Dataset Information

Open Data to Support Agricultural Diversification (version October 2020).

ABSTRACT: Following the development of a database that was specifically designed to store value chain information, particularly for underutilised crops, this article describes the data that are currently stored in the database and accessible through its web portal. The data includes various datasets on utilisation status, agro-ecological requirements and season lengths, potential yield and nutritional composition of crops. The data are stored in the form of tables with fixed data elements (column attributes). This article outlines the standard procedures (SOPs) that were developed in-house for data collection, metadata creation and data curation. These processes were used to ensure the quality and reusability of the data that is made available publicly through the database interface. Various statistics and example visualisations are provided to demonstrate the significance of such data for developing solutions for sustainable agricultural diversification.

SUBMITTER: Nizar NMM

PROVIDER: S-EPMC7844438 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Open Data to Support Agricultural Diversification (version October 2020).

Nizar Nur Marahaini Mohd NMM Jahanshiri Ebrahim E Sinin Siti Sarah Mohd SSM Wimalasiri Eranga M EM Suhairi Tengku Adhwa Syaherah Tengku Mohd TASTM Gregory Peter J PJ Azam-Ali Sayed N SN

Data in brief 20210121

Following the development of a database that was specifically designed to store value chain information, particularly for underutilised crops, this article describes the data that are currently stored in the database and accessible through its web portal. The data includes various datasets on utilisation status, agro-ecological requirements and season lengths, potential yield and nutritional composition of crops. The data are stored in the form of tables with fixed data elements (column attribut ...[more]

PMID: 33553528

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Project description:BackgroundCOVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19's infection ratio (IR) is essential for formulating policies and interventions.ObjectiveThe present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India's socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level.Data and methodsWe used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio.ResultsThe results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran's- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15-59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts.ConclusionThe COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

Dataset Information

Open Data to Support Agricultural Diversification (version October 2020).

Publications

Open Data to Support Agricultural Diversification (version October 2020).

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