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The Randolph Glacier Inventory (RGI) is a globally complete inventory of glacier outlines (excluding the ice sheets in Greenland and Antarctica). It is a subset of the database compiled by the Global Land Ice Measurements from Space (GLIMS) initiative. While GLIMS is a multi-temporal database with an extensive set of attributes, the RGI is intended to be a snapshot of the world’s glaciers at a specific target date, which in RGI 7.0 and all previous versions has been set as close as possible to the year 2000 (although in fact its range of dates can still be substantial in some regions). The RGI includes outlines of all glaciers larger than 0.01 km², which is the recommended minimum of the World Glacier Inventory.

The RGI was not designed for the measurement of glacier-by-glacier rates of area change, for which the greatest possible accuracy in dating, delineation and georeferencing is essential. While many RGI outlines meet these requirements, the primary focus of the RGI is on achieving global coverage, consistency, and proximity in a specific year. The strength of the RGI lies in its ability to handle large numbers of glaciers simultaneously. This allows, for example, for the estimation of glacier volumes and rates of elevation change at regional and global scales, as well as the simulation of cryospheric responses to climatic forcing.

Who develops and hosts the RGI? The RGI has been developed in an international community-driven effort of glaciologists starting in 2010. The inventory was named after “Randolph”, a town in New Hampshire, USA, where the team met for one of their meetings [Pfeffer et al., 2014]. In 2014 development of the RGI became the responsibility of the Working Group on the Randolph Glacier Inventory and Infrastructure for Glacier Monitoring, which operated under the International Association of Cryospheric Sciences (IACS). In 2019, a new Working Group was established to build upon the previous achievements and further expand its objectives: the IACS Working Group on the Randolph Glacier Inventory (RGI) and its role in future glacier monitoring and GLIMS.

The RGI datasets are listed on glims.org, and the RGI files can be downloaded through the data portal at the National Snow and Ice Data Center (NSIDC), which is the host for GLIMS.

Glacier product: includes outlines, attributes and auxiliary data for each individual glacier.

The Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) project established a new long-term collaboration of international experts to establish and sustain a global reference hydrological network (RHN), through common standards, protocols, indicators, and data infrastructure. ‘Reference Hydrometric Networks’ (RHNs), consist of gauging stations whose catchments are relatively undisturbed and record high quality data and little missing data. The concept of RHNs, their history and evolution are described in (Whitfield et al., 2012) previously and many countries have already established RHNs, however this is the first initiative to bring them together at a global level. The ROBIN Full Dataset consists of 3,060 stations in 30 countries, however the dataset described here is the ROBIN Public Dataset which contains metadata records for all 3,060 stations and daily streamflow data for a total of 2,386 stations. This tiered approached was due to data sharing restrictions in some countries. More information about the ROBIN Network and dataset can be found on the project website: https://www.ceh.ac.uk/our-science/projects/robin

Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.

The Caravan dataset that was released together with the paper. Since Version 1.6, the dataset is published in two different Zenodo repositories, depending on the filetype of the timeseries data.

Outcome of the 'Visual Inspection for Defining the Safety Upgrading Strategies’ (VISUS) approach to assess the school safety in the Chimanimani District after the Cyclone Idai. A VISUS survey across 15 schools in the Chimanimani district was conducted to gauge rehabilitation needs and identify key areas to build resilience.

This dataset contains the official materials from the launch event of the IHP-WINS (International Hydrological Programme – Water Information Network System) platform, held on 28 April 2023. It includes the launch brochure, the presentation delivered during the event, and the full recording of the webinar. These resources provide an overview of the platform’s objectives, functionalities, and relevance in supporting data sharing, open science, and collaborative water resources management. The dataset serves as a reference for stakeholders, partners, and contributors interested in learning about the vision and practical applications of IHP-WINS.