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NEW HORIZONS SDC JUPITER ENCOUNTER CALIBRATED V4.0
data.nasa.gov | Last Updated 2023-01-26T20:41:44.000ZThis data set contains Calibrated data taken by the New Horizons Student Dust Counter instrument during the Jupiter encounter mission phase. This is VERSION 4.0 of this data set. For the Jupiter encounter mission phase, SDC collected no science data during the Jupiter flyby, as the requisite spacecraft configuration prevented SDC from operating. There were some very sparse data taken from December, 2006 through April, 2007, and some of very short (or zero) duration after the Jupiter flyby from April, 2007 through June, 2007. The changes in Version 4.0 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. No new observations were added with Version 4.0.
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Fire Dispatches: 2012 - 2018
opendata.howardcountymd.gov | Last Updated 2019-11-14T15:48:05.000ZEmergency incidents created by HCPD's 911 Dispatch Center and classified as Fire, Rescue or Emergency Medical Service (EMS) calls. Not all of these incidents are created as cases, and thus not all of these incidents have a matching record in the Howard County Fire & Rescue Incidents table.
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HS Dataset CARES Mortgage Program
internal.open.piercecountywa.gov | Last Updated 2021-09-23T16:44:24.000Z - API
dietz3
data.marincounty.org | Last Updated 2024-04-05T13:30:40.000ZEmergency Medical Service ambulance dispatch incidents in Marin County, CA, for the period beginning March 1, 2013 through March 31, 2017. Data is updated quarterly. Data includes time stamps of events for each dispatch, nature of injury, and location of injury. Data also includes geocoding of most incident locations, however, specific street address locations are "obfuscated" and are generally shown within a block and are not, therefore, exact locations. Geocoding results are also based on the quality of the address information provided, and should therefore not be considered 100% accurate. Some of the data may be interpreted incorrectly without adequate knowledge of the clinical context. Please contact EMS@marincounty.org if you have any questions about the interpretation of fields in this dataset.
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Elections Absentee Ballot Drop Box Daily Count: September 29, 2020 Special Election
sharefulton.fultoncountyga.gov | Last Updated 2023-01-30T16:55:21.000ZData on absentee ballots and applications for the September 29, 2020 Special Election
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Recreation & Parks Program Totals: Fall 2015 - Summer 2016
opendata.howardcountymd.gov | Last Updated 2018-12-10T14:53:26.000ZData encompasses the number of participants enrolled in Howard County Recreation & Parks programs (I.e. Sports, Fitness, Nature & Environment, Camps etc...), by Season.
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Case Closing without a New Offense CY 2018 By County as Reported by the Juvenile Court Judges' Commission
data.pa.gov | Last Updated 2022-10-17T20:21:58.000ZThe juvenile justice system has a responsibility to protect the community from known juvenile offenders. Juveniles who do not commit a new offense while under court supervision have a higher probability of remaining crime free. This data represents the number and percentage of juveniles who successfully completed supervision without a new offense resulting in a Consent Decree, Adjudication of Delinquency, ARD, Nolo Contendere, or finding of guilt in a criminal procedure. <br/> CY - Calendar Year This dataset is contained within the Juvenile Court Judges' Commission’s 2018 Statewide Juvenile Justice Outcome Measure report: this report reflects outcomes of juvenile offenders whose cases were closed during the report period and who have received a period of supervision from a county juvenile probation department. These reported outcomes are associated with community protection, accountability, and competency development; three core goals of Pennsylvania's juvenile justice system. Since 2009, county juvenile probation departments have supervised and closed 126,006 cases. The proportion of cases closed successfully (without a new offense) during this time is 84.1%. For the year 2018, the proportion of cases closed successfully was 85.4%.
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Regional Comparison of Violent Crimes
internal.open.piercecountywa.gov | Last Updated 2023-11-07T19:38:38.000ZNumber of burglary, theft, arson, and destruction of property crimes in Pierce County. <br /><br />Crime data derived from the "Crimes in Washington" annual report (by year) compiled from data submitted to the Washington State Uniform Crime Reporting Program of the Washington Association of Sheriffs and Police Chiefs by Washington State law enforcement agencies. <br /><br />Only specific crimes are highlighted in the crime rates presented here. These numbers represent total numbers of reported crimes in each category (not arrests which may occur over a prolonged period). <br /><br />The following categories represent the violent crimes considered in this data: Murder, Manslaughter, Forcible Sex, Assault, Kidnapping/Abduction, Human Trafficking, and Robbery.<br /><br />The following categories represent the property crimes considered in this data: Burglary, Theft, Arson, and Destruction of Property. <br /><br />Each set of crimes is totaled, then the rate per 1,000 people is calculated using the total # of crimes and the current population of each jurisdiction per year as provided in the same report. <br /><br />This is a voluntary program and as such, some law enforcement agencies do not participate or have only recently participated, which is also reflected in this table.
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TRMM (TMPA-RT) Near Real-Time Precipitation L3 1 day 0.25 degree x 0.25 degree V7 (TRMM_3B42RT_Daily) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:59:46.000ZTMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06). This daily accumulated precipitation product is generated from the Near Real-Time 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42RT). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). Although the grid is from 60S to 60N, the high latitudes (beyond 50S/N) near real-time retrievals are considered very unreliable and thus are screened out from the daily accumulations. The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available. Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. The latency of this derived daily product is about 7 hours after the UTC day is closed. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product. The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format. The following describes the derivation in more details. The daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have Pdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]} where: Pdaily - Daily accumulation (mm) Pi - 3-hourly input, in (mm/hr) Nf - Number of 3-hourly files per day, Nf=8 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day. Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value. On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the "true" daily total. These events are easily detectable using "counts" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf. There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied: Perr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] ) where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, is accumulating within the 3-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate: rms_err = { (Perr_daily/3) ^2 / Ncnt
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Índice de Gobierno Digital 2018 entidades orden nacional
www.datos.gov.co | Last Updated 2024-04-20T20:18:18.000ZEl Índice de Gobierno Digital permite medir el desempeño y cumplimiento de las entidades públicas en la Política de Gobierno Digital. Este Índice permite al Ministerio de Tecnologías de la Información y las Comunicaciones determinar los avances específicos en cada temática de la Política de Gobierno Digital, buenas prácticas de implementación y estrategias focalizadas de acompañamiento. Así mismo, la información generada a través del Índice de Gobierno Digital permite a las entidades públicas tomar decisiones y definir acciones orientadas a mejorar su desempeño y cumplimiento de la Política de Gobierno Digital.