The Region Acceptance Process (RAP) is a component of the Integrated Regional Water Management (IRWM) Program Guidelines and is used to evaluate and accept an IRWM region into the IRWM grant program. The RAP is not a grant funding application; however, acceptance of the composition of an IRWM region (including the IRWM region’s boundary) into the IRWM grant program is required for DWR IRWM grant funding eligibility.This dataset includes:the boundaries of the most current IRWM Regions (as submitted to DWR by the respective IRWM planning region)their RAP status (Accepted, Not Accepted or Conditional) as conferred by DWR the year each entity participated in the RAPa descriptive field noting the date of any subsequent IRWM boundary changes submitted and accepted by DWR

This is a Treatment Episode Data Set (TEDS) short report showing the age of substance use initiation among treatment admissions aged 18 to 30 in 2011. The Treatment Episode Data Set (TEDS) is a national data system of annual admissions to substance abuse treatment facilities. TEDS received treatment admission records from 46 states, the District of Columbia, and Puerto Rico in 2011.

Evolution acceptance comparison between religious and no religious participants.

The global electrical acceptance testing market is booming, driven by renewable energy growth and stringent safety regulations. Discover key trends, market size projections (2025-2033), leading companies, and regional analysis in this comprehensive market report. Learn about high-voltage testing, low-voltage testing, and more.

Credit Acceptance - 현재 값, 이력 데이터, 예측, 통계, 차트 및 경제 달력 - Nov 2025.Data for Credit Acceptance including historical, tables and charts were last updated by Trading Economics this last November in 2025.

raw and clean data, code for reliability, group comparison in terms of perceptions, interaction, ANCOVA

Australia Liabilities: Flow: Non Life Insurance Corporations: Acceptance of Bills of Exchange Drawn by: Banks data was reported at 0.000 AUD mn in Dec 2024. This stayed constant from the previous number of 0.000 AUD mn for Sep 2024. Australia Liabilities: Flow: Non Life Insurance Corporations: Acceptance of Bills of Exchange Drawn by: Banks data is updated quarterly, averaging 0.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 27.000 AUD mn in Dec 2011 and a record low of -31.000 AUD mn in Dec 2017. Australia Liabilities: Flow: Non Life Insurance Corporations: Acceptance of Bills of Exchange Drawn by: Banks data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB025: SNA08: SESCA08: Funds by Sector: Financial Corporations: Non Life Insurance Corporations: Flow.

NCHS has linked data from various surveys with Medicare program enrollment and health care utilization and expenditure data from the Centers for Medicare & Medicaid Services (CMS). Linkage of the NCHS survey participants with the CMS Medicare data provides the opportunity to study changes in health status, health care utilization and costs, and prescription drug use among Medicare enrollees. Medicare is the federal health insurance program for people who are 65 or older, certain younger people with disabilities, and people with End-Stage Renal Disease.

Bradford Beck is the largest watercourse running through Bradford city centre. This dataset consists of a multipart polygon describing the spatial extents of modelled flooding for a rainfall event that has a 5% probability in any year plus 30% additional rainfall for a climate change adjustment.Typically, river and sewer systems are modelled with tools which are tailored for those purposes. Although the hydraulic processes modelling at the core of the different tools may be similar, the user interfaces, data management systems and hydrological modelling are designed to meet different requirements and reflect the differences in the nature of the different drainage systems. The diversity of the river system and the land use presents a number of choices with respect to the modelling methodology.To the west of Bradford, the predominantly rural land use and open (natural) watercourses are best represented by river modelling software. However, the urban land use and the nature of the drainage system with culverted watercourses are better represented by sewer simulation software.The City of Bradford Metropolitan District Council (CBMDC) acquired InfoWorks CS 2D to carry out this type of modelling. This software is accepted by the Environment Agency as being suitable for this type of assessment. The Bradford Beck system is represented by a 1D network of nodes and conduits, the information used to construct the network being obtained from a mixture of historic and contemporary surveys. The 2D surface networks have been created from the 1m horizontal resolution LIDAR data set procured by CBMDCThe hydraulic model is driven by hydrographs generated using the Revitalised Flood Hydrograph (ReFH) method2. Hydrographs were generated at the start of each main branch in the model and at key intermediate points along the length of the model.The simulations modelled in InfoWorks use a 1 metre horizontal resolution Digital Elevation Model (DEM), which results in a much higher resolution of detail (including vertical resolution). It is important to remember that the hydrological and hydraulic representations in the modelling are simplifications of reality and that it is important not to place too much emphasis on perceptions of accuracy resulting from the increased resolution.

MY NASA DATA (MND) is a tool that allows anyone to make use of satellite data that was previously unavailable.Through the use of MND’s Live Access Server (LAS) a multitude of charts, plots and graphs can be generated using a wide variety of constraints. This site provides a large number of lesson plans with a wide variety of topics, all with the students in mind. Not only can you use our lesson plans, you can use the LAS to improve the ones that you are currently implementing in your classroom.

The use of data from Washington County indicates the acceptance of and agreement to be legally bound by the terms of Washington County printed below. Disclaimer. Washington County has provided these Geographic Information System maps and data as a public information service. Every reasonable effort has been made to assure the accuracy of these maps and associated data. However, the maps and data being provided herein are intended for informational purposes only. No guarantee is made as to the accuracy of the maps and data and they should not be relied upon for any purpose other than general information. No LiabilityWashington County assumes no liability arising from the use of these maps or data. The maps and data are provided without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Furthermore, Washington County assumes no liability for any errors, omissions, or inaccuracies in the information provided regardless of the cause of such or for any decision made, action taken, or action not taken by the user in reliance upon any maps or data provided herein. Please consult official County maps and records for official information. IndemnificationIf user disseminates said data in any form or fashion to a third party, the user agrees to indemnify and hold harmless Washington County and its officials and employees from any and all claims, liability, damages, injuries, and suits, including court costs and reasonable attorney’s fees, arising from the use of the Washington County data by the user and any third party.

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].

Signal acceptance of $(\tilde{H},\tilde{B})$ simplified models (N2N3-hh) by their most relevant SRs, evaluated using MC simulation. The acceptance is given...

The dataset consists of closed cases that resulted in penalty assessments by EBSA since 2000. This data provides information on EBSA's enforcement programs to enforce ERISA's Form 5500 Annual Return/Report filing requirement focusing on deficient filers, late filers and non-filers.

Dataset tables listing: EBSA Data Dictionary, EBSA Metadata and EBSA OCATS.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Link to the Open Data site for the United States Census Bureau.

This data set was collected by TE-01 to provide a set of soil properties for BOREAS investigators in the SSA. The soil samples were collected at sets of soil pits. Each set of soil pits was in the vicinity of one of the five flux towers in the BOREAS SSA. The collected soil samples were sent to a lab, where the major soil properties were determined. These properties include, but are not limited to, soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus; particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur.

The Maine Geological Survey and the USGS coordinate the colletction of snow measurements each winter for the Maine River Flow Advisory Commission's flood prediction report. These measurements are sent to MGS monthly in January and February and weekly in March, April and May as long as there is snow on the ground. The dataset contains all the raw snow survey measurements (depth (inches), water content (inches), and density), their locations, data quality, and other qualitative comments or observations. These measurements are used to create the snow survey statewide maps.

From https://www.bts.gov/faf/county:The Freight Analysis Framework (FAF) database provides estimates of the weight and value of shipments throughout the United States for all commodity types and forms of transportation using a geographic system of 132 FAF zones. The Bureau of Transportation Statistics (BTS) developed an experimental county-to-county commodity flow product to provide the user community with more geographically granular commodity flow data to support planning, policymaking, and operational decisions at the state and local levels. Users can download state-specific files or the entire set of disaggregation factors to create customized queries. This experimental product provides flows for five commodity groups and five mode categories (see documentation for more details). BTS welcomes users to email FAF@dot.gov with feedback on this experimental product.The state FIPS code is also shown next to the state. Each zip file contains four tables with 1) county-level OD flows for the state of interest and every adjacent state, 2) county-to-FAF OD flows from the multi-state area to all other FAF zones, 3) FAF-to-county OD flows from all other FAF zones to the multi-state area, and 4) FAF-to-FAF OD flows from all other FAF zones to all other FAF zones. The files use county-level geography for the state of interest and states adjacent to this state. FAF zones represent flows outside of this area.The main Freight Analysis Framework files are loaded to Data Lumos separately here: https://www.datalumos.org/datalumos/project/231642/version/V1/view. Additional documentation is available at that link.The faf5_county_readme.txt and faf5_county_readme.xlsx were created for this upload and were not created by the DOT. The direct url to download each state-level dataset is in faf5_county_readme.xlsx.

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR collected AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data were collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and was completed in 2023. Additional information about the project can be found on the Statewide AEM Survey website. See the publication below for an overview of the project and a preliminary analysis of the AEM data.

California’s Statewide Airborne Electromagnetic Surveys and Preliminary Hydrogeologic Interpretations

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area:

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology and Geophysical Logs

NEW! Revisions to digitized lithology and geophysical logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page.

• AEM 3D Viewer (Beta) (computer only)

• AEM Profile Viewer

• SGMA Data Viewer (Basin Characterization tab)

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

• Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps

• Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal

• SkyTEM Instrument Comparison for Airborne EM

2018-2020 AEM Pilot Studies

Three AEM pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.