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A Data Storage, Analysis, and Project Administration Engine (TMFdw) for Small- to Medium-Size Interdisciplinary Ecological Research Programs with Full Raster Data Capabilities

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system n...

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Main Authors: Grigusova, Paulina, Beilschmidt, Christian, Dobbermann, Maik, Drönner, Johannes, Mattig, Michael, Sanchez, Pablo, Farwig, Nina, Bendix, Jörg
Format: Article
Language:English
Published: Philipps-Universität Marburg 2024
Subjects:
Geografie, Reisen
area-wide microclimate
raster engine
big raster data
working database
use case
Geography & travel
Online Access:PDF Full Text
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Summary:Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs. Dataset: The link to the datasets is as follows: https://respect.app.geoengine.io (accessed on 29 November 2024).
Item Description:Gefördert durch den Open-Access-Publikationsfonds der UB Marburg.
DOI:10.3390/data9120143

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