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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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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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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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City of Carnation Council Position No. 3
data.kingcounty.gov | Last Updated 2018-12-15T00:44:48.000Z(Initial posting at 8:15 p.m. on Election Day) Election results, November 2011 general election. To see when this was updated, click "About" on the far right of this page. See the schedule for when results are posted, at http://www.kingcounty.gov/elections/elections/201111/resultsschedule.aspx
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TRMM Microwave Imager (TMI) Gridded Oceanic Rainfall Product (TRMM Product 3A11) V7
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:52:56.000ZThe Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. TRMM was successfully launched on November 27, at 4:27 PM (EST) from the Tanegashima Space Center in Japan. The TRMM Microwave Imager (TMI) is a nine-channel passive microwave radiometer, which builds on the heritage of the Special Sensor Microwave/Imager (SSM/I) instrument flown aboard the Defense Meteorological Satellite Program (DMSP) platforms. Microwave radiation is emitted by the Earth's surface and by water droplets within clouds. However, when layers of large ice particles are present in upper cloud regions - a condition highly correlated with heavy rainfall - microwave radiation tends to scatter at frequencies above 19 GHz. The TMI detects radiation at five frequencies chosen to discriminate among these processes, thus revealing the likelihood of rainfall. The key to accurate retrieval of rainfall rates by this method is the deduction of cloud precipitation consistent with the radiation measurement at each frequency. The TMI frequencies are 10.65, 19.35, 37 and 85.5 GHz (dual polarization), and 21 GHz (vertical polarization only). The TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input. Statistics of the monthly rainfall, including number of samples, standard deviation, goodness-of-fit (of the brightness temperature histogram to the lognormal rainfall distribution function) and rainfall probability are also included in the output for each grid box. Spatial coverage is between 40 degrees North and 40 degrees South owing to the 35 degree inclination of the TRMM satellite. TMI brightness temperature histograms at 1 degree intervals are generated based on the 19, 21 and 19-21 GHz combination channels obtained from the Level 1B (calibrated brightness temperature) TMI product. Monthly rainfall indices over the ocean are derived by statistically matching monthly histograms of brightness temperatures with model calculated rainfall Probability Distribution Functions (PDF) using the 19-21 GHz combination data. Retrieved monthly rainfall data must pass a quality test based on the quality of the PDF fit. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the TMI measurements. A file contains 12 arrays of rainfall data and supporting information each of dimension 72 x 16, with a file size of about 40 KB (uncompressed). The HDF-EOS "grid" structure is used to accommodate the actual geophysical data arrays. There is 1 file of TMI 3A11 data produced per month.
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Election Results, Special Election Runoff, December 5, 2017
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:27.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, December 5, 2017 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Election Results, Special Election Runoff, February 3, 2015
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:06.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, February 3, 2015, to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Average Trend Percent by WRIA
data.wa.gov | Last Updated 2022-09-07T23:23:38.000ZSummer Low Flow Trend Indicator results, statewide, updated through Oct 2013. This information is updated annually with an additional year of flow data. These results are provided to the Puget Sound Partnership for their Vital Signs (http://www.psp.wa.gov/vitalsigns/summer_stream_flows.php) and to the Governor's Salmon Recovery Office for the "State of Salmon in WAtersheds" report (http://stateofsalmon.wa.gov/statewide/indicators/water-quantity). The attached document "WR Indicator Outcomes Memo - 10-24-10.pdf" describes the methodology for developing these indicators. The attached document "Low Flow Indicator Metadata.pdf" describes the contents of each column. Dept. of Ecology home page: http://www.ecy.wa.gov/ Disclaimer: Information provided by Ecology on this Web site is accurate to the best of Ecology's knowledge and is subject to change on a regular basis, without notice. Ecology cannot and does not warrant that the information on this Web site is absolutely current, although every effort is made to ensure that it is kept as current as possible. Ecology cannot and does not warrant the accuracy of these documents beyond the source documents, although every attempt is made to work from authoritative sources. Links to related sites are provided as a courtesy, but Ecology is not responsible for their availability, content or policies.
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United States Senate 2020
data.nashville.gov | Last Updated 2024-03-22T12:30:13.000ZUnofficial results for multiple Davidson County elections over time. (Use the Filter option by Election Date to limit results to a single election. Results may be delayed during elections.)
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Classification of Aeronautics System Health and Safety Documents
data.nasa.gov | Last Updated 2020-01-29T01:57:57.000ZMost complex aerospace systems have many text reports on safety, maintenance, and associated issues. The Aviation Safety Reporting System (ASRS) spans several decades and contains over 700 000 reports. The Aviation Safety Action Plan (ASAP) contains over 12 000 reports from various airlines. Problem categorizations have been developed for both ASRS and ASAP to enable identification of system problems. However, repository volume and complexity make human analysis difficult. Multiple experts are needed, and they often disagree on classifications. Even the same person has classified the same document differently at different times due to evolving experiences. Consistent classification is necessary to support tracking trends in problem categories over time. A decision support system that performs consistent document classification quickly and over large repositories would be useful. We discuss the results of two algorithms we have developed to classify ASRS and ASAP documents. The first is Mariana---a support vector machine (SVM) with simulated annealing, which is used to optimize hyperparameters for the model. The second method is classification built on top of nonnegative matrix factorization (NMF), which attempts to find a model that represents document features that add up in various combinations to form documents. We tested both methods on ASRS and ASAP documents with the latter categorized two different ways. We illustrate the potential of NMF to provide document features that are interpretable and indicative of topics. We also briefly discuss the tool that we have incorporated Mariana into in order to allow human experts to provide feedback on the document categorizations.