AutoScraping’s Travel Agents Contact Data is a unique and comprehensive dataset that provides detailed and verified information on 9,000 travel agents across the USA, LATAM, and Europe. This data product is specifically designed for businesses looking to expand their reach in the travel and tourism industry. It offers key insights and direct access to professionals in one of the most dynamic and globally connected sectors.

What Makes Our Data Unique? Our dataset stands out for its high level of accuracy and completeness. Each record is meticulously verified to ensure the highest quality, providing up-to-date contact information, including email addresses and phone numbers. The data is sourced from reliable industry channels and continuously updated to maintain relevance and integrity. Additionally, all data is GDPR-compliant, guaranteeing that your marketing and outreach efforts are effective and legally sound.

How Is the Data Generally Sourced? The data is collected through automated scraping techniques and manual verification processes. We target reputable sources such as travel industry directories, professional networking sites, and publicly available information from company websites. Each record is then cross-referenced and validated to confirm its authenticity and accuracy, providing you with a trustworthy dataset that you can rely on for various business applications.

Primary Use-Cases and Verticals The primary use cases for this data product include:

Marketing and Sales Outreach: Leverage our comprehensive contact information to reach out directly to travel agents in your target regions. This is ideal for travel, hospitality, and tourism companies looking to promote their services, launch marketing campaigns, or establish partnerships. Business Development: Use this dataset to identify potential partners, collaborators, or clients in the travel industry. With detailed insights into the agents' locations and areas of specialization, you can tailor your business development strategies effectively. Market Research and Analysis: Analyze trends and opportunities within the travel industry across different regions. This data is valuable for market researchers and analysts who want to understand the competitive landscape and make data-driven decisions. Event Promotion: If you organize events, workshops, or conferences in the travel sector, this dataset allows you to invite and connect with key industry players most likely to be interested in your events. Coverage and Attributes The dataset covers many geographies, including the United States, Latin America, and Europe, making it a versatile resource for global business strategies. It includes:

Geographic Coverage: USA, LATAM, Europe. Contact Information: Verified email addresses and phone numbers. Industry Focus: Travel agents with expertise in various travel segments such as leisure, corporate travel, luxury travel, and more. Compliance: GDPR-compliant, ensuring your outreach efforts are legally protected. Scale & Quality Indicators Our dataset includes 9,000 meticulously verified records, ensuring a high degree of accuracy and completeness. We use a rigorous verification process to cross-check and validate each entry, reducing the likelihood of outdated or incorrect information. This makes it an excellent choice for businesses that require reliable data for high-stakes applications, such as sales, marketing, and strategic planning.

How Does This Data Product Fit into Our Broader Data Offering? AutoScraping is dedicated to providing high-quality, customized datasets across various industries and geographies. Our Travel Agents Contact Data is part of our broader portfolio of data products, which includes datasets tailored to sectors like finance, real estate, and e-commerce. Our mission is to empower businesses with the data they need to make informed decisions, identify opportunities, and drive growth.

This dataset complements our other offerings by providing specific, actionable insights into the travel industry, characterized by its rapid growth and high competition. By incorporating this dataset into your business intelligence arsenal, you can gain a competitive edge and build stronger connections in the travel market.

Why Choose AutoScraping? With over a decade of experience in data extraction and verification, AutoScraping is a trusted partner for businesses seeking high-quality, reliable data. Our commitment to accuracy, compliance, and customer satisfaction sets us apart in the industry. Whether you want to expand your market presence, refine your business strategies, or conduct in-depth market research, our datasets are designed to meet your needs.

Investing in our Travel Agents Contact Data means you are not just acquiring information but gaining a powerful tool to drive your business forward. Our team is here to support you with any customization needs, ensuring that the data you receive perfectly al...

The number of Youtube users in Europe was forecast to continuously increase between 2024 and 2029 by in total 7.8 million users (+3.61 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 223.61 million users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like North America and Australia & Oceania.

European Union Imports from United States was US$372.59 Billion during 2022, according to the United Nations COMTRADE database on international trade. European Union Imports from United States - data, historical chart and statistics - was last updated on March of 2025.

The number of Facebook users in Europe was forecast to continuously increase between 2024 and 2028 by in total 15.5 million users (+3.91 percent). According to this forecast, in 2028, the Facebook user base will have increased for the sixth consecutive year to 412.26 million users. User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Facebook users in countries like South America and North America.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

The Gross Domestic Product per capita in European Union was last recorded at 34601.03 US dollars in 2023. The GDP per Capita in European Union is equivalent to 274 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Intelligent Energy – Europe (IEE) offers a helping hand to organisations willing to improve energy sustainability. Launched in 2003 by the European Commission, the programme is part of a broad push to create an energy-intelligent future for us all. It supports EU energy efficiency and renewable energy policies, with a view to reaching the EU 2020 targets (20% cut in greenhouse gas emissions, 20% improvement in energy efficiency and 20% of renewables in EU energy consumption).

The programme is a pillar of the EU's Competitiveness and Innovation Framework Programme (CIP).

More than 600 projects across Europe have been co-funded by programme since 2003. These projects cover fields as diverse as education and electrical appliances, yet all contribute to building a greener and more secure energy future for the EU. You can find the list of beneficiaries and projects funded by the programme.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the GPLv3, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

ch_cantons.csv

• Mapping between Swiss Cantons and NUTS3 regions
• Source: https://en.wikipedia.org/wiki/Data_codes_for_Switzerland
• Extract from Terms of Use:

Creative Commons Attribution-ShareAlike 3.0 Unported License

• https://en.wikipedia.org/wiki/Data_codes_for_Switzerland

EIA_hydro_generation_2000_2014.csv

• Hydroelectricity generation per country and year
• Source: https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016
• Extract from Terms of Use:

Public domain and use of EIA content: U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."

• https://www.eia.gov/about/copyrights_reuse.php

GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards:

1. Immigrants, Refugees, Muslims, Hispanics, Venezuelans
2. News Media Consumption
3. Trust in News Media and Societal Institutions
4. Frequency and Valence of Intergroup Contact
5. Realistic and Symbolic Intergroup Threat
6. Right-wing Authoritarianism
7. Social Dominance Orientation
8. Political Efficacy
9. Personality Characteristics
10. Perceived COVID-threat, and
11. Socio-demographic Characteristics

1. Austria
2. Belgium
3. Germany
4. Hungary
5. Italy
6. Spain
7. Sweden

1. United States of America
2. Colombia

The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

Access consumer behavior data for 700M+ consumer goods and electronics professionals globally with Success.ai. Includes detailed contact information, professional histories, and business locations. GDPR-compliant. Best price guaranteed.

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally the researchers chose to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, researchers combined some subcategories. First, they were interested in total tax revenue, as well as the shares of total revenue coming from direct and indirect taxes. Further, they measured two sub-categories of direct taxation, namely taxes on property and income. For indirect taxes, they separated excises, consumption, and customs.

What information do you include in your sales and marketing database?

When you buy one of our sales and marketing databases, all records we supply to you hold the below information (where it is available):

-Company name -Address (including town, county and postcode) -Phone number (including area code) -Website address -Company registration number and date of incorporation -Financial information (for example, net worth, turnover band, profit and loss, profit percentage change, sales increase or decrease) -Senior decision maker name -Senior decision maker email address (if we do not have this information, we will provide the next best email for example, a departmental email address) -Top-level market sector and granular industry classification -Standard Industrial Classification (SIC) code -Number of employees (both onsite and nationally) -Premise type and description -Number of branches -Legal status (for example, sole trader, partnership, private limited company)

Why use Metric Central?

There are several marketing database providers out there, but these are the reasons why you should choose us for your prospecting requirements.

The most up to date information on the market: We refresh our database every day, meaning when you buy from us, you will have the most up to date and accurate details, without having to worry about database decay, duplicated records

Reach out directly to the key decision-maker: Unlike some lists which will only give you a generic company email address, we will provide you with the name and details of the company’s key decision-maker, meaning you can directly reach out to the person with purchasing power, without being held back by the company gatekeeper 100% GDPR compliance: All of our records come with a full General Data Protection Regulation (GDPR) compliance audit trail, meaning you can reach out to prospects with the peace of mind you are compliant with the latest legislation

Buy online and use straightaway: With a lot of companies providing a similar product to ours, you have to reach out to them for a quote before you receive your product. Our sales and marketing database can be bought online and downloaded straight away, meaning you can start reaching out to decision-makers the very same day you buy. We even tell you how many companies your data will contain before you commit to purchasing!

A wide range of contacts: We have contact details for prospective customers across a wide range of different industries meaning no matter what your product or service is, we have your needs covered. Check out our website for a full list of databases we can provide CRM ready data: Our list can be uploaded straight into your CRM system of choice, meaning your sales team can start reaching out to prospective customers immediately.

Competitive pricing: We’re one of the most cost-effective companies on the market, combining a high-quality product with an accessible price point. Decades of experience: With several years of experience in the sales agency, our team at Metric Central is well-equipped to understand your pain points and provide you with the data you need to help you sell your product or service

If you would like to know more about the data services we offer and how we can help you grow your business, please don’t hesitate to get in touch with us today, and one of our experienced team reach out to you with the information you need.

Please note: All data is supplied under legitimate interest. The term of supply is as standard of a 12-month multi licence agreement which allows unlimited postal and telephone communication with 12 cold email sends.

The List of Invasive Alien Species of Union concern (also called the Union list) is a species checklist dataset published by the Research Institute of Nature and Forest (INBO). This list contains regulated invasive alien species (IAS) that require action at European Union Level, and is at the core of Regulation (EU) 1143/2014 on the prevention and management of the introduction and spread of IAS (the IAS Regulation). The list has an evidence base in risk assessments, showing potentially significant adverse impacts on biodiversity or related ecosystem services. The species included on the Union list are subject to restrictions and measures set out in the Regulation. These include restrictions on keeping, importing, selling, breeding and growing. Member States are required to take action on pathways of unintentional introduction, to take measures for the early detection and rapid eradication of these species, and to manage species that are already widely spread in their territory.

The list is dynamic and is subject to regular updates when new species are added. Here, it is published as a standardized Darwin Core Archive and includes for each species: the scientific name, higher classification and stable taxon identifier (in the taxon core), the English, Dutch, French and German common names in line with www.iasregulation.be, the date of entry into force (the date the provisions of the Regulation enter into force for the species, as mentioned in the Commission Implementing Regulation (EU) 2016/141, 2017/1263, 2019/1262, 2022/1203). Issues with the dataset can be reported at https://github.com/riparias/union-list/issues. For more information on the EU IAS Regulation and the list of IAS of Union concern, please see the European Commission webpage on IAS (https://environment.ec.europa.eu/topics/nature-and-biodiversity/invasive-alien-species_en), the IUCN website on the IAS Regulation (https://www.iucn.org/our-work/topic/invasive-alien-species) and the European Alien Species Information Network.

We have released this dataset to the public domain under a Creative Commons Zero waiver. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be.

This dataset was published as open data for the LIFE RIPARIAS project (Reaching Integrated and Prompt Action in Response to Invasive Alien Species https://www.riparias.be/), with technical support provided by the Research Institute for Nature and Forest (INBO).

This paper utilizes data on subjective probabilities to study the impact of the stock market crash of 2008 on households' expectations about the returns on the stock market index. We use data from the Health and Retirement Study that was fielded in February 2008 through February 2009. The effect of the crash is identified from the date of the interview, which is shown to be exogenous to previous stock market expectations. We estimate the effect of the crash on the population average of expected returns, the population average of the uncertainty about returns (subjective standard deviation), and the cross-sectional heterogeneity in expected returns (disagreement). We show estimates from simple reduced-form regressions on probability answers as well as from a more structural model that focuses on the parameters of interest and separates survey noise from relevant heterogeneity. We find a temporary increase in the population average of expectations and uncertainty right after the crash. The effect on cross-sectional heterogeneity is more significant and longer lasting, which implies substantial long-term increase in disagreement. The increase in disagreement is larger among the stockholders, the more informed, and those with higher cognitive capacity, and disagreement co-moves with trading volume and volatility in the market.

European Union Exports of beverages, spirits and vinegar to United States was US$11.78 Billion during 2022, according to the United Nations COMTRADE database on international trade. European Union Exports of beverages, spirits and vinegar to United States - data, historical chart and statistics - was last updated on March of 2025.

This page displays a table with European Union Imports By Country in U.S. dollars, according to the United Nations COMTRADE database on international trade.

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

Invasive species - American bullfrog (Lithobates catesbeianus) in Flanders, Belgium is a species occurrence dataset published by the Research Institute for Nature and Forest (INBO). The dataset contains over 7,500 occurrences (25% of which are American bullfrogs) sampled between 2010 until now, in the months April to October. The data are compiled from different sources at the INBO, but most of the occurrences were collected through fieldwork for the EU co-funded Interreg project INVEXO (http://www.invexo.eu). In this project, research was conducted on different methods for the management of American bullfrog populations, an alien invasive species in Belgium. Captured bullfrogs were almost always removed from the environment and humanely killed, while the other occurrences are recorded bycatch, which were released upon catch (see bibliography for detailed descriptions of the methods). Therefore, caution is advised when using these data for trend analysis, distribution range calculation, or other. Issues with the dataset can be reported at https://github.com/LifeWatchINBO/data-publication/tree/master/datasets/invasive-bullfrog-occurrences We strongly believe an open attitude is essential for tackling the IAS problem (Groom et al. 2015). To allow anyone to use this dataset, we have released the data to the public domain under a Creative Commons Zero waiver (http://creativecommons.org/publicdomain/zero/1.0/). We would appreciate it however if you read and follow these norms for data use (http://www.inbo.be/en/norms-for-data-use) and provide a link to the original dataset (https://doi.org/10.15468/2hqkqn) whenever possible. If you use these data for a scientific paper, please cite the dataset following the applicable citation norms and/or consider us for co-authorship. We are always interested to know how you have used or visualized the data, or to provide more information, so please contact us via the contact information provided in the metadata, opendata@inbo.be or https://twitter.com/LifeWatchINBO.