Ecolonical LAB presents its post-COP16Colombia report, which examines the ethical challenges and emerging perspectives at the intersection of biotechnology, artificial intelligence, and genetic resource management. This document places special emphasis on the importance of ecosystem sovereignty and the rights of communities to make decisions regarding their biological resources. We invite policymakers, academics, communities, and the general public to read the report, reflect, and contribute to a constructive dialogue that prioritizes sovereignty and ecological integrity in the face of rapid technological advancement.
Data science is an interdisciplinary field that leverages scientific methods, algorithms, statistics, and various systems to extract knowledge from heterogeneous datasets. By employing rigorous analytical techniques, data science aims to uncover meaningful patterns, insights, and relationships within data. This involves applying statistical analysis, data mining, machine learning, and visualization techniques to explore and interpret complex datasets.
Combining quantitative approaches as data science and Artificial Intelligence (AI) techniques such as machine learning and deep learning, whit qualitative and social science approaches as design thinking and sociological approaches. This concern the use or not of technology-based solutions for biodiversity, water, energy, utilities and urban / territorial solutions.
Mycle Schneider took up this definition of citizen science: "Citizen science can be defined as the participatory and combined effort of research, analysis, and public education that strictly pursues, as a basic principle, the goal of collective well-being of present and future generations of beings: humans on the planet and the biosphere."
Can AI-generated sequences bypass biodiversity laws? As genetic data moves to the digital realm, is governance keeping pace, or is regulation already obsolete?
Who controls geospatial intelligence? Discover how AI, data governance, and ethics shape access to geospatial data and decision-making.
From data privacy to indigenous rights, explore whether AI is truly the solution or if it risks creating new challenges in conservation efforts.
This article explores the integration of EBVs and remote sensing to enhance biodiversity monitoring whit data analytics.
How green hydrogen and decentralized renewable energy systems can shape the future of sustainable, independent energy solutions?
Regenerative agriculture promises not just to sustain but to restore ecosystems. Yet, is this ambitious model feasible at scale, or are we romanticizing a niche practice?
We explore the intersection of AI and ecological studies. Apply AI techniques to biodiversity conservation, species monitoring, and ecological modeling.
A synthesis of the latest advances in data analytics applied to water quality.
