Designing Institutional Infrastructure for E-Science

A new generation of information and communication infrastructures, including advanced Internet computing and Grid technologies, promises more direct and shared access to more widely distributed computing resources than was previously possible. Scientific and technological collaboration, consequently, is more and more dependent upon access to, and sharing of digital research data. Thus, the U.S. NSF Directorate committed in 2005 to a major research funding initiative, “Cyberinfrastructure Vision for 21st Century Discovery”. These investments are aimed at enhancement of computer and network technologies, and the training of researchers. Animated by much the same view, the UK e-Science Core Programme has preceded the NSF effort in funding development of an array of open standard middleware platforms, intended to support Grid enabled science and engineering research. This proceeds from the sceptical view that engineering breakthroughs alone will not be enough to achieve the outcomes envisaged. Success in realizing the potential of e-Science—through the collaborative activities supported by the "cyberinfrastructure," if it is to be achieved, will be the result of a nexus of interrelated social, legal, and technical transformations.e-science, cyberinfrastructure, information sharing, research

Inside a Data Science Team: Data Crafting in Generating Strategic Value from Analytics

Current research agrees that the value of data lies in analytics that generate valuable insights for strategic purposes. However, little is known about how these insights are derived by data scientists. This research reports on the work of an embedded data science team at an organization striving to use people analytics to improve its strategic human resource management. We find that to create strategically valuable analytics, data scientists engage in data crafting, an approach to data science work that relies on broadcasting the potential value of data science towards the organization, cultivating a shared vision of value within the team, and creating value-adding data products with organizational customers. To do so, the team requires appropriate positioning and autonomy within the organization. Our findings have implications on understanding the role of data science teams and organizational data with respect to strategy, and practical insights for realizing strategic value from analytics

Mapping citizen science contributions to the UN Sustainable Development Goals

The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact

SMART Cables for Observing the Global Ocean: Science and Implementation

The ocean is key to understanding societal threats including climate change, sea level rise, ocean warming, tsunamis, and earthquakes. Because the ocean is difficult and costly to monitor, we lack fundamental data needed to adequately model, understand, and address these threats. One solution is to integrate sensors into future undersea telecommunications cables. This is the mission of the SMART subsea cables initiative (Science Monitoring And Reliable Telecommunications). SMART sensors would “piggyback” on the power and communications infrastructure of a million kilometers of undersea fiber optic cable and thousands of repeaters, creating the potential for seafloor-based global ocean observing at a modest incremental cost. Initial sensors would measure temperature, pressure, and seismic acceleration. The resulting data would address two critical scientific and societal issues: the long-term need for sustained climate-quality data from the under-sampled ocean (e.g., deep ocean temperature, sea level, and circulation), and the near-term need for improvements to global tsunami warning networks. A Joint Task Force (JTF) led by three UN agencies (ITU/WMO/UNESCO-IOC) is working to bring this initiative to fruition. This paper explores the ocean science and early warning improvements available from SMART cable data, and the societal, technological, and financial elements of realizing such a global network. Simulations show that deep ocean temperature and pressure measurements can improve estimates of ocean circulation and heat content, and cable-based pressure and seismic-acceleration sensors can improve tsunami warning times and earthquake parameters. The technology of integrating these sensors into fiber optic cables is discussed, addressing sea and land-based elements plus delivery of real-time open data products to end users. The science and business case for SMART cables is evaluated. SMART cables have been endorsed by major ocean science organizations, and JTF is working with cable suppliers and sponsors, multilateral development banks and end users to incorporate SMART capabilities into future cable projects. By investing now, we can build up a global ocean network of long-lived SMART cable sensors, creating a transformative addition to the Global Ocean Observing System

The Importance of Spatial Data to Open - Access National Archaeological Databases and the Development of Paleodemography Research

With generous support from the National Science Foundation, we have spent the past four years developing an archaeological radiocarbon database for the United States. Here, we highlight the importance of spatial data for open-access, national-scale archaeological databases and the development of paleodemography research. We propose a new method for analyzing radiocarbon time series in the context of paleoclimate models. This method forces us to confront one of the central challenges to realizing the full potential of national-scale databases: the quality of the spatial data accompanying radiocarbon dates. We seek to open a national discussion on the use of spatial data in open-source archaeological databases

Defining data science: a new field of inquiry

Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in 40+ disciplines, hundreds of research areas, and thousands of applications. Millions of data science publications contain myriad definitions of data science and data science problem solving. Due to its infancy, many definitions are independent, application-specific, mutually incomplete, redundant, or inconsistent, hence so is data science. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework using a data science journal for the data science community to achieve such a definition. This paper provides candidate definitions for essential data science artifacts that are required to discuss such a definition. They are based on the classical research paradigm concept consisting of a philosophy of data science, the data science problem solving paradigm, and the six component data science reference framework (axiology, ontology, epistemology, methodology, methods, technology) that is a frequently called for unifying framework with which to define, unify, and evolve data science. It presents challenges for defining data science, solution approaches, i.e., means for defining data science, and their requirements and benefits as the basis of a comprehensive solution

Math or Science? Using Longitudinal Expectations Data to Examine the Process of Choosing a College Major

Due primarily to the difficulty of obtaining ideal data, much remains unknown about how college majors are determined. We take advantage of longitudinal expectations data from the Berea Panel Study to provide new evidence about this issue, paying particular attention to the choice of whether to major in math and science. The data collection and analysis are based directly on a simple conceptual model which takes into account that, from a theoretical perspective, a student’s final major is best viewed as the end result of a learning process. We find that students enter college as open to a major in math or science as to any other major group, but that a large number of students move away from math and science after realizing that their grade performance will be substantially lower than expected. Further, changes in beliefs about grade performance arise because students realize that their ability in math/science is lower than expected rather than because students realize that they are not willing to put substantial effort into math or science majors. The findings suggest the potential importance of policies at younger ages which lead students to enter college better prepared to study math or science.

Challenges, Strategies, and Impacts of Doing Citizen Science with Marginalised and Indigenous Communities: Reflections from Project Coordinators

Citizen science is growing and increasingly realizing its potential in terms of benefiting science and society. However, there are significant barriers to engaging participants in non-Western, non-educated, non-industrialised, non-rich and non-democratic contexts. By reflecting on the experiences of 15 citizen science project coordinators, this paper contributes to the small but growing body of knowledge attempting to identify barriers and opportunities of doing citizen science with marginalised and Indigenous communities. Challenges affecting participation in the analysed projects include issues that range from lack of basic infrastructure and participant safety to unbalanced knowledge hierarchies and data rights. We found that, to overcome these challenges, projects have used several strategies, from promoting decentralized and low-tech solutions to engaging in bottom-up actions from a human-rights approach. Finally, our analysis of project impacts supports the idea that doing citizen science with marginalised and Indigenous communities might have a greater impact for participants than for science, as scientific achievements (although valuable) were not among the most important impacts highlighted in terms of project success. By providing stories from the field in a structured way, we aim to guide, to inform, and to inspire other citizen science projects, and to, ultimately, contribute to broader participation in citizen science in the futur

Challenges, Strategies, and Impacts of Doing Citizen Science with Marginalised and Indigenous Communities : Reflections from Project Coordinators

Citizen science is growing and increasingly realizing its potential in terms of benefiting science and society. However, there are significant barriers to engaging participants in non-Western, non-educated, non-industrialised, non-rich and non-democratic contexts. By reflecting on the experiences of 15 citizen science project coordinators, this paper contributes to the small but growing body of knowledge attempting to identify barriers and opportunities of doing citizen science with marginalised and Indigenous communities. Challenges affecting participation in the analysed projects include issues that range from lack of basic infrastructure and participant safety to unbalanced knowledge hierarchies and data rights. We found that, to overcome these challenges, projects have used several strategies, from promoting decentralized and low-tech solutions to engaging in bottom-up actions from a human-rights approach. Finally, our analysis of project impacts supports the idea that doing citizen science with marginalised and Indigenous communities might have a greater impact for participants than for science, as scientific achievements (although valuable) were not among the most important impacts highlighted in terms of project success. By providing stories from the field in a structured way, we aim to guide, to inform, and to inspire other citizen science projects, and to, ultimately, contribute to broader participation in citizen science in the future