94 skills found · Page 1 of 4
rockita / Environmental IntelligenceData for Environmental Intelligence: A mega list of Earth System Datasets covering earth observations, climate, water, forests, biodiversity, ecology, protected areas, natural hazards, marine and the tracking of UN's Sustainable Development Goals
pyinat / PyinaturalistPython client for iNaturalist
ArctosDB / ArctosArctos is a museum collections management system
Dogiye12 / AI Based Biodiversity Hotspot MappingThis project uses AI to map biodiversity hotspots by analyzing environmental variables and species richness data. A machine learning model predicts high-biodiversity areas, supporting conservation planning and ecological research with spatially explicit outputs.
captain-project / Captain ProjectCAPTAIN is a Python program to optimize conservation planning based on biodiversity data and simulations using reinforcement learning
pyinat / NaturtagTag your nature photos with iNat taxonomy and observation metadata
tdwg / BdqBiodiversity Data Quality (BDQ) Interest Group
Beckybams / AI Powered Agroforestry Planning AI-Powered Agroforestry Planning uses machine learning and data analytics to design sustainable tree–crop systems. By analyzing soil, climate, and land data, it recommends optimal agroforestry combinations, improving productivity, biodiversity, and climate resilience while supporting smart data.
AnneChao / SpadeRSpadeR provides simple R functions to compute various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
michstrauch / CoMOLACoMOLA is a generic python tool for Constrained Multi-objective Optimization of Land use Allocation. It offers a framework to explore a landscape’s potential for ecosystem service supply and biodiversity conservation considering land use conversion and areal coverage constraints. CoMOLA can be used immediately by inputting a raster map representing the status-quo land use, ready-to-run models written in R including their input data, and (optional) information on constraints. The end product is a set of pareto-optimal solutions (land use maps) from which decision makers can discuss and select appropriate solutions according to their preferences.
vijaybarve / BdvisBiodiversity data Visualizations in R
vboussange / WSLJuliaWorkshop2023Practical introduction to Julia for modelling and data analysis in biodiversity and earth sciences
Geraldine-Winston / Biodiversity Loss Prediction Under Climate Change Using Ensemble Deep Learning Models.This project leverages an ensemble of deep learning models, including MLP, CNN, and LSTM, to predict biodiversity loss under various climate change scenarios, enhancing prediction accuracy using climate and environmental data.
Beckybams / Biodiversity Monitoring Using Acoustic DataBiodiversity monitoring using acoustic data leverages sound recordings to detect, identify, and track species in natural environments. By analyzing animal vocalizations with signal processing and machine learning, it enables non-invasive, scalable, and continuous ecosystem assessment.
brunobrr / BdcCheck out the vignettes with detailed documentation on each module of the bdc package
ropensci / OccCiteQuerying database aggregators and citing primary sources of resulting occurrence records.
Beckybams / AI Based Coral Reef Health AssessmentAI-Based Coral Reef Health Assessment uses machine learning to analyze satellite imagery, sensor data, and underwater images to detect bleaching, disease, and biodiversity changes. The system provides early warnings, supports conservation decisions, tracks reef recovery over time.
pyinat / Pyinaturalist ConvertData conversion tools for iNaturalist observations and taxonomy
leisux / IpybdPowerful Data Cleaner For Biodiversity
LEEClab / Atlantic SeriesATLANTIC SERIES: biodiversity hotspot data papers
