The restricted version of the Myers Abortion Facility Database provides detailed information on the identities, locations, and dates of operation of all publicly-identifiable abortion facilities in the United States from January 1, 2009 through the most recent available vintage. The database is updated quarterly. To access the application, select this component and then navigate to the "Files" folder within the component.

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 publicly available dataset has 33 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. This dataset requires a registration process and a data use agreement.

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)

Requesting Access to the COVID-19 Case Surveillance Restricted Access Detailed Data Please review the following documents to determine your interest in accessing the COVID-19 Case Surveillance Restricted Access Detailed Data file: 1) CDC COVID-19 Case Surveillance Restricted Access Detailed Data: Summary, Guidance, Limitations Information, and Restricted Access Data Use Agreement Information 2) Data Dictionary for the COVID-19 Case Surveillance Restricted Access Detailed Data The next step is to complete the Registration Information and Data Use Restrictions Agreement (RIDURA). Once complete, CDC will review your agreement. After access is granted, Ask SRRG (eocevent394@cdc.gov) will email you information about how to access the data through GitHub. If you have questions about obtaining access, email eocevent394@cdc.gov.

Overview

The COVID-19 case surveillance database includes individual-level data reported to 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 are collected by jurisdictions and reported voluntarily to CDC.

COVID-19 case surveillance data are collected by jurisdictions and are shared voluntarily with CDC. For more information, visit: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.

The deidentified data in the restricted access dataset include demographic characteristics, state and county of residence, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and comorbidities.

All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 case reports have been routinely submitted using standardized case reporting forms.

On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

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. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases.

On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations:

Restricted Data Files Available at the Data Centers Researchers and users with approved research projects can access restricted data files that have not been publicly released for reasons of confidentiality at the AHRQ Data Center in Rockville, Maryland. Qualified researchers can also access restricted data files through the U.S. Census Research Data Center (RDC) network (http://www.census.gov/ces/dataproducts/index.html -- Scroll down the page and click on the Agency for Health Care Research and Quality (AHRQ) link.) For information on the RDC research proposal process and the data sets available, read AHRQ-Census Bureau agreement on access to restricted MEPS data.

Sensitive Regulated Data: Permitted and Restricted UsesPurposeScope and AuthorityStandardViolation of the Standard - Misuse of InformationDefinitionsReferencesAppendix A: Personally Identifiable Information (PII)Appendix B: Security of Personally Owned Devices that Access or Maintain Sensitive Restricted DataAppendix C: Sensitive Security Information (SSI)

Author: Rodrigo A. Moreira (C) 2023 https://orcid.org/0000-0002-7605-8722 LICENSE: CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/)

rbLEC - Local Euler Charactersitics - from CATH database

A. rbLEC NETWORK

[I] The networks for each PDB[1] structure is defined by the PDB atoms N,CA,C of each residue as nodes of a graph G.
[II] An edge of G is set if the distance between two atom in [I] is greater than 2.0 Angstrons.
[III] The graph G is defined in the files with extensions ".network_backboneRE_heavy_gt2"

Equation (1) [6,7] \begin{equation} \chi = \sum_{k=1}^{N} \kappa_k = \sum_{k=1}^{N} \underbrace{ \left(1 + \sum_{l=1}^{\infty} (-1)^{l} \frac{v_{l-1}}{l+1} \right)_{k}}_{\kappa_k} \end{equation}

Equation (2) \begin{equation} LEC = \sum_{m \in R} \kappa_m = \kappa_{N} + \kappa_{CA} + \kappa_{C} \end{equation}

B. FILENAME EXTENSIONS

B.1 Basic files

".fixed" PDB file after use of pdbfixer[2] in structures from CATH database.

".dssp" Output of DSSP[3] software

".stride" Output of STRIDE[4] software

B.2 Data files

".network_backboneRE_heavy_gt2" - Generate by D.2 below. Describe the network graph, as described in A. above.

".knill_curvature" - Generate by D.1 below. Contain the filtration of kappas for each vertice of the network.

".residues_curvature" - Generate by D.1 below. They are the filtration of LEC, Equation (2) above, for each residue, namely summation of 3 kappas from respective '.knill_curvature', correspoings to PDB atoms N,CA and C, describe in A. above.

".label" - Generated by D.3 below Extra file for easier assesment of structures. They have the same information about LEC as described in respective ".residue_curvature" file extensions, but merge also the information from ".dssp" and ".stride" classes as well as residue name and residue ID for each molecule. Format of columns: cutoff resname resid DSSP_class STRIDE_class LEC

C. FOLDERS

CATH_FIXED (after uncompress cath_fixed.tar.xz, approximately 13GB)
  contains the fixed PDBs and LECs from CATH[5] database

D. SOFTWARE D.1 lec.py: compute the kappas in Equation (1) above. Example usage: $ python3 lec.py CATH_FIXED/2x0qA02/2x0qA02 It will create the files with extension ".kappas" and ".relec", which reproduces the respectively the files with extension ".knill_curvature" and ".residue_curvature".

 D.2 pdb2network.lua: creates rbLEC network file (number of nodes and edges list) from PDB to be used as input by lec.py.
  Example usage:
    $ lua pdb2rbLEC.lua CATH_FIXED/2x0qA02/2x0qA02.fixed
  Output reproduces the file CATH_FIXED/2x0qA02/2x0qA02.pdb.network_backboneRE_heavy_gt2


 D.3 label.lua: create files with extension '*.label' from files '*.pdb.stride', '*.pdb.dssp' and '*.pdb.network_backboneRE_heavy_gt2.residues_curvature.
  Example usage:
     $ lua label.lua CATH_FIXED/2x0qA02/2x0qA02.pdb
  Output reproduces the file CATH_FIXED/2x0qA02/2x0qA02.pdb.network_backboneRE_heavy_gt2.residues_curvature.label

REFERENCES [1] Herman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., & Bourne, P. (2000). The protein data bank. Nucleic acids research, 28, 235–42. [2] Eastman, P., Swails, J., Chodera, J., McGibbon, R., Zhao, Y., Beauchamp, K., Wang, L.P., Simmonett, A., Harrigan, M., Stern, C., & others (2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS computational biology, 13(7), e1005659. [3] Kabsch, W., & Sander, C. (1983). Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers: Original Research on Biomolecules, 22(12), 2577–2637. [4] Frishman, D., & Argos, P. (1995). Knowledge-based protein secondary structure assignment. Proteins: Structure, Function, and Bioinformatics, 23(4), 566–579. [5] Knudsen, M., & Wiuf, C. (2010). The CATH database. Human genomics, 4(3), 1–6. [6] Levitt, N. (1992). The Euler characteristic is the unique locally determined numerical homotopy invariant of finite complexes. Discrete & computational geometry, 7, 59–67. [7] Knill, O. (2011). A graph theoretical Gauss-Bonnet-Chern theorem. arXiv preprint arXiv:1111.5395.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

The Contaminated Sites Register dataset is a spatial representation of individual sites recorded and classified by the Department of Water and Environmental (DWER) under the Contaminated Sites Act 2003 (the Act). The Act requires certain parties to report known or suspected contaminated sites. Show full description

The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) is the largest publicly available all-payer pediatric inpatient care database in the United States, containing data from two to three million hospital stays each year. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, such as congenital anomalies, as well as uncommon treatments, such as organ transplantation. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The KID is a sample of pediatric discharges from 4,000 U.S. hospitals in the HCUP State Inpatient Databases yielding approximately two to three million unweighted hospital discharges for newborns, children, and adolescents per year. About 10 percent of normal newborns and 80 percent of other neonatal and pediatric stays are selected from each hospital that is sampled for patients younger than 21 years of age.

The KID contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes discharge status, diagnoses, procedures, patient demographics (e.g., sex, age), expected source of primary payment (e.g., Medicare, Medicaid, private insurance, self-pay, and other insurance types), and hospital charges and cost.

Restricted access data files are available with a data use agreement and brief online security training.

This documentation has been created by ICPSR for the restricted version of Census 2000 distributed by the Bureau of the Census. The restricted data is based on questions from the long form questionnaire, and was collected from one in six households in the United States. Topics covered include income, ancestry, citizenship status, home values, commute time to work, occupation, education, veteran status, language ability, migration, place of birth, and many others. The documentation available here provides files summaries, variable information, and facilitates sorting of the data by race or by a wide variety of geographical units. ICPSR is not distributing the restricted data, only the documentation for it. Users who wish to access the restricted data can find more information at the Michigan Census Research Data Center Web site. Users should also note that the data for the public versions of Census 2000 are available from ICPSR.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses.

The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals.

Restricted access data files are available with a data use agreement and brief online security training.

National Center for Health Statistics (NCHS) population health survey data have been linked to VA administrative data containing information on military service history and VA benefit program utilization. The linked data can provide information on the health status and access to health care for VA program beneficiaries. In addition, researchers can compare the health of Veterans within and outside the VA health care system and compare Veterans to non-Veterans in the civilian non-institutionalized U.S. population. Due to confidentiality requirements, the Restricted-use NCHS-VA Linked Data Files are accessible only through the NCHS Research Data Center (RDC) Network. All interested researchers must submit a research proposal to the RDC. Please see the NCHS RDC website (https://www.cdc.gov/rdc/index.htm) for instructions on submitting a proposal.

This dataset contains income restricted housing units across the 4-county PSRC region (King, Kitsap, Pierce, and Snohomish Counties). They are summarized here by county, and then grouped into various AMI (area median income) bands. A breakdown of unit size by number of bedrooms is also included. This dataset was updated May 1, 2025 to capture improvements in the geographic placement of properties, and corrections to the unit counts for some properties in Snohomish CountyAll jurisdictions within the 4-county PSRC region are included, even those with zero income restricted units. Note that while we attempt to capture all income restricted units in the region, the IRHD (Income Restricted Housing Database) is not an exhaustive list. Some properties also include units that are not income restricted - it was not always possible to disaggregate these units. For example, the bedroom size data includes some market rate units. Where the data was available in King County, units created through various incentive programs were included, such as IZ (incentive zoning), MHA (Mandatory Housing Affordability) and MFTE (Multi Family Tax Exemption) units. Units created under these programs across the region are undercounted due to data availability.

This dataset tracks the updates made on the dataset "HCUP State Inpatient Databases (SID) - Restricted Access File" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "HCUP Nationwide Ambulatory Surgery Sample (NASS) Database – Restricted Access" as a repository for previous versions of the data and metadata.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Database of Communities of National Environmental Significance stores maps, taxonomic, ecological, and management information about Communities of National Environmental Significance listed in the Environment Protection and Biodiversity Conservation (EPBC) Act 1999 as threatened ecological communities.

Credit:

State and Commonwealth Herbaria, Museums and Conservation Agencies Centre for Plant Biodiversity Research Australian Government Department of the Environment, Environmental Resources Information Network

External accuracy:

The positional accuracy of spatial data is a statistical estimate of the degree to which planimetric coordinates and elevations of features agree with their real world values. The planimetric accuracy attainable in the vector data will be composed of errors from three sources:

1. The positional accuracy of the source material

2. Errors due to the conversion processes.

3. Errors due to the manipulation processes.

This specification cannot prescribe a figure for the planimetric accuracy of the existing source material used for capture of community distributions as it has already been produced. The errors due to the digitising process depend on the accuracy of the digitising table set-up or the scanner resolution, systematic errors in the equipment, errors due to software and errors specific to the operator. An accepted standard for digitising is that the line accuracy should be within half a line width.

Non Quantitative accuracy:

Tests are undertaken to ensure that there are no errors in attributes:

• The spatial resolution of the data is reflected in the Presence Categories

• Presence categories are one of:

* Community known to occur within area

* Community likely to occur within area

* Community may occur within area (general indication only)

Conceptual consistency:

Tests undertaken for logical consistency:

• Names of export files and data quality table are correct

• Table names are valid

• Item names in coverages are valid

• Item names are present in coverage attribute files

• Label points and entity point features have only one coordinate pair

• The Arc/Info coverages can be generated, have attributes attached and be 'built'

• In polygon coverages there are no label errors i.e. every polygon has one and only one polygon label point

• Data format, projection and data type are correct

• There are no overshoots, i.e. arc overhangs at intersections (1% error acceptable)

• There are no undershoots, i.e. arcs failing to meet at intersections (0.5% error acceptable)

• There are no new polygons smaller than the minimum specified area (5% error acceptable)

• There are no new linear features shorter than the minimum length (5% error acceptable)

• There are no artefacts such as spikes or deviations visible at 1:125 000 (5% error acceptable)

• Separate covers have exactly coincident lines where intended (5% error acceptable)

Completeness omission:

The database is continually being updated as the lists of threatened ecological communities on schedules of the EPBC Act are amended.

The Species of National Environmental Significance database is available at

https://www.environment.gov.au/science/erin/databases-maps/snes

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Spatial information is stored in a geographic information system and links to the Species Profile tables through the community identifier.

Source data were provided from a range of government, industry and non-government organisations.

Testing is carried out using a combination of expert opinion and on-screen checks.

Dataset Citation

Department of the Environment (2015) Communities of National Environmental Significance Database - RESTRICTED - Metadata only. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/c01c4693-0a51-4dbc-bbbd-7a07952aa5f6.

Hong Kong DTC: FC: Assets: NCD: Issued by Restricted Licence Banks data was reported at 0.000 HKD mn in May 2018. This stayed constant from the previous number of 0.000 HKD mn for Apr 2018. Hong Kong DTC: FC: Assets: NCD: Issued by Restricted Licence Banks data is updated monthly, averaging 0.000 HKD mn from Jan 1991 (Median) to May 2018, with 329 observations. The data reached an all-time high of 934.000 HKD mn in Aug 1996 and a record low of 0.000 HKD mn in May 2018. Hong Kong DTC: FC: Assets: NCD: Issued by Restricted Licence Banks data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong – Table HK.KB004: Balance Sheet: Deposit Taking Companies.

Link Function: information

This dataset contains income restricted housing units across the 4-county PSRC region (King, Kitsap, Pierce, and Snohomish Counties). They are summarized here by county, and then grouped into various AMI (area median income) bands. A breakdown of unit size by number of bedrooms is also included. This dataset was updated May 1, 2025 to capture improvements in the geographic placement of properties, and corrections to the unit counts for some properties in Snohomish CountyAll jurisdictions within the 4-county PSRC region are included, even those with zero income restricted units. Note that while we attempt to capture all income restricted units in the region, the IRHD (Income Restricted Housing Database) is not an exhaustive list. Some properties also include units that are not income restricted - it was not always possible to disaggregate these units. For example, the bedroom size data includes some market rate units. Where the data was available in King County, units created through various incentive programs were included, such as IZ (incentive zoning), MHA (Mandatory Housing Affordability) and MFTE (Multi Family Tax Exemption) units. Units created under these programs across the region are undercounted due to data availability.

This layer displays boundaries for Marine Managed Areas designated as "Restricted Area" in the Southeast Atlantic.

The purpose of this data is to increase awareness of restrictions and protections of marine life. To achieve this goal, this data lists the area name, its purpose, an overview of what is restricted and allowed, and its geographic boundary. This dataset includes Federal and State data from internet searches and downloads from official sources. However, additional municipally managed sites may exist that are not included.

The MPA Inventory is a comprehensive catalog that provides detailed information for existing marine protected areas in the United States. Additionally, this feature class includes records included in the 2014 MPA Center Inventory that were not included in the updated inventory produced by the MPA Center and the Anthropocene Institute. The inventory provides geospatial boundary information (in polygon format) and classification attributes that seek to define the conservation objectives, protection level, governance and related management criteria for all sites in the database. The comprehensive inventory of federal, state and territorial MPA sites provides governments and stakeholders with access to information to make better decisions about the current and future use of place-based conservation. The information also will be used to inform the development of the national system of marine protected areas as required by Executive Order 13158.