Abstract
Addressing global challenges and taking action towards a sustainable future-in regards to ecology, biodiversity, water and food security and climate-requires both solution-driven research and a deep understanding of Earth and space systems. Trustworthy, reproducible science requires that data, including their provenance, be accessible for evaluation and further study. International collaboration and open data sharing are essential for addressing these challenges and promoting new scientific advances. Although strides have been made in improving data sharing platforms, as well as data and metadata standards, there is still work to be done to facilitate careful collection, stewardship and reuse of data across the Earth and space sciences. To achieve an ecosystem where data are consistently collected, well described, curated, shared, preserved and reused there needs to be significant continuous evolution in (i) equitable access to data and infrastructure, (ii) a change in the research culture and the scientific rewarding system to value data contributions, and (iii) standards around describing and documenting data across disciplines and domains. This statement takes the broadest interpretation of the term 'data' according to the Beijing Declaration on Research Data: data can be collected, generated or compiled by humans or machines and include (but not limited to) metadata, samples, methods, software and algorithms. Infrastructure includes hardware, intangible assets such as software and human capital. Ensure equitable access to trusted and reusable data Researchers have a responsibility to collect, document and share data-raw and processed-in an ethical manner that is as open and transparent as possible. Persistent funding is crucial for maintaining open and trusted data infrastructures, such as repositories, to facilitate ease of deposit and long-term reuse. This infrastructure supports the management and curation of data throughout the research process, ensuring that data are findable, accessible, interoperable and reusable (FAIR) for both people and machines. To meet these expectations, investment in support staff, such as data stewards and IT developers, along with research and development of data architectures is imperative.
