AMECO is the annual macro-economic database of the European Commission's Directorate General for Economic and Financial Affairs (DG ECFIN). The database is regularly cited in DG ECFIN's publications and is indispensable for DG ECFIN's analyses and reports. To ensure that DG ECFIN's analyses are verifiable and transparent to the public, AMECO data is made available free of charge. AMECO contains data for EU-27, the euro area, EU Member States, candidate countries and other OECD countries (United States, Japan, Canada, Switzerland, Norway, Iceland, Mexico, Korea, Australia and New Zealand).

Here, we present a comprehensive traits database for the butterflies and macro-moths of Great Britain and Ireland. The database covers 968 species in 21 families. Ecological traits fall into four main categories: life cycle ecology and phenology, host plant specificity and characteristics, breeding habitat, and morphological characteristics. The database also contains data regarding species distribution, conservation status, and temporal trends for abundance and occupancy. This database can be used for a wide array of purposes including further fundamental research on species and community responses to environmental change, conservation and management studies, and evolutionary biology. A more recent version of the dataset is available at https://doi.org/10.5285/33a66d6a-dd9b-4a19-9026-cf1ffb969cdb entitled 'Traits data for the butterflies and macro-moths of Great Britain and Ireland, 2022'. Full details about this dataset can be found at https://doi.org/10.5285/5b5a13b6-2304-47e3-9c9d-35237d1232c6

These macro-invertebrate data incorporate the results from the national river water quality network (NRWQN) from 66 sites throughout New Zealand for the purpose of monitoring long-term trends. Data included: 1990 to 2008. The NRWQN was funded by the Foundation for Research, Science, & Technology through NIWA's Nationally Significant Database: Water Resources & Climate programme. Current funding (from July 2011) comes from the NIWA Environmental Information/Monitoring programme core funding. The data are collected annually in summer, and data collection was initiated in January 1989.

This dataset contains comprehensive information about the global alien spread and distribution of macrofungi species during the last centuries (1753-2018)

The FAO Macro-Economic Indicators database provides country and regional-level macroeconomic indicators related to the total economy, agriculture, forestry, fishing, manufacturing, and specific sub-industries, including time series for national accounts variables like GDP and value-added.

The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details.

This collection includes only a subset of indicators from the source dataset.

These macro-invertebrate data incorporate the results from the national river water quality network (NRWQN) from 66 sites throughout New Zealand for the purpose of monitoring long-term trends. Data included: 2009 onward. The NRWQN was funded by the Foundation for Research, Science, & Technology through NIWA's Nationally Significant Database: Water Resources & Climate programme. Current funding (from July 2011) comes from the NIWA Environmental Information/Monitoring programme core funding. The data are collected annually in summer, and data collection was initiated in January 1989.

This data is used for article of macroeconomic of some Asian countries in long period which explained about four Asian countries, such as Indonesia, Malaysia, Singapore, and South Korea. This data has taken from World Bank Development Indicators (WDI) database and is formed by Vector Auto Regression (VAR) model, then empirical result is executed by Granger causality model on E-views 11 program to gauge the relationship between gross domestic product, exchange rate, inflation rate, foreign direct investment, net export, government expenditures, unemployment rate, and savings. The results showed that most of gross domestic product of sample and other macro-economy variables have not causality relationship.

In April and May 2019, we compiled the “BenBio” part of the “BenBioDen database” following the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) Statement for systematic reviews and meta-analyses. In the first PRISMA step, the “Identification” step, we identified 1,373 articles in the Web of Science using the key words “marine meiofauna biomass”, “marine macrofauna biomass”, “marine megafauna biomass”, “marine meiobenth* biomass”, “marine macrobenth* biomass”, “marine megabenth* biomass”, “nematode biomass”, and “benthic ‘standing stock’”. We located an additional 201 publications based on expert knowledge. A search of the PANGAEA® Data Publisher (https://www.pangaea.de/) identified 1,488 datasets representing 148 publications using the key words “meiofauna biomass”, “macrofauna biomass” and “megafauna biomass”. Further 30 datasets were found in the EOL data archive (http://data.eol.ucar.edu/), through citations in review papers, and based on expert knowledge...

China Macro-economic Climate Index: Coincident Index data was reported at 98.800 2019=100 in Nov 2024. This records a decrease from the previous number of 98.900 2019=100 for Oct 2024. China Macro-economic Climate Index: Coincident Index data is updated monthly, averaging 98.600 2019=100 from Dec 2022 (Median) to Nov 2024, with 24 observations. The data reached an all-time high of 100.100 2019=100 in Nov 2023 and a record low of 95.305 2019=100 in Dec 2022. China Macro-economic Climate Index: Coincident Index data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OF: Economic Climate Indicator.

We analyze the contribution of credit spread, house and stock price shocks to the US economy based on a time-varying parameter vector autoregressive model. We find that the contribution of financial shocks to gross domestic product growth fluctuates from about 20% in normal times to more than 50% during the Great Recession. The Great Recession and the subsequent weak recovery can largely be traced back to negative housing shocks. Housing shocks have become more important for the real economy since the early 2000s, and negative housing shocks are more important than positive ones. Unexpected increases in credit spreads have not been deflationary recently.

This page provides the data of the manuscript: Martínez, C. G. B., Niediek, J., Mormann, F. & Andrzejak,R. G. Seizure onset zone lateralization using a nonlinear analysis of micro versus macro electroencephalographic recordings during seizure-free stages of the sleep-wake cycle from epilepsy patients. Frontiers in Neurology 11, 1057, 2020. If you use any of this data, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

In a resource-constrained world with growing population and demand for energy, goods, and services with commensurate environmental impacts, we need to understand how these trends relate to various aspects of economic activity. 7see-GB is a computational model that links energy demand through to final economic consumption, and is used to explore decadal scenarios for the UK macroeconomy. This dataset includes two published models (*.vpm) from the source model 7see-GB, version 5-10 (22Apr15). They show how results were created for the paper 'A Robust Data-driven Macro-socioeconomic-energy Model'. The source model was developed in Vensim(r) (5.8b) and these published models can be viewed with the Vensim Reader, as provided with this dataset. There are instructions on how to navigate the published models and inspect variables shown in the paper. The .exe and .dmg files are free 'Model Reader' executables for Windows/OSX which allow a user to run the model without buying the Vensim simulator.

This dataset contains the latest macro data collected from various websites. The dataset covers different economies (AU, CN, SG, ES, EA, RU, CA, US, BR, KR, JP, GB, IT, AR, DE, ZA, FR, MX, IN, TR, ID, SA, IF, EU, OP, G2, WL, G7). Survey consensus figures are provided displaying the average forecast among a representative group of economists. Forecasts correspond to predictions from a moving average (ARIMA) model.

X-STR database for White British population

Comprehensive nutritional calculation data based on established scientific formulas and dietary guidelines

Copes Rule Database.csv file with database of phenotypic rates of change for traitsCopesRuleDB.csv

We show that the conditional distribution of forecasted GDP growth depends on financial conditions in a panel of 11 advanced economies. Financial conditions have a larger effect on the lower 5th percentile of conditional growth—which we call growth-at-risk (GaR)—than the median. In addition, the term structure of GaR reflects that when initial financial conditions are loose, downside risks are lower in the near-term but increase in later quarters. This intertemporal tradeoff for loose financial conditions is amplified when credit-to-GDP growth is rapid. Using granular instrumental variables, we also provide evidence that the relationship from loose financial conditions to future downside risks is causal. Our results suggest that models of macrofinancial linkages should incorporate the endogeneity of higher-order moments to systematically account for downside risks to growth in the medium run.

These files include the underlying data and the code to replicate all analyses in the paper and the Supporting Information (SI).

Argentina Banco Macro SA: Liabilities data was reported at 10,067,975,001.000 ARS th in Jan 2025. This records a decrease from the previous number of 10,129,428,138.000 ARS th for Dec 2024. Argentina Banco Macro SA: Liabilities data is updated monthly, averaging 41,163,434.000 ARS th from May 2001 (Median) to Jan 2025, with 285 observations. The data reached an all-time high of 10,129,428,138.000 ARS th in Dec 2024 and a record low of 490.600 ARS th in Nov 2001. Argentina Banco Macro SA: Liabilities data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.KB033: Balance Sheet: Banco Macro S.A..

Data Repository: MoCEGS Project

The data for each year in the MoCEGS project are in zipped file. The names of the files are saved as the following context: [Area]_[Starting Date DDMMYYYY]_[Ending Date DDMMYYYY]_[Project Name]

Within the zipped file, the following folder structure exist: - capacity: This is 30-minute generation capacity (by energy source) data that is directly queried from ENTSOE API. - The name of the station is directly used here as the name of the csv file. E.g. [code name of station].csv. Refer to the "areas.csv" file to get the definition of the station. - The source of the energy is labelled as B[number]. Refer to the 'env.csv' file to get a reference to the source of generation.

• flow: This is the 30-minute power flow data that is directly queried from ENTSOE API. (This is an optional query)

  • The name of the file is arranged as [code name of source station]_[code name of sink station].csv. Refer to the "areas.csv" file to get the definition of the station.

• load: This is the 30-minute power load (consumption) that is directly queried from ENTSOE API.

  • The name of the station is directly used here as the name of the csv file. E.g. [code name of station].csv. Refer to the "areas.csv" file to get the definition of the station.

• price: This is the 30-minute power price that is directly queried from ENTSOE API.

  • The name of the file is arranged as [code name of source station]_[code name of sink station].csv. Refer to the "areas.csv" file to get the definition of the station.