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Combining Discrete Element Modeling, Finite Element Analysis, and Experimental Calibrations for Modeling of Granular Material Systems Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:43:27.000ZThe 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.
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Health Home Quality Measures: Beginning 2013
health.data.ny.gov | Last Updated 2019-08-07T19:18:45.000ZThis dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). To support ongoing assessment of the effectiveness of the Health Home model, the CMS has established a recommended Core Set of health care quality measures that it intends to promulgate in the rulemaking process. The data used in the Health Home Quality Measures are taken from the following sources: • Medicaid Data Mart: Claims and encounters data generated from the Medicaid Data Warehouse (MDW). • QARR Member Level Files: Sample of the health plan eligible member’s quality. • New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse: Claims and encounters data generated from the Medicaid Data Warehouse (MDW). Please refer to the Overview document for additional information.
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Medicaid Chronic Conditions, Inpatient Admissions and Emergency Room Visits by County: Beginning 2012
health.data.ny.gov | Last Updated 2016-12-13T21:25:28.000ZThis dataset contains information on selected chronic health conditions in the Medicaid population at the county level. The chronic health conditions were identified through 3M Clinical Risk Group software and Medicaid enrollment/eligibility, encounter, claim and pharmacy data over a 12-month period.
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HCD Property Investments FY10-Q1 24
data.memphistn.gov | Last Updated 2024-05-21T16:49:51.000ZAddress-level dataset of of properties receiving HCD services from July 1, 2009 - March 31, 2024 (FY10-Q1 24). Data was combined in June-July 2020 from multiple sources, including IDIS (HUD data and reimbursement system), tracking spreadsheets maintained by HCD managers, and WAPez (weatherization data system).
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Uninsured Population Census Data 1-year estimates 2017-Current Statewide Human Services and Insurance
data.pa.gov | Last Updated 2022-02-21T19:25:46.000ZThe American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. This dataset provides estimates for Health Insurance Coverage in Pennsylvania and is summarized from summary table S2701: SELECTED CHARACTERISTICS OF HEALTH INSURANCE COVERAGE IN THE UNITED STATES. A blank cell within the dataset indicates that either no sample observations or too few sample observations were available to compute the statistic for that area. Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level. While an ACS 1-year estimate includes information collected over a 12-month period, an ACS 5-year estimate includes data collected over a 60-month period. In the case of ACS 1-year estimates, the period is the calendar year (e.g., the 2015 ACS covers the period from January 2015 through December 2015).
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START-UP NY Tax-Free Area Locations
data.ny.gov | Last Updated 2023-06-06T13:40:23.000ZEmpire State Development (ESD) produces an annual report with a cumulative list of College and University Sponsors approved in the START-UP NY Program. The dataset displays the name of the Sponsor, the street address, city and county of the tax-free areas (TFA) designated by that college or university, square footage of each building space and acreage of each parcel of vacant land, the name of buildings designated and the START-UPNY website of the college.
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Managed Care Individual Provider Network Data: December 31, 2016
health.data.ny.gov | Last Updated 2017-06-07T16:49:28.000ZThe primary purpose for the Provider Network Data System is to collect data needed to evaluate the provider networks including physicians, hospitals, labs, home health agencies, durable medical equipment providers, etc., for all types of health plans in New York State. Beginning in 2017, the PNDS includes Medicaid Managed Care (MMC), HIV Special Need Plans (SNP), Health and Recovery Plans (HARP), Child Health Plus (CHP), Programs of All-Inclusive Care for the Elderly (PACE), Non-PACE Managed Long-Term Care (MLTC) plans, Qualified Health Plans (QHP), Essential Plans (EP), and commercial plans (commercial plan reporting will be incomplete until Q2 2017). This dataset reflects individual provider data. Provider Network Data System information is self-reported by health plans. The PNDS data dictionary can be found at http://www.health.ny.gov/health_care/managed_care/docs/dictionary.pdf. To use the NYS Provider & Health Plan Look-Up Tool, click on the following link, https://pndslookup.health.ny.gov/.
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NEW HORIZONS SDC PLUTO CRUISE RAW V2.0
data.nasa.gov | Last Updated 2023-01-26T20:54:05.000ZThis data set contains Raw data taken by the New Horizons Student Dust Counter instrument during the pluto cruise mission phase. This is VERSION 2.0 of this data set. SDC collected science data intermittently during the hibernation years following the Jupiter encounter, designated as the PLUTOCRUISE phase. There were also Annual Checkouts (ACOs), STIM calibrations, Noise calibrations, and an anomaly in November, 2007. SDC's main science data collection periods were during hibernation. During ACOs, science data are taken intermittently but the user must be careful in analyzing these data since there is usually more activity on the spacecraft during hibernation. STIM and Noise refer to scheduled calibrations and are done with a regular cadence of one per year after the Jupiter encounter; they occurred sporadically in the early years of the mission. Note that some SDC data files have the same stop and start time and a zero exposure time. The reason for this is that the start and stop time for SDC data files are the event times for the first and last events in the files, so for files that contain a single event, these two values are the same. The changes in Version 2.0 were re-running of the ancillary data in the data product, updated geometry from newer SPICE kernels, minor editing of the documentation, catalogs, etc., and resolution of liens from the December, 2014 review, plus those from the May, 2016 review of the Pluto Encounter data sets. New observations added with this version (V2.0) include ongoing cruise observations from August, 2014 through January, 2015.
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Maternal Opioid Use Hospital Stays 2016-2017 County Health Care Cost Containment Council (PHC4)
data.pa.gov | Last Updated 2022-10-17T20:23:36.000ZCountywide counts of maternal hospital stays with opioid use and countywide rates of maternal hospital stays with opioid use per 1,000 maternal stays. Maternal stays include those involving a delivery, as well as other pregnancy-related stays. Opioid use, or opioid use disorder, is a diagnosis indicating opioid dependence, abuse, or use. Some opioid drugs may be prescribed as part of medication-assisted treatment to relieve withdrawal symptoms and psychological cravings often associated with opioid use disorders. Opioid use during pregnancy can lead to Neonatal Abstinence Syndrome (NAS) for newborns. This analysis is restricted to maternal hospital stays for Pennsylvania-state residents who were hospitalized in Pennsylvania hospitals. Disclaimer: PHC4’s database contains statewide hospital discharge data submitted to PHC4 by Pennsylvania hospitals. Every reasonable effort has been made to ensure the accuracy of the information obtained from the Uniform Claims and Billing Form (UB-82/92/04) data elements. Computer collection edits and validation edits provide opportunity to correct specific errors that may have occurred prior to, during or after submission of data. The ultimate responsibility for data accuracy lies with individual providers. PHC4 agents and staff make no representation, guarantee, or warranty, expressed or implied that the data received from the hospitals are error-free, or that the use of this data will prevent differences of opinion or disputes with those who use published reports or purchased data. PHC4 will bear no responsibility or liability for the results or consequences of its use.
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Workforce Innovation Opportunity Act (WIOA) Title III Performance Accountability Metrics PY 2017-2018 - Current Annual Labor and Industry
data.pa.gov | Last Updated 2022-06-09T15:50:25.000ZA comprehensive collection of data that assesses the effectiveness of Pennsylvania in achieving positive outcomes for individuals served by the workforce development system’s Title III Wagner-Peyser (Labor Exchange) program. Data is compiled in compliance with US Department of Labor’s Employment and Training Administration guidance on Workforce Innovation and Opportunity Act (WIOA) Performance Accountability. Data is available for the state and each of the CareerLink® offices in the commonwealth.