191 views (8 recent) Catches in FAO area 27 by country, species, area and year as provided by the national authorities. Source: Eurostat/ICES data compilation of catch statistics - ICES 2020, Copenhagen. Format: Archived dataset in .xlsx and .csv formats. Version: 22-06-2020. https://www.ices.dk/data/dataset-collections/Pages/Fish-catch-and-stock-assessment.aspx

Nominal Statistical Discrepancy for United States was 0.00000 Domestic Currency in January of 2021, according to the United States Federal Reserve. Historically, Nominal Statistical Discrepancy for United States reached a record high of 5000.00000 in July of 1951 and a record low of -100.00000 in October of 1950. Trading Economics provides the current actual value, an historical data chart and related indicators for Nominal Statistical Discrepancy for United States - last updated from the United States Federal Reserve on November of 2025.

This dataset provides values for GDP NOMINAL reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Key information about Jordan Nominal GDP Growth

• Jordan Nominal GDP Growth was reported at 4.972 % in Jun 2025.
• This records an increase from the previous number of 4.900 % for Mar 2025.
• Jordan Nominal GDP Growth data is updated quarterly, averaging 5.051 % from Mar 1993 to Jun 2025, with 130 observations.
• The data reached an all-time high of 35.849 % in Sep 2008 and a record low of -5.104 % 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.

CEIC calculates quarterly Nominal GDP Growth from quarterly Nominal GDP. The Department of Statistics provides Nominal GDP in local currency based on SNA 2008. Nominal GDP Growth prior to Q1 2009 is based on SNA 1993.

Graph and download economic data for Nominal Statistical Discrepancy for Republic of Korea (NSDGDPSAXDCKRQ) from Q1 1960 to Q1 2025 about residual and Korea.

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.

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.*

Annual nominal catches of more than 200 species of fish and shellfish in the Northeast Atlantic region. Data are presented in the live weight equivalent of landings per year, country, species, and fishing area. This dataset includes catches starting from 2006.

Wage and Payroll Statistics - Table 220-19023 : Nominal Indices of Payroll per Person Engaged by industry division (Q1 1999 = 100)

Graph and download economic data for Nominal Households Final Consumption Expenditure for United States (NCPHISAXDCUSQ) from Q1 1959 to Q4 2023 about consumption expenditures, consumption, households, and USA.

Nominal Statistical Discrepancy for Great Britain was 881.00000 Domestic Currency in January of 2025, according to the United States Federal Reserve. Historically, Nominal Statistical Discrepancy for Great Britain reached a record high of 1052.00000 in January of 2024 and a record low of 0.00000 in April of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for Nominal Statistical Discrepancy for Great Britain - last updated from the United States Federal Reserve on December of 2025.

Overview with Chart & Report: Nominal Budget Balance reflects the difference between the government's income and expenditure at the end of the budget period. The nominal budget calculation also includes debt servicing costs. A

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

Wage and Payroll Statistics - Table 220-19001 : Nominal Wage Indices for employees up to supervisory level by industry section (September 1992 = 100)

While in 2018, digitally transformed enterprises accounted for **** trillion U.S. dollars of the global nominal GDP, in 2023 they are forecast to account for **** trillion U.S. dollars, more than half of the overall nominal GDP. This signals that digital supremacy in the global economy is near. Digital transformation: benefits and necessity  Among many other benefits, digital transformation is essential for helping businesses to create better customer experiences as well as improve their process efficiency through automation. Digitally transformed enterprises are equipped to drive new revenue and stay ahead of the competition. The difficult economic situation brought about by the global coronavirus (COVID-19) pandemic, however, has made digital transformation a necessity rather something nice to have - leading DX activities has become CIOs’ top task to help business preserve through the COVID disruption. Digital transformation challenges   Despite evidence of widespread benefits through digital transformation, its implementation is not without challenges. Businesses worldwide cite skill gaps and cultural differences as major challenges in their pursuit of digital transformation.A lack of clarity on transformation strategy and insufficient alignment across organizational departments are top reasons as to why some businesses stall momentum in their digital transformation efforts.

Key information about Spain Nominal GDP

• Spain Nominal GDP reached 377.3 USD bn in Mar 2023, compared with 352.5 USD bn in the previous quarter.
• Nominal GDP in Spain is updated quarterly, available from Mar 1970 to Mar 2023, with an average number of 150.1 USD bn.
• The data reached an all-time high of 435.8 USD bn in Jun 2008 and a record low of 9.2 USD bn in Mar 1970.

CEIC converts quarterly Nominal GDP into USD. National Statistics Institute provides Nominal GDP in EUR. Federal Reserve Board average market exchange rate is used for currency conversions. Nominal GDP prior to Q1 1995 is sourced from the International Monetary Fund.


Related information about Spain Nominal GDP

• In the latest reports, Spain GDP expanded 3.8 % YoY in Mar 2023.
• Its GDP deflator (implicit price deflator) increased 6.2 % in Mar 2023.
• Spain GDP Per Capita reached 27,002.6 USD in Dec 2020.
• Its Gross Savings Rate was measured at 24.2 % in Dec 2022.
• For Nominal GDP contributions, Investment accounted for 19.1 % in Mar 2023.
• Public Consumption accounted for 19.2 % in Mar 2023.
• Private Consumption accounted for 58.1 % in Mar 2023.

The Mars Express PFS data set contains raw (CODMAC Level 2) measurements from the Planetary Fourier Spectrometer collected during the first extension Mars orbit phases.

When adjusted for inflation, the 2024 federal minimum wage in the United States is over 40 percent lower than the minimum wage in 1970. Although the real dollar minimum wage in 1970 was only 1.60 U.S. dollars, when expressed in nominal 2024 dollars this increases to 13.05 U.S. dollars. This is a significant difference from the federal minimum wage in 2024 of 7.25 U.S. dollars.

Korea, Republic of (South Korea) - Nominal Statistical Discrepancy for Republic of Korea was 398400.00000 Domestic Currency in January of 2025, according to the United States Federal Reserve. Historically, Korea, Republic of (South Korea) - Nominal Statistical Discrepancy for Republic of Korea reached a record high of 481200.00000 in April of 2021 and a record low of -794000.00000 in January of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for Korea, Republic of (South Korea) - Nominal Statistical Discrepancy for Republic of Korea - last updated from the United States Federal Reserve on November of 2025.

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.