Open data could have helped us learn from another mining dam disaster

Paulo A. de Souza Jr. at Nature: “The recent Brumadinho dam disaster in Brazil is an example of infrastructure failure with catastrophic consequences. Over 300 people were reported dead or missing, and nearly 400 more were rescued alive. The environmental impact is massive and difficult to quantify. The frequency of these disasters demonstrates that the current assets for monitoring integrity and generating alerting managers, authorities and the public to ongoing change in tailings are, in many cases, not working as they should. There is also the need for adequate prevention procedures. Monitoring can be perfect, but without timely and appropriate action, it will be useless. Good management therefore requires quality data. Undisputedly, management practices of industrial sites, including audit procedures, must improve, and data and metadata available from preceding accidents should be better used. There is a rich literature available about design, construction, operation, maintenance and decommissioning of tailing facilities. These include guidelines, standards, case studies, technical reports, consultancy and audit practices, and scientific papers. Regulation varies from country to country and in some cases, like Australia and Canada, it is controlled by individual state agencies. There are, however, few datasets available that are shared with the technical and scientific community more globally; particularly for prior incidents. Conspicuously lacking are comprehensive data related to monitoring of large infrastructures such as mining dams.

Today, Scientific Data published a Data Descriptor presenting a dataset obtained from 54 laboratory experiments on the breaching of fluvial dikes because of flow overtopping. (Re)use of such data can help improve our understanding of fundamental processes underpinning industrial infrastructure collapse (e.g., fluvial dike breaching, mining dam failure), and assess the accuracy of numerical models for the prediction of such incidents. This is absolutely essential for better management of floods, mitigation of dam collapses, and similar accidents. The authors propose a framework that could exemplify how data involving similar infrastructure can be stored, shared, published, and reused…(More)”.