The statistic shows the distribution of employment in El Salvador by economic sector from 2013 to 2023. In 2023, 14.75 percent of the employees in El Salvador were active in the agricultural sector, 23.39 percent in industry and 61.86 percent in the service sector.

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.

MEDDOPROF Silver Standard

This repository contains the MEDDOPROF Silver Standard, a collection of clinical case reports of multiple specialties that have been automatically annotated with occupations, working statuses and activities using named entity recognition models trained on the MEDDOPROF corpus. MEDDOPROF and this Silver Standard have been created by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis.

About the MEDDOPROF Corpus

MEDDOPROF is a corpus and shared task focused on the detection and normalization of occupations and related information such as working statuses and non-paid activities in clinical texts in Spanish. All mentions in the corpus have been normalized to SNOMED CT, with occupations also being normalized to the European Skills, Competences, and Occupations classification ESCO. MEDDOPLACE was released as part of the IberLEF 2021 workshop, which in turn was held within the SEPLN 2021 conference.

About this Silver Standard

The Silver Standard includes 13,357 documents and a total of 5,568,844 tokens. The documents belong to over 10 different medical specialties, including occupational health, oncology, radiology, infectology, dermatology, psichiatry and more.

The corpus includes three different labels: PROFESION (occupation), SITUACION-LABORAL (working status) and ACTIVIDAD (activity/hobby). There is a total of 4,332 predictions (1,325 unique), with the most common label being PROFESION with 3,599 predictions (1,181 unique), followed by SITUACION-LABORAL with 682 (180 unique) and ACTIVIDAD with 51 (44 unique).

The annotations are presented in .ann format, which is the format used by the annotation tool brat.

For more information, please check the attached README file.

Citation

If you use this Silver Standard or any MEDDOPROF materials, please cite the task's overview paper: Lima-López, Salvador, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, & Martin Krallinger. “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts.” Procesamiento del Lenguaje Natural [Online], 67 (2021): 243-256.

License This work is licensed under a Creative Commons Attribution 4.0 International License.

Contact If you have any questions or suggestions, please contact us at:

• Salvador Lima-López ()
• Martin Krallinger ()

Acknowledgements MEDDOPROF was promoted by the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4ProfHealth - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).

Related Links

• MEDDOPROF website: https://temu.bsc.es/meddoprof/

• MEDDOPROF Gold Standard: https://doi.org/10.5281/zenodo.5070540

• MEDDOPROF Annotation Guidelines: https://doi.org/10.5281/zenodo.4694675