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FY2017 08: Chief Technology Officer Budget by Department - Graph
data.montgomeryschoolsmd.org | Last Updated 2016-08-28T19:03:52.000Z - API
FY2016 08: Chief Operating Officer Budget by Department - Graph
data.montgomeryschoolsmd.org | Last Updated 2016-08-27T18:06:17.000ZFY2016 MCPS Operating Budget
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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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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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NCHS - Drug Poisoning Mortality by County: United States
healthdata.gov | Last Updated 2023-07-25T17:57:16.000ZThis dataset contains model-based county estimates for drug-poisoning mortality. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8). REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. 2. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. 3. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013. 4. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014. 5. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf. 6. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e. 7. National Center for Health Statistics. County geog
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Dallas Animals Field Data Fiscal Year 2016 - 2017
www.dallasopendata.com | Last Updated 2021-08-17T14:48:04.000ZDallas Animal Services data that pertains to operations by Animal Services Officers (ASO) who respond to calls in the field throughout the City of Dallas. ASO’s document their work using Chameleon software, an animal shelter software program. The document will be updated on a daily basis, so that citizens have a greater understanding of what ASO’s are doing in the neighborhoods of Dallas. “Helping Dallas be a safe, compassionate, and healthy place for people and animals”. Start date is October 01, 2016
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SBUV2/NOAA-16 Ozone (O3) Profile and Total Column Ozone 1 Month Zonal Mean L3 Global 5.0 degree Latitude Zones V1 (SBUV2N16L3zm) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:51:01.000ZThe Solar Backscattered Ultraviolet (SBUV) from NOAA-16 Level-3 monthly zonal mean (MZM) product (SBUV2N16L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 154 months of data from October 2000 through July 2013. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). The MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions: 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 degrees). 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 degrees; large discrepancy between profile total and best total ozone). NOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angles where the B-Pair total ozone estimate is not as reliable as the ozone profile under these conditions. This error flag may be removed in future version of algorithm. The zonal means computed for each month are screened according to the following statistical criteria: 1) Number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month. 2) Mean latitude of good retrievals less than or equal to 1 degree from center of latitude band. 3) Mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15).
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Vital Signs: Transit Cost-Effectiveness – by operator
data.bayareametro.gov | Last Updated 2018-07-06T18:04:51.000ZVITAL SIGNS INDICATOR Transit Cost-Effectiveness (T13) FULL MEASURE NAME Net cost per transit boarding (cost per boarding minus fare per boarding) LAST UPDATED May 2017 DESCRIPTION Transit cost-effectiveness refers to both the total and net costs per transit boarding, both of which are adjusted to reflect inflation over time. Net costs reflect total operating costs minus farebox revenue (i.e. operating costs that are not directly funded by system users). The dataset includes metropolitan area, regional, mode, and system tables for net cost per boarding, total cost per boarding, and farebox recovery ratio. DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm Bureau of Labor Statistics: Consumer Price Index http://www.bls.gov/data/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data. Financial data was inflation-adjusted to match 2015 dollar values using metro-specific Consumer Price Indices.
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NLDAS Secondary Forcing Data L4 Monthly Climatology 0.125 x 0.125 degree V002
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:55:49.000ZThis data set contains the monthly climatology data of the secondary forcing data for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over the thirty years (1980 - 2009) of the NLDAS-2 monthly data. The file format is WMO GRIB-1. Brief description about the NLDAS-2 hourly and monthly primary forcing data can be found from the GCMD DIFs for GES_DISC_NLDAS_FORB0125_H_V002 and GES_DISC_NLDAS_FORB0125_M_V002 at http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_FORB0125_H_V002 and http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_FORB0125_M_V002. Details about the generation of the NLDAS-2 forcing datasets can be found in Xia et al. (2012). The NLDAS-2 monthly climatology land surface forcing fields are grouped into two GRIB files, "File A" and "File B". "File B" is the secondary (optional) forcing file and contains ten fields. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_FORB_monthly.002.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. For more information, please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS2.pdf.
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SBUV2/NOAA-11 Ozone (O3) Profile and Total Column Ozone Monthly L3 Global 5.0deg Lat Zones V1
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:54:41.000ZThe Solar Backscattered Ultra Violet (SBUV) from NOAA-11 Level-3 monthly zonal mean (MZM) product (SBUV2N11L3zm) is derived from the Level-2 retrieved ozone profiles. Ozone retrievals are generated from the v8.6 SBUV algorithm. A Level-3 MZM file computes zonal means covering 5 degree latitude bands for each calendar month. For this product there are 147 months of data from January 1989 through March 2001. There are a total of 36 latitudinal bands, 18 in each hemisphere. Profile data are provided at 21 layers from 1013.25, 639.318, 403.382,254.517, 160.589, 101.325,63.9317, 40.3382, 25.4517, 16.0589, 10.1325, 6.39317,4.03382, 2.54517, 1.60589, 1.01325,0.639317, 0.403382, 0.254517, 0.160589 and 0.101325 hPa (measured at bottom of layer). NOTE: Some profiles have 20 layers and do not report the top most layer. Mixing ratios are reported at 15 layers from 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0 and 50.0 hPa (measured at middle of layer). The MZM product averages retrievals that meet the criteria for a good retrieval as determined by error flags in the Level 2 data. A good retrieval is defined as satisfying the following conditions: 1) Profile Error Flag = 0 or 1 (0 = good retrieval; 1 = solar zenith angle > 84 deg.) 2) Total Error Flags = 0, 1, 2 or 5 (0 = good retrieval; 1 = not used; 2 = solar zenith angle > 84 deg; large discrepancy between profile total and best total ozone) NOTE - Total error flag = 5 is anomalously applied at high latitudes and high solar zenith angle where B-Pair total ozone estimate is not as reliable as profile under these conditions. This error flag may be removed in future version of algorithm. The zonal means computed for each month are screened according to the following statistical criteria: 1) number of good retrievals for the month greater than or equal to 2/3 of the samples for a nominal month. 2) mean latitude of good retrievals less than or equal to 1 degree from center of latitude band. 3) mean time of good retrievals less than or equal to 4 days from center of month (i.e., day = 15)