From the Web site: The Post gained access to the Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System, known as ARCOS, as the result of a court order. The Post and HD Media, which publishes the Charleston Gazette-Mail in West Virginia, waged a year-long legal battle for access to the database, which the government and the drug industry had sought to keep secret.

The version of the database published by The Post allows readers to learn how much hydrocodone and oxycodone went to individual states and counties, and which companies and distributors were responsible.

Also: Guidelines for using this data Fill out the form below to establish a connection with our team and report any issues downloading the data. This will also allow us to update you with any additional information as it comes out and answer questions you may have. Because of the volume of requests, we ask you use this channel rather than emailing our reporters individually. If you publish an online story, graphic, map or other piece of journalism based on this data set, please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project. Post reporting and graphics can be used on-air. We ask for oral or on-screen credit to The Washington Post. For specific requests, including interview with Post journalists, please email postpr@washpost.com.

Credit report of Dea Drug Enforcement Administration Survivors Benefit Fund contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

117 Global import shipment records of Dea with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This feature class contains road data derived from applying Infra data to a national forest's road GIS data. Infrastructure (Infra) is a collection of applications which house information related to an assets managed by the Forest Service (including but not limited to, Roads, Bridges, Buildings, Water Systems, Waste Water Systems, Dams, Trails, and Recreation Sites). The feature class contains records for all roads that are in each database and are correctly configured. This data would include only existing roads, ones that permit motorized use as well as those that do not. For roads that are legally open for motorized use, it identifies the authorized modes of travel and season of use. This data may not represent a forest's currently published Motor Vehicle Use Map (MVUM). This feature class is derived from the Infra table II_MVUM_ROAD_ALLOW. Access and Travel Management (ATM) data included is pulled from the Allowed Uses tab in the Infra ATM for Roads form. Since this feature class is a current snapshot of Infra data, it is different than the currently published MVUM data and thus is for internal use only, primarily for review of Infra data during development or update of MVUM. This feature class will not be published for public use.

Context

Opiates are used for pain management in the USA and elsewhere.
Data from The Washington Post. Pain Pills in the USA (2006-2012)

Content

https://www.washingtonpost.com/graphics/2019/investigations/dea-pain-pill-database/#download-resources

https://www.washingtonpost.com/national/2019/07/18/how-download-use-dea-pain-pills-database/

arcos_handbook.pdf (definitions)

arcos_all_washpost.tsv (raw data)

interesting columns = ['BUYER_NAME','BUYER_ADDRESS1', 'BUYER_ADDRESS2', 'BUYER_CITY', 'BUYER_STATE', 'BUYER_ZIP', 'BUYER_COUNTY','DRUG_NAME', 'QUANTITY', 'UNIT', 'TRANSACTION_DATE', 'CALC_BASE_WT_IN_GM', 'DOSAGE_UNIT', 'Product_Name', 'Ingredient_Name','Revised_Company_Name', 'Reporter_family']

Acknowledgements

If you publish an online story, graphic, map or other piece of journalism based on this data set, please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project.

Photo by Gesina Kunkel on Unsplash

The 2013-14 South African National Land-cover dataset produced by GEOTERRAIMAGE as a commercial data product has been generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land-cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land-cover dataset, which 36 covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land-cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land-cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. The data remains the property of GEOTERRAIMAGE, and is protected by copyright laws. All Intellectual Property rights pertaining to the data remain with GEOTERRAIMAGE at all times.

WMS Service of the WMS Service. Topographic Map of Andalusia 1:10,000 raster (year 2013). The Topographic Map of Andalusia (MTA) is conceived as a topographical base, which includes the general elements found in the territory, and oriented for its exploitation by computer systems as well as for the elaboration of cartographic outputs (maps). The reference scale is 1/10,000, so the level of detail and geometric precision of the entities must be consistent with this value. The extension of its scope is limited to the territory of the autonomous community of Andalusia. In order to facilitate the exchange of data with other autonomous communities and following the premises of the Superior Geographical Council (CSG), the data model of the MTA starts from the definitions established in the Topographic Base of Andalusia (BTA) developed by the CENG. Currently, the BTA model is being improved after having been adopted in some Autonomous Communities and in order to bring it closer to the specifications of INSPIRE. Mapped and edited map of the Cartographic Base of Andalusia, a symbology has been assigned to the different geometries and the toponymy has been modeled. The topographic data come from the photogrammetric restitution of PNOA flights 2010-2011. Note that the contour lines are generated from the restored terrain elements and the digital PNOA 2010-2011 terrain model. La base cartográfica se completa con información temática procedente de otros organismos productores como los usos de suelo a partir de los datos generados en el proyecto SIOSE, el límite de espacios naturales a partir de la información facilitada por REDIAM, la toponimia procedente del proyecto Nomenclátor Geográfico de Andalucía (NGA) o los límites administrativos procedentes de Datos Espaciales de Andalucía (DEA).

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Abstract Digital Earth Australia Coastlines is a continental dataset that includes annual shorelines and rates of coastal change along the entire Australian coastline from 1988 to the present. The product combines satellite data from Geoscience Australia's Digital Earth Australia program with tidal modelling to map the most representative location of the shoreline at mean sea level for each year. The product enables trends of coastal retreat and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades. The ability to map shoreline positions for each year provides valuable insights into whether changes to our coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting our coastlines and potentially assist planning and forecasting for future scenarios. The DEA Coastlines product contains five layers:

Annual shorelines Rates of change points Coastal change hotspots (1 km) Coastal change hotspots (5 km) Coastal change hotspots (10 km)

Annual shorelines Annual shoreline vectors that represent the median or ‘most representative’ position of the shoreline at approximately 0 m Above Mean Sea Level for each year since 1988. Dashed shorelines have low certainty. Rates of change points A point dataset providing robust rates of coastal change for every 30 m along Australia’s non-rocky coastlines. The most recent annual shoreline is used as a baseline for measuring rates of change. Points are shown for locations with statistically significant rates of change (p-value <= 0.01; see sig_time below) and good quality data (certainty = "good"; see certainty below) only. Each point shows annual rates of change (in metres per year; see rate_time below), and an estimate of uncertainty in brackets (95% confidence interval; see se_time). For example, there is a 95% chance that a point with a label -10.0 m (±1.0 m) is retreating at a rate of between -9.0 and -11.0 metres per year. Coastal change hotspots (1 km, 5 km, 10 km) Three points layers summarising coastal change within moving 1 km, 5 km and 10km windows along the coastline. These layers are useful for visualising regional or continental-scale patterns of coastal change. Currency Date modified: August 2023 Modification frequency: Annually Data extent Spatial extent North: -9° South: -44° East: 154° West: 112° Temporal extent From 1988 to Present Source information

Product description and metadata Digital Earth Australia Coastlines catalog entry Data download Interactive Map

Lineage statement The DEA Coastlines product is under active development. A full and current product description is best sourced from the DEA Coastlines website. For a full summary of changes made in previous versions, refer to Github. Data dictionary Layer attribute columns Annual shorelines

Attribute name Description

OBJECTID Automatically generated system ID

year The year of each annual shoreline

certainty A column providing important data quality flags for each annual shoreline (see the Quality assurance section of the product description and metadata page for more detail about each data quality flag)

tide_datum The tide datum of each annual shoreline (e.g. "0 m AMSL")

id_primary The name of the annual shoreline's Primary sediment compartment from the Australian Coastal Sediment Compartments framework

Rates of change points and Coastal change hotspots

Attribute name Description

OBJECTID Automatically generated system ID

uid A unique geohash identifier for each point

rate_time Annual rates of change (in metres per year) calculated by linearly regressing annual shoreline distances against time (excluding outliers). Negative values indicate retreat and positive values indicate growth

sig_time Significance (p-value) of the linear relationship between annual shoreline distances and time. Small values (e.g. p-value < 0.01 or 0.05) may indicate a coastline is undergoing consistent coastal change through time

se-time Standard error (in metres) of the linear relationship between annual shoreline distances and time. This can be used to generate confidence intervals around the rate of change given by rate_time (e.g. 95% confidence interval = se_time * 1.96).

outl_time Individual annual shoreline are noisy estimators of coastline position that can be influenced by environmental conditions (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal modelling results or limited clear satellite observations). To obtain reliable rates of change, outlier shorelines are excluded using a robust Median Absolute Deviation outlier detection algorithm, and recorded in this column

dist_1990, dist_1991, etc Annual shoreline distances (in metres) relative to the most recent baseline shoreline. Negative values indicate that an annual shoreline was located inland of the baseline shoreline. By definition, the most recent baseline column will always have a distance of 0 m

angle_mean, angle_std The mean angle and standard deviation between the baseline point to all annual shorelines. This data is used to calculate how well shorelines fall along a consistent line; high angular standard deviation indicates that derived rates of change are unlikely to be correct

valid_obs, valid_span The total number of valid (i.e. non-outliers, non-missing) annual shoreline observations, and the maximum number of years between the first and last valid annual shoreline

sce Shoreline Change Envelope (SCE). A measure of the maximum change or variability across all annual shorelines, calculated by computing the maximum distance between any two annual shorelines (excluding outliers). This statistic excludes sub-annual shoreline variability like tides, storms and seasonal effects

nsm Net Shoreline Movement (NSM). The distance between the oldest (1988) and most recent annual shoreline (excluding outliers). Negative values indicate the coastline retreated between the oldest and most recent shoreline; positive values indicate growth. This statistic does not reflect sub-annual shoreline variability, so will underestimate the full extent of variability at any given location

max_year, min_year The year that annual shorelines were at their maximum (i.e. located furthest towards the ocean) and their minimum (i.e. located furthest inland) respectively (excluding outliers). This statistic excludes sub-annual shoreline variability

certainty A column providing important data quality flags for each annual shoreline (see the Quality assurance section of the product description and metadata page for more detail about each data quality flag)

id_primary The name of the point's Primary sediment compartment from the Australian Coastal Sediment Compartments framework

Contact Geoscience Australia, clientservices@ga.gov.au

Le jeu de données recense les différents Défibrillateurs Automatisés Externes (DEA) présents dans la collectivité, leur localisation géographique et les personnels en charge des DEA. Le jeu de données est envisagé dans une perspective de prévention à l'égard des citoyens, pour notamment les informer de l'emplacement des différents DAE dans leur commune. Lien vers une carte interactive concernant l’emplacement des DAE (défibrillateurs): https://umap.openstreetmap.fr/fr/map/defibrillateurs-automatises-externes-dae_838481

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The AUV-acquired Side Scan Sonar (SSS) data during RV SONNE cruise SO242_1 of the entire DEA is provided as GeoTIFF here (0.5 x 0.5 m resolution, UTM 16S).

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Localisation des défibrillateurs (DEA) installés par la Ville de Bruxelles « Face à un arrêt cardiaque, la clé de la survie et d'un bon rétablissement réside dans la rapidité de réaction.En effet, à chaque minute qui passe sans massage cardiaque ni choc externe pour relancer le cœur, les chances de survie diminuent de 10%.Si la victime reste sans aide durant les 4 à 6 minutes qui suivent l'arrêt cardiaque, le risque de décès grimpe à 90 ou 95%.En cas d’arrêt cardiaque, votre plus grande chance de survie n'est pas le meilleur médecin l’hôpital, mais les personnes qui vous entourent. »OSEZ INTERVENIR ! 1. Avertissez les services de secours au numéro d’urgence 1122. Réanimez la victime3. Cherchez un défibrillateur (DEA) et utilisez-leLien vidéo : https://www.youtube.com/watch?v=I1tWDBsVEcY

Plus d’info : https://www.monrythmecardiaque.be/reanimatie.php

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The 1990 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 4/5 multispectral imagery, acquired between April 1989 and October 1993. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 4/5 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 4/5 standard map projection format as provided by the USGS. Class 35–39 of the 1990 South African National land cover dataset was clustered to derive South African land cover 1990 Class N Mines for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs