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License information was derived automatically
United States Liabilities: Flow: Issuers of Asset Backed Securities (ABS) data was reported at 11.004 USD bn in Jun 2018. This records an increase from the previous number of -15.049 USD bn for Mar 2018. United States Liabilities: Flow: Issuers of Asset Backed Securities (ABS) data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Jun 2018, with 267 observations. The data reached an all-time high of 234.662 USD bn in Dec 2006 and a record low of -185.149 USD bn in Jun 2013. United States Liabilities: Flow: Issuers of Asset Backed Securities (ABS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB028: Funds by Sector: Flows and Outstanding: Issuers of Asset-Backed Securities.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Gulf Stream population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Gulf Stream.
The dataset constitues the following two datasets across these two themes
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
https://opensource.org/licenses/BSD-3-Clausehttps://opensource.org/licenses/BSD-3-Clause
The datasets in this release support the results presented in the paper
P. Jamshidi, G. Casale, "An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems", accepted for presentation at MASCOTS 2016.
An open access to the paper is available at https://arxiv.org/abs/1606.06543
Also open source code is available at https://github.com/dice-project/DICE-Configuration-BO4CO
The archive contains 10 comma separated datasets representing performance measurements (throughput and latency) for 3 different stream benchmark applications. These have been experimentally collected on 5 different cloud cluster over the course of 3 months (24/7). Each row in the datasets represents a different configuration setting for the application and the last two columns represent the average performance of the application measured over the course of 10 minutes under that specific configuration setting. The datasets contains a full factorial and exhaustive measurements for all possible settings limited to a predetermined interval for each variable. Each dataset is named in the following format: "benchmark_application-dimensions-cluster_name". For example, "wc-6d-c1" refers to WordCount benchmark application with 6 dimensions (i.e., we varied 6 configuration parameters) and the application was deployed on c1 cluster (OpenNebula, see Appendix). This resulted in a dataset of size 2880, i.e., it has taken 2880*10m=480h=20days for collecting the data!
For more information about the data refer to the appendix of the paper: https://arxiv.org/abs/1606.06543.
When referring to the dataset or code please cite the paper above.
The Commodity Flow Survey (CFS) is undertaken through a partnership between the U.S. Census Bureau, U.S. Department of Commerce, and the Research and Innovation Technology Administration, Bureau of Transportation Statistics (BTS), U.S. Department of Transportation. This survey produces data on the movement of goods in the United States. It provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of manufacturing, mining, wholesale, and select retail and services establishments. The data from the CFS are used by public policy analysts and for transportation planning and decision making to access the demand for transportation facilities and services, energy use, and safety risk and environmental concerns. This dataset provides data for the Temperature Series.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States Flow: ABS: saar: Gross Saving data was reported at 0.000 USD bn in Mar 2018. This stayed constant from the previous number of 0.000 USD bn for Dec 2017. United States Flow: ABS: saar: Gross Saving data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 3.124 USD bn in Sep 1998 and a record low of 0.000 USD bn in Mar 2018. United States Flow: ABS: saar: Gross Saving data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB028: Funds by Sector: Flows and Outstanding: Issuers of Asset-Backed Securities.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Gulf Stream population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Gulf Stream. The dataset can be utilized to understand the population distribution of Gulf Stream by age. For example, using this dataset, we can identify the largest age group in Gulf Stream.
Key observations
The largest age group in Gulf Stream, FL was for the group of age 60 to 64 years years with a population of 163 (19.22%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Gulf Stream, FL was the 25 to 29 years years with a population of 4 (0.47%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Gulf Stream Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States Assets: Flow: MA: DS: ABS: Mortgage Backed data was reported at -33.096 USD bn in Jun 2018. This records a decrease from the previous number of -10.561 USD bn for Mar 2018. United States Assets: Flow: MA: DS: ABS: Mortgage Backed data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Jun 2018, with 267 observations. The data reached an all-time high of 236.636 USD bn in Mar 2009 and a record low of -86.398 USD bn in Dec 2010. United States Assets: Flow: MA: DS: ABS: Mortgage Backed data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB011: Funds by Sector: Flows and Outstanding: Monetary Authority.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States Flow: ABS: Discrepancy data was reported at 0.000 USD bn in Mar 2018. This stayed constant from the previous number of 0.000 USD bn for Dec 2017. United States Flow: ABS: Discrepancy data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. United States Flow: ABS: Discrepancy data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB028: Funds by Sector: Flows and Outstanding: Issuers of Asset-Backed Securities.
The Commodity Flow Survey (CFS) is undertaken through a partnership between the U.S. Census Bureau, U.S. Department of Commerce, and the Research and Innovation Technology Administration, Bureau of Transportation Statistics (BTS), U.S. Department of Transportation. This survey produces data on the movement of goods in the United States. It provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of manufacturing, mining, wholesale, and select retail and services establishments. The data from the CFS are used by public policy analysts and for transportation planning and decision making to access the demand for transportation facilities and services, energy use, and safety risk and environmental concerns. This dataset provides data for the Exports Series.
This dataset represents predictions made to individual, local NHDPlusV2 stream segments. Attributes were calculated for every local NHDPlusV2 stream segment. (See Supplementary Info for Glossary of Terms). These predictions were made to provide estimates of reference-condition stream temperatures in support of the 2008-2009 and 2013-2014 (forthcoming) National Rivers and Streams Assessments. These predictions were based on a set of published models (Hill et al. 2013; http://www.journals.uchicago.edu/doi/abs/10.1899/12-009.1). From Hill et al. (2013): "We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual stream temperature. These models used a set of least-disturbed USGS stations and sites to model stream temperatures from a set of landscape metrics. To build reference-condition models, we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. These data are summarized to produce local stream segment-level metrics as a continuous data type.
The Commodity Flow Survey (CFS) is undertaken through a partnership between the U.S. Census Bureau, U.S. Department of Commerce, and the Research and Innovation Technology Administration, Bureau of Transportation Statistics (BTS), U.S. Department of Transportation. This survey produces data on the movement of goods in the United States. It provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of manufacturing, mining, wholesale, and select retail and services establishments. The data from the CFS are used by public policy analysts and for transportation planning and decision making to access the demand for transportation facilities and services, energy use, and safety risk and environmental concerns. This dataset provides data for the Hazardous Materials Series.
The UK censuses took place on 27 March 2011. They were run by the Northern Ireland Statistics & Research Agency (NISRA), National Records of Scotland (NRS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.
Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics and underpin funding allocation to provide public services. This is the home for all UK census data.
Census flow data involve flows of individuals in the UK between origins and destinations. These flows are either the residential migrations of individuals from one place of usual residence to another, or of commuters making journeys from home to workplace or place of study.Traffic Flow Census data in Hong Kong There are 3 kinds of spatial data file format avaliable: File GeoDatabase(FGDB, provided in ZIP): Users can read this consolidated list to enquire the data resource and file names. KML: Users can read this consolidated list to enquire the data resource and file names. GML + GFS:Users can read this consolidated list to enquire the data resource and file names.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset presents information about superannuation income. The data covers the financial years 2011-12 to 2017-18, and is based on Statistical Area Level 4 (SA4) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).
Superannuation income includes the following data items on the Individual Tax Returns (ITR):
Australian annuities and superannuation income streams taxable component taxed element
Australian annuities and superannuation income streams taxable component untaxed element
Australian annuities and superannuation income streams lump sum in arrears taxable component taxed element
Australian annuities and superannuation income streams lump sum in arrears taxable component untaxed element
Australian superannuation lump sum payments taxed element
Australian superannuation lump sum payments untaxed element
Bonuses from life insurance companies and friendly societies
A change to legislation relating to superannuation, taking effect from 1 July 2007, meant that people aged 60 years and over who receive superannuation income in the form of a lump sum or income stream (such as a pension) from a taxed source, receive that income tax free. Therefore, if a person has no other income, or their total income is below the tax-free threshold, or any tax payable is mitigated by a tax offset (such as Senior Australian Tax Offset), then this person is not required to lodge a tax return. Due to such changes, the superannuation statistics (persons, income) included in this release are regarded as partial, subject to under-coverage.
All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the Australian Bureau of Statistics (ABS) to closely align to ABS definitions of income.
The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.
Please note:
All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.
To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.
Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.
For further information please visit the Australian Bureau of Statistics.
AURIN has made the following changes to the original data:
Spatially enabled the original data.
Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Grand Lake Stream plantation population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Grand Lake Stream plantation. The dataset can be utilized to understand the population distribution of Grand Lake Stream plantation by age. For example, using this dataset, we can identify the largest age group in Grand Lake Stream plantation.
Key observations
The largest age group in Grand Lake Stream Plantation, Maine was for the group of age 65 to 69 years years with a population of 43 (22.87%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Grand Lake Stream Plantation, Maine was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Grand Lake Stream plantation Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset presents information about superannuation income. The data covers the financial years 2011-12 to 2017-18, and is based on Greater Capital City Statistical Areas (GCCSA) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).
Superannuation income includes the following data items on the Individual Tax Returns (ITR):
Australian annuities and superannuation income streams taxable component taxed element
Australian annuities and superannuation income streams taxable component untaxed element
Australian annuities and superannuation income streams lump sum in arrears taxable component taxed element
Australian annuities and superannuation income streams lump sum in arrears taxable component untaxed element
Australian superannuation lump sum payments taxed element
Australian superannuation lump sum payments untaxed element
Bonuses from life insurance companies and friendly societies
A change to legislation relating to superannuation, taking effect from 1 July 2007, meant that people aged 60 years and over who receive superannuation income in the form of a lump sum or income stream (such as a pension) from a taxed source, receive that income tax free. Therefore, if a person has no other income, or their total income is below the tax-free threshold, or any tax payable is mitigated by a tax offset (such as Senior Australian Tax Offset), then this person is not required to lodge a tax return. Due to such changes, the superannuation statistics (persons, income) included in this release are regarded as partial, subject to under-coverage.
All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the Australian Bureau of Statistics (ABS) to closely align to ABS definitions of income.
The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.
Please note:
All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.
To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.
Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.
For further information please visit the Australian Bureau of Statistics.
AURIN has made the following changes to the original data:
Spatially enabled the original data.
Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.
Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automatically
This dataset, released in October 2019, contains the statistics of the Family stream, 2016.
The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).
Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.
For more information please see the data source notes on the data.
Source: Compiled by PHIDU based on the ABS Census and Migrants Integrated Dataset, August 2016.
AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States Assets: Flow: Issuers of Asset Backed Securities (ABS) data was reported at 4.969 USD bn in Sep 2018. This records a decrease from the previous number of 5.248 USD bn for Jun 2018. United States Assets: Flow: Issuers of Asset Backed Securities (ABS) data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Sep 2018, with 268 observations. The data reached an all-time high of 234.633 USD bn in Dec 2006 and a record low of -185.149 USD bn in Jun 2013. United States Assets: Flow: Issuers of Asset Backed Securities (ABS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB028: Funds by Sector: Flows and Outstanding: Issuers of Asset-Backed Securities.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Carol Stream population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Carol Stream. The dataset can be utilized to understand the population distribution of Carol Stream by age. For example, using this dataset, we can identify the largest age group in Carol Stream.
Key observations
The largest age group in Carol Stream, IL was for the group of age 55 to 59 years years with a population of 3,005 (7.62%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Carol Stream, IL was the 80 to 84 years years with a population of 557 (1.41%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Carol Stream Population by Age. You can refer the same here
description: The Commodity Flow Survey provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of commodities from manufacturing, mining, wholesale, and selected retail and services establishments. It is undertaken through a partnership between the Bureau of the Census, U.S. Department of Commerce, and the Bureau of Transportation Statistics, Research and Innovative Technology Administration.; abstract: The Commodity Flow Survey provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of commodities from manufacturing, mining, wholesale, and selected retail and services establishments. It is undertaken through a partnership between the Bureau of the Census, U.S. Department of Commerce, and the Bureau of Transportation Statistics, Research and Innovative Technology Administration.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States Liabilities: Flow: Issuers of Asset Backed Securities (ABS) data was reported at 11.004 USD bn in Jun 2018. This records an increase from the previous number of -15.049 USD bn for Mar 2018. United States Liabilities: Flow: Issuers of Asset Backed Securities (ABS) data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Jun 2018, with 267 observations. The data reached an all-time high of 234.662 USD bn in Dec 2006 and a record low of -185.149 USD bn in Jun 2013. United States Liabilities: Flow: Issuers of Asset Backed Securities (ABS) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.AB028: Funds by Sector: Flows and Outstanding: Issuers of Asset-Backed Securities.