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Category: Data

Description: Initially, the release of data platforms will follow these steps and considerations: 1) To ensure that the selected databases contain the necessary information, the first step will be to make a preliminary request for the metadata of each database. This approach allows for an initial verification of the structure and content of the databases, ensuring that they include the required data for the study. This optimizes the process by avoiding the complete release procedure for databases that do not meet specific criteria, allowing the focus to remain only on those that truly contain the variables of interest. 2) After reviewing the metadata, the documentation and access policies will be consulted. Some platforms offer open public data, while others require permissions or specific usage agreements. 3) Access request (when applicable): For platforms that require authorization, a formal request for data access will be made. This may involve submitting detailed research proposals justifying the use of the data, filling out specific forms, and adhering to confidentiality agreements or terms of use (for example, platforms such as MapBiomas or ICMBio, which have specific data protection policies). 4) Institutional authorizations: Some platforms may require institutional authorization for data use, which will be facilitated by the research affiliation with the institution. 5) Integration with existing systems: For platforms that offer application programming interfaces (APIs) or direct access via queries, automated scripts will be developed to extract and continuously update the data. Platforms such as NASA or INPE, for example, may provide real-time data, requiring appropriate technological integration to ensure data updates and synchronization. 6) Usage registration and licensing: For platforms requiring formal usage registration, the data will be used under the appropriate licensing terms, and researchers will ensure compliance with any citation or usage license requirements when using third-party data. This includes crediting data sources and adhering to any redistribution or modification restrictions. 7) Data security and protection: To ensure data protection and privacy, even when public—especially when dealing with sensitive data, even in an aggregated form, such as health or socioeconomic indicators—appropriate security protocols will be followed, with secure database storage to prevent unauthorized access. Once data access is granted and extraction is completed, the compiled database will be structured and organized in stages: 1) Data integration in KNIME: The data extracted from each platform will be combined into a centralized database, using unique identifiers for each study region. 2) Standardization of variables: After extracting data from each platform, socioeconomic, climatic, environmental, and health variables will be reviewed and standardized to ensure consistency across different sources. Each variable will have a unified nomenclature and a specific coding system, such as household income, pollution level, NDVI, among others. 3) Creation of relational tables: Each table will represent a specific set of variables (e.g., a table for climatic variables, a table for socioeconomic variables, etc.). 4) Data cleaning process: During consolidation, data will undergo a cleaning process, eliminating duplicates, correcting inconsistencies, and handling missing values. 5) Geospatialization of data: To facilitate the analysis of environmental and geographical impacts, the data will be georeferenced. A spatial data layer will be created to analyze the variables based on geographic location, integrating climatic and environmental information with the study areas. 6) Documentation and metadata: Each stage of data extraction and integration will be properly documented, with the creation of a detailed data dictionary and metadata. Following the release of access to the databases, a Data Management Plan (DMP Tool) will be implemented to ensure the organization, security, and accessibility of the data used and generated throughout the research. This plan establishes detailed guidelines for data collection, storage, processing, and sharing, ensuring that all stages comply with best practices in open science and data management. The DMP Tool also defines backup and data security procedures, ensuring protection against information loss and appropriate access control. Additionally, it includes full documentation of variables and data manipulation processes, facilitating the study’s replication by other researchers and archiving in publicly accessible data repositories, when applicable, to promote transparency and scientific reuse.