Data generation for exploration geochemistry: Past, present and future

The paper provides a comprehensive overview of how geochemical data is generated and used in the field of geosciences, focusing on its role in resource exploration and environmental assessment. Here's a clear description based on the title and abstract:

1. Review of Traditional Geochemical Data Generation: The paper begins by examining how geochemical data has traditionally been generated. This includes the methods used in the past to collect and analyze geochemical data, highlighting the characteristics and limitations of these older techniques.
2. Impact of Recent Developments: The paper then explores recent advancements that have impacted geochemical data generation, particularly in the realms of data science and artificial intelligence. These modern developments have significantly disrupted traditional practices by introducing new tools and methods that enhance data analysis and interpretation.
3. Future Directions: Looking ahead, the paper envisions what geochemical data generation might look like in the future. It discusses the need for new strategies that align with the evolving demands of data users, which include integrating automation, improving analytical techniques, and leveraging advanced computing capabilities.
4. Challenges and Solutions: The paper identifies several challenges in adapting to the new data landscape. These include:
   
   • A shift from scientific reductionism to system-level approaches.
   • Advances in geometallurgy, which require different types of data.
   • Skill gaps in geoscientific education.
   • Increasing demand for raw materials, which pushes for more comprehensive data.
   • To address these challenges, the paper suggests that traditional methods will still be valuable but emphasizes the need for developing and implementing new methods to generate larger and more useful geochemical data sets. Solutions involve:
     
   • Evolving geoscientific education to meet new demands.
   • Utilizing modern technology and methods.
   • Clearly defining the roles and responsibilities of data users and generators.
   • Updating data management practices to handle bigger datasets effectively.