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.

Abstract (en): The data contain records of arrests made by agents of the Drug Enforcement Administration (DEA) during fiscal year 1994. The data were constructed from the DEA Defendant Statistical System file, and include only those arrests made within the United States and its territories. Suspects arrested by the DEA may immediately be transferred to state or local jurisdictions. The data file contains variables from the original DEA data file as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 1.4-1.5. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. Arrests made by Drug Enforcement Administration agents in the United States and territories during fiscal year 1994. 2011-03-08 All parts are being moved to restricted access and will be available only using the restricted access procedures. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. 2011-06-15 This data collection has been deaccessioned and is no longer available.

The global Lauramide DEA market is experiencing robust growth, driven by increasing demand from the cosmetic and personal care industries, as well as the household and industrial cleaning sectors. Lauramide DEA, a versatile surfactant, is valued for its foaming, emulsifying, and cleaning properties. While precise market size figures for 2025 require confidential data access, we can estimate based on available industry reports and growth trends. Let's assume a 2025 market size of $500 million, representing a significant increase from previous years. A conservative Compound Annual Growth Rate (CAGR) of 4% from 2025-2033 is projected, leading to an estimated market value of approximately $710 million by 2033. This growth trajectory is fueled by several factors: the rising global population, increasing disposable incomes leading to higher consumption of personal care products, and a sustained demand for effective cleaning solutions across various sectors. The market's segmentation reflects the diverse applications of Lauramide DEA, with liquid formulations currently dominating the market. However, solid forms are gaining traction due to their ease of handling and storage. The major players in the Lauramide DEA market, including BASF, Stepan, and Solvay, are strategically investing in research and development to innovate new formulations and improve product efficiency. Geographical expansion, particularly in emerging economies of Asia-Pacific and South America, presents significant opportunities for market growth. However, potential restraints include fluctuating raw material prices and increasing environmental concerns related to the surfactant's potential impact on aquatic ecosystems. Companies are actively working on sustainable alternatives and improving manufacturing processes to mitigate environmental concerns. Further research into biodegradable Lauramide DEA alternatives and stricter regulations on surfactant usage will likely shape the market's future trajectory. The competitive landscape is characterized by established global players alongside regional manufacturers, creating a dynamic environment ripe for both innovation and consolidation. This in-depth report provides a comprehensive analysis of the global Lauramide DEA market, projecting a market value exceeding $800 million by 2028. It delves into production figures, market segmentation, key players, emerging trends, and future growth potential, offering invaluable insights for industry stakeholders. Keywords: Lauramide DEA Market, Lauramide DEA Production, DEA, Cosmetics, Cleaning Products, Chemical Industry, Market Analysis, Market Trends, Market Size, Market Growth, Market Segmentation.

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Anstee, J. M., Botha, E. J. and Dekker, A. G. (2009): Study on the remote sensing of estuarine macrophytes and saltmarsh vegetation in Wallis Lake; CSIRO Water for a Healthy Country Flagship report prepared for: NSW MER Program, Estuarine Theme Team); CSIRO Land and Water and Water for a Healthy Country: Canberra, Australia, 2030

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Analysis Ready Data (ARD) takes medium resolution satellite imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. This product is a single, cohesive ARD package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections.

ARD consists of sub products, including :

1) NBAR Surface Reflectance which produces standardised optical surface reflectance data using robust physical models which correct for variations and inconsistencies in image radiance values. Corrections are performed using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR).

2) NBART Surface Reflectance which performs the same function as NBAR Surface Reflectance, but also applies terrain illumination correction.

3) OA Observation Attributes product which provides accurate and reliable contextual information about the data. This 'data provenance' provides a chain of information which allows the data to be replicated or utilised by derivative applications. It takes a number of different forms, including satellite, solar and surface geometry and classification attribution labels.

ARD enables generation of Derivative Data and information products that represent biophysical parameters, either summarised as statistics, or as observations, which underpin an understanding of environmental dynamics. The development of derivative products to monitor land, inland waterways and coastal features, such as: - urban growth - coastal habitats - mining activities - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping) - water extent

Derivative products include: - Water Observations from Space (WOfS) - National Intertidal Digital Elevation Model (NIDEM) - Fractional Cover (FC) - Geomedian

ARD and Derivative products are reproduced through a period collection upgrade process for each sensor platform. This process applied improvements to the algorithms and techniques and benefits from improvements applied to the baseline data that feeds into the ARD production processes.

Value: These data are used to understand distributions of and changes in surface character, environmental systems, land use.

Scope: Australian mainland and some part of adjacent nations.

Access data via the DEA web page - https://www.dea.ga.gov.au/products/baseline-data

An Australia-wide vegetation height was generated using Global Ecosystem Dynamics Investigation (GEDI) LiDAR Altimetry (from 2019) and used to train a random forest model to provide vegetation height from Landsat data available in Digital Earth Australia. The Landsat data used for extrapolating vegetation height images were the Annual Fractional Cover product and the Annual Geomedian product. The random forest model was used to generate annual vegetation height from 1988 to 2021. To reduce errors in irrigated agriculture, vegetation height below three metres was set to zero. Refer to the metadata for a description of the method and validation of this product and the Readme.txt for the data format (see Supporting files). This method was developed through the CSIRO Digiscape Future Science Platform and updated as part of the Regional Land and Ecosystem Accounts project, which is funded through the Australian Department of Climate Change, Energy the Environment and Water (DCCEEW). Lineage: GEDI data are available to download from the NASA EarthData Search website (https://search.earthdata.nasa.gov/search). The Landsat Annual Fractional Cover and Annual Geomedian products data are available through Digital Earth Australia as part of the Collection 3 data (Fractional Cover Percentiles – https://cmi.ga.gov.au/data-products/dea/630/dea-fractional-cover-percentiles-landsat and Geometric Median and Median Absolute Deviation – https://cmi.ga.gov.au/data-products/dea/645/dea-geometric-median-and-median-absolute-deviation-landsat ). Processing was performed on the Australian National Computational Infrastructure (NCI) and the CSIRO Earth Analytics Science and Innovation (EASI) platform, tested using Jupyter notebooks, and batch-processed as python scripts. Images were processed as tiles, then mosaicked to form annual Australia-wide layers.

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

This dataset provides model-based provisional estimates of the weekly numbers of drug overdose, suicide, and transportation-related deaths using “nowcasting” methods to account for the normal lag between the occurrence and reporting of these deaths. Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data. For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11–12 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see:

Rossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. DOI: https://doi.org/10.15620/ cdc:101132

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Botha, E.J., Dekker, A.G., and Park, Y.J. (2009) Remote sensing of previously unmapped marine habitats on the south coast of Western Australia; National Research Flagship Wealth from Oceans Report to WA-DEC, WA South Coast NRM, WA South West Catchments Council. Canberra, Australia, pp: 53. https://doi.org/10.3390/rs12142247

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Abstract (en): The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year. DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug-related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit. The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 22 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals. DAWN includes a set of complex sample design variables to calculate estimates for the entire universe of DAWN-eligible hospitals in the United States from the sampled hospitals participating in DAWN. The primary sampling weights reflect the probability of selection, and separate adjustment factors are included to account for sampling of ED visits, nonresponse, data quality, and the known total of ED visits delivered by the universe of eligible hospitals. DAWN design variables include: variance estimation stratum (STRATA), PSU, replicate (REPLICATE), PSU frame count (PSUFRAME), and case weight (CASEWGT). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Response Rates: For 2009, 242 hospitals submitted data that were used for estimation. The overall weighted response rate was 31.8 percent. For the 12 oversampled metropolitan areas and divisions, the individual response rates ranged from 28.5 percent in the Ho...

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Leiper, I., Phinn, S. & Dekker, A.G. (2012): Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef, Australia, International Journal of Remote Sensing, 33:12, 3946-3965. http://dx.doi.org/10.1080/01431161.2011.637675

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Roelfsema, Christiaan M; Phinn, Stuart R; Joyce, Karen (2016): Spectral reflectance library of algal, seagrass and substrate types in Moreton Bay, Australia. PANGAEA, https://doi.org/10.1594/PANGAEA.864316 Publication: Roelfsema, Christiaan M; Phinn, Stuart R; Dennison, William C; Dekker, Arnold G; Brando, Vittorio E (2006): Monitoring toxic cyanobacteria Lyngbya majuscula (Gomont) in Moreton Bay, Australia by integrating satellite image data and field mapping. Harmful Algae, 5(1), 45-56, https://doi.org/10.1016/j.hal.2005.05.007

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Blackburn, D. T, and Dekker, A. G. (2007). “Remote sensing study of marine and coastal features and interpretation of changes in relation to natural and anthropogenic processes. Final Technical Report”. ACWS Technical Report No.6 prepared for the Adelaide Coastal Waters Study Steering Committee, July 2007 David Blackburn Environmental Pty Ltd and CSIRO Land and Water.

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Jupp, D., Byrne, G. T., Anstee, J. M. McDonald, E.R., McVicar, T.R. ,Parkin, D; 1996 Port Phillip Bay benthic habitat mapping project task G2.2, CSIRO Division of Water Resources, Consultancy Report 96/43, 47 pp, CSIRO Australia

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Rodney Borrego-Acevedo, Chris M. Roelfsema, Stuart R. Phinn & Alistair R. Grinham (2014) Predicting distribution of microphytobenthos abundance on a reef platform by combining in situ underwater spectrometry and pigment analysis, Remote Sensing Letters, 5:5, 461-470, DOI: 10.1080/2150704X.2014.922723

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Record for source data hosted in the National Spectral Database (NSD) Aquatic Library

Citation: Phinn, S. Roelfsema, C. Scarth, P., Dekker, A.G., Brando, V.E., Anstee, J.M. and Marks, A., (2005) An integrated remote sensing approach for adaptive management of complex coastal waters. Final Report – Moreton Bay Remote Sensing Tasks (MR2). Phinn, S. and Dekker, A.G (eds), Published by the CRC for Coastal Zone, Estuary and Waterway Management, Indooroopilly, Qld, Australia Publication: Dekker A.G., Phinn S.R., Anstee J.M., Bissett P. Brando V.E., Casey B., Fearns P., Hedley J., Klonowski W., Lee Z.P., Lynch M., Lyons M., Mobley C. and Roelfsema C. (2011) Intercomparison of shallow water bathymetry, hydro-optics and benthos mapping techniques in Australian and Caribbean coastal environments; Limnology & Oceanography Methods. 9:pp 396-425. | https://doi.org/10.4319/lom.2011.9.396

For further information and instructions to access the database go to the following URL: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database