Concerns about secondary use of data and limited opportunities for benefit-sharing have focused attention on the tension that Indigenous communities feel between (1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and ( 2) supporting open data, machine learning, broad data sharing, and big data initiatives. The International Indigenous Data Sovereignty Interest Group (within the Research Data Alliance) is a network of nation-state based Indigenous data sovereignty networks and individuals that developed the 'CARE Principles for Indigenous Data Governance' (Collective Benefit, Authority to Control, Responsibility, and Ethics) in consultation with Indigenous Peoples, scholars, non-profit organizations, and governments. The CARE Principles are people-and purpose-oriented, reflecting the crucial role of data in advancing innovation, governance, and self-determination among Indigenous Peoples. The Principles complement the existing data-centric approach represented in the 'FAIR Guiding Principles for scientific data management and stewardship' (Findable, Accessible, Interoperable, Reusable). The CARE Principles build upon earlier work by the Te Mana Raraunga Maori Data Sovereignty Network, US Indigenous Data Sovereignty Network, Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective, and numerous Indigenous Peoples, nations, and communities. The goal is that stewards and other users of Indigenous data will 'Be FAIR and CARE.' In this first formal publication of the CARE Principles, we articulate their rationale, describe their relation to the FAIR Principles, and present examples of their application.

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Much attention has been given to the challenges of handling massive data volumes in modern data-intensive science. This paper examines an equally daunting challenge -the diversity of interdisciplinary data, notably research data, and the need to interrelate these data to understand complex systemic problems such as environmental change and its impact. We use the experience of the International Polar Year 2007-8 (IPY) as a case study to examine data management approaches seeking to address issues around complex interdisciplinary science. We find that, while technology is a critical factor in addressing the interdisciplinary dimension of the data intensive science, the technologies developing for exa-scale data volumes differ from those that are needed for extremely distributed and heterogeneous data. Research data will continue to be highly heterogeneous and distributed and will require technologies to be much simpler and more flexible. More importantly, there is a need for both technical and cultural adaptation. We describe a vision of discoverable, open, linked, useful, and safe collections of data, organized and curated using the best principles and practices of information and library science. This vision provides a framework for our discussion and leads us to suggest several short-and long-term strategies to facilitate a socio-technical evolution in the overall science data ecosystem.

ABSTRACT

A scientific publication is fundamentally an argument consisting of a set of ideas and expectations supported by observations and calculations that serve as evidence of its veracity. An argument without evidence is only a set of assertions. Consider the difference between the statement “The hairy woodpecker population is declining in the northwest region of the United States” and the statement “Hairy woodpecker populations in the northwest region of the United States have declined by 11% between 1992 and 2003, according to data from the Institute for Bird Populations (http://www.birdpop.org/).” Both or neither of these statements could be true, but only the second one can be verified. Scientific papers do, of course, present specific data points as evidence for their arguments, but how well do papers guide readers to the body of those data, where the the data's integrity can be further examined? In practice, a chasm may lie across the path of a reviewer seeking the source data of a scientific argument.