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The CAMALIOT mobile app for crowdsourcing is used to collect data transmitted by the Global Satellite Navigation System (GNSS) for scientific applications such as weather forecasting and improving positional accuracy

OSCAR is a model of reduced-complexity that describes the interactions between large-scale components of the Earth system that relate to anthropogenic climate change. Its modules are calibrated to emulate the behavior of complex process-based models.

This is a GIS-based model which explicitly accounts for the interplay between national and local ex-ante measures, e.g., investment in prevention/mitigation measures (on the part of the public authorities, the citizens and the insurance industry) and ex-post policies for sharing the financial costs after the disaster.

A set of global zoning layers are provided that identify broad homologues of foodscape classes. These are comparable in a minimum set of biophysical and management characteristics and can help to plan for possible interventions and leverage points for more sustainable agriculture.

The Integrated Catastrophe Analysis and Management Modeling (ISCRiMM) approach addresses systemic risks by analyzing socio-economic and structural vulnerabilities, employing statistical and machine learning methods, and enabling stakeholders to update vulnerability indices for effective risk management.

Stakeholder engagement is a crucial part of informed and sustainable decision-making. The first step in stakeholder engagement is to understand their vision, their roles, and their expectations. The Stakeholders Exploratory Vision Analysis (SEVA) is a flexible exploratory tool to analyze the stakeholders’ vision. SEVA is designed to be easy to use yet provide flexibility so it can be applied in different fields for stakeholder engagement.

Data on built-up surfaces around the world were collected via a Geo-Wiki campaign, where this data set is intended for use as an independent validation of global maps of built-up surfaces.

Data on the key drivers of tropical forest loss were collected using the Geo-Wiki platform. The data, collected within a geographical area of 30 degrees North and South of the equator, are openly available and are already being used in different studies.

SHELscape is a spatially-explicit agent-based model for understanding short-run post-natural disasters non-linear adjustment processes in a multi-market framework.

The GROW Citizen Observatory was developed in a Horizon 2020 funded project to engage citizens (interested in growing food and improving the health of their soils) in sharing their data with a large community of growers across Europe.

Geo-Wiki is an online application for the visualization and crowdsourcing of land cover and land use data, where the first data collection campaigns ran during 2011 and 2012.

Data on agricultural field size has been collected via Geo-Wiki campaigns, where this type of data can provide some indication of agricultural practices, and it helps us to determine what types of satellite data are needed for agricultural monitoring in different parts of the world.

A Geo-Wiki campaign on capturing data on cropland extent took place in September 2016, collecting data at 36,000 unique locations around the world

Data collection using mobile technology has facilitated the collection of in situ or ground-based data on land use/land cover including geo-tagged photographs that document the landscape.

Several data collection activities related to forest cover, forest biomass and forest management have been coordinated by IIASA’s NoDES group in the past

Crop Observe App is an application for collecting crop type, phenological stage, visible damage and management practices.

Assessing management impacts on the BioGeoChemistry of ecosystems

The IIASA/ESM based BGC-MAN model was calibrated and validated with TrEco data for the dominant ecosystems and land use forms of the Congo basin, including virgin forests, managed forests, forest fallows after shifting cultivation and savannahs.

A tool to assess temporal verification of net greenhouse gas emission changes under the Kyoto Protocol.

A dataset to understand near-term emissions mitigation and adaptation efforts