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9 results for “geocode”
African Swine Fever Worldwide Epidemiology Data - OIE Webscrape example - Geocoded using Google API and Manual
<p>Example African Swine Fever dataset generated by programs described in following publication </p> <p>Title: Web-scraping programmatic techniques in aggregating difficult to access OIE WAHIS animal disease outbreak information; using African Swine Fever in Europe as an example.</p> <p>Short running title: Methods for web-scraping OIE WAHIS data.</p> <p>Abstract: This study describes and makes available new methods for acquiring difficult to access, publicly available, disease surveillance data. It uses World Organisation for Animal Heath (OIE) data on African Swine Fever (ASF) outbreaks in Belarus and its neighbouring European countries to showcase the importance of adequate disease surveillance data to inform decision-making. The data acquired from these methods allow for large-scale, geospatial outbreak mapping and summary statistics of any terrestrial disease listed on the OIE World Animal Health Information System (WAHIS) database. These techniques will make important epidemiological data more accessible to the scientific community and aid in gaining further insight into the occurrence and spread of OIE listed diseases in a timely manner, fulfilling an important function of disease surveillance.</p>
Early Christian baptisteries, 3rd-12th centuries: A geocoded dataset
<p>This table is an updated, expanded, and geocoded digital version of Sebastian Ristow, Frühchristliche Baptisterien (Münster: Aschendorffsche Verlagsbuchhandlung, 1998). It provides a dataset of 1100+ detailed records of Christian baptisteries from the 3rd until the 12th century. The records are provided with geographic coordinates (at the settlement level, not the building level), construction date range, building orientation, piscina depth, piscina shape, the presence of a ciborium, and other variables.</p> <p>The records' main author is Sebastian Ristow. Under the supervision of David Zbíral, the records have been transferred from the printed book and structured by Hana Hořínková, who also provided most geographic coordinates. Some records have been checked by Sebastian Ristow, who also provided new records not present in the original printed book.</p>
Geocoded variant of United Nations Code for Trade and Transport Locations
<div></div> <div> <div> <div> <div>The <a href="https://unece.org/trade/uncefact/unlocode">United Nations Code for Trade and Transport Locations</a> dataset provides information on the codes known as "UN/LOCODE". </div> <div>This version of the dataset, based on the csv files of version 232, has extra information based on pycountry (names of administrative regions, country names, flags) and on most important on location.</div> <div> </div> <div> </div> <div>The dataset has the following extra fields:<br> <table> <tbody> <tr> <td>Column name</td> <td>Example</td> <td>Description</td> <td>Source</td> </tr> <tr> <td> <pre>alpha_2_country</pre> </td> <td> <pre>AD</pre> </td> <td>2 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>alpha_3_country</pre> </td> <td> <pre>AND</pre> </td> <td>3 letter based country code, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>flag_country</pre> </td> <td> <pre>🇦🇩</pre> </td> <td>unicode flag</td> <td>pycountry</td> </tr> <tr> <td> <pre>name_country</pre> </td> <td> <pre>Andorra</pre> </td> <td>Country name, ISO 3166-1</td> <td>pycountry</td> </tr> <tr> <td> <pre>numeric_country</pre> </td> <td> <pre>020</pre> </td> <td>Country code</td> <td>pycountry</td> </tr> <tr> <td> <pre>official_name_country</pre> </td> <td> <pre>Principality of Andorra</pre> </td> <td>Country official name</td> <td>pycountry</td> </tr> <tr> <td> <pre>score</pre> </td> <td>95</td> <td>Confidence code of geocoding [0-100/nan]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geocoded</pre> </td> <td>True</td> <td>Was the coordinate derived from geocoding [true/false]</td> <td>ArcGIS</td> </tr> <tr> <td> <pre>geometry</pre> </td> <td> <pre>POINT (1.516666666666667 42.5)</pre> </td> <td>Point coordinate</td> <td>ArcGIS if geocoded else original UNLO </td> </tr> </tbody> </table> <br>The figure below shows the locations (n=23663) that have been geocoded in this dataset in green and the existing locations in pink. </div> <div><br></div> <div> </div> <div>Ownership of the original data recides at UNLO and the participants. </div> </div> </div> </div>
Replication files for: "Censuscoding: a privacy-preserving alternative to geocoding"
<p>Geocoding is an important tool for research, but precise street addresses and geospatial coordinates may risk revealing personally identifiable information if not used carefully. Censuscoding is a self-contained tool for determining the Census block group that contains a street address, to solve this challenge. Censuscoding maintains privacy by providing an anonymous view of location through block groups, which vary between 600 to 3,000 individuals in population.</p> <p>These replication files are used to build the lookup data distributed with censuscoding v0.2.0. Please see the corresponding GitHub repositories https://github.com/ripl-org/censuscoding and https://github.com/ripl-org/censuscoding-data.</p>
geocoder
<p>A geocoder that relies on offline TIGER/Line data useful for geocoding private health information</p>
Delta-X: AirSWOT L2 Geocoded Water Surface Elevation, MRD, LA, 2021, V3
This dataset contains Level 2 (L2) AirSWOT geocoded products, including estimated water surface elevation. The AirSWOT instrument is a Ka-band interferometer and for this study is flown on the King Air B200 platform. Data were collected during the DeltaX airborne campaign over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA. Flights occurred during the Delta-X Spring 2021 deployment from 2021-03-26 to 2021-04-18 and the Delta-X Fall 2021 deployment from 2021-08-21 to 2021-09-12. AirSWOT is capable of producing high resolution (3.6 m) digital elevation models over land and water bodies using near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. The instrument includes six antennas that form multiple baseline pairs for along-track and across-track interferometry. AirSWOT elevation data are useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions and is an airborne calibration and validation instrument for the Surface Water and Ocean Topography (SWOT) satellite. This Version 3 dataset provides updated data files due to an updated Calumet survey that changed the water level by 0.138 m. This resulted in all the AirSWOT water levels changing by that same amount. For these L2 products, only the estimated water surface elevation in respect to the WGS84 ellipsoid surface, and estimated height above the NAVD88 (GEOID12B) vertical datum files changed. Note that data acquired on September 1 and September 5, 2021 do not meet the expected MAE in-situ comparison and should be used with caution. This dataset contains cloud optimized GeoTIFF rasters in UTM map coordinates for each flight line. In addition, a text file provides basic metadata, including flight line ID, start and end UTC times of data acquisition, processor version number, and the date and time of different processing stages.
BOREAS Landsat TM Level-3p Imagery: Geocoded and Scaled At-Sensor Radiance
For BOREAS, the level-3p Landsat TM data were used to supplement the level-3s Landsat TM products. Along with the other remotely sensed images, the Landsat TM images were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar to the level-3s Landsat TM products, the level-3p images were processed with ground control information which improved the accuracy of the geographic coordinates provided. Geographically, the level-3p images cover the BOREAS NSA and SSA. Temporally, the four images cover the period of 20-Aug-1988 to 07-Jun-1994. Except for the 07-Jun-1994 image which contains 7 bands, the other three only contain 3 bands. Companion files include (1)an image inventory listing to inform users of the images that are available and (2) example thumbnail images that may be viewed using a convenient viewer utility.
ARIA Sentinel-1 Geocoded Unwrapped Interferograms
Level-2 interferometric products generated by the Jet Propulsion Lab (JPL) ARIA project. The creation, discovery, and distribution of these products support InSAR science around tectonically active regions, volcanoes, or areas of subsidence/uplift. The generation of the ARIA-S1-GUNW products was in part funded through collaborations with the AWS Open Data Program and NASA ROSES.
Geocoded Disasters (GDIS) Dataset
The Geocoded Disasters (GDIS) Dataset is a geocoded extension of a selection of natural disasters from the Centre for Research on the Epidemiology of Disasters' (CRED) Emergency Events Database (EM-DAT). The data set encompasses 39,953 locations for 9,924 disasters that occurred worldwide in the years 1960 to 2018. All floods, storms (typhoons, monsoons etc.), earthquakes, landslides, droughts, volcanic activity and extreme temperatures that were recorded in EM-DAT during these 58 years and could be geocoded are included in the data set. The highest spatial resolution in the data set corresponds to administrative level 3 (usually district/commune/village) in the Global Administrative Areas database (GADM, 2018). The vast majority of the locations are administrative level 1 (typically state/province/region).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.