Published: June 19,2024

Originally published in English:: 2022-04-21
Publication of the Spanish version: 2024-5-8
Working Group: Artificial Intelligence
Status: Current


The goal of this guide is to provide mining industry stakeholders with best practices for data sharing based on existing initiatives, so that they can benefit from the opportunities that open data offers. This guide is intended for readers who wish to share data with others, those involved in the approval process, and users who wish to use open data shared by the mining industry.

Management Considerations

Before sharing and publishing data, a data license is often used to describe the intended use of data providers while providing protection for them. It also provides clarity to data consumers by preventing them from potentially infringing on the rights of data owners. There are different types of licenses for different purposes. License types are usually divided into open (without technical or legal restrictions), non-commercial, partially open or restricted use and closed. Existing frameworks, such as Creative Commons and Montreal Data License, can be used to cover general requirements.

Sharing data brings benefits, including supporting innovation and research and allowing the public to access information to help improve decision-making in operations. Before implementation, it is critical to address the cost, legal issues, storage, privacy, and common language challenges associated with collecting, managing, internally communicating, and maintaining open data to minimize the challenges and maximize the benefits of sharing. the data.

Implementation Considerations

Before implementation, it is very important to determine what data should be shared and what should not. The data set must be well documented, reliable, usable, accurate, relevant and in an accessible format. If a data set is commercially sensitive, contains personally identifiable information (PII) or sensitive data, or poses a security risk, sharing the data sets should be avoided unless these risks can be mitigated. A risk assessment should be performed based on the organization’s policies and risk tolerances.

When a data set becomes open, it must be submitted in a machine-readable format that is open and logical. If possible, any community consensus on the format(s) of existing data should be prioritized. It is also important to identify appropriate anonymization requirements and techniques.

It is recommended that a formal approval process be adopted when data is released. Documentation provided for data release approval typically includes information that provides an overview of the original data and its structure, a description of the anonymization procedures, an overview of the resulting data, and a certification or approval from key stakeholders that the data set can be shared. Selecting the appropriate hosting and listing platform is the last step before opening the data set.


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