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

LAC is the most water-rich region in the world by most metrics; however, water resource distribution throughout the region does not correspond demand. To understand water risk throughout the region, this dataset provides population and land area estimates for factors related to water risk, allowing users to explore vulnerability throughout the region to multiple dimensions of water risk. This dataset contains estimates of populations living in areas of water stress and risk in 27 countries in Latin America and the Caribbean (LAC) at the municipal level. The dataset contains categories of 18 factors related to water risk and 39 indices of water risk and population estimates within each with aggregations possible at the basin, state, country, and regional level. The population data used to generate this dataset were obtained from the WorldPop project 2020 UN-adjusted population projections, while estimates of water stress and risk come from WRI’s Aqueduct 3.0 Water Risk Framework. Municipal administrative boundaries are from the Database of Global Administrative Areas (GADM). For more information on the methodology users are invited to read IADB Technical Note IDB-TN-2411: “Scarcity in the Land of Plenty”, and WRIs “Aqueduct 3.0: Updated Decision-relevant Global Water Risk Indicators”.

dataplor's location intelligence dataset for Central America, South America & the Caribbean covers 45M+ locations across 15 countries and territories, making it one of the most comprehensive global datasets available. Our datasets are particularly valued for updates at weekly increments. dataplor's Point of Interest and mobility datasets are used by businesses worldwide to enhance their operational strategies, including and not limited to the following use cases:

• Third-party logistics / order fulfillment
• Consumer Product Goods (CPGs)
• Telecommunication
• Finance and Investment
• Audience Extension
• Store Location Data
• Real Estate Intelligence
• Audience Targeting Data
• Travel Booking Data

Temporally-Rich: Our listings data covers up to 20 years of historical business insights.

Up-To-Date: At dataplor, we're transparent with our customers - we update our datasets weekly or monthly for countries which customers are subscribed; and undergoes our home-grown proprietary QA/QC system designed by PhD data scientists - which is the keystone of dataplor's competitive edge; enabling dataplor to be the highest ranked POI provider for accuracy in the industry.

Point of Interest datasets may be purchased by country.

This data set consists of records for over 900 mineral facilities in Latin America and Canada. The mineral facilities include mines, plants, smelters, or refineries of aluminum, cement, coal, copper, diamond, gold, iron and steel, nickel, platinum-group metals, salt, and silver, among others. Records include attributes such as commodity, country, location, company name, facility type and capacity if applicable, and generalized coordinates. The data were compiled from multiple sources, including the 2003 and 2004 USGS Minerals Yearbooks (Latin America and Candada volume), data to be published in the 2005 Minerals Yearbook Latin America and Canada Volume, minerals statistics and information from the USGS minerals information Web site (http://minerals.usgs.gov/minerals/), and data collected by USGS minerals information country specialists. Data reflect the most recent published table of industry structure for each country. Other sources include statistical publications of individual countries, annual reports and press releases of operating companies,and trade journals. Due to the sensitivity of some energy commodity data, the quality of these data should be evaluated on a country-by-country basis. Additional information and explanation is available from the country specialists.

Citation: Title: Mineral Operations of Latin America and CanadaCredits: Rachel Bernstein,J.M. Eros,Meliany Quinatana-VelazquezPublication Date: 2006Publisher: U.S. Geological SurveyOnline Linkages: http://mrdata.usgs.gov/Larger Works:Title: Mineral Facilities of Latin America and CanadaCredits: Rachel Bernstein, J.M. Eros, Meliany Quinatana-VelazquezPublication Date: 2006Publisher: U.S. Geological SurveyOnline Linkages: http://pubs.usgs.gov/of/2006/1375

('This layer package was loaded using Data Basin..',)('Click here to go to the detail page for this layer package in Data Basin',), where you can find out more information, such as full metadata, or use it to create a live web map.

This dataset displays the roads in North and South America in a linear format. This shapefile data layer is comprised of 72099 derivative vector framework library features derived based on 1:3 000 000 data originally from RWDBII. The layer provides nominal analytical/mapping at 1:3 000 000. Data processing complete globally. Data Source: http://www.fao.org/geonetwork/srv/en/metadata.show?id=29044&currTab=simple Access Date: October 16, 2007 Notes: Please visit the previous link for more information regarding this particular dataset. This map is a portion of entire world map.

The major source of geo-referenced soil data of Latin America and the Caribbean at a scale of 1:5 M is the Soil Map of the World (SMW) of FAO/Unesco (1974-1981). For this part of the globe the information was collected before 1974, the year of publication of the Latin American map sheets. Collection of soil survey information by the national institutes responsible for soil survey continued after publication and a large amount of new data is available at the national level. Since 1991 the FAO is actualizing the SMW information of Latin America with support from national soils institutes in the concerned countries. This has resulted in the acquisition of new 1:5 M soil maps of most Latin American countries. New soil maps with a revised soil classification legend (FAO, 1990) of Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay and Venezuela were obtained by FAO through subcontracts with the national soil institutes. Since 1988 the World Soils and Terrain Database Programme (SOTER) is operational in various Latin American countries at a scale of 1:1 M, in particular in Argentina, Brazil and Uruguay with UNEP funding. The major objective of the SOTER methodology is to use information technology, like geographic information systems and database management systems, for the creation of a world soils and terrain database with digital maps and attributes and their interpretations. At the moment SOTER databases at scale 1:1 M are available for the whole of Uruguay, the northern part of Argentina (460,000 km2) and the south of Brazil (100,000 km2). In 1992 FAO formally endorsed SOTER and decided to use the methodology to store and update soils and terrain data at a global level. The SOTER Procedures Manual was published jointly by FAO, ISRIC, ISSS and UNEP in 1993 and in the following year also as No. 74 in the series of World Soil Resources Bulletins. During the same year an agreement was signed between FAO and UNEP to develop a soils and terrain database of Latin America at scale 1:5 M, jointly with the updating of the SMW. ISRIC was asked to coordinate the activities of the SOTERLAC 1:5 M project in the countries to be included.

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.

Analysis of ‘Bank Credit Allocation in Latin America and the Caribbean’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/search/dataset/0041062/ on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Despite their importance, data on the structure of bank credit by maturity are scarce. For Latin America and the Caribbean, data are particularly difficult to obtain, as few banks report loan maturity data in commercial data sets such as Bankscope. With support from the Association of Supervisors of Banks of the Americas, this study assembled a novel data set on the structure of bank credit allocation in Latin America and the Caribbean covering 21 countries during 2004-14. This paper uses Bankscope and International Financial Statistics data to extended the coverage to more than 100 countries, creating the largest data set so far on credit by maturity. Benchmarking credit structure in Latin America and the Caribbean, the paper finds that the region is financially underdeveloped, because the ratio of short-term credit to gross domestic product is lower than in peers; long-term credit is at par; and consumer and commercial loans are lower.

--- Original source retains full ownership of the source dataset ---

Techsalerator’s Job Openings Data in Latin America provides a thorough and insightful dataset designed to deliver businesses, recruiters, labor market analysts, and job seekers with a comprehensive view of employment opportunities across the Latin American region. This dataset aggregates job postings from a diverse array of sources on a daily basis, ensuring that users have access to the most current and extensive collection of job openings available throughout Latin America.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates job postings from a variety of sources, including company career sites, job boards, recruitment agencies, and professional networking platforms. This comprehensive coverage ensures that users receive a broad spectrum of job opportunities from multiple channels. Daily Updates:

Data is updated daily, providing real-time insights into job market conditions. This frequent updating ensures that the dataset reflects the latest job openings and market trends. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, manufacturing, and more. This segmentation allows users to analyze trends and opportunities within specific industries. Regional Breakdown:

Detailed information is provided on job openings across different countries and key regions within Latin America. This regional breakdown helps users understand job market dynamics and opportunities in various geographic areas. Role and Skill Analysis:

The dataset includes information on job roles, required skills, qualifications, and experience levels. This feature assists job seekers in identifying opportunities that match their expertise and helps recruiters find candidates with the desired skill sets. Company Insights:

Users can access information about the companies posting job openings, including company names, industries, and locations. This data provides insights into which companies are hiring and where demand for talent is highest. Historical Data:

The dataset may include historical job posting data, enabling users to perform trend analysis and comparative studies over time. This feature supports understanding changes and developments in the job market. Latin American Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking, but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Recruitment: Recruiters and HR professionals can use the data to identify hiring trends, understand competitive practices, and optimize their recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, enhancing the efficiency and effectiveness of their job search. Workforce Planning: Companies can gain valuable insights into the availability of talent across Latin America, assisting with decisions related to market entry, expansion, and talent acquisition. Techsalerator’s Job Openings Data in Latin America is an essential tool for understanding the diverse and evolving job markets across the region. By providing up-to-date and detailed information on job postings, it supports effective decision-making for businesses, job seekers, and labor market analysts.

This database presents the largest dataset of Chilean wild bees (Apoidea) with 36,010 records from 167 species, comprising over 40 years of data. This collection is held by the Pontificia Universidad Catolica de Valparaiso, being the largest in the country. Prof. Haroldo Toro started this collection and prof. Luisa Ruz continued his legacy. A first effort to digitize this information was made in 2008 through the IABIN project that gathered pollinator datasets from Latin America, but when the funding was over, the database became offline. Now, in the frame of the SURPASS2 project, we recovered the PUCV wild bee collection, updated the taxonomy, estimated geographic coordinates for most of the records, and standardized it to DarwinCore to make it freely available through GBIF. This endeavor aims to open this natural legacy to everyone and encourage more research in this field.

This statistic shows a ranking of the estimated per capita consumer spending on healthcare in 2020 in Latin America and the Caribbean, differentiated by country. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 06. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.The shown forecast is adjusted for the expected impact of the COVID-19 pandemic on the local economy. The impact has been estimated by considering both direct (e.g. because of restrictions on personal movement) and indirect (e.g. because of weakened purchasing power) effects. The impact assessment is subject to periodic review as more data becomes available.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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Mature Support Notice: This item is in mature support as of July 2021. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map presents transportation data, including highways, roads, railroads, and airports for the world.The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.You can add this layer on top of any imagery, such as the Esri World Imagery map service, to provide a useful reference overlay that also includes street labels at the largest scales. (At the largest scales, the line symbols representing the streets and roads are automatically hidden and only the labels showing the names of streets and roads are shown). Imagery With Labels basemap in the basemap dropdown in the ArcGIS web and mobile clients does not include this World Transportation map. If you use the Imagery With Labels basemap in your map and you want to have road and street names, simply add this World Transportation layer into your map. It is designed to be drawn underneath the labels in the Imagery With Labels basemap, and that is how it will be drawn if you manually add it into your web map.

We present the GLOBAL ROADKILL DATA, the largest worldwide compilation of roadkill data on terrestrial vertebrates. We outline the workflow (Fig. 1) to illustrate the sequential steps of the study, in which we merged local-scale survey datasets and opportunistic records into a unified roadkill large dataset comprising 208,570 roadkill records. These records include 2283 species and subspecies from 54 countries across six continents, ranging from 1971 to 2024.Large roadkill datasets offer the advantage ofpreventing the collection of redundant data and are valuable resources for both local and macro-scale analyses regarding roadkill rates, road and landscape features associated with roadkill risk, species more vulnerable to road traffic, and populations at risk due to additional mortality. The standardization of data - such as scientific names, projection coordinates, and units - in a user-friendly format, makes themreadily accessible to a broader scientific and non-scientific community, including NGOs, consultants, public administration officials, and road managers. The open-access approach promotes collaboration among researchers and road practitioners, facilitating the replication of studies, validation of findings, and expansion of previous work. Moreover, researchers can utilize suchdatasets to develop new hypotheses, conduct meta-analyses, address pressing challenges more efficiently and strengthen the robustness of road ecology research. Ensuring widespreadaccess to roadkill data fosters a more diverse and inclusive research community. This not only grants researchers in emerging economies with more data for analysis, but also cultivates a diverse array of perspectives and insightspromoting the advance of infrastructure ecology.MethodsInformation sources: A core team from different continents performed a systematic literature search in Web of Science and Google Scholar for published peer-reviewed papers and dissertations. It was searched for the following terms: “roadkill* OR “road-kill” OR “road mortality” AND (country) in English, Portuguese, Spanish, French and/or Mandarin. This initiative was also disseminated to the mailing lists associated with transport infrastructure: The CCSG Transport Working Group (WTG), Infrastructure & Ecology Network Europe (IENE) and Latin American & Caribbean Transport Working Group (LACTWG) (Fig. 1). The core team identified 750 scientific papers and dissertations with information on roadkill and contacted the first authors of the publications to request georeferenced locations of roadkill andofferco-authorship to this data paper. Of the 824 authors contacted, 145agreed to sharegeoreferenced roadkill locations, often involving additional colleagues who contributed to data collection. Since our main goal was to provide open access to data that had never been shared in this format before, data from citizen science projects (e.g., globalroakill.net) that are already available were not included.Data compilation: A total of 423 co-authors compiled the following information: continent, country, latitude and longitude in WGS 84 decimal degrees of the roadkill, coordinates uncertainty, class, order, family, scientific name of the roadkill, vernacular name, IUCN status, number of roadkill, year, month, and day of the record, identification of the road, type of road, survey type, references, and observers that recorded the roadkill (Supplementary Information Table S1 - description of the fields and Table S2 - reference list). When roadkill data were derived from systematic surveys, the dataset included additional information on road length that was surveyed, latitude and longitude of the road (initial and final part of the road segment), survey period, start year of the survey, final year of the survey, 1st month of the year surveyed, last month of the year surveyed, and frequency of the survey. We consolidated 142 valid datasets into a single dataset. We complemented this data with OccurenceID (a UUID generated using Java code), basisOfRecord, countryCode, locality using OpenStreetMap’s API (https://www.openstreetmap.org), geodeticDatum, verbatimScientificName, Kingdom, phylum, genus, specificEpithet, infraspecificEpithet, acceptedNameUsage, scientific name authorship, matchType, taxonRank using Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters) and link of the associatedReference (URL).Data standardization - We conducted a clustering analysis on all text fields to identify similar entries with minor variations, such as typos, and corrected them using OpenRefine (http://openrefine.org). Wealsostandardized all date values using OpenRefine. Coordinate uncertainties listed as 0 m were adjusted to either 30m or 100m, depending on whether they were recorded after or before 2000, respectively, following the recommendation in the Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters).Taxonomy - We cross-referenced all species names with the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy using Java and GBIF’s API (https://doi.org/10.15468/39omei). This process aimed to rectify classification errors, include additional fields such as Kingdom, Phylum, and scientific authorship, and gather comprehensive taxonomic information to address any gap withinthe datasets. For species not automatically matched (matchType - Table S1), we manually searched for correct synonyms when available.Species conservation status - Using the species names, we retrieved their conservation status and also vernacular names by cross-referencing with the database downloaded from the IUCNRed List of Threatened Species (https://www.iucnredlist.org). Species without a match were categorized as "Not Evaluated".Data RecordsGLOBAL ROADKILL DATA is available at Figshare27 https://doi.org/10.6084/m9.figshare.25714233. The dataset incorporates opportunistic (collected incidentally without data collection efforts) and systematic data (collected through planned, structured, and controlled methods designed to ensure consistency and reliability). In total, it comprises 208,570 roadkill records across 177,428 different locations(Fig. 2). Data were collected from the road network of 54 countries from 6 continents: Europe (n = 19), Asia (n = 16), South America (n=7), North America (n = 4), Africa (n = 6) and Oceania (n = 2).(Figure 2 goes here)All data are georeferenced in WGS84 decimals with maximum uncertainty of 5000 m. Approximately 92% of records have a location uncertainty of 30 m or less, with only 1138 records having location uncertainties ranging from 1000 to 5000 m. Mammals have the highest number of roadkill records (61%), followed by amphibians (21%), reptiles (10%) and birds (8%). The species with the highest number of records were roe deer (Capreolus capreolus, n = 44,268), pool frog (Pelophylax lessonae, n = 11,999) and European fallow deer (Dama dama, n = 7,426).We collected information on 126 threatened species with a total of 4570 records. Among the threatened species, the giant anteater (Myrmecophaga tridactyla, VULNERABLE) has the highest number of records n = 1199), followed by the common fire salamander (Salamandra salamandra, VULNERABLE, n=1043), and European rabbit (Oryctolagus cuniculus, ENDANGERED, n = 440). Records ranged from 1971 and 2024, comprising 72% of the roadkill recorded since 2013. Over 46% of the records were obtained from systematic surveys, with road length and survey period averaging, respectively, 66 km (min-max: 0.09-855 km) and 780 days (1-25,720 days).Technical ValidationWe employed the OpenStreetMap API through Java todetect location inaccuracies, andvalidate whether the geographic coordinates aligned with the specified country. We calculated the distance of each occurrence to the nearest road using the GRIP global roads database28, ensuring that all records were within the defined coordinate uncertainty. We verified if the survey duration matched the provided initial and final survey dates. We calculated the distance between the provided initial and final road coordinates and cross-checked it with the given road length. We identified and merged duplicate entries within the same dataset (same location, species, and date), aggregating the number of roadkills for each occurrence.Usage NotesThe GLOBAL ROADKILL DATA is a compilation of roadkill records and was designed to serve as a valuable resource for a wide range of analyses. Nevertheless, to prevent the generation of meaningless results, users should be aware of the followinglimitations:- Geographic representation – There is an evident bias in the distribution of records. Data originatedpredominantly from Europe (60% of records), South America (22%), and North America (12%). Conversely, there is a notable lack of records from Asia (5%), Oceania (1%) and Africa (0.3%). This dataset represents 36% of the initial contacts that provided geo-referenced records, which may not necessarily correspond to locations where high-impact roads are present.- Location accuracy - Insufficient location accuracy was observed for 1% of the data (ranging from 1000 to 5000 m), that was associated with various factors, such as survey methods, recording practices, or timing of the survey.- Sampling effort - This dataset comprised both opportunistic data and records from systematic surveys, with a high variability in survey duration and frequency. As a result, the use of both opportunistic and systematic surveys may affect the relative abundance of roadkill making it hard to make sound comparisons among species or areas.- Detectability and carcass removal bias - Although several studies had a high frequency of road surveys,the duration of carcass persistence on roads may vary with species size and environmental conditions, affecting detectability. Accordingly, several approaches account for survey frequency and target speciesto estimate more

Introduction

Welcome to the Native American Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 3,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Dataset Updates &

• Attention and interest on international issues related to different countries.
• Most important objectives for Spain in international politics.
• Image of the Ibero-American countries in Spain and for the interviewee.
• Spain's relations with Latin America in economic, political, cultural, scientific/technical and sporting aspects.
• Most important objectives for Spain in international policy with Latin America.
• Similarities, common interests and union in the future between Spain and Latin American or European countries.
• Knowledge of the annual celebration of the Ibero-American Summit.
• Importance of the Ibero-American Summit for the countries of Latin America and for Spain.
• Agreement with various statements on the Ibero-American Summits: They strengthen political and economic ties, poor practical results, boost cooperation, and recent summits have lost relevance.
• Main problems that currently exist in the world. Opinion on the cooperation of Spain to the development of other peoples and the role of the State in international aid and cooperation.
• Countries to which Spanish development cooperation is directed and to which it should be directed.
• Evaluation of the resources that Spain dedicates to international cooperation for development and knowledge of the reduction of funds in recent years due to the crisis. Knowledge of the objective of industrialized countries to devote 0.7% of their GDP to help less developed countries. Opinion on whether Spain should dedicate 0.7% of its GDP. Knowledge of the percentage of Spanish GDP that is dedicated to helping less developed countries.
• Important aspects in development cooperation: human rights, health, indigenous peoples, poverty,... Knowledge of the United Nations Millennium Goals. Likelihood of achieving the various Goals. Knowledge of the resources that your Autonomous Community or City Council dedicates to cooperation with developing countries. Opinion on whether they should devote part of their resources.
• Agreement on the form of financing of NGOs.
• Most important task to be performed by NGDOs. Composition of NGOs to make their work effective.
• Participation in the interviewee's development cooperation.
• Box marked for allocation in the Income Statement.
• Influence on relations between Spain and Latin America of Latin American immigrants.
• Main cause of the immigration that Spain receives. Changes in the image of Latin America due to the presence of Latin American immigrants and the degree of agreement with a series of opinions about them.
• Beneficiary of the work done by immigrants from less developed countries.
• Agreement that there would be fewer immigrants if cooperation between countries were greater.
• Effect of the economic situation on immigrant and Spanish workers.
• Opinions on Spanish immigration policy and changes that should be introduced.
• Knowledge of Spanish companies with interests in Latin America. Investment.
• Effect of the performance of Spanish companies investing in Latin America has on the image of Spain.
• Equal performance of Spanish companies in Spain and Latin America.
• The Spanish Government should encourage greater involvement of Spanish companies abroad.
• Opinion on the action of Spanish companies in Latin America Larina in environmental and social matters
• Effect of the performance of Spanish companies investing in Latin America has on relations between Spain and these countries.
• Opinion on who benefits from the presence of Spanish companies in Latin America.
• Ideological self-location scale (1-10).
• Electoral participation in the 2008 general elections.
• Religious practice.

The data consist of transcripts of interviews with 19 individuals from Brazil and 5 individuals from Colombia, who are all involved in Black and Indigenous activist organisations or in state agencies that are charged with promoting anti-racism and/or human rights. Each transcript begins with a paragraph giving contextual informationLatin America has often been held up as a region where racism is less of a problem than in regions such as the United States or Europe. Because most people are 'mestizos' (mixed race) and mixture is often seen as the essence of national identity, clear racial boundaries are blurred, resulting in comparatively low levels of racial segregation and a traditionally low public profile for issues of race. In Europe and the United States, the racial mixture and interaction across racial boundaries, which are typical of Latin America and are becoming more visible elsewhere, are heralded by some observers as leading towards a 'post-racial' reality, where anti-racism and multiculturalism - seen in this view as divisive policies that accentuate social differences - become unnecessary. Critics point out that mixture is not an antidote to racial inequality and racism in Latin America: they all coexist. This severely qualifies claims that mixture can lead to a 'post-racial' era. This project will investigate anti-racist practices and ideologies in Bolivia, Brazil, Colombia and Mexico. The project will contribute to conceptualising and addressing problems of racism, racial inequality and anti-racism in the region. We also propose that Latin America presents new opportunities for thinking about racism and anti-racism in a 'post-racial' world. Understanding how racism and anti-racism are conceived and practised in Latin America - in contexts in which mixture is pervasive - can help us to understand how to think about racism and anti-racism in other regions of the world, where notions of race have been changing in some respects towards Latin American patterns. It is also crucial to show the variety of ways in which mixture operates and co-exists with racism in Latin America - a region that is far from homogeneous. Research teams in each country, working with a range of organisations concerned with racism and discrimination, will explore how the organisations conceptualise and address key problems, which are becoming more salient in other regions, which confront similar scenarios. First, how to practice anti-racism when most people are mixed and when they may deny the importance of race and racism and themselves be both victims and the perpetrators of racism. Second, how to conceptualise and practice anti-racism when 'culture' seems to be the dominant discourse for talking about difference, but when physical difference (skin colour, hair type, etc.) remain powerful but often unacknowledged signs that move people to discriminate. Third, how to understand racism and combat it when race and class coincide to a great extent and make it easy to deny that race and racism are important factors. Fourth, how to make sure anti-racism addresses gender difference effectively, in a context in which mixture between white men and non-white women has been seen as the founding act of the nation. Fifth, how to pursue anti-racism when it is often claimed that there is little overt racist violence and that this is evidence of racial tolerance. We will explore how these elements structure - and may constrain - ideas about (anti-)racism within institutions, organisations and everyday practice. Our project will work with organisations in Bolivia, Brazil, Colombia and Mexico - countries that capture a good range of the region's diversity - to explore how racism and anti-racism are conceptualised and addressed in state and non-state circles, in legislation and the media, and in a variety of campaigns and projects. We aim to strengthen anti-racist practice in Latin America by feeding back our findings and by helping build networks; and to provide useful insights for understanding racism and anti-racism within and outside the region. The project carried out research in four countries, Brazil, Colombia, Ecuador and Mexico. We started by scoping out a broad range of organizations and individuals who were working in a direct or indirect fashion to challenge racism and racial inequality. We then selected seventeen case studies (over a third of which were Indigenous), with which we worked in depth, while also touching on about twenty other cases in a less intensive way. The cases were selected in order to include both Black and Indigenous organisations and cases, and to include a range of cases from government bodies to grassroots activist movements, plus some legal processes in which a variety of actors and organizations were involved. Our methods were mainly ethnography and interviews, undertaken principally by the four postdoctoral researchers, each of whom worked in one country. Some interviews were done with the assistance of a research assistant hired in the country. The interviews were conducted mostly in 2017, with some in 2018, in localities appropriate to the case study, such as an organization’s offices, an individual’s residence, or an agreed neutral location (e.g. a café, a village square, a classroom). Some interviews were informal conservations, but most were at least semi-structured. Common interview guides were not used, as each interview was specific to the case in question. Many interviews were audio-recorded (some were video-recorded) and selected interviews were transcribed in full or in part. Files with the original audio recordings and the transcripts are stored on a secure server in the University of Manchester. The files uploaded here are a selection of the transcribed interviews.

Metadata: Title: Rainfall Erosivity in the WorldDescription: This map provides a complete rainfall erosivity dataset for the whole World based on 3625 precipitation stations and around 60,000 years of rainfall records at high temporal resolution (1 to 60 minutes). Gaussian Process Regression(GPR) model was used to interpolate the rainfall erosivity values of single stations and to generate the R-factor map. In addition, we explore an approach to derive an R-factor based on satellite data.Spatial coverage: WorldPixel size: 30 arc-seconds (~1 km at the Equator).Measurement Unit: MJ mm ha-1 h-1 yr-1Projection: ETRS89 Lambert Azimuthal Equal AreaTemporal coverage: 30-40 years - Predominant in the last decade: 2000 - 2010 Global R-factor The purpose of this study is to assess rainfall erosivity inthe World in the form of the RUSLE R-factor, based on the best available datasets in the Globe. We used the Global Rainfall Erosivity Database (GloREDa) which contains 3,625 precipitation stations from 63 countires in the Globe with temporal resolutions of 1 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 52 years. The average time series per precipitation station is around 16.8 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 30 arc-seconds (~1 km at the Equator). Globally, the mean rainfall erosivity is estimated to be 2,190 MJ mm ha-1 h-1 yr-1 and broadly reflects climatic patterns, with the highest values, (which are 3 three times highergreater than the mean) are found in South America (especially around the Amazon Basin) and the Caribbean countries, Central and parts of east Western Africa and South East Asia. The lowest values are mainly found in mid and high latitude regions such as Canada, the Russian Federation, Northern Europe, Northern Africa, the and Middle East and southern Australia. It should be noted that high rainfall erosivity does not necessarily mean high erosion as factors such as soil characteristics, vegetative cover and land use are also important factors.The new global erosivity map is a critical input to global and continental assessments of soil erosion by water, flood risk and natural hazard prevention. Current global estimates of soil erosion by water are very uncertain, ranging over one order of magnitude (from around 20 to over 200 Pg per year). More accurate global predictions of rill and interrill soil erosion rates can only be achieved when the rainfall erosivity factor is thoroughly computed. GloREDa: Global Rainfall Erosivity Database At global scale, this is the first time ever that an erosivity database of such dimension is compiled. The Global Rainfall Erosivity Database, named hereafter as GloREDa, contains erosivity values estimated as R-factors (refer to the method section) from 3,625 stations distributed in 63 countries worldwide. This is the result of an extensive data collection of high temporal resolution rainfall data from the maximum possible number of countries in order to have a representative sample across different climatic and geographic gradients. GloREDa has three components, which are described in the relevant publication: The Rainfall Erosivity database at European Scale (REDES) 1,865 stations from 23 countries outside Europe (Australia, New Zealand, South Korea, Japan, China, India, Malaysia, Iran, Kuwait, Israel, Turkey, Russian Federation, United States of America, Mexico, Costa Rica, Jamaica, Colombia, Suriname, Chile, Brazil, Algeria, South Africa, Mauritius). 85 stations collected from a literature review (12 countries) The number of GloREDa stations varied greatly among continents. Europe had the largest contribution to the dataset, with 1,725 stations (48% of total), while South America had the lowest number of stations (141 stations or ~4% of total). Africa has very low density of GloREDa stations (5% of the total). In North America and the Caribbean, we collected erosivity values from 146 stations located in 6 countries (Unites States, Canada, Mexico, Cuba, Jamaica and Costa Rica). Finally, Asia and the Middle East were well represented in GloREDa, with 1,220 stations (34% of the total) distributed in 10 countries including the Siberian part of the Russian Federation, China, India, Japan. GloREDa Database: You can also download the updated GloREDa 1.2 includes measured erosivity (R-factor) data for 3939 stations. In addition, we added the monthly component to GloREDa and we calculated the mean monthly R-factor per station. For 94% (3702 stations) of GloREDa, it was possible to add the monthly R-factor values summarizing 44,424 monthly records. This also includes the he derived twelve (12) global monthly erosivity maps. Additional - derived datasets The Global R-factor data and the poin data have contributed to develop additional datasets such as a) Satellite-based R-factor b) Global assessment of storm disaster-prone areas. a) Satellite-based R-factor In addition, we developed two alternatives for erosivity map based on a) satellite-based rainfall data and b) erosivity density concept. We used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity.The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. The global erosivity map and the satellite derived one are publicly available and can be used by other research groups to perform national, continental and global soil erosion modelling. b) Global assessment of storm disaster-prone areas Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. By using measured Ranfall Erosivity Density (RED) for 3,625 raingauges worldwide and applying kriging methodologies, we identify the damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 hm-2 h-1 yr-1, respectively). We have analysed for the first time the spatial pattern of hydrological hazard associated with rainfall erosivity in a global-scale visualisation. The results indicated that about 31% and 19% of the world’s land area have a greater than 50% probability of exceeding the warning and alert thresholds of 1.5 and 3.0 hm-2 h-1 yr-1, respectively. Data The Global erosivity map (GeoTIFF format) at 30 arc-seconds (~1 km) resolution is available for free download in the European Soil Data Centre (ESDAC). The calculated erosivity values per station in GloREDa will become available in the future pending on the agreed copyright issues with data providers. We also share the recenlty developed global erosivity maps based on satellite high resolution temporal data (CMORPH) and the erosivity density concept. GloREDa calcualted erosivity values can be shared in case of scientific collaborations. The point measured data for 3,625 stations can be requested from contact author (for scientific developments). GloREDa has contributed in developing the Global Rainfall Projections for 2050 and 2070. To get access to the all datasets and the code, please compile the request form ; instructions will then follow how to download the datasets. More information about Global Rainfall erosivity in the corresponding section. References A complete description of the methodology and the application in World is described in the paper:Panagos P., Borrelli P., Meusburger K., Yu B., Klik A., Lim K.J., Yang J.E, Ni J., Miao C., Chattopadhyay N., Sadeghi S.H., Hazbavi Z., Zabihi M., Larionov G.A., Krasnov S.F., Garobets A., Levi Y., Erpul G., Birkel C., Hoyos N., Naipal V., Oliveira P.T.S., Bonilla C.A., Meddi M., Nel W., Dashti H., Boni M., Diodato N., Van Oost K., Nearing M.A., Ballabio C., 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports 7: 4175. DOI: 10.1038/s41598-017-04282-8. GloREDa reference: Panagos, P., Hengl, T., Wheeler, I., Marcinkowski, P., Rukeza, M.B., Yu, B., Yang, J.E., Miao, C., Chattopadhyay, N., Sadeghi, S.H. and Levi, Y., et al. 2023. Global Rainfall Erosivity database (GloREDa) and monthly R-factor data at 1km spatial resolution. Data in Brief, 50, Art.no.109482. DOI: 10.1016/j.dib.2023.109482

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

BackgroundCaesarean section (CS) rates continue to evoke worldwide concern because of their steady increase, lack of consensus on the appropriate CS rate and the associated additional short- and long-term risks and costs. We present the latest CS rates and trends over the last 24 years.MethodsWe collected nationally-representative data on CS rates between 1990 to 2014 and calculated regional and subregional weighted averages. We conducted a longitudinal analysis calculating differences in CS rates as absolute change and as the average annual rate of increase (AARI).ResultsAccording to the latest data from 150 countries, currently 18.6% of all births occur by CS, ranging from 6% to 27.2% in the least and most developed regions, respectively. Latin America and the Caribbean region has the highest CS rates (40.5%), followed by Northern America (32.3%), Oceania (31.1%), Europe (25%), Asia (19.2%) and Africa (7.3%). Based on the data from 121 countries, the trend analysis showed that between 1990 and 2014, the global average CS rate increased 12.4% (from 6.7% to 19.1%) with an average annual rate of increase of 4.4%. The largest absolute increases occurred in Latin America and the Caribbean (19.4%, from 22.8% to 42.2%), followed by Asia (15.1%, from 4.4% to 19.5%), Oceania (14.1%, from 18.5% to 32.6%), Europe (13.8%, from 11.2% to 25%), Northern America (10%, from 22.3% to 32.3%) and Africa (4.5%, from 2.9% to 7.4%). Asia and Northern America were the regions with the highest and lowest average annual rate of increase (6.4% and 1.6%, respectively).ConclusionThe use of CS worldwide has increased to unprecedented levels although the gap between higher- and lower-resource settings remains. The information presented is essential to inform policy and global and regional strategies aimed at optimizing the use of CS.