Under SO2024-0008386, passed by City Council on 4/17/2024, the Department of Family and Support Services publishes data on the City-operated and City-funded emergency new arrivals shelters. This series of datasets contains data related to the Limited Stay Policy.

Under this policy, a shelter stay can be extended beyond the initial 60-day period under certain circumstances. This dataset shows the original exit date of those who have exited due to this policy. This dataset does not represent their actual exit date, but rather the date their initial 60-day stay would have ended barring any extensions.

The Denominator File combines Medicare beneficiary entitlement status information from administrative enrollment records with third-party payer information and GHP enrollment information. The Denominator File contains data on all Medicare beneficiaries enrolled and or entitled in a given year. It is an abbreviated version of the Enrollment Data Base (EDB) (selected data elements). It does not contain data on all beneficiaries ever entitled to Medicare. The file contains data only for beneficiaries who were entitled during the year of the data. These data are available annually in May of the current year for the prior year.

The National Post-acute and Long-term Care Study (NPALS) is a biennial study of major post-acute and long-term care providers and their services users. Seven provider settings are in included. NPALS collects survey data on the residential care community and adult day services sectors, and uses administrative data (available from CMS) for home health, nursing home, hospice, inpatient rehabilitation, and long-term care hospital sectors. The goals of the study are to: estimate the supply of paid, regulated post-acute and long-term care services providers; estimate key policy-relevant characteristics and practices of these providers; estimate the number of post-acute and long-term care services users; estimate key policy-relevant characteristics of these users; produce national and state estimates where feasible; compare across provider sectors; and monitor trends over time.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and aut

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 32 data element restricted access dataset.

The following apply to the public use datasets and the restricted access dataset: - Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf. - Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. - Some data are suppressed to protect individual privacy. - Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the previously updated datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured. - Datasets are updated monthly. - Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy. - For more information about data collection and reporting, please see wwwn.cdc.gov/nndss/data-collection.html. - For more information about the COVID-19 case surveillance data, please see www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html.

Overview

The COVID-19 case surveillance database includes patient-level data reported by U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as "immediately notifiable, urgent (within 24 hours)" by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020 to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data collected by jurisdictions are shared voluntarily with CDC. For more information, visit: wwwn.cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/case-definition/2020/08/05/.

COVID-19 Case Reports

COVID-19 case reports are routinely submitted to CDC by pu

This data set describes the service areas where NBN Co Limited is the Statutory Infrastructure Provider (SIP). This data set forms part of the SIP register which is managed by the ACMA. The SIP …Show full descriptionThis data set describes the service areas where NBN Co Limited is the Statutory Infrastructure Provider (SIP). This data set forms part of the SIP register which is managed by the ACMA. The SIP register is located on the ACMA’s website at https://www.acma.gov.au/sip-register. The data represented here is provided by NBN Co to the ACMA as required under Part 19 of the Telecommunications Act 1997. The ACMA also publishes NBN Co’s geospatial data to the National Map. The copyright in the data is owned by NBN Co, and users must comply with the terms of use for the data as set out on this website. The ACMA does not guarantee, and accepts no legal liability for any loss whatsoever arising from or in connection with the accuracy, reliability, currency, completeness or fitness for purpose of the data. The technology planned or delivered for premises or areas by NBN Co, and the availability of the NBN Co network at a premise, may be subject to change over time. More up to date information may be available on https://www.nbnco.com.au/.

For PPRTs, the environmental code defines a single category of zones for zones (L515-15 et seq.): areas at risk. Unlike natural RPPs, PPRTs never have restricted areas that are not directly exposed to risk.Depending on the hazard level, each TPP area is subject to enforceable regulation. The PPRT regulations generally distinguish between two types of areas:1- “Building prohibited areas”, known as “red zones”, where the hazard level is high and the general rule is the construction ban;2- “prescribed areas”, so-called “blue zones”, where the hazard level is medium and the projects are subject to requirements adapted to the type of issue. The instructions of the PPRT development guide add a gradation within the “red zones” and “blue zones”.

Regulatory zoning of the Risk Prevention Plan Withdrawal of the Argiles of the commune of Monlaur-Bernet in the department of Gers. The Regulations of the RPP describe the different requirements and recommendations intended to apply to each of the areas of the regulatory map. These requirements are essentially constructive provisions and are mainly aimed at the construction of new houses. However, some of them also apply to existing constructions. Depending on the type of construction (existing or future), some of these requirements are mandatory or simply recommended. The approved RPP is public utility easement and is enforceable against third parties.

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0002 (DS0002) contains the data from the State Design Data. This file contains 7 variables and 82,139 cases. The state identifier in the State Design file reflects the participant's state of residence at the time of selection and recruitment for the PATH Study. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth and Parent Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state Federal Information Processing System (FIPS), state abbreviation, and full name of the state). The State Identifier values in these datasets represent participants' state of residence at the time of Wave 1, which is also their state of residence at the time of recruitment. Dataset 1611 (DS1611) contains the Tobacco Universal Product Code (UPC) data from Wave 1. This data file contains 32 variables and 8,601 cases. This file contains UPC values on the packages of tobacco products used or in the possession of adult respondents at the time of Wave 1. The UPC values can be used to identify and validate the specific products used by respon

The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Research Programmes, NIHR Centres of Excellence and Facilities, plus the NIHR Academy. NIHR awards from all NIHR Research Programmes and the NIHR Academy that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion criteriaNIHR awards are categorised as public health awards if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health awards across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR awards categorised as public health awards from NIHR Research Programmes and the NIHR Academy. This dataset does not currently include public health awards or projects funded by any of the three NIHR Research Schools or any of the NIHR Centres of Excellence and Facilities. Therefore, awards from the NIHR Schools for Public Health, Primary Care and Social Care, NIHR Public Health Policy Research Unit and the NIHR Health Protection Research Units do not feature in this curated portfolio.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. This caveat applies to all data within the dataset irrespective of the funding NIHR Research Programme or NIHR Academy award.Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further informationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows: NIHR School for Public Health here. NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here. NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here. NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

DE-SynPUF is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,300,000 persom (2.3m) data sets in the OMOP Common Data Model format. The DE-SynPUF was created with the goal of providing a realistic set of claims data in the public domain while providing the very highest degree of protection to the Medicare beneficiaries’ protected health information. The purposes of the DE-SynPUF are to:

1. allow data entrepreneurs to develop and create software and applications that may eventually be applied to actual CMS claims data;
2. train researchers on the use and complexity of conducting analyses with CMS claims data prior to initiating the process to obtain access to actual CMS data; and,
3. support safe data mining innovations that may reveal unanticipated knowledge gains while preserving beneficiary privacy. The files have been designed so that programs and procedures created on the DE-SynPUF will function on CMS Limited Data Sets. The data structure of the Medicare DE-SynPUF is very similar to the CMS Limited Data Sets, but with a smaller number of variables. The DE-SynPUF also provides a robust set of metadata on the CMS claims data that have not been previously available in the public domain. Although the DE-SynPUF has very limited inferential research value to draw conclusions about Medicare beneficiaries due to the synthetic processes used to create the file, the Medicare DE-SynPUF does increase access to a realistic Medicare claims data file in a timely and less expensive manner to spur the innovation necessary to achieve the goals of better care for beneficiaries and improve the health of the population.

The regulated zoning of the Natural Hazard Prevention Plan (PPRN) is digitised in accordance with the national requirements of COVADIS. For natural PPRs, the Environmental Code defines two categories of zones (L562-1): risk-exposed areas and areas that are not directly exposed to risks but where measures can be foreseen to avoid exacerbating the risk. Depending on the hazard level, each area is subject to an enforceable settlement. The regulations generally distinguish three types of zones: 1- ‘Building prohibited areas’, known as ‘red areas’, where the hazard level is high and the general rule is the prohibition on construction; 2- ‘prescribed areas’, known as ‘blue zones’, where the hazard level is average and the projects are subject to requirements adapted to the type of issue; 3- areas not directly exposed to risks but where constructions, works, developments or farms, agricultural, forestry, craft, commercial or industrial could aggravate risks or cause new ones, subject to prohibitions or requirements (cf. Article L562-1 of the Environmental Code). The latter category applies only to natural RPPs.

For natural PPRs, the Environmental Code defines two categories of zones (L562-1): risk-exposed areas and areas that are not directly exposed to risks but where measures can be foreseen to avoid exacerbating the risk.

Depending on the hazard level, each area is subject to an enforceable settlement. This PPR defines 2 types of zones:

— red zone corresponding to the urbanised areas located in strong hazard and non-urbanised areas, regardless of the level of hazard (fields of expansion of floods to be preserved). In these areas, only new constructions and developments directly linked to the management, maintenance and operation of space, subject to compliance with requirements to reduce vulnerability and very limited extensions of existing buildings. It is subdivided into 3 sub-areas: — UK: area of strong hazards in urban center — RD: zoning of the hazard study produced by Constellium and validated by the Prefect on January 5, 2015 — R: other red zone

— orange area corresponding to urbanised areas affected by medium or low hazard where urbanisation is allowed provided that the vulnerability is not aggravated

Risk Prevention Plans (RPPs) are the key government instrument for risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a set of spatial data organised into several data sets. This dataset describes the restricted areas of the plan once approved. RPP regulations generally distinguish: — ‘Building prohibited areas’, known as ‘red areas’, where the hazard level is high and the general rule is the prohibition on construction; — ‘areas subject to requirements’, known as ‘blue zones’, where the hazard level is average and the projects are subject to requirements adapted to the type of issue and areas not directly exposed to risks but subject to prohibitions or requirements

This data is obtained from our Asset Inventory Database and is refreshed weekly. Please note that while all due care has been taken in the preparation and provision of this service, Auckland Transport (AT) does not give any warranty that the information contained is accurate and accepts no liability whatsoever for any loss or damage arising from the use of the data. Users of this data should apply and rely upon their own skill and judgement when using the information, and consider the consequences arising from its use. This data should not be used in isolation from other sources of advice and information.

This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Land Estate - Other Activities category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction. Each feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://dnr.alaska.gov/projects/las/ Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

For natural PPRs, the Environmental Code defines two categories of zones (L562-1): risk-exposed areas and areas that are not directly exposed to risks but where measures can be foreseen to avoid exacerbating the risk. Depending on the hazard level, each area is subject to an enforceable settlement. The regulations generally distinguish three types of zones: 1- ‘Building prohibited areas’, known as ‘red areas’, where the hazard level is high and the general rule is the prohibition on construction; 2- ‘prescribed areas’, known as ‘blue zones’, where the hazard level is average and the projects are subject to requirements adapted to the type of issue; 3- areas not directly exposed to risks but where constructions, works, developments or farms, agricultural, forestry, craft, commercial or industrial could aggravate risks or cause new ones, subject to prohibitions or requirements (cf. Article L562-1 of the Environmental Code). The latter category applies only to natural RPPs.

This RPP defines 2 main areas: — red: principle of prohibition on construction — orange: principle of permission to build with prescription

These two areas are themselves subdivided into 6 sectors: R1: Flood Expansion Fields in Low Hazard R2: Flood Expansion Fields in Medium Hazardous R3: Zone of strong hazards R3u: Urban centre area subjected to a severe hazard O1: low-risk urbanised area O2: urbanised Medium Hazardous Area

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits under a medical benefit plan in that calendar year and month. A recipient may have received benefits from multiple plans in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.\ NOTE: On February 14 2019, the enrollment

This dataset contains restricted areas (surface objects) of PPRT Risk industrialist of Lacq Mont (L_ZONE_REG_PPRT_20130259_064) Risk prevention plans (RPPs) are the key instrument of the state in risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a spatial dataset organised into several data sets. This game of data describes the restricted areas of the plan once approved. Regulations of RPPs generally distinguish between ‘construction ban areas’, known as ‘red areas’, where the hazard level is strong and the general rule is the prohibition on construction; the ‘prescribed areas’, referred to as ‘blue zones’ where the hazard level is and that the projects are subject to requirements adapted to the type of issue and the areas not directly exposed to risks but subject to prohibitions or requirements.

Several bureaus within the Department of Interior compiled available information from seabird observation datasets from the Atlantic Outer Continental Shelf into a single database, with the goal of conducting research and informing coastal and offshore planning activities. The cooperators were the Bureau of Ocean Energy Management's (BOEM) Environmental Studies Program (http://www.boem.gov/Environmental-Stewardship/Environmental-Studies/Environmental-Studies.aspx), the U.S. Fish and Wildlife Service's (USFWS) Division of Migratory Bird Management (http://www.fws.gov/migratorybirds/) and the U.S. Geological Survey's (USGS) Patuxent Wildlife Research Center (http://www.pwrc.usgs.gov). The resulting product is the Atlantic Offshore Seabird Dataset Catalog, which characterizes the survey effort and bird observations that have been collected across space and time. Currently, the database contains ~60 datasets from 1906-2009 with over 260,000 records of seabird observations. Data will initially be provided as summary web mapping services, with web feature services (for downloading and looking at single-species data) at the linkage given elsewhere in this document. USAGE: Seabirds provide unique challenges even when using estimation techniques to sample populations (e.g., Tasker et al. 1984, Spear et al. 1992). To date, there has been little consistency among survey designs. Surveys have varied by the type of vessel from which they are conducted (ship, plane), the methodology that counts are made, the width of the area being counted, and equipment used, among many other differences. Under such circumstances, comparing results and making inferences can be difficult. Because these estimates of effort-adjusted counts do not account for detection probability, they are likely biased by factors that affect this parameter such as weather, survey method, observer, or other environmental variables (MacKenzie et al. 2006). Such results may be considered naïve in that they do not account for factors that can affect the ability to detect an animal. Furthermore, these results contain data collected over a 30 year period without regard for any long term temporal changes that may have occurred with species or the environment. Further analysis is necessary to determine if such changes have occurred with any species. While it is possible to separate data collected recently from historical (>20 years old) datasets, the amount of recent data is limited and therefore maps showing only these data may be limited spatially. Finally, effort calculations do not account for survey width, while normally static during a survey, can be reduced during certain conditions and does vary by survey method, especially boat vs. plan surveys. The vast majority of survey data collected offshore U.S. Atlantic waters were collected using similar techniques and so effort data will not be greatly affected by such discrepancies. Still, such differences do exist and were not accounted for; therefore, this estimate of survey effort is a rough surrogate for effort. Consequently, the effort-adjusted counts will also be affected by differences in survey methodology and should be considered only roughly offset by effort.