Explore the historical Whois records related to acdd.club (Domain). Get insights into ownership history and changes over time.

An interactive atlas that demonstrates the precise locations of the areas where the Agricultural Chemicals Distribution Control Act 1966 applies and does not apply.

Repository that contains alerts that will be sent to SSA employees when certain conditions exist, to inform them of work that needs to be done, is being reviewed, or has been completed.

🇮🇪 아일랜드

🇮🇪 아일랜드

Animal tracklines as a product of processing and cross referencing data in the Core 1/2 IOOS ATN databases _NCProperties=version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.2 acknowledgement=NOAA IOOS,Axiom Data Science,Wildlife Computers,Argos,SMRU,Navy ONR,NOAA NMFS,Stanford Block Lab cdm_data_type=Trajectory cdm_trajectory_variables=genus,common_name,species,taxon,event_id,topp_id,deploy_year,workgroup Conventions=CF-1.6,ACDD-1.3 Easternmost_Easting=179.9558 eventid=111100500 featureType=Trajectory geospatial_lat_max=73.478 geospatial_lat_min=-82.44846 geospatial_lat_units=degrees_north geospatial_lon_max=179.9558 geospatial_lon_min=-179.9715 geospatial_lon_units=degrees_east geospatial_vertical_positive=down history=NetCDF file obtained by IOOS ATN DAC created by a state space model and combined with metadata from the CORE1 database. infoUrl=https://atn.ioos.us institution=IOOS Animal Telemetry Network Metadata_Conventions=COARDS, CF-1.6, ACDD-1.3, Unidata Dataset Discovery v1.0 metadata_link=https://ioos.noaa.gov/project/atn/ naming_authority=gov.noaa.ioos.atn ncei_template_version=NCEI_NetCDF_Trajectory_Template_v2.0 Northernmost_Northing=73.478 number_of_tags=1 project=IOOS Animal Telemetry Network ptt=-9999 rerun=true source=ATN DAC Block Lab Core 1 data transfer sourceUrl=(local files) Southernmost_Northing=-82.44846 standard_name_vocabulary=CF Standard Name Table v57 subsetVariables=genus,common_name,species,taxon,event_id,topp_id,deploy_year,workgroup tag_model=DD2193 time_coverage_duration=P170D time_coverage_end=2018-01-29T17:55:00Z time_coverage_start=2002-01-12T05:00:00Z Westernmost_Easting=-179.9715 year=2011

This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Bolinas map area, California. The vector data file is included in "Faults_OffshoreBolinas.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreBolinas/data_catalog_OffshoreBolinas.html. The Offshore of Bolinas map area straddles the right-lateral transform boundary between the North American and Pacific plates and is cut by several active faults that cumulatively form a distributed shear zone, including the San Andreas Fault, the eastern strand of the San Gregorio Fault, the Golden Gate Fault, and the Potato Patch Fault (Bruns and others, 2002; Ryan and others, 2008). These faults are covered by sediment (mostly unit Qms) with no seafloor expression, and are mapped using seismic-reflection data (see field activities S-8-09-NC and L-1-06-SF). The San Andreas Fault is the primary plate-boundary structure and extends northwest through the southern part of the map area before passing onshore at Bolinas Lagoon. This section of the San Andreas Fault has an estimated slip rate of 17 to 24 mm/yr (U.S. Geological Survey, 2010), and the devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas a few kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005). The San Andreas Fault forms the boundary between two distinct basement terranes, Upper Jurassic and Lower Cretaceous melange and graywacke sandstone of the Franciscan Complex to the east, and Late Cretaceous granitic and older metamorphic rocks of the Salinian block to the west. Franciscan Complex rocks (unit KJf, undivided) form seafloor outcrops adjacent to the shoreline southeast of Stinson Beach that are commonly continuous with onshore coastal outcrops. Faults were primarily mapped by interpretation of seismic reflection profile data (see field activities S-8-09-NC and L-1-06-SF). The seismic reflection profiles were collected between 2006 and 2009. References Cited Bolt, B.A., 1968, The focus of the 1906 California earthquake: Bulletin of the Seismological Society of America, v. 58, p. 457-471. Bruns, T.R., Cooper, A.K., Carlson, P.R., and McCulloch, D.S., 2002, Structure of the submerged San Andreas and San Gregorio fault zones in the Gulf of Farallones as inferred from high-resolution seismic-reflection data, in Parsons, T. (ed.), Crustal structure of the coastal and marine San Francisco Bay region, California: U.S. Geological Survey Professional Paper 1658, p. 77-117. Lomax, A., 2005, A reanalysis of the hypocentral location and related observations for the Great 1906 California earthquake: Bulletin of the Seismological Society of America, v. 95, p. 861-877. Ryan, H.F., Parsons, T., and Sliter, R.W., 2008. Vertical tectonic deformation associated with the San Andreas fault zone offshore of San Francisco, California. Tectonphysics, 429 (1-2), p. 209-224. U.S. Geological Survey and California Geological Survey, 2010, Quaternary fault and fold database for the United States, accessed April 5, 2012, from USGS website: http://earthquake.usgs.gov/hazards/qfaults/.

According to a global gaming audience survey in the fourth quarter of 2020, console gamers were most likely to have purchased gaming add-ons in the past 12 months. Overall, ** percent of responding console gamers stated that they had purchased DLC for a video game, while only ** and ** percent of PC and mobile gamers respectively claimed the same. In-game currency for in-game content was the most popular type of in-game purchase.

Multi-page reports produced for each Geological Conservation Review (GCR) site. The reports contain geological and conservation information, comprehensive site photography and local information. The Geological Conservation Review looked at identifying geological sites across the UK, that make up a unique contribution to Britain’s Earth Heritage. The Geological Conservation Review started in 1997 when a more systematic approach to site selection occurred. The objective of the review was to identify those sites needed to show all the key scientific elements of the Earth heritage of Britain.

description: This NOAA Climate Data Record (CDR) of AVHRR reflectance and brightness temperatures was produced by the University of Wisconsin using the AVHRR Pathfinder Atmospheres - Extended (PATMOS-X) Version 5.3 processing system. The CDR spans from 1979 to the present with daily, global coverage generated from between two and ten NOAA and MetOp satellite passes per day. The source AVHRR data points, with a sensor resolution of 1.09 km near nadir, have been fitted to a 0.1 x 0.1 degree equal-angle grid in ascending and descending files. The calibrated reflectance channels in the visible spectrum (0.63, 0.86, and 1.6 microns) portion of the record were delivered as a CDR in Version 5.2 in 2010. This updated Version 5.3 extends the product to include brightness temperatures from the 3.75, 11 and 12 micron channels, as well as a suite of cloud products (the PATMOS-x Cloud Properties CDR). Version 5.3 also extends the period of record through the present and applies the newest calibration coefficients. In total, there are seven (7) AVHRR channel variables. Not counting the coordinate variables, there are 48 data and ancillary data variables in the netCDF data file to facilitate proper data usage. The file format was converted from HDF to netCDF-4 with metadata following the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD). The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.; abstract: This NOAA Climate Data Record (CDR) of AVHRR reflectance and brightness temperatures was produced by the University of Wisconsin using the AVHRR Pathfinder Atmospheres - Extended (PATMOS-X) Version 5.3 processing system. The CDR spans from 1979 to the present with daily, global coverage generated from between two and ten NOAA and MetOp satellite passes per day. The source AVHRR data points, with a sensor resolution of 1.09 km near nadir, have been fitted to a 0.1 x 0.1 degree equal-angle grid in ascending and descending files. The calibrated reflectance channels in the visible spectrum (0.63, 0.86, and 1.6 microns) portion of the record were delivered as a CDR in Version 5.2 in 2010. This updated Version 5.3 extends the product to include brightness temperatures from the 3.75, 11 and 12 micron channels, as well as a suite of cloud products (the PATMOS-x Cloud Properties CDR). Version 5.3 also extends the period of record through the present and applies the newest calibration coefficients. In total, there are seven (7) AVHRR channel variables. Not counting the coordinate variables, there are 48 data and ancillary data variables in the netCDF data file to facilitate proper data usage. The file format was converted from HDF to netCDF-4 with metadata following the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD). The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

Automated Weather Station and AWS-like networks are the primary source of surface-level meteorological data in remote polar regions. These networks have developed organically and independently, and deliver data to researchers in idiosyncratic ASCII formats that hinder automated processing and intercomparison among networks. Moreover, station tilt causes significant biases in polar AWS measurements of radiation and wind direction. Researchers, network operators, and data centers would benefit from AWS-like data in a common format, amenable to automated analysis, and adjusted for known biases. This project addresses these needs by developing a scientific software workflow called "Justified AWS" (JAWS) to ingest Level 2 (L2) data in the multiple formats now distributed, harmonize it into a common format, and deliver value-added Level 3 (L3) output suitable for distribution by the network operator, analysis by the researcher, and curation by the data center. Polar climate researchers currently face daunting problems including how to easily: 1. Automate analysis (subsetting, statistics, unit conversion) of AWS-like L2 ASCII data. 2. Combine or intercompare data and data quality from among unharmonized L2 datasets. 3. Adjust L2 data for biases such as AWS tilt angle and direction. JAWS addresses these common issues by harmonizing AWS L2 data into a common format, and applying accepted methods to quantify quality and estimate biases. Specifically, JAWS enables users and network operators to 1. Convert L2 data (usually ASCII tables) into a netCDF-based L3 format compliant with metadata conventions (Climate-Forecast and ACDD) that promote automated discovery and analysis. 2. Include value-added L3 features like the Retrospective, Iterative, Geometry-Based (RIGB) tilt angle and direction corrections, solar angles, and standardized quality flags. 3. Provide a scriptable API to extend the initial L2-to-L3 conversion to newer AWS-like networks and instruments. Polar AWS network experts and NSIDC DAAC personnel, each with decades of experience, will help guide and deliberate the L3 conventions implemented in Stages 2-3. The project will start on July 1, 2017 at entry Technology Readiness Level 3 and will exit on June 30, 2019 at TRL 6. JAWS is now a heterogeneous collection of scripts and methods developed and validated at UCI over the past 15 years. At exit, JAWS will comprise three modular stages written in or wrapped by Python, installable by Conda: Stage 1 ingests and translates L2 data into netCDF. Stage 2 annotates the netCDF with CF and ACDD metadata. Stage 3 derives value-added scientific and quality information. The labor-intensive tasks include turning our heterogeneous workflow into a robust, standards-compliant, extensible workflow with an API based on best practices of modern scientific information systems and services. Implementation of Stages 1-2 may be straightforward though tedious due to the menagerie of L2 formats, instruments, and assumptions. The RIGB component of Stage 3 requires ongoing assimilation of ancillary NASA data (CERES, AIRS) and use of automated data transfer protocols (DAP, THREDDS). The immediate target recipient elements are polar AWS network managers, users, and data distributors. L2 borehole data suffers from similar interoperability issues, as does non-polar AWS data. Hence our L3 format will be extensible to global AWS and permafrost networks. JAWS will increase in situ data accessibility and utility, and enable new derived products (both are AIST goals). The PI is a long-standing researcher, open source software developer, and educator who understands obstacles to harmonizing disparate datasets with NASA interoperability recommendations. Our team participates in relevant geoscience communities, including ESDS working groups, ESIP, AGU, and EarthCube.

This dataset contains the final rule/correction notice cost thresholds by Medicare Severity Diagnosis Related Group (MS-DRG) for the cost criteria for new technology add on payment applications for applications for FY 2018.

This dataset tracks annual distribution of students across grade levels in Anderson Add B School

CFRF - WHOI Shelf Research Fleet | The CFRF, in partnership with scientists at the Woods Hole Oceanographic Institution (WHOI), operates a fishing vessel research fleet to conduct bi-weekly oceanographic sampling across the continental shelf and slope south of Rhode Island. The purpose of the project is to study changes in oceanographic conditions, particularly temperature, in order to better understand how these changes may impact the distribution and abundance of key fisheries resources. acknowledgment=We would like to aknowledge the captains and crew of the Shelf Research Fleet who have collected this vital information. cdm_data_type=Point Conventions=COARDS, CF-1.10, ACDD-1.3 conventions=CF-1.7, ACDD-1.3 data_contact=Linus Stoltz, Data Manager Easternmost_Easting=-68.0957573 email_contact=data@cfrfoundation.org featureType=Point file_name=236225_20250723_1040.rsk geospatial_lat_max=43.18329563333333 geospatial_lat_min=39.426957 geospatial_lat_units=degrees_north geospatial_lon_max=-68.0957573 geospatial_lon_min=-74.00729295804538 geospatial_lon_units=degrees_east infoUrl=https://www.cfrfoundation.org/shelf-research-fleet institution=CFRF | WHOI keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=43.18329563333333 references=https://www.cfrfoundation.org/shelf-research-fleet sensor_manufacturer=RBR Global sensor_model=Concerto CTD sensor_serial_number=236225 sensor_specifications=https://rbr-global.com/products/standard-loggers/rbrduo-ct/ sourceUrl=(local files) Southernmost_Northing=39.426957 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=latitude, longitude, profile_id, project_id testOutOfDate=now-135days time_coverage_end=2025-07-29T16:09:45Z time_coverage_start=2014-11-29T17:24:35Z vessel_id=328 Westernmost_Easting=-74.00729295804538

Glider deployment #118 - otn200 2020-09-20 (realtime). Slocum gliders are small, free-swimming, unmanned vehicles that use changes in buoyancy to move vertically and horizontally through the water column in a saw-tooth pattern. They are deployed for days to several months and gather detailed information about the physical, chemical and biological processes of the world's oceans. The Slocum glider was designed and built by Teledyne Webb Research Corporation, Falmouth, MA, USA. cdm_data_type=TrajectoryProfile cdm_profile_variables=profile_id, depth cdm_trajectory_variables=trajectory comment=Labrador current study with support from Nunatsiavut Government and in collaboration with Oceans North and OTN. contributor_name=Eric Oliver, Tyler Byrne, Adam Comeau, Joey, Kate Patterson, Xiang Ling Conventions=CF-1.6, COARDS, ACDD-1.3 deployment_number=118 deployment_title=Lab Sea 2020 Easternmost_Easting=-58.795297 featureType=TrajectoryProfile format_version=IOOS_Glider_NetCDF_v3.0-noqartod.nc geospatial_bounds=POLYGON ((56.964920 -60.299610, 56.964920 -60.299610, 56.964920 -60.299610, 56.964920 -60.299610, 56.964920 -60.299610)) geospatial_lat_max=57.738732937638 geospatial_lat_min=56.537753 geospatial_lat_units=degrees_north geospatial_lon_max=-58.795297 geospatial_lon_min=-61.690748 geospatial_lon_units=degrees_east geospatial_vertical_max=354.454 geospatial_vertical_min=0.0 geospatial_vertical_positive=down geospatial_vertical_units=m history=2021-03-08T02:10:24Z - Created with the GUTILS package: "/home/slocum/gdp/GUTILS_RUN/GDP/gdp" id=otn200-2020-09-20 16:00:00 infoUrl=https://www.dal.ca/ institution=Dalhousie, Oceanography keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=CF-1.6, COARDS, ACDD-1. naming_authority=org.oceantrackingnetwork Northernmost_Northing=57.738732937638 platform_type=Slocum Glider processing_level=Dataset taken from glider native file format and is provided as is with no expressed or implied assurance of quality assurance or quality control. project=Lab Coast - Oceans North sea_name=Labrador Sea source=Observational data from a profiling glider sourceUrl=(local files) Southernmost_Northing=56.537753 standard_name_vocabulary=CF Standard Name Table v60 testing_mission=False time_coverage_duration=P0DT0H1M35.179380178S time_coverage_end=2020-10-24T00:34:30Z time_coverage_start=2020-09-16T17:56:25Z Westernmost_Easting=-61.690748

The’ Qu'Appelle Valley Lakes system – Topography and Imagery’ series consists of topographic and imagery data for lakes within the Qu'Appelle River Valley in central Saskatchewan. This data was collected in the fall of 2008 and consists of contour lines, shorelines, spot heights, tile index, and imagery

The majority of Polish console gamers spent between ** and ** zloty on average per game add-ons purchase in 2020. Only five percent of respondents stated that they invested 100 zloty and more in a single console game add-ons purchase.

According to a global gaming audience survey in the fourth quarter of 2020, Nintendo Switch gamers were most likely to have purchased gaming add-ons in the past 12 months. Overall, 40 percent of responding Switch gamers stated that they had purchased DLC for a video game, while only 36 and 30 percent of Xbox and PlayStation gamers respectively claimed the same. In-game currency for in-game content was the second-most popular type of in-game purchase.