Historical dataset showing East Asia & Pacific life expectancy by year from 1960 to 2023.

Context

Life expectancy at birth is a key metric reflecting the average number of years a person can expect to live from birth, considering current mortality rates. Across the globe, life expectancy varies widely due to factors such as healthcare access, socio-economic conditions, and lifestyle choices. Developed nations often boast higher life expectancies, typically ranging from 75 to 85 years, owing to advanced healthcare systems and improved living standards. In contrast, developing nations often face shorter life expectancies, frequently falling below 70 years, largely due to inadequate healthcare infrastructure and prevailing socio-economic challenges. These disparities underscore the critical importance of global efforts to enhance healthcare access and address socio-economic inequalities.

Content

This dataset comprises historical information encompassing various indicators concerning Life Expectancy at Birth on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Life Expectancy at Birth from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Life Expectancy at Birth from 1990 - 2021 - Life Expectancy at Birth from year 1990 to 2021 (32 Columns.)

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/upczekR.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Freepik

Thumbnail by: Image by Quality of life icons created by Paul J. - Flaticon

Introduction

The East Asian Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.

Facial Image Data

The dataset includes over 1500 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.

Diversity and Representation

Quality and Image Conditions

To ensure robust model training and generalizability, images are captured under varied natural conditions:

Metadata

Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:

This metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.

Applications

This dataset is ideal for a wide range of computer vision use cases, including:

Ethical Collection and Data Security

We maintain the highest ethical and security standards throughout the data lifecycle:

Introduction

Welcome to the East Asian Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 10,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

The UN life expectancy dataset is a real-world dataset used to demonstrate the proposed method.

Context

The Gender Development Index (GDI) is a composite measure designed to assess gender disparities and inequalities in a society by considering factors related to human development. It is an extension of the Human Development Index (HDI) and focuses on three key dimensions: health, education, and income. In the GDI, these dimensions are assessed separately for males and females, allowing for a comparison of gender-based development gaps. Health indicators typically include life expectancy at birth for both genders. Education indicators encompass literacy rates and enrollment in primary, secondary, and tertiary education for both males and females. The income component typically examines income levels and workforce participation for both genders.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, and Gender Development Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Gender Development Index from 1990 to 2021 - Gender Development Index from 1990 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/NI4UY57.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Freepik

Thumbnail by: Freepik

Objectives: We examined the magnitude and determinants of socioeconomic disparities in disability-free life expectancy and life expectancy at age 65 (DFLE65 and LE65) in China.Methods: Data from Chinese Longitudinal Healthy Longevity Survey collected during 2011–2018 (8,184 participants aged ≥65) were used. Socioeconomic status (SES) was measured by economic status (ES), and education, respectively. Multistate Markov models and microsimulations were fitted to estimate DFLE65 and LE65.Results: LE65 between high- and low-ES groups differed by 2.20 years for males and 2.04 years for females. The DFLE65 disparity in ES was 1.51 and 1.29 years for males and females, respectively. Not undergoing physical examinations, inadequate fruit/vegetable intake, and stress contributed to 35.10% and 57.36% of DFLE65 disparity in ES, as well as 26.36% and 42.65% of LE65 disparity for males and females, respectively. These disparities in education and ES were of a similar magnitude, while the above factors contributed little to education disparity.Conclusion: Socioeconomic disparities in DFLE65 and LE65 existed in China. Physical examination, fruit/vegetable intake and stress partly explained these disparities.

In the Chinese Food Life Cycle Assessment Database (CFLCAD), Greenhouse Gas Emissions (GHGE) for 80 food items, Water Use (WU) for 93 food items and Land Use (LU) for 50 food items are collected through a literature review. The CFLCAD applies conversion factors for the edible portion of food, food loss ratio and processing, storage, packaging, transportation, and food preparation stages to estimate the environmental footprints of food from production to consumption. Similar food groups and recipes are used to match those food items without LCA value in the Chinese food composition table, resulting in a total of 17 food groups in the database.

Summary characteristics of the study cities by regions.

Introduction

Welcome to the Chinese Language Image Captioning Dataset! A collection of Images with associated text captions to facilitate the development of AI models capable of generating high-quality captions for images. This dataset is meticulously crafted to support research and innovation in computer vision and natural language processing.

Image Data

This dataset features over 5,000 high-resolution images sourced from diverse categories and scenes. Each image is meticulously selected to encompass a wide array of contexts, objects, and environments, ensuring comprehensive coverage for training robust image captioning models.

Caption Data

Each image in the dataset is paired with a high-quality descriptive caption. These captions are carefully crafted to provide detailed and contextually rich descriptions of the images, enhancing the dataset's utility for training sophisticated image captioning algorithms.

Metadata

Each image-caption pair is accompanied by comprehensive metadata to facilitate informed decision-making in model development:

Usage and Applications

The Image Captioning Dataset serves various applications across different domains:

The scorpion has been extensively used in various pharmacological profiles or as food supplies. The exploration of scorpion venom has been reported due to the presence of recombinant peptides. rBmαTX14 is an α-neurotoxin extracted from the venom gland of the East Asian scorpion Buthus martensii Karsch and can affect ion channel conductance. Here, we investigated the functions of rBmαTX14 using the Caenorhabditis elegans model. Using western blot analysis, rBmαTX14 was shown to be expressed both in the cytoplasm and inclusion bodies in the E.coli Rosetta (DE3) strain. Circular dichroism spectroscopy analysis demonstrated that purified rBmαTX14 retained its biological structures. Next, feeding nematodes with E.coli Rosetta (DE3) expressing rBmαTX14 caused extension of the life span and promoted the locomotion of the nematodes. In addition, we identified several genes that play various roles in the life span and locomotion of C. elegans through microarray analysis and quantitative real-time PCR. Furthermore, if the amino acid site H15 of rBmαTX14 was mutated, rBmαTX14 no longer promoted the C. elegans life span. In conclusion, the results not only demonstrated the functions and mechanism of rBmαTX14 in C. elegans, but also provided the new sight in the utility of recombinant peptides from scorpion venom.