Ruth Duerr

In the age of big data and open science, what processes are needed to follow open science protocols while upholding Indigenous Peoples’ rights? The Earth Data Relations Working Group (EDRWG), convened to address this question and envision a research landscape that acknowledges the legacy of extractive practices and embraces new norms across Earth science institutions and open science research. Using the National Ecological Observatory Network (NEON) as an example, the EDRWG recommends actions, applicable across all phases of the data lifecycle, that recognize the sovereign rights of Indigenous Peoples and support better research across all Earth Sciences.

As the problems humanity faces become ever more obvious and dangerous, the need for interdisciplinary, cross-disciplinary and trans-disciplinary research and solutions becomes ever more apparent. T...

As the problems humanity faces become ever more obvious and dangerous, the need for interdisciplinary, cross-disciplinary and trans-disciplinary research and solutions becomes ever more apparent. The problems themselves are often intertwined in complex ways - for example the impact of climate change on human health, food & water security, disasters, and so on and how all of these are exacerbated by human population growth and a general lack of recognition that humanity is part of the ecosystem upon which Earthly life depends. Underpinning our ability to understand and solve these complex problems are data of all kinds, more importantly the information that has and continues to be garnered through obtaining and analyzing these and most importantly their inclusion in the larger body of knowledge and ways of knowing that hopefully leads to the wisdom needed to directly address the root causes of each problem. Over the past decades, through involvement in projects on science topics as diverse as quasars and stellar pediatrics, solar physics, social science, and a wide variety of Earth Science topics; where informatics topics such as data management and curation, systems and framework development; and tools and methodologies such as Use Cases, Natural Language Processing, Deep Learning, Knowledge Graphs, Information Models, etc. were included; a number of principles and lessons learned which unite the technical underpinnings of these projects have come to the fore. These will be discussed in this presentation.

The five divisions of NASA’s Science Mission Directorate (SMD) represent a very broad spectrum of academic disciplines, ranging from Astronomy, to Planetary science, to Heliophysics, Earth science, Biology and Physical science with measurement scales ranging from components of atoms to the structure of the entire universe. In addition, the systems that support access to these data range from systems based on formal and broadly accepted OWL ontologies, to those based on current and historical disciplinary metadata standards, to ad-hoc or bespoke systems dating back to NASA’s very earliest missions; all generally developed to support the mission or, more recently, discipline focussed data users. Consequently the access mechanisms, data structures, vocabularies, terms in use, etc. vary widely across the divisions making cross-disciplinary research at best difficult if not impossible. Currently NASA SMD is working to improve support for cross-disciplinary/transdisciplinary research by developing a system that supports discovery across all of SMD’s data products, a model that can be extended to all forms of scientific output including software, tools, models, publications, etc. The core underpinnings of such a system is an information model being developed using the methodology developed by Dr. Peter Fox and Dr. Deborah McGuinness. Here we discuss the model (a knowledge graph), lessons learned along the way, and key findings for other systems attempting to bridge across broad disciplinary challenges.

As noted in the session description any research object produced should be made ’FAIR’; but ‘FAIR’ objects require attribution. What does that mean for the vast variety of research objects? What roles need to be acknowledged, how and where? These are questions the Federation of Earth Science Data Partners (ESIP) Research Object Citation cluster have been addressing. In particular, we have been applying the lens of the CREDiT taxonomy roles in these discussions. Here our conclusions to date will be described.

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.

In this review, we adopt the definition that 'Data citation is a reference to data for the purpose of credit attribution and facilitation of access to the data' (TGDCSP 2013: CIDCR6). Furthermore, access should be enabled for both humans and machines (DCSG 2014). We use this to discuss how data citation has evolved over the last couple of decades and to highlight issues that need more research and attention. Data citation is not a new concept, but it has changed and evolved considerably since the beginning of the digital age. Basic practice is now established and slowly but increasingly being implemented. Nonetheless, critical issues remain. These issues are primarily because we try to address multiple human and computational concerns with a system originally designed in a non-digital world for more limited use cases. The community is beginning to challenge past assumptions, separate the multiple concerns (credit, access, reference, provenance, impact, etc.), and apply different approaches for different use cases.