This dataset contains seasonally adjusted employment data for New York City. Data is reported at the industry level (in units of thousands) and aggregated to total nonfarm and total private levels. Updates are posted after the not-seasonally-adjusted data is published by the NYS Department of Labor – typically monthly but with irregularities due to annual benchmark revisions.

The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data. NOTE: The LAUS Seasonally Adjusted Benchmark 2023 data was last revised in 2024. The newly revised Benchmark 2024 data will be available in mid-2025.

Dataset Card for "redline_bench"

More Information needed

Benchmark data and results (2nd revision) for scCODA is A Bayesian model for compositional single-cell data analysis (Büttner et al., 2021). See README.txt and Github repository for instructions on how to use.

This dataset contains information about the number and percentage of managers by gender and age, 15 years and over (2006-07 and 2016-17).

(a) Data was calculated as an average of four quarters (August, November, February, May) in the financial year.

(b) Occupation is classified according to the ABS Australian and New Zealand Standard Classification of Occupations (ANZSCO), 2006 (cat. no. 1220.0).

(c) Until recently, ABS policy has been to revise benchmarks for labour force data on a five-yearly basis following final rebasing of population estimates to the latest Census of Population and Housing data. However, labour force population benchmarks are now updated more frequently when preliminary population estimates become available, and again when these preliminary estimates are subsequently revised. For this release of Gender Indicators, Australia, labour force estimates dating back to (and including) 2014-15 have been revised in accordance with this new benchmarking process. Future revisions to benchmarks will then take place every time a new year of labour force data becomes available for publishing in the Gender Indicators publication. Re-benchmarking historical data has not resulted in any material change to unemployment rates, participation rates or employment to population ratios. For more information see ABS Labour Force, Australia, Jun 2016 (cat. no. 6202.0).
Source: ABS data available on request, Labour Force Survey

Regional accounts give a description of the economic process in the regions of a country in conformity with the national accounts. Elements in the economic process distinguished in national accounts are production, distribution of income, spending and financing. Regional accounts focus on the description of the production processes in the various regions. Data available from: 1996 -2010. Frequency: discontinued. Status of the figures: These figures are based on the Standard Industrial Classification (SIC) '93. The figures from 1996 are definite. The three most recent years still have a (more detailled) provisional character. For the first time figures are available based on the first provisional estimate of the economy of the Netherlands. Following the benchmark revision of the national accounts, the regional accounts have been revised starting from reporting year 2001. Subsequently time series have been compiled for the years 1995-2000.

Heavy trucks include trucks with more than 14,000 pounds gross vehicle weight. Prior to the 2003 Benchmark Revision heavy trucks were more than 10,000 pounds. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

GDP: sa: Changes in Inventories data was reported at 1,548.000 EUR mn in Dec 2024. This records an increase from the previous number of 1,401.700 EUR mn for Sep 2024. GDP: sa: Changes in Inventories data is updated quarterly, averaging 289.900 EUR mn from Mar 1995 (Median) to Dec 2024, with 120 observations. The data reached an all-time high of 3,544.000 EUR mn in Mar 2007 and a record low of -2,198.400 EUR mn in Mar 2009. GDP: sa: Changes in Inventories data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Greece – Table GR.OECD.MEI: Gross Domestic Product: Seasonally Adjusted: OECD Member: Quarterly. [STAT_CONC_DEF] Methodological changes have been applied to data from 2010-Q1 at the occasion of the December 2020 benchmark revision. These methodological changes still need to be incorporated to back data. Therefore there is a break in data in 2010-Q1. Furthermore, volume data before 2010 is still based on the old reference year 2010.

Heavy trucks include trucks with more than 14,000 pounds gross vehicle weight. Prior to the 2003 Benchmark Revision heavy trucks were more than 10,000 pounds. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

This table shows the indicators of the macroeconomic scoreboard. Furthermore, some additional indicators are shown. To identify in a timely manner existing and potential imbalances and possible macroeconomic risks within the countries of the European Union in an early stage, the European Commission has drawn up a scoreboard with fourteen indicators. This scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains quarterly and annual figures for both these fourteen indicators and nine additional indicators for the Netherlands.

The fourteen indicators in the macroeconomic scoreboard are: - Current account balance as % of GDP, 3 year moving average - Net international investment position, % of GDP - Real effective exchange rate, % change on three years previously - Share of world exports, % change on five years previously - Nominal unit labour costs, % change on three years previously - Deflated house prices, % change on one year previously - Private sector credit flow as % of GDP - Private sector debt as % of GDP - Government debt as % of GDP - Unemployment rate, three year moving average - Total financial sector liabilities, % change on one year previously - Activity rate, % of total population aged 15-64, change in percentage points on three years previously - Long-term unemployment rate, % of active population aged 15-74, change in percentage points on three years previously - Youth unemployment rate, % of active population aged 15-24, change in percentage points on three years previously

The additional indicators are: - Real effective exchange rate, index - Share of world exports, % - Nominal unit labour costs, index - Households credit flow as % of GDP - Non-financial corporations credit flow as % of GDP - Household debt as % of GDP - Non-financial corporations debt as % of GDP - Activity rate, % of total population aged 15-64 - Youth unemployment rate, % of active population aged 15-24

Data available from: first quarter of 2006.

Status of the figures: Annual and quarterly data are provisional.

Changes as of 3 July 2019: For all indicators except for the long-term unemployment, figures on the first quarter of 2019 have been added. For the long-term unemployment the figure for the fourth quarter of 2018 is added, as well as the annual figure for 2018. Due to the benchmark revision of 2016, national accounts data changed for the period from 1016 onwards. As a result MIP indicators differ from previous data deliveries as well.

When will new figures be published? New data are published within 120 days after the end of each quarter. The first quarter may be revised in October, the second quarter in January. Quarterly data for the previous three quarters are adjusted along when the fourth quarter figures are published in April. This corresponds with the first estimate of the annual data for the previous year. The annual and quarterly data for the last three years are revised together with the publication of the first quarter in July.

The Japan Industrial Productivity Database 2015 (JIP Database 2015) comprises, for the period 1970-2012, various types of annual data necessary for estimating total factor productivity (TFP) in 108 industries covering Japan's economy as a whole, including capital service input indices and capital costs, quality-adjusted labor service input indices and labor costs, nominal and real output and intermediate inputs, as well as growth accounting results, including estimates of TFP growth rates. The JIP Database 2015 extends the JIP Database 2014 (released on October 6, 2014) by one year to 2012, employing exactly the same estimation approach and industry classifications. Moreover, as in the JIP Database 2014, the 2000 Benchmark Revision of the National Accounts is used for control totals. We plan to revise the estimation approach and the industry classifications in 2016 based on the 2005 Benchmark Revision of the National Accounts. The global economic crisis triggered by the collapse of Lehman Brothers in September 2008 has posed severe challenges to the Japanese economy and economies around the globe. How TFP and employment have been affected by the global economic crisis is an issue that has attracted the interest of researchers worldwide. The JIP Database 2015 covers part of this period of crisis, and we hope that it will contribute to research on structural changes in inputs of factors of production and productivity.

This table contains cash-based data on a monthly basis about revenue and expenditure and the central government balance sheet. Data are provided by the accounting departments of the relevant public institutions. The figures in this table are presented on a cash basis, which means that the criterion for the booking is the moment the payment is made. The figures presented here do not comply with the publications on National Accounts. With this table, the Netherlands meets the requirements as laid down in the Directive EU 2011/85. This directive is part of the Enhanced Economic Governance package ('Sixpack'), adopted by the European Council in 2011. Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP and total government expenditures have been adjusted upwards as a result of the revision. As part of the revision, the determination of the population of central government has been improved qualitatively. From the first month of 2018 onwards, administrative data of the central government will be published after revision in this table. The figures for the previous months have not been adjusted based on the revision. Between December 2017 and January 2018, a break occurs as a result of the benchmark revision. Figures available from: January 2014. Status of the figures: Figures for April, May and June 2023 are provisional. Figures for January, February and March 2023 are revised provisional and figures for 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022 are final. Changes as from 1 August 2023: Figures for June 2023 have been added. Figures for 2022 have been adjusted. Figures for 2022 are final. When will new figures be published? New monthly figures will be published one month after the month under review. With the publication of a new month, provisional figures for the previous month can be adjusted. The figures referring to the three months of a quarter will be revised at the end of the next quarter. Six months after the end of the year, figures will be set to 'definite'.

The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these categories. Data from the survey provide information on major labour market trends such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates, employment including the self-employed, full and part-time employment, and unemployment. It publishes monthly standard labour market indicators such as the unemployment rate, the employment rate and the participation rate. The LFS is a major source of information on the personal characteristics of the working-age population, including age, sex, marital status, educational attainment, and family characteristics. Employment estimates include detailed breakdowns by demographic characteristics, industry and occupation,job tenure, and usual and actual hours worked. This dataset is designed to provide the user with historical information from the Labour Force Survey. The tables included are monthly and annual, with some dating back to 1976. Most tables are available by province as well as nationally. Demographic, industry, occupation and other indicators are presented in tables derived from the LFS data. The information generated by the survey has expanded considerably over the years with a major redesign of the survey content in 1976 and again in 1997, and provides a rich and detailed picture of the Canadian labour market. Some changes to the Labour Force Survey (LFS) were introduced which affect data back to 1987. There are three reasons for this revision: The revision enables the use of improved population benchmarks in the LFS estimation process. These improved benchmarks provide better information on the number of non-permanent residents. There are changes to the data for the public and private sectors from 1987 to 1999. In the past, the data on the public and private sectors for this period were based on an old definition of the public sector. The revised data better reflects the current public sector definition, and therefore result in a longer time series for analysis. The geographic coding of several small Census Agglomerations (CA) has been updated historically from 1996 urban centre boundaries to 2001 CA boundaries. This affects data from January 1987 to December 2004. It is important to note that the changes to almost all estimates are very minor, with the exception of the public sector series and some associated industries from 1987 to 1999. Rates of unemployment, employment and participation are essentially unchanged, as are all key labour mark et trends. The article titled Improvements in 2006 to the LFS (also under the LFS Documentation button) provides an overview of the effect of these changes on the estimates. The seasonally-adjusted tables have been revised back three years (beginning with January 2004) based on the latest seasonal output.

Light trucks include trucks with up to 14,000 pounds gross vehicle weight, including minivans and sport utility vehicles. Prior to the 2003 Benchmark Revision, light trucks were up to 10,000 pounds. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

Light trucks include trucks with up to 14,000 pounds gross vehicle weight, including minivans and sport utility vehicles. Prior to the 2003 Benchmark Revision, light trucks were up to 10,000 pounds. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

GDP: CC: DD: Public data was reported at 0.000 % in Sep 2016. This records a decrease from the previous number of 0.100 % for Jun 2016. GDP: CC: DD: Public data is updated quarterly, averaging 0.000 % from Mar 1995 (Median) to Sep 2016, with 87 observations. The data reached an all-time high of 2.300 % in Mar 1996 and a record low of -1.100 % in Mar 2003. GDP: CC: DD: Public data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Japan – Table JP.A067: SNA 93: Benchmark Year=2005: Contribution to Changes. Changed from SNA 1993 to SNA 2008 Replacement series ID: 383038937

The Berlin SPARQL Benchmark (BSBM) is a suite of benchmarks built around an e-commerce use case [1]. We generated 21 versions of the dataset with different scale factors. The first dataset, with a scale factor of 100, contains about 7,000 vertices and 75,000 edges. We generated versions with scale factors between 2,000 and 40,000 in steps of 2,000. The largest dataset contains about 1.3 M vertices and 13 M edges. For our experiments in [2], we first use the different versions ordered from smallest to largest (version 0 to 20) to simulate a growing graph database. Subsequently, we reverse the order to emulate a shrinking graph database. Over all versions, the mean degree is 8.1 (+- 0.5), the mean in-degree is 4.6 (+- 0.3), and the mean out-degree is 9.8 (+- 0.2).

1. Christian Bizer, Andreas Schultz: The Berlin SPARQL Benchmark. Int. J. Semantic Web Inf. Syst. 5(2): 1-24 (2009)

2. Till Blume, David Richerby, Ansgar Scherp: Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs. CIKM 2020: 75-84

Japan Gross National Income: 2015p: Contribution to Changes data was reported at 2.100 % in 2024. This records an increase from the previous number of 0.400 % for 2023. Japan Gross National Income: 2015p: Contribution to Changes data is updated yearly, averaging 0.800 % from Mar 1996 (Median) to 2024, with 29 observations. The data reached an all-time high of 3.600 % in 1996 and a record low of -4.900 % in 2009. Japan Gross National Income: 2015p: Contribution to Changes data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Japan – Table JP.A025: SNA 2008: Benchmark year=2015: Contribution to Changes: Chain Linked: 2015 Price: Fiscal Year.

This dataset represents CLIGEN input parameters for locations in 68 countries. CLIGEN is a point-scale stochastic weather generator that produces long-term weather simulations with daily output. The input parameters are essentially monthly climate statistics that also serve as climate benchmarks. Three unique input parameter sets are differentiated by having been produced from 30-year, 20-year and 10-year minimum record lengths that correspond to 7673, 2336, and 2694 stations, respectively. The primary source of data is the NOAA GHCN-Daily dataset, and due to data gaps, records longer than the three minimum record lengths were often queried to produce the needed number of complete monthly records. The vast majority of stations used at least some data from the 2000's, and temporal coverages are shown in the Excel table for each station. CLIGEN has various applications including being used to force soil erosion models. This dataset may reduce the effort needed in preparing climate inputs for such applications. Revised input files added on 11/16/20. These files were revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months. Second revision input files added on 2/12/20. A formatting error was fixed that affected transition probabilities for 238 stations with zero recorded precipitation for one or more months. The affected stations were predominantly in Australia and Mexico. Resources in this dataset:Resource Title: 30-year input files. File Name: 30-year.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files. File Name: 20-year.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files. File Name: 10-year.zipResource Description: CLIGEN .par input files based on 10-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: Map Layer. File Name: MapLayer.kmzResource Description: Map Layer showing locations of the new CLIGEN stations. This layer may be imported into Google Earth and used to find the station closest to an area of interest.Resource Software Recommended: Google Earth,url: https://res1wwwd-o-tgoogled-o-tcom.vcapture.xyz/earth/ Resource Title: Temporal Ranges of Years Queried. File Name: GHCN-Daily Year Ranges.xlsxResource Description: Excel tables of the first and last years queried from GHCN-Daily when searching for complete monthly records (with no gaps in data). Any ranges in excess of 30 years, 20 years and 10 years, for respective datasets, are due to data gaps.Resource Title: 30-year input files (revised). File Name: 30-year revised.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: CLIGEN v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files (revised). File Name: 20-year revised.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: Cligen v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files (revised). File Name: 10-year revised.zipResource Description: CLIGEN .par input files based on 10-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: Cligen v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 30-year input files (revised 2). File Name: 30-year revised 2.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files (revised 2). File Name: 20-year revised 2.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files (revised 2). File Name: 10-year revised 2.zipResource Description: CLIGEN *.par input files based on 10-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/

We present a new analysis of exchange and dispersion effects for calculating halogen-bonding interactions in a wide variety of complex dimers (69 total) within the XB18 and XB51 benchmark sets. Contrary to previous work on these systems, we find that dispersion plays a more significant role than exact exchange in accurately calculating halogen-bonding interaction energies, which are further confirmed by extensive SAPT analyses. In particular, we find that even if the amount of exact exchange is nonempirically tuned to satisfy known DFT constraints, we still observe an overall improvement in predicting dissociation energies when dispersion corrections are applied, in stark contrast to previous studies (Kozuch, S.; Martin, J. M. L. J. Chem. Theory Comput. 2013, 9, 1918−1931). In addition to these new analyses, we correct several (14) inconsistencies in the XB51 set, which is widely used in the scientific literature for developing and benchmarking various DFT methods. Together, these new analyses and revised benchmarks emphasize the importance of dispersion and provide corrected reference values that are essential for developing/parametrizing new DFT functionals, specifically for complex halogen-bonding interactions.