a dramatic increase in data volume and diversity. Managing access to this expanding data landscape requires scalable and flexible policy management frameworks. Organizations are seeking solutions that can integrate seamlessly with existing identity and access management (IAM) systems, support granular policy definition, and provide real-time insights into access activities. The integration of advanced analytics and machine learning capabilities into data access policy management solutions is enabling proactive risk identification and policy optimization, further driving market expansion.

From a regional perspective, North America continues to dominate the Data Access Policy Management market, owing to the presence of leading technology providers, stringent regulatory requirements, and high awareness of data security best practices. Europe follows closely, driven by strong regulatory enforcement and increasing digitalization across industries. The Asia Pacific region is witnessing the fastest growth, propelled by rapid economic development, increasing digital adoption, and evolving regulatory frameworks. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions ramp up their investments in data security and compliance infrastructure. The global nature of data flows and the interconnectedness of business ecosystems underscore the importance of robust data access policy management across all regions.

Component Analysis

The Data Access Policy Management market is segmented by component into software and services, each playing a pivotal role in the overall value proposition. The software segment encompasses standalone policy management platforms as well

This CNIG data standard concerns local planning documents (LDPs) and land use plans (POSs that are PLU). This data standard provides a technical framework describing in detail how to dematerialise these town planning documents in a spatial database that can be used by a GIS tool and interoperable. This standard of data covers both the graphical plans of sectors and the information overlaying them. This CNIG data standard was developed on the basis of the specifications for the dematerialisation of planning documents created in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The CNIG data standard provides definitions and a structure for organising and storing spatial data from communal maps in an infrastructure, while the CNIG specifications are used to frame the digitisation of these data. The ‘Data Structure’ section presented in this CNIG standard provides additional recommendations for the storage of data files. These are specific choices for the common data infrastructure of the ministries responsible for agriculture and sustainable development, which do not apply outside their context.

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.

This data set was extracted from an original set that shows the coal fields of Alaska and the conterminous United States. Most of the material for the conterminous United States was collected from James Trumbull's "Coal Fields of the United States, Conterminous United States" map (sheet 1, 1960). The Gulf Coast region was updated using generalized, coal-bearing geology obtained from State geologic maps. The Alaska coal fields were collected from Farrell Barnes's "Coal Fields of the United States, Alaska" map (sheet 2, 1961). (National Atlas of the United States, 2002) Purpose: These data are intended for geographic display and analysis at the National level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:5,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. Shapefiles were obtained from the National Atlas of the United States web site. See the Full Metadata page for process step information pertaining to the creation of the original data.

Speed Limit Data Services Market Outlook

According to our latest research, the global speed limit data services market size in 2024 stands at USD 2.1 billion, with a robust compound annual growth rate (CAGR) of 13.8% projected through the forecast period. By 2033, the market is anticipated to reach USD 6.2 billion, driven by the rapid adoption of intelligent transportation systems, the proliferation of connected vehicles, and the growing need for real-time road safety solutions. This growth is further fueled by increasing regulatory mandates for speed compliance, advancements in data analytics, and the integration of artificial intelligence in mobility ecosystems.

A key growth factor for the speed limit data services market is the surging demand for advanced navigation and driver assistance systems across the automotive industry. As vehicles become increasingly connected and autonomous, the need for accurate, dynamic, and up-to-date speed limit data has become critical for ensuring road safety and regulatory compliance. Automakers and technology providers are investing heavily in integrating real-time speed limit information into navigation systems, adaptive cruise control, and advanced driver-assistance systems (ADAS), reducing the risk of speeding incidents and enhancing the overall driving experience. Additionally, the rise in smart city initiatives worldwide has accelerated the deployment of digital infrastructure capable of supporting real-time data exchange between vehicles and central traffic management systems, further propelling market growth.

Another major driver is the exponential growth in the adoption of fleet management and telematics solutions. Logistics and transportation companies are leveraging speed limit data services to optimize route planning, monitor driver behavior, and ensure compliance with local traffic regulations. By integrating these data services, fleet operators can reduce operational costs, improve fuel efficiency, and minimize the likelihood of traffic violations, which directly translates to lower insurance premiums and enhanced brand reputation. The increasing emphasis on road safety by government agencies and insurance providers is also encouraging the widespread use of speed limit data analytics to assess risk profiles, determine insurance premiums, and develop targeted safety programs for high-risk areas.

Furthermore, technological advancements in data collection, processing, and analytics have significantly expanded the capabilities of speed limit data services. The integration of crowdsourced data, sensor-based inputs, and government databases allows for the creation of highly accurate and context-aware speed limit maps. Artificial intelligence and machine learning algorithms are being employed to predict changes in speed limits due to construction, weather conditions, or special events, enabling proactive decision-making for both drivers and traffic management authorities. The shift towards cloud-based platforms and open data standards is fostering greater interoperability among stakeholders, enhancing the scalability and reliability of speed limit data services across global markets.

From a regional perspective, North America and Europe remain the frontrunners in the adoption of speed limit data services, owing to their mature automotive industries, stringent road safety regulations, and early investments in intelligent transportation infrastructure. Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, increasing vehicle ownership, and government-led smart mobility initiatives. Countries such as China, Japan, and South Korea are witnessing significant investments in digital mapping technologies and autonomous vehicle testing, creating lucrative opportunities for market players. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing awareness of road safety and the implementation of digital traffic management solutions. These regional dynamics underscore the global nature of the speed limit data services market and highlight the need for tailored solutions to address diverse regulatory and infrastructural challenges.