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1. Content: Tea leaf image 2. Format of image: JPG 3. Number of Class: 7 4. Number of image: 5276

The folder contains replication code of the Paper A Multi-Factor GDP Nowcast Model for India. The readme file contains all the instruction, and different subfolders contains code for creating different figures and tables of the paper.

In this study, four types of drained peatlands (slight-drained peatlands, light-drained peatlands, moderate-drained peatlands and deep-drained peatlands) were selected in order to quantitatively evaluate the degree of peat subsidence at different depths, as well as the changes in peat carbon stock, decomposition potential and Q10. Meanwhile, we synthesized 915 observations from relevant studies and used meta-analysis to reveal the universal effect of drainage on peat carbon loss.

This dataset is a result of the "Integrating national open databases for a comprehensive view on food, environment and health in Brazil", an initiative that integrates diverse national open databases to provide insights into food production, consumption, environmental impact, and public health in Brazil from 1974 to 2022. The primary hypothesis behind the dataset is that analyzing food systems, nutrition, and agricultural practices over time will reveal patterns that can inform sustainable food policies and health interventions. We hypothesize that by aggregating and harmonizing data from various national sources, it will be possible to identify critical relationships between food production, nutrient availability, and environmental sustainability. This integrated dataset allows us to evaluate how agricultural practices and food consumption patterns have evolved in response to changing environmental, economic, and societal conditions in Brazil. Food Production and Nutrient Data: The dataset includes detailed information on harvested areas, food production (in tons), and nutrient availability (e.g., energy, protein, fiber, vitamins, and minerals) for various food groups across different Brazilian regions and states. Water and Environmental Data: Water usage, deficit, and environmental impact data are linked to agricultural activities, allowing the assessment of water efficiency in food production. Pesticides and Residue Monitoring: Information on pesticide usage, maximum residue levels (MRL), and residue percentage in food items is provided, with additional details on authorized pesticide types, toxicity classifications, and environmental risks. Food Consumption: Data on food acquisition and consumption patterns, broken down by food groups (e.g., beans, vegetables, fruits), are presented, highlighting dietary trends across various population groups. Data Collection and Methodology: The dataset was constructed by linking several government datasets, including agricultural statistics, water use reports, and residue monitoring programs. Each dataset was meticulously harmonized to ensure compatibility and completeness, following Extract, Transform, Load (ETL) processes. The data was collected over several decades, representing longitudinal trends in food systems and health. The harmonization process ensures that all data fields are consistent and comparable across time and regions. Interpretation and Use: This dataset can be used to explore the connections between food systems, health, and the environment. It supports studies in agrifood systems, sustainability, public health, and climate change. Researchers can investigate the impacts of food production on nutrient availability, water consumption, and pesticide use over time. It can also be used to model future food system sustainability under different climate scenarios and dietary patterns, contributing to the development of food policies that balance human and planetary health.

Analyses of mineral dust collected at six locations on San Jacinto Peak in Southern California between August 2019 and March 2022. Dust was collected at approximately four month intervals. Our purpose was to constrain the geochemical properties and flux of dust deposited at various elevations in an alpine ecosystem.

This is the research data for the analyzing mental workload of navigators in HSC bridge operations.

THIS PAPER ENABLES THE MARKETING FIRMS ABOUT HOW AI WILL HELP TO BRING REVOLUTIONS IN ADVERTISING RESEARCH TRENDS

This dataset includes the original images of agarose gel electrophoresis, sanger sequencing results of the proband and normal individuals, and sequencing results from minigene validation. Open the sequence file using SnapGene software.

Dataset for Malay version PSC-12 scale validation study

Hematopoietic stem cells (HSC) possess the capacity to regenerate the entire hematopoietic system. However, the precise HSC dynamics in the early post-transplantation phase remains an enigma. Clinically, the initial hematopoiesis in the post-transplantation period is critical, necessitating strategies to accelerate hematopoietic recovery. Here, we uncovered the spatiotemporal dynamics of early active hematopoiesis, “Hematopoietic Cell Inflation”, using a highly sensitive in vivo imaging system. Hematopoietic Cell Inflation occurs in three peaks in the spleen after transplantation, with common myeloid progenitors (CMPs), notably characterized by HSC-like signatures, playing a central role. Leveraging these findings, we developed expanded CMPs (exCMPs), which exhibit a gene expression pattern that selectively proliferates in the spleen and promotes hematopoietic expansion. Moreover, universal exCMPs supported early hematopoiesis in allogeneic transplantation. Human universal exCMPs have the potential to be a viable therapeutic enhancement for all HSC transplant patients.