Big data and data quality

Big Data, classically understood, is characterized by its volume and variety of data and the speed with which it is managed; and in our conception we will add an additional property: quality. It is necessary to analyze the quality of the alphanumeric and spatial information of the available data. In the same way, it is necessary to see if the access, maintenance and propagation of the metadata is adequate and consistent with the processes of generation, editing and transformation of the databases from which they come.

Big data and data quality projects

Que es Big data and data quality?

Environmental Big Data refers to the collection, management and analysis of large volumes of data related to the environment. This data can come from various sources, such as sensors, satellite images, and field studies. The ability to process and interpret these large volumes of data allows for a better understanding of changes in ecosystems, climate trends and the impact of human activities on the environment. Advances in Big Data facilitate the identification of patterns, the prediction of future trends and the making of more informed decisions in environmental management.

How to work with Big Data in environmental research?

Optimization of Geospatial Databases: We research and develop optimal formats for geospatial information, including long time series of remote sensing images. This includes studies on data compression, preservation and computational optimization.
Open Data: We promote and integrate open science initiatives, facilitating free access to data and metadata to promote transparency and collaboration in research.
Quality Standards: We implement innovative tools for the visualization and search of geospatial information, establishing quality standards to guarantee the reliability and consistency of the data.
Applications in Ecology: We use Big Data for stoichiometric ecology, ecometabolomics and functional ecology, creating mathematical models that relate carbon concentrations and flows to species diversity and global change factors.