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Here, we present a framework for a beach litter monitoring process, based on free and open-source software (FOSS), which allows customization for any sampling design. The framework was developed by means of a GIS project (QGIS), a GIS collector (QField), and an R code, allowing further adjustments according to the area to be surveyed and research questions. The aim is to improve data collection, accessibility, and interoperability, as well as to help to fill the currently existing gap between fieldwork and data analysis, preventing typos and allowing better data processing. Therefore, it is expected to take less than an hour from ending fieldwork to obtaining up-to-date products. To test the developed open-source geospatial framework, it was applied in different sectors and dates on an important southern Brazilian touristic beach. Results obtained from the open-source geospatial framework application produce baseline information on beach litter issues, such as amounts, sources, and spatial and temporal patterns. Adoption of the framework can facilitate data collection by local and regional stakeholders, and the results obtained from it can be applied to support management strategies. For researchers, it produces spatialized data for each item in an already tidy format, which can be used for robust and complex models. A series of supplementary files support reproducibility and provide a guide to future users.
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http://dx.doi.org/10.1007/s10661-020-08602-w | DOI Listing |
There is a large and growing number of trait databases in ecology and evolution. Structured and open-access data repositories are important resources for allowing researchers to explore, visualize, and download such data for analysis. In this paper, we detail the design and structure of a simple Shiny web app intended to deploy trait databases as interactive web platforms.
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August 2025
Center of Drone Excellence (CODEX), Marine Mammal Institute, Oregon State University, Newport, OR, United States of America.
Drones have revolutionized researchers' ability to obtain morphological data on megafauna, particularly cetaceans. The last decade has seen a surge in studies using drones to distinguish morphological differences among populations, calculate energetic reserves and body condition, and identify decreasing body sizes over generations. However, standardized workflows are needed to guide data collection, post-processing, and incorporation of measurement uncertainty, thereby ensuring that measurements are comparable within and across studies.
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December 2025
Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia.
The rapid proliferation of scientometric and bibliometric analyses has emphasized the need for robust, scalable methods to visualize complex, large-scale research data. Conventional geospatial visualization techniques-most notably choropleth maps-often introduce significant distortions due to their inability to adequately account for spatial heterogeneity and overdispersion in bibliometric distributions. To address these methodological shortcomings, we propose GeoBM (Geographic Bibliometric Mapping), a computational framework that enables enhanced geovisualization of global scientific output and collaboration patterns.
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July 2025
Smith School of Enterprise and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
Agriculture sector is a major contributor to greenhouse gas emissions, yet the lack of asset-level farm data, including ownership, land use, and production, hinders effective transition finance and decarbonisation efforts. To address this gap, we developed an open-source farm-level dataset using natural language processing (NLP) and unsupervised learning, mapping farm names to spatial polygons to fill ownership and entity gaps. In England, this approach identified 117,116 farming entities with essential attributes such as addresses, land areas, crop types, production output, and geospatial coordinates.
View Article and Find Full Text PDFEnviron Monit Assess
July 2025
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India.
Forest structural connectivity denotes the physical linkage among forest landscape elements, and is crucial for sustaining ecological, hydrological, and socio-economic benefits. However, forest fragmentation increasingly threatens forest connectivity, particularly in emerging economies like India, which face intense development pressures and resource constraints. This study presents the first comprehensive analysis of forest structural connectivity dynamics across Indian states using free and open-source geospatial tools.
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