Link Function: information

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. This Climate Data Record (CDR) contains solar spectral irradiance (SSI) as a function of time and wavelength created with the Naval Research Laboratory model for spectral and total irradiance (version 2). Solar spectral irradiance is the wavelength-dependent energy input to the top of the Earth's atmosphere, at a standard distance of one Astronomical Unit from the Sun. Its units are W per m2 per nm. Also included is the value of total (spectrally integrated) solar irradiance in units W per m2. The dataset was created by Judith Lean (Space Science Division, Naval Research Laboratory), Odele Coddington and Peter Pilewskie (Laboratory for Atmospheric and Space Science, University of Colorado). The daily- and monthly-averaged SSI data range from 1882 to the present, and annual-averaged SSI data begin in 1610. The data file format is netCDF-4 following CF metadata conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

United States Number of Housing Unit: Minnesota data was reported at 2,437,711.000 Unit in 2017. This records an increase from the previous number of 2,419,560.000 Unit for 2016. United States Number of Housing Unit: Minnesota data is updated yearly, averaging 2,329,371.500 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 2,437,711.000 Unit in 2017 and a record low of 2,073,863.000 Unit in 2000. United States Number of Housing Unit: Minnesota data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB012: Number of Housing Units: By States.

Objective The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models. Results During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health r..., Aggregated data from 2020-05-16 to 2022-01-17 regarding Bordeaux Hospital EHR. Bordeaux hospital data warehouse was used, during the pandemic, to describe the current state of the epidemic at the hospital level on a daily basis. Those data were then used in the forecast model including: hospitalizations, hospital and ICU admission and discharge, ambulance service notes and emergency unit notes. Concepts related to COVID-19 were extracted from notes by dictionary-based approaches (e.g. cough, dyspnoea, covid-19). Dictionaries were manually created based on manual chart review to identify terms used by practitioners. Then, the number and proportion of ambulance service calls or hospitalization in emergency units mentioning concepts related to covid-19 were extracted. Due to different data acquisition mechanisms, there was a delay between the occurrence of events and the data acquisition. It was of 1 day for EHR data, 5 days for department hospitalizations and RT-PCR, 4 days for weather, 2..., Data are stored in a .rdata file. Please use R (https://www.r-project.org/) software to open the data.

This dataset contains counts and rates of young people aged 15–24 years who were not in employment, education, or training (NEET) during the 2015 calendar year. The report containing maps of this data can be found at www.stats.govt.nz/about_us/what-we-do/partnerships.... The data was provided by the Integrated Data Infrastructure (IDI) which brings together a wide range of data from government administrative sources and surveys. Disclaimer Any person who has had access to the unit-record data has certified that they have been shown, have read, and have understood section 81 of the Tax Administration Act 1994, which relates to secrecy. Access to the anonymised data used in this study was provided by Stats NZ in accordance with security and confidentiality provisions of the Statistics Act 1975. Only people authorised by the Statistics Act 1975 are allowed to see data about a particular person, household, business and or organisation and the results in these tables have been confidentialised to protect these groups from identification. Careful consideration has been given to the privacy, security and confidentiality issues associated with using administrative and survey data in the IDI. Any person who has had access to the unit-record data has certified that they have been shown, have read, and have understood section 81 of the Tax Administration Act 1994, which relates to secrecy. Any discussion of data limitations or weaknesses is in the context of using the IDI for statistical purposes, and is not related to the data's ability to support Inland Revenue's core operational requirements. Values of -999 are supressed to protect confidentiality. Citation Stats NZ (2017). Otago youth not in employment, education, or training (NEET): Collaborative research between Stats NZ Methodist Mission Southern using integrated data. Retrieved from www.stats.govt.nz.

This list includes detail information about every water rights record in the State Water Resources Control Board's "Electronic Water Rights Information Management System" (EWRIMS) database. Each row correspond with a unique application ID and its associated data. The list include basic summary information about the Water Right record, such as the type and status, the location of the Points of Diversion, the amount of water allowed (Face Value), and summary data associated with the electronic Water Right record. This file is in flat file format and may not include all information associated to a water right such all Points of Diversion, all uses and seasons or the amounts reported used for every month. That information may be available in the associated flat files for each category.

Customs records of are available for CHRISTIE DIGITAL SYSTEMS UNIT. Learn about its Importer, supply capabilities and the countries to which it supplies goods

This data set is part of an ongoing project to consolidate interagency fire point data. The incorporation of all available historical data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).

Abstract (en): The Public Use Microdata Sample (PUMS) 1-Percent Sample contains household and person records for a sample of housing units that received the "long form" of the 1990 Census questionnaire. Data items include the full range of population and housing information collected in the 1990 Census, including 500 occupation categories, age by single years up to 90, and wages in dollars up to $140,000. Each person identified in the sample has an associated household record, containing information on household characteristics such as type of household and family income. All persons and housing units in the United States. A stratified sample, consisting of a subsample of the household units that received the 1990 Census "long-form" questionnaire (approximately 15.9 percent of all housing units). 2006-01-12 All files were removed from dataset 85 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 83 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 82 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.1998-08-28 The following data files were replaced by the Census Bureau: the state files (Parts 1-56), Puerto Rico (Part 72), Geographic Equivalency File (Part 84), and Public Use Microdata Areas (PUMAS) Crossing State Lines (Part 99). These files now incorporate revised group quarters data. Parts 201-256, which were separate revised group quarters files for each state, have been removed from the collection. The data fields affected by the group quarters data revisions were POWSTATE, POWPUMA, MIGSTATE and MIGPUMA. As a result of the revisions, the Maine file (Part 23) gained 763 records and Part 99 lost 763 records. In addition, the following files have been added to the collection: Ancestry Code List, Place of Birth Code List, Industry Code List, Language Code List, Occupation Code List, and Race Code List (Parts 86-91). Also, the codebook is now available as a PDF file. (1) Although all records are 231 characters in length, each file is hierarchical in structure, containing a housing unit record followed by a variable number of person records. Both record types contain approximately 120 variables. Two improvements over the 1980 PUMS files have been incorporated. First, the housing unit serial number is identified on both the housing unit record and on the person record, allowing the file to be processed as a rectangular file. In addition, each person record is assigned an individual weight, allowing users to more closely approximate published reports. Unlike previous years, the 1990 PUMS 1-Percent and 5-Percent Samples have not been released in separate geographic series (known as "A," "B," etc. records). Instead, each sample has its own set of geographies, known as "Public Use Microdata Areas" (PUMAs), established by the Census Bureau with assistance from each State Data Center. The PUMAs in the 1-Percent Sample are based on a distinction between metropolitan and nonmetropolitan areas. Metropolitan areas encompass whole central cities, Primary Metropolitan Statistical Areas (PMSAs), Metropolitan Statistical Areas (MSAs), or groups thereof, except where the city or metropolitan area contains more than 200,000 inhabitants. In that case, the city or metropolitan area is divided into several PUMAs. Nonmetropolitan PUMAs are based on areas or groups of areas outside the central city, PMSA, or MSA. PUMAs in this 1-Percent Sample may cross state lines. (2) The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. The dataset contains three MSU channel-based, monthly gridded atmospheric layer temperature Climate Data Records generated by merging nine NOAA polar orbiting satellites, TRIOS-N and NOAA-6 through NOAA-14. These are temperatures of middle-troposphere (TMT), upper-troposphere (TUT, also known as temperature troposphere stratosphere), and lower-stratosphere (TLS), corresponding to measurements from MSU channels 2, 3, and 4, respectively. These products have global coverage with a 2.5 latitudes by 2.5 longitude grid resolution. Time period is from November 1978 through September 2006. Adjustments of observations included limb-adjustment, diurnal drift corrections, warm target temperature effect, and residual inter-satellite bias removal.

This dataset contains key characteristics about the data described in the Data Descriptor Data-driven curation process for describing the blood glucose management in the intensive care unit. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

The dataset contains three channel-based, monthly gridded atmospheric layer temperature Climate Data Records generated by merging nine MSU NOAA polar orbiting satellites, TIROS-N and NOAA-6 through NOAA-14, and six AMSU-A polar orbiting satellites, NOAA-15 through NOAA-18, MetOp-A, and NASA AQUA. These are temperatures of middle-troposphere (TMT), upper-troposphere (TUT, also known as temperature troposphere stratosphere), and lower-stratosphere (TLS), corresponding to measurements from MSU/AMSU-A channels 2/5, 3/7, and 4/9, respectively. Adjustments of observations included limb-adjustment, diurnal drift corrections, warm target temperature effect, and residual inter-satellite bias removal. Data coverage is from November 1978 to present; It is monthly global gridded dataset with 2.5 latitude by 2.5 longitude resolution. The dataset is updated each month with full Period of Record (POR) files in order to monitor operationally the change of upper air temperature.

United States Number of Housing Unit: Kansas data was reported at 1,273,742.000 Unit in 2017. This records an increase from the previous number of 1,265,805.000 Unit for 2016. United States Number of Housing Unit: Kansas data is updated yearly, averaging 1,231,387.000 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 1,273,742.000 Unit in 2017 and a record low of 1,134,548.000 Unit in 2000. United States Number of Housing Unit: Kansas data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB012: Number of Housing Units: By States.

Information on the fetal death data tape file was abstracted from the Report of Fetal Death forms received in all the States and the District of Columbia, with a record on the data file for each report of a fetal death received. The data is provided to the National Center for Health Statistics (NCHS) through the Vital Statistics Cooperative Program by the registration offices of all States, the District of Columbia, and New York City. Data from New York, excluding New York City, were submitte d in machine readable form. All other 1992 data were coded and keyed by the U.S. Bureau of the Census. Fetal death data are limited to deaths occurring within the United States to U.S. residents and nonresidents. Fetal deaths occurring to U.S. citizens outside the United States are not included in this data file. In NCHS tabulations by place of residence, fetal deaths to nonresidents of the United States are excluded. The foreign resident records can be identified by code 4 in tape location 7 of the data tape. In addition, the majority of fetal death tables published by NCHS include only those fetal deaths with stated or presumed gestation of 20 weeks or more (see the Technical Appendix). Those records identified with a 2 in tape location 5 are included in these tabulations. All other records are excluded. Effective January 1, 1989, a revised U-S. Standard Report of Fetal Death replaced the 1978 revision. The 1989 revision provides a wide variety of new information on maternal and fetal health characteristics. Questions on complications of labor and delivery and congenital anomalies of fetus were changed from an open-ended question to a checkbox format to improve reporting of information. Several new items were added that improve the data files value for monitoring and research of factors affecting fetal mortality. The Office of Management and Budget revised its designation of metropolitan statistical areas based on figures from the 1990 Census. Effective with the 1990 data file, NCHS has been using these new definitions and codes as indicated in the listing of 320 Metropolitan Statistical Areas (MSAS), Primary Metropolitan Statistical Areas (PMSAS), and New England County Metropolitan Ar eas (NEaSS) included in this documentation. There are also 20 Consolidated Metropolitan Statistical Areas (mSAS), which are made up of PMSAS. Other geographic changes based on the 1990 Census will be implemented later. NCHS has adopted a new policy on release of vital statistics unit record data files. This new policy was implemented with the 1989 vital event files to prevent the inadvertent disclosure of individuals and institutions. As a result, this file does not contain the actual day of the death. The geographic detail is also restricted-only counties and cities of 100,000 or more population based on the 1980 Census as well as metropolitan areas of 100,000 or more population based on the 1990 Census, are identified. NOSB = Note to Users: This CD is part of a collection located in the Data Archive at the Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

The metropolitan survey is conducted in even-numbered years, cycling through a set of 41 metropolitan areas, surveying each one about once every 6 years. This data collection provides information on the characteristics of a metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. The data are presented in seven separate parts: Part 1, Work Done Record (Replacement or Addition to the House), Part 2, Journey to Work Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, and Part 7, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

This dataset contains key characteristics about the data described in the Data Descriptor PIC, a paediatric-specific intensive care database. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 
     Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

A. SUMMARY This dataset reports the number of new residential units made available for occupancy in San Francisco since January 2018. Each row in this dataset shows the change in the number of new units associated with a building permit application. Each row also includes the date those units were approved for occupancy, the type of document approving them, and their address. Values in the column [Number of Units Certified] can be added together to produce a count of new units approved for occupancy since January 2018. These records provide a preliminary count of new residential units. The San Francisco Planning Department issues a Housing Inventory Report each year that provides a more complete account of new residential units, and those results may vary slightly from records in this dataset. The Housing Inventory Report is an in-depth annual research project requiring extensive work to validate information about projects. By comparison, this dataset is meant to provide more timely updates about housing production based on available administrative data. The Department of Building Inspection and Planning Department will reconcile these records with future Housing Inventory Reports. B. METHODOLOGY At the end of each month, DBI staff manually calculate how many new units are available for occupancy for each building permit application and enters that information into this dataset. These records reflect counts for all types of residential units, including authorized accessory dwelling units. These records do not reflect units demolished or removed from the city’s available housing stock. Multiple records may be associated with the same building permit application number, which means that new certifications or amendments were issued. Only changes to the net number of units associated with that permit application are recorded in subsequent records. For example, Building Permit Application Number [201601010001] located at [123 1st Avenue] was issued an [Initial TCO] Temporary Certificate of Occupancy on [January 1, 2018] approving 10 units for occupancy. Then, an [Amended TCO] was issued on [June 1, 2018] approving [5] additional units for occupancy, for a total of 15 new units associated with that Building Permit Application Number. The building will appear as twice in the dataset, each row representing when new units were approved. If additional or amended certifications are issued for a building permit application, but they do not change the number of units associated with that building permit application, those certifications are not recorded in this dataset. For example, if all new units associated with a project are certified for occupancy under an Initial TCO, then the Certificate of Final Completion (CFC) would not appear in the dataset because the CFC would not add new units to the housing stock. See data definitions for more details. C. UPDATE FREQUENCY This dataset is updated monthly. D. DOCUMENT TYPES Several documents issued near or at project completion can certify units for occupation. They are: Initial Temporary Certificate of Occupancy (TCO), Amended TCO, and Certificate of Final Completion (CFC). • Initial TCO is a document that allows for occupancy of a unit before final project completion is certified, conditional on if the unit can be occupied safely. The TCO is meant to be temporary and has an expiration date. This field represents the number of units certified for occupancy when the TCO is issued. • Amended TCO is a document that is issued when the conditions of the project are changed before final project completion is certified. These records show additional new units that have become habitable since the issuance of the Initial TCO. • Certificate of Final Completion (CFC) is a document that is issued when all work is completed according to approved plans, and the building is ready for complete occupancy. These records show additional new units that were not accounted for in the Initial o

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in nine separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Mover Group Record, Part 8, Recodes (One Record per Housing Unit), and Part 9, Weights. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

Customs records of Chi are available for UNIT UNIT B.. Learn about its Importer, supply capabilities and the countries to which it supplies goods