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- API data.sunshinecoast.qld.gov.au | Last Updated 2022-07-13T22:48:07.000Z
"The Obstacle Limitation Surface (OLS) (line) layer forms part of the Airport Environs Overlay Maps within the Sunshine Coast Planning Scheme 2014. The Obstacle Limitation Surface (OLS) (line) layer identifies the Obstacle Limitation Surface. The purpose of the Runway Separation Distances (polygon) is to trigger the Runway Seperation Distances in the search function of the Sunshine Coast Planning Scheme interactive mapping. This layer is for the purpose of the Sunshine Coast Planning Scheme 2014 only. Please contact Council on 5475 7526 or email email@example.com for more information on the Sunshine Coast Planning Scheme 2014. Notes on Airport Environs Overlay Maps ─ * Overlays provide a trigger for consideration of an overlay issue to be verified by further on-site investigations. * In certain circumstances pre-existing development approvals may override the operation of an overlay."
- API data.sunshinecoast.qld.gov.au | Last Updated 2022-07-14T23:58:21.000Z
"The Obstacle Limitation Surface (OLS) (polygon) layer forms part of the Airport Environs Overlay Maps within the Sunshine Coast Planning Scheme 2014. The purpose of this layer is to trigger the Obstacle Limitation Surface in the search function of the Sunshine Coast Planning Scheme interactive mapping. The Obstacle Limitation Surface (line) layer identifies the Obstacle Limitation Surface. This layer is for the purpose of the Sunshine Coast Planning Scheme 2014 only. Please contact Council on 5475 7526 or email firstname.lastname@example.org for more information on the Sunshine Coast Planning Scheme 2014. Notes on Airport Environs Overlay Maps ─ * Overlays provide a trigger for consideration of an overlay issue to be verified by further on-site investigations. * In certain circumstances pre-existing development approvals may override the operation of an overlay."
- API data.ny.gov | Last Updated 2019-06-10T18:02:12.000Z
The Behavioral Health Organization (BHO) initiative oversees the transition to managed care for Medicaid recipients who receive mental health (MH) and substance use disorder (SUD) services in New York State. The metrics emphasize improving rates of timely follow-up treatment post discharge, timely filling of appropriate medication prescriptions post discharge, and reducing rates of readmission.The BHO MH Engagement in Care dataset is designed to assess the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving two or more outpatient mental health visits within thirty days of discharge and the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving four or more outpatient mental health visits within 60 days of discharge. The year 2015 saw the conclusion of the first phase of the Behavioral Health Organization initiative (BHO). A new Behavioral Health Managed Care Transition phase II is underway. The data contained in the BHO metrics span 2010 to 2014, using the 2010 calendar year for a baseline. Earlier in the program (2011‐2012) the metrics were calculated quarterly and on a year‐to‐date basis, later in (2013‐2014), New York State Office of Mental Health opted for semi‐annual and year‐to‐date aggregations.
- API data.pa.gov | Last Updated 2022-10-17T20:05:23.000Z
This data set provides an estimate of the number of people aged 15-34 years with newly identified confirmed chronic (or past/present) hepatitis C infection, by county and by year. The dataset is limited to persons aged 15 to 34 because hepatitis C infection is usually asymptomatic for decades after infection occurs. Cases are usually identified because they have finally become symptomatic, or they were screened. Until very recently, screening for hepatitis C was not routinely performed. This makes it very challenging to identify persons with recent infection. Limiting the age of newly identified patients to 15-34 years makes it more likely that the cases included in the dashboard were infected fairly recently. It is not meant to imply that the opioid crisis’ effect on hepatitis C transmission is limited to younger people. The process by which case counts are determined is as follows: Case reports, which include lab test results and address data, are sent to Pennsylvania’s electronic disease surveillance system (PA-NEDSS). Confirmation status is determined by public health investigators who evaluate test results against the CDC case definition for hepatitis C in place for the year in which the patient was first reported (https://wwwn.cdc.gov/nndss/conditions/hepatitis-c-chronic/). Reportable disease data, including hepatitis C, is extracted from PA-NEDSS, combined with similar data sent by the Philadelphia Department of Public Health (PDPH, which uses a separate surveillance system), and sent to CDC. Case data sent to CDC (from PA-NEDSS and PDPH combined) are used to create a statewide reportable disease dataset. This statewide file was used to generate the dashboard dataset. Note that the term that CDC has used to denote persons with hepatitis C infection that is not known to be acute has switched back and forth between “Hepatitis C, past or present” and “Hepatitis C, chronic” over the past several years. The CDC case definition for hepatitis C, chronic (or past or present) changed in 2005, 2010, 2011, 2012, and 2016. Persons reported as confirmed in one year may not have been considered confirmed in another year. For example, patients with a positive radioimmunoblot assay (RIBA) or elevated enzyme immunoassay (EIA) signal-to-cutoff level were counted as confirmed in 2012, but not counted as confirmed in 2016. Data sent to CDC’s National Notifiable Disease Surveillance System use a measure for aggregating cases by year called the MMWR year. The MMWR, or the Morbidity and Mortality Weekly Report, is an official publication by CDC and the means by which CDC has historically presented aggregated case count data. Since data in the MMWR are presented by week, the MMWR year always starts on the Sunday closest to Jan 1 and ends on the Saturday closest to Dec 31. The most recent year for which case counts are finalized is 2016. Annual case counts are finalized in May of the following year. The patient zip code, as submitted to PA-NEDSS, is used to determine the case’s county of residence at the time of initial case report. In some instances, the patient zip code is unavailable. In those circumstances, the zip code of the provider that ordered the lab test is used as a proxy for patient zip code. Users should note that the state prison system routinely screens all incoming inmates for hepatitis C. If these inmates are determined to be confirmed cases, they are assigned to the county in which they were incarcerated when their confirmed hepatitis C was first identified. Hepatitis C case counts in counties with state prisons should be interpreted cautiously in light of this enhanced screening activity.
- API healthdata.gov | Last Updated 2021-07-17T03:04:24.000Z
This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Percentage of Californians who receive care in an integrated system, defined as a Health Maintenance Organization that is tracked by the Department of Managed Health Care. Managed care refers to health care coverage that organizes doctors, hospitals and other providers into groups in order to enhance the quality and cost effectiveness of medical treatment. Today, 58 California counties receive their health care through six main models of managed care: Two-Plan, County Organized Health Systems (COHS), Geographic Managed Care (GMC), Regional Model (RM), Imperial, and San Benito. County enrollment information is compiled by Department of Managed Health Care Licensed Full Service Health Plans. This enrollment information is not standardized and may be designated by the member’s place of employment or home resulting in reporting inaccuracies.
RSBS SMO: Kitchen Appliances, New York State Residential Statewide Baseline Study: Single and Multifamily Occupant Telephone or Web Surveydata.ny.gov | Last Updated 2019-11-15T22:21:25.000Z
How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes 2,982 single-family and 379 multifamily occupant survey completes for a total of 3,361 responses. The survey involved 2,285 Web, 1,041 telephone, and 35 mini-inspection surveys. The survey collected information on the following building characteristics: building shell, kitchen appliances, heating and cooling equipment, water heating equipment, clothes washing and drying equipment, lighting, pool and spa equipment, small household appliances, miscellaneous energy consuming equipment, as well as behaviors and characteristics of respondents.
- API nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:30.000Z
<p> During the past 2 decades, various concepts for mitigating the impact threats from NEOs have been proposed, but many of these concepts were impractical and not technically credible. In particular, all non-nuclear techniques require mission lead times larger than 10 years. However, for the most probable impact threat with a warning time less than 10 years, the use of high-energy nuclear explosives in space becomes inevitable for proper fragmentation and dispersion of an NEO in a collision course with Earth. However, the existing nuclear subsurface penetrator technology limits the impact velocity to less than 300m/s because higher impact velocities destroy prematurely the detonation electronic equipment. Thus, an innovative space system architecture utilizing high-energy nuclear explosives must be developed for a worst-case intercept mission resulting in relative closing velocities as high as 5-30km/s. An advanced system concept is proposed for nuclear subsurface explosion missions. The concept blends a hypervelocity kinetic-energy impactor with nuclear subsurface explosion, and exploits a 2-body space vehicle consisting of a fore body and an aft body. These 2 spacecraft bodies may be connected by a deployable boom. The fore body provides proper kinetic impact crater conditions for an aft body carrying nuclear explosives to make a deeper penetration into an asteroid body. For such a complex mission architecture design study, non-traditional, multidisciplinary research efforts in the areas of hypervelocity impact dynamics, nuclear explosion modeling, high-temperature thermal shielding, shock-resistant electronic systems, and advanced space system technologies are required. Expanding upon the current research activities, the Iowa State Asteroid Deflection Research Center will develop an innovative, advanced space system architecture that provides the planetary defense capabilities needed to enable a future real space mission more efficient, affordable, and reliable.</p>
Combining Discrete Element Modeling, Finite Element Analysis, and Experimental Calibrations for Modeling of Granular Material Systems Projectnasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:43:27.000Z
The current state-of-the-art in DEM modeling has two major limitations which must be overcome to ensure that the technique can be useful to NASA engineers and the commercial sector: the computational intensive nature of the software, and the lack of an established methodology to determine the particle properties to best accurately model a given physical system. The proposed work will address both of these limitations. We will look at two approaches to overcome the particle count limitations of DEM: investigate the scaling up of particle size; and combine FEA and DEM to look at problems of densely packed solids. We will explore regimes where DEM and FEA are applicable and establish a coupling methodology that can be further developed during phase II. To address the lack of an established methodology to determine the particle properties to best accurately model a given physical system, we will investigate several small scale experiments that can be used to characterize DEM models. The proposed work will advance the state-of-the-art in DEM. At the end of phase I we will show the feasibility of developing modeling approaches to overcome the main limitations of DEM.
- API opendata.utah.gov | Last Updated 2019-04-19T09:27:46.000Z
This data set contains the 5-star ratings for nursing homes in Utah by the Center for Medicare and Medicaid Services. Ratings for Health inspections, Staffing and Quality measures are included. The 5-star quality rating system isn't a substitute for visiting the nursing home. This system can give you important information, help you compare nursing homes by topics you consider most important, and help you think of questions to ask when you visit the nursing home. Use the 5-star ratings together with other sources of information.
- API data.memphistn.gov | Last Updated 2019-10-25T20:04:39.000Z
The data describes the hospital locations in Memphis (and Collierville) with 24 hour Emergency Departments. Each hospital location listed is a facility the Memphis Fire Department's Emergency Medical Services (EMS) Bureau provides transportation services to when patients require immediate medical attention beyond the EMS scope of practice.