Industrial Production in Mexico decreased 2.40 percent in September of 2025 over the same month in the previous year. This dataset provides - Mexico Industrial Production - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This is an update of the dataset documented in: Andrew, R.M., 2019. Global CO2 emissions from cement production, 1928–2018. Earth System Science Data 11, 1675–1710. https://doi.org/10.5194/essd-11-1675-2019. Data in this release cover the period 1880–2020. Note that emissions from use of fossil fuels in cement production are not included in this dataset since they are usually included elsewhere in global datasets of fossil CO2 emissions. The process emissions in this dataset, which result from the decomposition of carbonates in the production of cement clinker, amounted to ~1.6 Gt CO2 in 2020, while emissions from combustion of fossil fuels to produce the heat required amounted to an additional ~0.9 Gt CO2 in 2020. July 2021 release (210723): Major changes Updated to data releases from USGS published in 2021. Additionally updated through 2020 with country-specific data for Afghanistan, China, Egypt, India, Iran, Jamaica, Japan, Saudi Arabia, South Korea, and Viet Nam. The Cement Production dataset Cement production data by country are primarily derived from USGS statistics. The construction of this dataset begins with production back-calculated from CDIAC's 2019 edition cement emissions data, which are a direct function of cement production (from the 2020 edition CDIAC has changed its methodology). Then using available data for some former Soviet states before the dissolution of the Soviet Union, Soviet states are disaggregated for all years before dissolution. Data obtained directly from USGS are used to overwrite from 1990 onwards, with a small number of additional corrections. Countries for which cement production is not available in the most recent years are extrapolated simply. Finally, country-specific cement production data are overwritten for the following countries: USA, China, India, Norway, Sweden, Iran, Saudi Arabia, South Korea, Jamaica, Moldova, Mexico, Namibia, Afghanistan, Argentina, Egypt. Note that many zeros in the cement production dataset are propagated from CDIAC and should probably be NODATA. The approach used for each country is summarised in the file "6. cement_production_method.csv". Emissions calculation Emissions for all UNFCCC Annex I countries ("developed" countries) are derived from their official submissions to the UNFCCC in Common Reporting Format (structured Excel files), for which data are available from 1990 (slightly earlier for some Economies in Transition). For non-Annex I countries clinker ratios derived from the Getting the Numbers Right (GNR) cement sustainability initiative are applied to the cement production dataset to derive approximate clinker production by country, from which emissions are calculated using IPCC default factors. Country-specific methods are used for China, India, Japan, Turkey, USA. The combined_cement_data.xlsx file is used to overwrite emissions with superior data, in most cases as reported in official reporting to the UNFCCC, e.g. Biennial Update Reports, National Communications, and National Inventory Reports. Some countries do not report time-series of emissions, but do supply some isolated estimates in their official reporting to the UNFCCC, and these are used in some cases to constrain estimates. A number of countries state in their official reporting to the UNFCCC that they have never produced clinker, so emissions are set to zero for all years for these countries. In other cases, statements are made that no clinker was produced before a certain year, and this information is also incorporated. The information available usually covers a number of years, up to 3 decades. These are then extrapolated by combining available data and assumptions about historical developments in clinker ratios to produce longer time series of emissions based on the longer cement production dataset. More detail on this method are given in the accompanying journal paper.

This data set contains five data files (.txt format). Three data files provide net primary productivity (NPP) estimates for a tropical dry deciduous forest within the 3,300-ha Chamela Biological Station, Mexico. There is one file for each of the three permanent watershed plots located along an elevational gradient from 60 to 160-m above sea level. NPP was estimated from field measurements obtained during wet and dry seasons between 1982 and 1995. A fourth NPP data file provides average nutrient fluxes into and out of five watersheds. The fifth file provides precipitation and minimum/maximum temperature data from measurements obtained onsite. Detailed data are available for above-ground NPP (ANPP) (fine litterfall, wood increment, and leaf herbivory plus an estimation of understory production), and below-ground NPP (BNPP) (fine root production and root increment). Biomass data and nutrient inputs/outputs (P, K, Ca, Mg) averaged from five watersheds are also included in the data set. Estimated ANPP ranged from 611 to 808 g/m2/year between the three sub-sites (average 682 g/m2/year), and total NPP ranged from 1,119 to 1,353 g/m2/year (average 1,206 g/m2/year). These estimates are thought to represent the lower bounds of NPP because root and stem herbivory have not been taken into account, although leaf herbivory is included. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.

Success.ai’s Prospect Data for the Manufacturing Sector in North America provides businesses with a powerful dataset to connect with manufacturers and industry leaders across the United States, Canada, and Mexico. This dataset offers verified contact details, detailed firmographic insights, and business location data for companies in a wide range of manufacturing sectors, including automotive, electronics, consumer goods, industrial equipment, and more.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures that your outreach, market research, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution is ideal for businesses looking to succeed in the dynamic North American manufacturing industry.

Why Choose Success.ai’s Manufacturing Prospect Data?

1. Verified Contact Data for Effective Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of manufacturing executives, plant managers, procurement officers, and engineers.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and optimizing communication efficiency.

2. Regional Focus on North American Manufacturing

   • Includes profiles of manufacturers across key markets such as the U.S., Canada, and Mexico, covering diverse sectors and specialties.
   • Gain insights into regional industry trends, operational practices, and supply chain dynamics unique to North America.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, production expansions, market shifts, and operational improvements.
   • Stay aligned with the rapidly evolving manufacturing sector to identify opportunities and maintain relevance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and compliant use of data for your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers, engineers, and operational leaders driving manufacturing innovation across North America.
• 30M Company Profiles: Access firmographic data, including revenue ranges, workforce sizes, and geographic locations.
• Verified Leadership Contacts: Connect directly with CEOs, COOs, plant managers, and procurement leads shaping manufacturing operations.
• Business Insights: Understand production capacities, supply chain networks, and technology adoption rates.

Key Features of the Dataset:

1. Manufacturing Decision-Maker Profiles

   • Identify and connect with leaders responsible for vendor selection, process optimization, and technology integration.
   • Target professionals making decisions on resource allocation, production planning, and quality control.

2. Firmographic and Geographic Data

   • Access detailed business information, including company sizes, production capacities, and geographic footprints.
   • Pinpoint manufacturing hubs, regional facilities, and distribution centers to enhance supply chain strategies.

3. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (automotive, electronics, consumer goods), geographic location, company size, or revenue range.
   • Align campaigns with specific manufacturing needs, such as sustainability, cost optimization, or digital transformation.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow you to craft personalized messaging, highlight unique value propositions, and improve engagement with manufacturing stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technologies designed to enhance manufacturing efficiency, cost savings, or production quality.
   • Build relationships with procurement managers, operations directors, and plant supervisors.

2. Market Research and Competitive Analysis

   • Analyze trends in North American manufacturing to identify growth opportunities, emerging markets, and industry challenges.
   • Benchmark against competitors to refine product offerings, pricing models, and go-to-market strategies.

3. Supply Chain and Vendor Development

   • Engage with manufacturers seeking reliable suppliers, logistics partners, or raw material sources.
   • Position your company as a strategic partner for supply chain optimization and sustainability initiatives.

4. Technology Integration and Innovation

   • Target R&D professionals and operations leaders exploring robotics, AI, IoT, or automation tools.
   • Offer solutions to support digital transformation, improve production scalability, or enhance workforce productivity.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality manufacturing data at competitive prices, ensuring maximum ROI for your outreach and strategic initiatives....

The transition to renewable energy is crucial for addressing pollution and greenhouse gas emissions from activities like electricity generation and transportation. However, the distribution of energy resources varies geographically and temporally, necessitating measurement and estimation to optimize production. While previous studies have examined renewable resources in isolation or as complementary, this paper uses a scoring system to evaluate renewable energy potential. Focusing on Northern Mexico, the paper assesses solar and wind power resources using data from the Servicio Meteorológico Nacional's automatic weather stations. Wind power density (WPD) was calculated from average wind speeds, and solar irradiance data were processed similarly to derive average values. Interpolation of resources availability was conducted using Inverse Distance Weighting (IDW), normalizing scores based on measured and maximum values. The study area includes Tamaulipas, Nuevo León, Coahuila, Chihuahua, and Sonora. Results show that northern Chihuahua and northwest Sonora have the highest WPD and solar irradiance, with central Nuevo León exhibiting the highest average irradiance. Overall, Chihuahua and Sonora scored highest in energy resource availability. This evaluation provides a valuable basis for policymakers and companies considering renewable energy projects in these regions.

Introduction

Welcome to the Mexican Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Mexican Spanish communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Mexican accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Mexican Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Spanish speech and language AI applications:

Introduction

This Mexican Spanish Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

Description: 📝

The Minerometallurgical Industry Statistics (EIMM) aims to provide reliable and timely indicators of the volume and value of minerometallurgical production. The data characterization extends nationally, by state, and to municipalities with mining activity in Mexico from 2001 to 2023. Both files contained in this dataset are the result of processing various relational tables to generate consolidated data. You can obtain more information about the different tables used by accessing the original source cited in the acknowledgments section.

This dataset consists of 2 files:

• Mining Production by Major States and Municipalities: Contains indicators related to the products and volume of minerometallurgical production by federal entities and municipalities, where mining activity exists.

• Minerometallurgical Production by Main Products: Contains indicators related to the products, volume, and value of national minerometallurgical production.

You can find both files available in both English and Spanish.

Information about each column:

For *Mining Production by Major States and Municipalities:*

• "COVERAGE": Geographic area to which the statistical indicators refer.
• "YEAR": Year corresponding to the information (YYYY).
• "MONTH": Contemplates the reference period of the information (MM).
• "STATE": identifies the federative entity where it is located.
• "MUNICIPALITY": identifies the municipality where it is located.
• "PRODUCT_GROUP": Classification of products according to their natural characteristics.
• "PRODUCT": Name of the mining product.
• "MEASUREMENT_UNIT": Specifies physical volume measurement, in kilograms or tons.
• "VOLUME": Numeric value of the product in a certain period.
• "STATUS": Status of the figures according to the Guidelines for Changes to the information disclosed in the statistical and geographical publications of INEGI.

• "COVERAGE": Geographic area to which the statistical indicators refer.
• "YEAR": Year corresponding to the information (YYYY).
• "MONTH": Contemplates the reference period of the information (MM).
• "PRODUCT_GROUP": Classification of products according to their natural characteristics.
• "PRODUCT": Name of the mining product.
• "MEASUREMENT_UNIT": Specifies physical volume measurement, in kilograms or tons.
• "VOLUME": Volume of production obtained from extraction, beneficiation, and/or refining.
• "VALUE": Monetary value of the minerometallurgical production at current prices (MXN).
• "INDEX": Indicator of production volume expressed in terms relative to a base year. If the value is greater than 100, there was an increase, and if it is lower, a decrease.
• "STATUS": Status of the figures according to the Guidelines for Changes to the information disclosed in the statistical and geographical publications of INEGI.

Acknowledgements:

You can find the original source at INEGI, Minería 2023 Publisher: Instituto Nacional de Estadística y Geografía, INEGI

The Spanish TTS Monologue Speech Dataset is a professionally curated resource built to train realistic, expressive, and production-grade text-to-speech (TTS) systems. It contains studio-recorded long-form speech by trained native Spanish voice artists, each contributing 1 to 2 hours of clean, uninterrupted monologue audio.

Unlike typical prompt-based datasets with short, isolated phrases, this collection features long-form, topic-driven monologues that mirror natural human narration. It includes content types that are directly useful for real-world applications, like audiobook-style storytelling, educational lectures, health advisories, product explainers, digital how-tos, formal announcements, and more.

All recordings are captured in professional studios using high-end equipment and under the guidance of experienced voice directors.

Recording & Audio Quality

Only clean, production-grade audio makes it into the final dataset.

Voice Artist Selection

All voice artists are native Spanish speakers with professional training or prior experience in narration. We ensure a diverse pool in terms of age, gender, and region to bring a balanced and rich vocal dataset.

Script Quality & Coverage

Scripts are not generic or repetitive. Scripts are professionally authored by domain experts to reflect real-world use cases. They avoid redundancy and include modern vocabulary, emotional range, and phonetically rich sentence structures.

Transcripts & Alignment

While the script is used during the recording, we also provide post-recording updates to ensure the transcript reflects the final spoken audio. Minor edits are made to adjust for skipped or rephrased words.

This data set contains five data files (.txt format). Three data files provide net primary productivity (NPP) estimates for a tropical dry deciduous forest within the 3,300-ha Chamela Biological Station, Mexico. There is one file for each of the three permanent watershed plots located along an elevational gradient from 60 to 160-m above sea level. NPP was estimated from field measurements obtained during wet and dry seasons between 1982 and 1995. A fourth NPP data file provides average nutrient fluxes into and out of five watersheds. The fifth file provides precipitation and minimum/maximum temperature data from measurements obtained onsite.

Detailed data are available for above-ground NPP (ANPP) (fine litterfall, wood increment, and leaf herbivory plus an estimation of understory production), and below-ground NPP (BNPP) (fine root production and root increment). Biomass data and nutrient inputs/outputs (P, K, Ca, Mg) averaged from five watersheds are also included in the data set.

Estimated ANPP ranged from 611 to 808 g/m2/year between the three sub-sites (average 682 g/m2/year), and total NPP ranged from 1,119 to 1,353 g/m2/year (average 1,206 g/m2/year). These estimates are thought to represent the lower bounds of NPP because root and stem herbivory have not been taken into account, although leaf herbivory is included.

Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2001.

Car Production in Mexico increased to 367.87 Thousand Units in October from 355.53 Thousand Units in September of 2025. This dataset provides - Mexico Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Introduction

This Mexican Spanish Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish speakers. Featuring over 30 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world scenario coverage.

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

This dataset is ideal for a range of voice AI and NLP applications:

This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.