The dataset used for training the inconsistent behaviour predictor of DeepGuard.

Researchers depend on observed regularities in macroeconomic data to guide the development of theory. One problem in developing monetary models of the business cycle is that there seems to be a great deal of instability in nominal data. Using data from 1959:Q1 to 1998:Q4, the authors document changes in the cyclical behavior of nominal data series that appear after 1979:Q3, when the Federal Reserve implemented a policy to end the acceleration of inflation. Such changes in cyclical behavior were not apparent in real variables. The authors conclude that in order to find regularities in nominal datasets, it may be necessary to examine and compare episodes with similar monetary policy regimes.

Graph and download economic data for Nominal Gross Domestic Product for United States (NGDPSAXDCUSQ) from Q1 1950 to Q2 2025 about GDP and USA.

CPI Price, nominal in Chile was reported at 185 in 2025, according to the World Bank collection of development indicators, compiled from officially recognized sources. Chile - CPI Price, nominal - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Key information about Denmark Nominal GDP

• Denmark Nominal GDP reached 97.0 USD bn in Dec 2022, compared with 96.7 USD bn in the previous quarter.
• Nominal GDP in Denmark is updated quarterly, available from Mar 1990 to Dec 2022, with an average number of 72.0 USD bn.
• The data reached an all-time high of 101.6 USD bn in Dec 2021 and a record low of 32.3 USD bn in Mar 1990.

• In the latest reports, Denmark GDP expanded 1.5 % YoY in Dec 2022.
• Its GDP deflator (implicit price deflator) increased 5.5 % in Dec 2022.
• Denmark GDP Per Capita reached 66,782.7 USD in Dec 2022.
• Its Gross Savings Rate was measured at 36.0 % in Dec 2022.
• For Nominal GDP contributions, Investment accounted for 26.2 % in Dec 2022.
• Public Consumption accounted for 22.6 % in Dec 2022.
• Private Consumption accounted for 44.0 % in Dec 2022.

Brazil PSBR: 12 Months Cumulative: Nominal data was reported at 479,231.092 BRL mn in Feb 2019. This records a decrease from the previous number of 480,024.316 BRL mn for Jan 2019. Brazil PSBR: 12 Months Cumulative: Nominal data is updated monthly, averaging 71,163.809 BRL mn from Dec 1991 (Median) to Feb 2019, with 327 observations. The data reached an all-time high of 644,381.482 BRL mn in Jan 2016 and a record low of 9.660 BRL mn in Dec 1991. Brazil PSBR: 12 Months Cumulative: Nominal data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.FB003: Public Sector Borrowing Requirement: Last 12 Months Accumulated.

Major differences from previous work: For level 2 catch: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines with identical strata but different effort units are duplicates reporting the same fishing activity with different measurement units. It is indeed not possible to infer strict equivalence between units, as some contain information about others (e.g., Hours.FAD and Hours.FSC may inform Hours.STD). in the case of WCPFC data, effort records were also kept in all originally reported units. Here, duplicates do not necessarily share the same “fishing_mode”, as SETS for purse seiners are reported with an explicit association to fishing_mode, while DAYS are not. This distinction allows SETS records to be separated by fishing mode, whereas DAYS records remain aggregated. Some limited harmonization—particularly between units such as NET-days and Nets—has not been implemented in the current version of the dataset, but may be considered in future releases if a consistent relationship can be established.

These are nominal yield curves, obtained by fitting a parametric form to the prices of off-the-run nominal Treasury coupon securities. The data are available at daily frequency, from 1961 to present.

Hungary Rent Index: Nominal data was reported at 216.400 2015=100 in Mar 2025. This records a decrease from the previous number of 216.600 2015=100 for Feb 2025. Hungary Rent Index: Nominal data is updated monthly, averaging 136.400 2015=100 from Jan 2016 (Median) to Mar 2025, with 111 observations. The data reached an all-time high of 216.600 2015=100 in Feb 2025 and a record low of 105.900 2015=100 in Feb 2016. Hungary Rent Index: Nominal data remains active status in CEIC and is reported by Hungarian Central Statistical Office. The data is categorized under Global Database’s Hungary – Table HU.EB005: Rent Index.

Key information about US Nominal GDP Growth

• United States Nominal GDP Growth was reported at 5.028 % in Dec 2024.
• This records a decrease from the previous number of 5.032 % for Sep 2024.
• US Nominal GDP Growth data is updated quarterly, averaging 6.138 % from Mar 1948 to Dec 2024, with 308 observations.
• The data reached an all-time high of 19.646 % in Mar 1951 and a record low of -6.835 % in Jun 2020.
• US Nominal GDP Growth data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Nominal GDP: Y-o-Y Growth: Quarterly: Seasonally Adjusted.

Austria Services Turnover Index: Nominal data was reported at 124.000 2010=100 in Dec 2017. This records an increase from the previous number of 117.100 2010=100 for Sep 2017. Austria Services Turnover Index: Nominal data is updated quarterly, averaging 108.900 2010=100 from Mar 2011 (Median) to Dec 2017, with 28 observations. The data reached an all-time high of 124.000 2010=100 in Dec 2017 and a record low of 99.400 2010=100 in Jun 2011. Austria Services Turnover Index: Nominal data remains active status in CEIC and is reported by Statistics Austria. The data is categorized under Global Database’s Austria – Table AT.H013: Nominal Services Turnover Index: 2010=100. Rebased from 2010=100 to 2015=100 Replacement series ID: 403929797

Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*

Slovenia Industrial Turnover Index: Nominal data was reported at 116.402 2021=100 in Feb 2025. This records an increase from the previous number of 113.515 2021=100 for Jan 2025. Slovenia Industrial Turnover Index: Nominal data is updated monthly, averaging 72.739 2021=100 from Jan 2000 (Median) to Feb 2025, with 302 observations. The data reached an all-time high of 135.274 2021=100 in Mar 2023 and a record low of 38.495 2021=100 in Aug 2000. Slovenia Industrial Turnover Index: Nominal data remains active status in CEIC and is reported by Statistical Office of the Republic of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.C001: Nominal and Real Industrial Turnover Index: 2021=100.

Russia Average Monthly Pension: Nominal data was reported at 13,396.300 RUB in Nov 2018. This records an increase from the previous number of 13,383.400 RUB for Oct 2018. Russia Average Monthly Pension: Nominal data is updated monthly, averaging 2,841.600 RUB from Jan 1995 (Median) to Nov 2018, with 287 observations. The data reached an all-time high of 17,425.600 RUB in Jan 2017 and a record low of 120.300 RUB in Jan 1995. Russia Average Monthly Pension: Nominal data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GC025: Nominal and Real Pension. Taking into account the lump sum monetary payment of 5 thou. rubles in January 2017 appointed according to the Federal law of November 22, 2016. С учетом единовременной денежной выплаты в январе 2017г. в размере 5 тысяч рублей, назначенной в соответствии с Федеральным законом от 22 ноября 2016г. № 385-ФЗ.

This is the supplemental data set for "Instantaneous habitable windows in the parameter space of Enceladus' ocean".nominal_salts_case.xlsx contains the output from the chemical speciation model described in the main text for the nominal salt case, with [Cl] = 0.1m and [DIC] = 0.03m. DIC is the sum of the molalities of CO2(aq), HCO3- (aq) and CO32-. The speciation was performed in intervals of 10 K and 0.5 pH units, between pH 7-12 and 273-473 K. high_salts_case.xlsx contains the output from the chemical speciation model described in the main text for the high salt case, with [Cl] = 0.2m and [DIC] = 0.1m. DIC is the sum of the molalities of CO2(aq), HCO3- (aq) and CO32-. The speciation was performed in intervals of 10 K and 0.5 pH units, between pH 7-12 and 273-473 K.low_salts_case.xlsx contains the output from the chemical speciation model described in the main text for the low salt case, with [Cl] = 0.05m and [DIC] = 0.01m. DIC is the sum of the molalities of CO2(aq), HCO3- (aq) and CO32-. The speciation was performed in intervals of 10 K and 0.5 pH units, between pH 7-12 and 273-473 K.CO2_activity_uncertainty.xlsx collects the activity of CO2 from the three files above into a single sheet. This is plotted in supplemental figure S2.independent_samples.zip contains a further 20 figures which show the variance caused by solely each of [CH4], [H2], n_ATP and k at a fixed temperature or pH as indicated by the file name. These show the deviation from the nominal log10(Power supply) e.g. Figure 3 in the main text if the named parameter were allowed to vary within its uncertainty defined in Table 1 in the main text.

CPI Price, nominal in Bulgaria was reported at 164 in 2025, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bulgaria - CPI Price, nominal - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Major differences from v1: For level 2 catch: Catches and number raised to nominal are only raised to exactly matching stratas or if not existing, to a strata corresponding with UNK/NEI or 99.9. (new feature in v4) When nominal strata lack specific dimensions (e.g., fishing_mode always UNK) but georeferenced strata include them, the nominal data are “upgraded” to match—preventing loss of detail. Currently this adjustment aligns nominal values to georeferenced totals; future versions may apply proportional scaling. This does not create a direct raising but rather allows more precise reallocation. (new feature in v4) IATTC Purse seine catch-and-effort are available in 3 separate files according to the group of species: tuna, billfishes, sharks. This is due to the fact that PS data is collected from 2 sources: observer and fishing vessel logbooks. Observer records are used when available, and for unobserved trips logbooks are used. Both sources collect tuna data but only observers collect shark and billfish data. As an example, a strata may have observer effort and the number of sets from the observed trips would be counted for tuna and shark and billfish. But there may have also been logbook data for unobserved sets in the same strata so the tuna catch and number of sets for a cell would be added. This would make a higher total number of sets for tuna catch than shark or billfish. Efforts in the billfish and shark datasets might hence represent only a proportion of the total effort allocated in some strata since it is the observed effort, i.e. for which there was an observer onboard. As a result, catch in the billfish and shark datasets might represent only a proportion of the total catch allocated in some strata. Hence, shark and billfish catch were raised to the fishing effort reported in the tuna dataset. (new feature in v4, was done in Firms Level 0 before) Data with resolution of 10degx10deg is removed, it is considered to disaggregate it in next versions. Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. (as v3) Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. (as v3) Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. (as v3) Strata for which catches in tons are raised to match nominal data have had their numbers removed. (as v3) Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. (as v3) Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. (as v3) The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") (as v3) This results in a raising of the data compared to v3 for IOTC, ICCAT, IATTC and WCPFC. However as the raising is more specific for CCSBT, the raising is of 22% less than in the previous version. Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines wi

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

IMS: Inductive Monitoring System The Inductive Monitoring System (IMS) is a tool that uses a data mining technique called clustering to extract models of normal system operation from archived data. IMS works with vectors of data values. IMS analyzes data collected during periods of normal system operation to build a system model. It characterizes how the parameters relate to one another during normal operation by finding areas in the vector space where nominal data tends to fall. These areas are called nominal operating regions and correspond to clusters of similar points found by the IMS clustering algorithm. These nominal operating regions are stored in a knowledge base that IMS uses for real-time telemetry monitoring or archived data analysis. During the monitoring operation, IMS reads real-time or archived data values, formats them into the predefined vector structure, and searches the knowledge base of nominal operating regions to see how well the new data fits the nominal system characterization. For each input vector, IMS returns the distance that vector falls from the nearest nominal operating region. Data that matches the normal training data well will have a deviation distance of zero. If one or more of the data parameters is slightly outside of expected values, a small non-zero result is returned. As incoming data deviates further from the normal system data, indicating a possible malfunction, IMS will return a higher deviation value to alert users of the anomaly. IMS also calculates the contribution of each individual parameter to the overall deviation, which can help isolate the cause of the anomaly.

Key information about Jordan Nominal GDP Growth

• Jordan Nominal GDP Growth was reported at 4.600 % in Sep 2024.
• This records an increase from the previous number of 3.975 % for Jun 2024.
• Jordan Nominal GDP Growth data is updated quarterly, averaging 6.364 % from Mar 1993 to Sep 2024, with 127 observations.
• The data reached an all-time high of 35.849 % in Sep 2008 and a record low of -5.102 % in Jun 2020.
• Jordan Nominal GDP Growth data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Nominal GDP: Y-o-Y Growth: Quarterly.