Explore macroeconomic statistics and indicators, including GDP, Gross Fixed Capital Formation, National Income, and more. This dataset covers a wide range of countries such as Afghanistan, Albania, Algeria, Australia, Brazil, China, Germany, India, United States, and many more.

GDP, Gross Domestic Product, Capita, GFCF, Gross Fixed Capital Formation, Value, Added, Gross, Output, National, Income, Manufacturing, Agriculture, Population, National Accounts

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela, Yemen, Zambia, Zimbabwe

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bob79846514/macro dataset hosted on Hugging Face and contributed by the HF Datasets community

Zenodo

The Macro or A1pp domain is a module of about 180 amino acids which can bind ADP-ribose (an NAD metabolite) or related ligands. Binding to ADP-ribose could be either covalent or non-covalent : in certain cases it is believed to bind non-covalently ; while in other cases (such as Aprataxin) it appears to bind both non-covalently through a zinc finger motif, and covalently through a separate region of the protein . This domain is found in a number of otherwise unrelated proteins. It is found at the C-terminus of the macro-H2A histone protein 4 and also in the non-structural proteins of several types of ssRNA viruses such as NSP3 from alpha-viruses and coronaviruses. This domain is also found on its own in a family of proteins from bacteria, archaebacteria and eukaryotes. The 3D structure of the SARS-CoV Macro domain has a mixed alpha/beta fold consisting of a central seven-stranded twisted mixed beta sheet sandwiched between two alpha helices on one face, and three on the other. The final alpha-helix, located on the edge of the central beta-sheet, forms the C terminus of the protein . The crystal structure of AF1521 (a Macro domain-only protein from Archaeoglobus fulgidus) has also been reported and compared with other Macro domain containing proteins. Several Macro domain only proteins are shorter than AF1521, and appear to lack either the first strand of the beta-sheet or the C-terminal helix 5. Well conserved residues form a hydrophobic cleft and cluster around the AF1521-ADP-ribose binding site .

hungryzebra/macro dataset hosted on Hugging Face and contributed by the HF Datasets community

Overview

• Introduction
• Object Classes
  • Graphic
  • Graphic Decoration
  • Graphic Head
  • Main Entry
  • Main Entry Continued
  • Main Head
  • Main List
  • Main Paragraph
  • Main Paragraph Catalogue Description
  • Margin Text Manuscript Addendum
  • Margin Text Note
  • Numbering
  • Running Title
  • Stamp
  • Stamp Sticker
  • Title Page

Introduction

This dataset provides a comprehensive collection for segmenting various zones in historical documents. The task is to accurately annotate different zones that help in categorizing text and graphical elements. The dataset consists of distinct classes such as textual, graphical, and decorative zones.

Object Classes

Graphic

Description

Regions primarily containing graphic representations or illustrations, often found centrally on a page.

Instructions

Annotate the entire area that contains central illustrations. Ensure to include complete borders if present. Do not include any peripheral text associated with the graphics.

Graphic Decoration

Description

Decorative elements often used as borders or fillers around main text or graphics.

Instructions

Focus on annotating smaller decorative elements that do not convey primary content, such as ornamental borders. Ensure to exclude surrounding main text.

Graphic Head

Description

Illustrative or decorative elements located at the heads of sections, often introducing the content.

Instructions

Identify and annotate header illustrations or decorations that introduce sections. Do not include text unless part of the illustration or decoration.

Main Entry

Description

The primary body of text in the document that serves as a main entry.

Instructions

Outline the main textual content without including any decorative elements or illustrations. Ensure the text is cleanly captured within boundaries.

Main Entry Continued

Description

Continuation of the main body text from a prior page or section.

Instructions

Annotate where the text resumes, maintaining continuity from the previous page. Ensure exclusion of any introductory headers or decorations transitioning into the continued text.

Main Head

Description

Headings or titles that introduce the main text sections.

Instructions

Encircle clearly identified headings that stand at the beginning of main sections. Do not include adjacent body text.

Main List

Description

Zones containing enumerated lists or series of items.

Instructions

Mark areas that contain ordered lists or bullet points. Ensure that complete list items are captured, avoiding adjacent explanatory paragraphs.

Main Paragarph

Description

Paragraphs of text excluding lists or numerical data.

Instructions

Enclose full paragraphs, differentiating them from lists or other formatted text, ensuring paragraph ends and beginnings are clearly defined.

Main Paragraph Catalogue Description

Description

Paragraphs specifically describing catalogue items.

Instructions

Highlight paragraphs focused on descriptive catalog entries, distinct from regular narrative text or headings. Capture explicit item descriptions without images.

Margin Text Manuscript Addendum

Description

Textual additions or comments typically found in the margins.

Instructions

Focus on texts residing outside the main body and note added commentary or references in margins. Exclude main body and footnotes.

Margin Text Note

Description

Small notes or annotations found typically in the margins of pages.

Instructions

Isolate smaller margin notes or brief annotations not part of the primary text. Exclude any marginal header or visual borders.

Numbering

Description

Sections of a page that display page or item numbers.

Instructions

Circle areas specifically containing numbers, whether for pagination or enumeration, irrespective of whether it's at the top or bottom of the page.

Running Title

Description

Titles or headers appearing at the top or bottom of the pages, serving as running titles.

Instructions

Encapsulate titles that repeat across pages as headers or footers. Exclude any body text unconventionally placed nearby.

Stamp

Description

Sections containing stamps or seals, often used for identification or authenticity.

Instructions

Recognize and encircle all stamped areas. Ensure to separate from adjacent text or graphics.

Stamp Sticker

Description

Regions where sticker labels or adhesive notes are placed.

Instructions

Anno

This article investigates the macro-level drivers of adult-age language learning with a focus on migration based on a new dataset on German language learning in 77 countries (including Germany) for 1992-2006. Fixed-effects regressions show that language learning abroad is strongly associated with immigration from countries of the European Union and the Schengen Area whose citizens enjoy free access to Germany, while language learning in Germany is strongly associated with immigration from countries with restricted access. The different degrees of uncertainty about access to Germany seem to be of importance for preparatory language learning. To shed light on country heterogeneities, we substitute the location fixed effects with a vector of country characteristics, which include several distance measures among others, and we estimate a random-effects model. Last, we provide some tentative arguments in favour of a causal interpretation. The main results related to the role of uncertainty are mostly unaffected. The Skilled Immigration Act from 2020 removes this uncertainty with potential positive effects on preparatory language learning and economic and social integration.

Measurements of biomass and productivity of seabed macrobenthic and megabenthic organisms, from studies in eastern North America from New England to the Canadian Arctic dating from 1954 to 2000, have been assembled into a comprehensive, georeferenced, database. Information sources include primary publications, technical reports and unpublished data from scientific studies, commercial fisheries surveys, and monitoring and baseline studies carried out for offshore petroleum exploration. See Stewart et al 2001 for more details. This resource contains biomass information extracted from the database (Stewart et al, 2001) for the following taxonomic groups: crustacea, echinodermata, mollusca, polychaeta and 'other'. This dataset contains 'absence' records as the source dbase includes valid biomass values of zero. Each data record includes a reference to the source in the DwC.associatedReferences field. Version 1 of this resource was created during the Census of Marine Life. Version 3 of this resource contains revised DwC records including occurrenceStatus (presence/absence) for the original 5 taxonomic groups plus fish. Content in this version was extracted from database tables.

Banco Macro reported ARS129.16B in Net Income for its fiscal quarter ending in December of 2024. Data for Banco Macro | BMA - Net Income including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Macro Bank total assets for the quarter ending September 30, 2024 were $12.871B, a 22.64% increase year-over-year. Macro Bank total assets for 2024 were $15.942B, a 37.56% decline from 2023. Macro Bank total assets for 2023 were $25.529B, a 58.63% increase from 2022. Macro Bank total assets for 2022 were $16.094B, a 53.38% increase from 2021.

Aquatic macroinvertebrate samples collected in Salt Lake County streams. Data collected and maintained by Salt Lake County Watershed Planning & Restoration. As a rule, 10 transects are identified per stream reach and 8 kick-samples are collected, then combined and analyzed as a composite reach-wide sample. The composite sample is sent to an offsite lab for processing and analysis, which provides aquatic invasive species identification and overall habitat and water quality indicators. To identify the reach locations, the data in this table can be related to the Water Quality Sample Sites point feature layer using the common field “SiteID. THIS DATA IS CONTINUALLY UPDATED AND MAY NOT HAVE BEEN CORRECTED FOR ERRORS, BY USING THIS DATASET IN ANY MANNER THE USER ACKNOWLEDGES THIS AND DOES NOT HOLD SALT LAKE COUNTY RESPONSIBLE. Consult the “QA/QC Complete” field in this table to determine if data has been verified. Download the metadata here.

Argentina Banco Macro SA: Financial Expense: Interest data was reported at -1,811,428,581.000 ARS th in Dec 2024. This records a decrease from the previous number of -1,682,585,246.000 ARS th for Nov 2024. Argentina Banco Macro SA: Financial Expense: Interest data is updated monthly, averaging -1,044,035.000 ARS th from May 2001 (Median) to Dec 2024, with 284 observations. The data reached an all-time high of -1.800 ARS th in Jan 2002 and a record low of -1,811,428,581.000 ARS th in Dec 2024. Argentina Banco Macro SA: Financial Expense: Interest 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.KB048: Income Statement: Banco Macro S.A..

This dataset is about books. It has 1 row and is filtered where the book is Shoot macro : professional macrophotography techniques for exceptional studio images. It features 7 columns including author, publication date, language, and book publisher.

Macro Defect Inspection Systems Market Outlook

The global macro defect inspection systems market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. This significant growth is driven by the increasing demand for high-quality semiconductor devices and the rising complexity of semiconductor manufacturing processes, which necessitate advanced defect detection and inspection systems.

One of the primary growth factors in the macro defect inspection systems market is the rapid advancement in semiconductor technologies. As semiconductor devices become smaller and more intricate, the need for precise inspection systems that can detect and rectify defects at a macro level becomes crucial. These systems ensure the production of high-quality chips, which are essential for the functioning of electronic devices. Moreover, the growing adoption of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) in inspection processes enhances the accuracy and efficiency of defect detection, further propelling market growth.

The automotive industry's increasing reliance on electronic components and semiconductor devices is another significant driver of the macro defect inspection systems market. Modern vehicles are equipped with numerous electronic systems for safety, entertainment, and navigation, all of which require high-quality semiconductors. The stringent quality standards in the automotive sector necessitate the use of advanced inspection systems to ensure the reliability and performance of these components. Consequently, the demand for macro defect inspection systems in automotive manufacturing is expected to witness substantial growth over the forecast period.

The growing trend of miniaturization in electronics, coupled with the rise of the Internet of Things (IoT), is also fueling the demand for macro defect inspection systems. As electronic devices become more compact and interconnected, the need for rigorous inspection and defect detection at the macro level becomes increasingly important. This trend is particularly evident in the consumer electronics and telecommunications sectors, where the demand for high-performance, defect-free devices is paramount. The integration of IoT devices in various applications further amplifies the need for reliable inspection systems, thereby driving market growth.

From a regional perspective, Asia Pacific dominates the macro defect inspection systems market, owing to the presence of major semiconductor manufacturing hubs in countries like China, Japan, South Korea, and Taiwan. The region's strong manufacturing base, coupled with significant investments in advanced technologies and infrastructure, makes it a key market for macro defect inspection systems. North America and Europe also hold substantial market shares, driven by the presence of leading technology companies and automotive manufacturers. These regions are characterized by technological advancements and stringent quality standards, which necessitate the adoption of advanced inspection systems.

Component Analysis

The macro defect inspection systems market is segmented by component into hardware, software, and services. The hardware segment encompasses the physical devices and instruments used in defect detection, including cameras, sensors, and inspection machines. This segment is a significant contributor to the market, driven by continuous advancements in hardware technologies that enhance the accuracy and efficiency of defect detection. High-resolution cameras and advanced sensors play a critical role in identifying defects at a macro level, ensuring the production of high-quality semiconductor devices.

Software components in macro defect inspection systems are equally vital, as they provide the algorithms and analytical tools necessary for defect detection and analysis. The software segment includes inspection software, defect classification algorithms, and data analysis tools. With the integration of AI and ML, software solutions have become more sophisticated, enabling real-time defect detection and predictive maintenance. These advancements help manufacturers in reducing downtime and improving production yield, thereby driving the growth of the software segment.

Services in the macro defect inspection systems market include installation, maintenance, training, and technical support. As inspection systems become more complex, the demand for specialized services has increased

This statistic displays the distribution of macro- and microplastics lost to the environment worldwide as of 2018, with a breakdown by geographical region. As of that year, around 20 percent of the global losses of microplastics to the environment took place in Asia.

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.

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 dataset is about books. It has 1 row and is filtered where the book is Macro-econophysics : new studies on economic networks and synchronization. It features 7 columns including author, publication date, language, and book publisher.

Dataset

This dataset was created by Nicholas Scherer

Released under Apache 2.0

Contents

Banco Macro reported 841.96B in Sales Revenues for its fiscal quarter ending in September of 2024. Data for Banco Macro | BMA - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last June in 2025.