Hydraulic transmissivity inferred from ice-sheet relaxation following Greenland supraglacial lake drainages

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lai, C.-Y., Stevens, L. A., Chase, D. L., Creyts, T. T., Behn, M. D., Das, S. B., & Stone, H. A. Hydraulic transmissivity inferred from ice-sheet relaxation following Greenland supraglacial lake drainages. Nature Communications, 12(1), (2021): 3955, https://doi.org/10.1038/s41467-021-24186-6.Surface meltwater reaching the base of the Greenland Ice Sheet transits through drainage networks, modulating the flow of the ice sheet. Dye and gas-tracing studies conducted in the western margin sector of the ice sheet have directly observed drainage efficiency to evolve seasonally along the drainage pathway. However, the local evolution of drainage systems further inland, where ice thicknesses exceed 1000 m, remains largely unknown. Here, we infer drainage system transmissivity based on surface uplift relaxation following rapid lake drainage events. Combining field observations of five lake drainage events with a mathematical model and laboratory experiments, we show that the surface uplift decreases exponentially with time, as the water in the blister formed beneath the drained lake permeates through the subglacial drainage system. This deflation obeys a universal relaxation law with a timescale that reveals hydraulic transmissivity and indicates a two-order-of-magnitude increase in subglacial transmissivity (from 0.8 ± 0.3 mm3 to 215 ± 90.2 mm3) as the melt season progresses, suggesting significant changes in basal hydrology beneath the lakes driven by seasonal meltwater input.C.-Y.L. and L.A.S thank Lamont-Doherty Earth Observatory for funding through the Lamont Postdoctoral Fellowships. D.L.C acknowledges support from the National Science Foundation (NSF) Graduate Research Fellowship. T.T.C. was supported by NSF’s Office of Polar Programs (NSF-OPP) through OPP-1643970, the National Aeronautics and Space Administration (NASA) through NNX16AJ95G, and a grant from the Vetlesen Foundation. S.B.D. and M.D.B. acknowledge funding from NSF-OPP and NASA’s Cryospheric Sciences Program through OPP-1838410, ARC-1023364, ARC-0520077, and NNX10AI30G. H.A.S. thanks the High Meadows Environmental Institute and the Carbon Mitigation Initiative at Princeton University. This publication was supported by the Princeton University Library Open Access Fund

Towards Principled Responsible Research and Innovation: Employing the Difference Principle in Funding Decisions

Responsible Research and Innovation (RRI) has emerged as a science policy framework that attempts to import broad social values into technological innovation processes whilst supporting institutional decision-making under conditions of uncertainty and ambiguity. When looking at RRI from a ‘principled’ perspective, we consider responsibility and justice to be important cornerstones of the framework. The main aim of this article is to suggest a method of realising these principles through the application of a limited Rawlsian Difference Principle in the distribution of public funds for research and innovation. There are reasons why the world's combined innovative capacity has spewed forth iPhones and space shuttles but not yet managed to produce clean energy or universal access to clean water. (Stilgoe 2013, xii) I derive great optimism from empathy's evolutionary antiquity. It makes it a robust trait that will develop in virtually every human being so that society can count on it and try to foster and grow it. It is a human universal. (de Waal 2009, 209) Responsible Research and Innovation (RRI) has emerged as a science policy framework that attempts to import broad social values into technological innovation processes whilst supporting institutional decision-making under conditions of uncertainty and ambiguity. In this respect, RRI re-focuses technological governance from standard debates on risks to discussions about the ethical stewardship of innovation. This is a radical step in Science & Technology (S&T) policy as it lifts the non-quantifiable concept of values into the driving seat of decision-making. The focus of innovation then goes beyond product considerations to include the processes and – importantly – the purposes of innovation (Owen et al. 2013, 34). Shared public values are seen as the cornerstone of the new RRI framework, while market mechanisms and risk-based regulations are of a secondary order. What are the values that could drive RRI? There are different approaches to the identification of public values. They can be located in democratically agreed processes and commitments (such as European Union treaties and policy statements) or they can be developed organically via public engagement processes. Both approaches have advantages and disadvantages. For instance, although constitutional values can be regarded as democratically legitimate, their application to specific technological fields can be difficult or ambiguous (Schroeder and Rerimassie 2015). On the other hand, public engagement can accurately reflect stakeholder values but is not necessarily free from bias and lobbyist agenda setting. We argue that if RRI is to be more successful in resolving policy dilemmas arising from poorly described and uncertain technological impacts, basic universal principles need to be evoked and applied. When looking at RRI from a ‘principled’ perspective, we consider responsibility and justice to be important cornerstones of the framework. One could describe them in the following manner: Research and innovation should be conducted responsibly. Publicly funded research and innovation should be focused fairly on socially beneficial targets. Research and innovation should promote and not hinder social justice. The main aim of this article is to suggest a method of realising these principles through the application of a limited Rawlsian Difference Principle in the distribution of public funds for research and innovation. This paper is in three parts. The first part discusses the above principles and introduces the Rawlsian Difference Principle. The second part identifies how RRI is currently applied by public funding bodies. The third part discusses the operationalisation of the Rawlsian Difference Principle in responsible funding decisions

Invest to Save: Report and Recommendations of the NSF-DELOS Working Group on Digital Archiving and Preservation

Digital archiving and preservation are important areas for research and development, but there is no agreed upon set of priorities or coherent plan for research in this area. Research projects in this area tend to be small and driven by particular institutional problems or concerns. As a consequence, proposed solutions from experimental projects and prototypes tend not to scale to millions of digital objects, nor do the results from disparate projects readily build on each other. It is also unclear whether it is worthwhile to seek general solutions or whether different strategies are needed for different types of digital objects and collections. The lack of coordination in both research and development means that there are some areas where researchers are reinventing the wheel while other areas are neglected. Digital archiving and preservation is an area that will benefit from an exercise in analysis, priority setting, and planning for future research. The WG aims to survey current research activities, identify gaps, and develop a white paper proposing future research directions in the area of digital preservation. Some of the potential areas for research include repository architectures and inter-operability among digital archives; automated tools for capture, ingest, and normalization of digital objects; and harmonization of preservation formats and metadata. There can also be opportunities for development of commercial products in the areas of mass storage systems, repositories and repository management systems, and data management software and tools.

A Review of High School Level Astronomy Student Research Projects over the last two decades

Since the early 1990s with the arrival of a variety of new technologies, the capacity for authentic astronomical research at the high school level has skyrocketed. This potential, however, has not realized the bright-eyed hopes and dreams of the early pioneers who expected to revolutionise science education through the use of telescopes and other astronomical instrumentation in the classroom. In this paper, a general history and analysis of these attempts is presented. We define what we classify as an Astronomy Research in the Classroom (ARiC) project and note the major dimensions on which these projects differ before describing the 22 major student research projects active since the early 1990s. This is followed by a discussion of the major issues identified that affected the success of these projects and provide suggestions for similar attempts in the future.Comment: Accepted for Publication in PASA. 26 page

A Review of the Open Educational Resources (OER) Movement: Achievements, Challenges, and New Opportunities

Examines the state of the foundation's efforts to improve educational opportunities worldwide through universal access to and use of high-quality academic content

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes

Lessons learned in effective community-university-industry collaboration models for smart and connected communities research

In 2017, the Boston University Hariri Institute for Computing and the Initiative on Cities co-hosted two workshops on “Effective Community-University-Industry Collaboration Models for Smart and Connected Communities Research,” with the support of the National Science Foundation (NSF). These efforts brought together over one hundred principal investigators and research directors from universities across the country, as well as city officials, community partners, NSF program managers and other federal agency representatives, MetroLab Network representatives and industry experts. The focus was on transdisciplinary “smart city” projects that bring technical fields such as engineering and computer science together with social scientists and community stakeholders to tackle community-sourced problems. Presentations, panel discussions, working sessions and participant white papers surfaced operational models as well as barriers and levers to enabling effective research partnerships. To capture the perspectives and beliefs of all participants, in addition to the presenters, attendees were asked to synthesize lessons on each panel topic. This white paper summarizes the opportunities and recommendations that emerged from these sessions, and provides guidance to communities and researchers interested in engaging in these types of partnerships as well as universities and funders that endeavor to nurture them. It draws on the collective wisdom of the assembled participants and the authors. While many of the examples noted are drawn from medium and large cities, the lessons may still be applicable to communities of various sizes.National Science Foundatio