EduPar-20 Keynote Speaker: The Computing and Information Science Landscape: A look forward

In this talk, I will briefly describe current robotics development and activities in the NSF Division of Information and Intelligent Systems (IIS), Directorate for Computer and Information Science and Engineering (CISE), focusing on Robust Intelligence. I will also briefly discuss my current research activities in distributed mobile robots and skill learning for humanoid robots. These research activities are part of my independent research development while serving at the National Science Foundation.

The rapid expansion of big data has transformed research practices across disciplines, yet disparities exist in its adoption among U.S. institutions of higher education. This study examines trends in NSF-funded big data-driven research across research domains, institutional classifications, and directorates. Using a quantitative approach and natural language processing (NLP) techniques, we analyzed NSF awards from 2006 to 2022, focusing on seven NSF research areas: Biological Sciences, Computer and Information Science and Engineering, Engineering, Geosciences, Mathematical and Physical Sciences, Social, Behavioral and Economic Sciences, and STEM Education (formally known as Education and Human Resources). Findings indicate a significant increase in big data-related awards over time, with CISE (Computer and Information Science and Engineering) leading in funding. Machine learning and artificial intelligence are dominant themes across all institutions’ classifications. Results show that R1 and non-minority-serving institutions receive the majority of big data-driven research funding, though HBCUs have seen recent growth due to national diversity initiatives. Topic modeling reveals key subdomains such as cybersecurity and bioinformatics benefiting from big data, while areas like Biological Sciences and Social Sciences engage less with these methods. These findings suggest the need for broader support and funding to foster equitable adoption of big data methods across institutions and disciplines.

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Through the CISE Pathways to Revitalized Undergraduate Computing Education (CPATH) program, NSF's Directorate for Computer and Information Science and Engineering (CISE) is challenging its partners - colleges, universities and other stakeholders committed to advancing the field of computing and its impact - to transform undergraduate computing education on a national scale, to meet the challenges and opportunities of a world where computing is essential to U.S. leadership and economic competitiveness across all sectors of society. [4] One approach to such revitalization is the introduction of interdisciplinary courses to expand the scope of computing education. This approach has its roots in programs such as Lynn Stein's small footprint core [2] and Georgia Tech's threads [3]. The basic idea is to have students from various disciplines work together on computing projects to expand their educational horizons and make computing courses more appealing. This panel brings together educators who have developed and taught interdisciplinary courses with these goals in mind. The panelists will share their experiences and solicit new ideas from the audience. We expect a lively discussion on the pros and cons of this approach.

Developing a major national resource such as the US National Science, Technology, Engineering, and Mathematics education Digital Library (NSDL) requires policy and technical expertise. Policy issues are as much a challenge as infrastructure in the construction of such a major digital library. This paper focuses on the policy making structures that have developed since 2000 when the US National Science Foundation began its funding for NSDL. The authors are the Vice Chair and Chair of the NSDL Policy Committee and report here on issues of voice, authority, and central issues for policy construction as these have emerged in the NSDL context.

The experimental work was carried out at National Tsing Hua University, Institute of Photonics Technologies, supported by the Ministry of Science and Technology, Taiwan (grants 105-2112-M-007-030-MY3, 105-2112-M-001-030 and 104-2112-M-007-012-MY3). The concept of isolated circularly polarized attosecond pulses was developed by C.H.-G., D.D.H., M.M.M., C.G.D., H.C.K., A.B. and A.J.-B.. C.H.-G. acknowledges support from the Marie Curie International Outgoing Fellowship within the EU Seventh Framework Programme for Research and Technological Development (2007–2013), under Research Executive Agency grant agreement no. 328334. C.H.-G. and L.P. acknowledge support from Junta de Castilla y Leon (SA046U16) and the Ministerio de Economia y Competitividad (FIS2013-44174-P, FIS2016-75652-P). C.H.-G. acknowledges support from a 2017 Leonardo Grant for Researchers and Cultural Creators (BBVA Foundation). M.M.M. and H.C.K. acknowledge support from the Department of Energy Basic Energy Sciences (award no. DE-FG02-99ER14982) for the concepts and experimental set-up. For part of the theory, A.B., A.J.-B., C.G.D., M.M.M. and H.C.K. acknowledge support from a Multidisciplinary University Research Initiatives grant from the Air Force Office of Scientific Research (award no. FA9550-16-1-0121). A.J.-B. also acknowledges support from the US National Science Foundation (grant no. PHY-1734006). This work utilized the Janus supercomputer, which is supported by the US National Science Foundation (grant no. CNS-0821794) and the University of Colorado, Boulder. This research made use of the high-performance computing resources of the Castilla y Leon Supercomputing Center (SCAYLE, www.scayle.es), financed by the European Regional Development Fund (ERDF). J.L.E. acknowledges support from the National Science Foundation Graduate Research Fellowship (DGE-1144083). L.R. acknowledges support from the Ministerio de Educacion, Cultura y Deporte (FPU16/02591).

Funding agencies and research councils around the world rely on peer review to assess the potential impacts of proposed research. This article compares the procedures of two major public science agencies — the US National Science Foundation and the European Commission's 7th Framework Programme — for evaluating ex ante the potential societal impact of research proposals. In this paper we survey the state of the art and discuss some of the conceptual questions that arise in using ex ante peer review to assess the societal impact of scientific research. Copyright , Beech Tree Publishing.

In the first years of the 21st century a lot of educative innovation has been developed. One of the most relevant innovations for higher education institutions are the Massive Open On-line Courses (MOOC). In the field of Computer and Information Sciences, a lot of MOOC have been performed. In this paper we analyze the courses that are available in Coursera, one of the most popular platforms for delivering MOOC. Our intention is to assess to which extent the subjects suggested for the careers of Computer and Information Sciences are covered by the courses in Coursera. Despite the fact that are many courses in certain subjects, our results show that some subjects are not very well covered. Another conclusion that can be elicited from our analysis is that there are strong relationships among the courses that are available in Coursera. Key Words Massive Open On-line Courses (MOOC), Distance education, Computer Science.

Now in its tenth year (1985-94) as a US National Science Foundation supercomputing center, the Cornell Theory Center in upstate New York is committed to advancing research in computational science and engineering in concert with the technologies of high-performance computing and communication. Specifically, the center is developing globally scalable parallel computing resources for its user community. CTC researchers and staff are applying their expertise in parallel algorithm development and optimization to problems ranging from molecular modeling to operations research. In addition, CTC research staff often function as test pilots for computational techniques in their respective fields. Such collaborations with the academic and industrial research communities are designed to address problems of national interest. These include Grand Challenge problems in computational science and National Challenge applications, as well as efforts in parallel performance evaluation and tools development.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper, based on an invited Thesis Eleven presentation (8 August 2019), provides a ‘map of technopolitics’ that springs from an investigation of the theoretical notion of technological convergence adopted by the US National Science Foundation, signaling a new paradigm of ‘nano-bio-info-cogno’ (NBIC) technologies. This integration at the nano-level is expected to drive the next wave of scientific research, technology and knowledge economy. The paper explores the concept of ‘technopolitics’ by investigating the links between Wittgenstein’s anti-scientism and Lyotard’s ‘technoscience’, reviewing the history of the notion in the work of the Belgium philosopher Gilbert Hottois. The ‘deep convergence’ representing a new technoscientific synergy is the product of long-term trends of ‘bioinformational capitalism’ that harnesses the twin forces of information and genetic sciences that coalesce in the least mature ‘cognosciences’ in their application to education and research. The map of technopolitics systematically identifies the political relations between Big Tech and ‘new digital publics’ to reveal that the new paradigm is based on the supreme value of cognitive efficiency. There are a closely-knit cluster of concerns that frame a map of political issues about the fifth-generation technological impacts on human beings, their bodies and minds, and public institutions, not least the logic of the distribution and ownership of data, information and knowledge, and its effects on democracy.

The US National Science Foundation (NSF) in 2015 awarded funding for a first-of-a-kind distributed cyberinfrastructure (DCI) system called Jetstream. Jetstream will be the NSF's first production cloud for general-purpose science and engineering research and education. Jetstream, scheduled for production in January 2016, will be based on the OpenStack cloud environment software with a menu-driven interface to make it easy for users to select a pre-composed Virtual Machine (VM) to perform a particular discipline-specific analysis. Jetstream will use the Atmosphere user interface developed as part of iPlant, providing a low barrier to use by practicing scientists, engineers, educators, and students, and Globus services from the University of Chicago for seamless integration into the national cyberinfrastructure fabric. The team implementing Jetstream has as their primary mission extending the reach of the NSF's eXtreme Digital (XD) program to researchers, educators, and research students who have not previously used NSF XD program resources, including those in communities and at institutions that traditionally lack significant cyberinfrastructure resources. We will, for example, use virtual Linux Desktops to deliver DCI capabilities supporting research and research education at small colleges and universities, including Historically Black Colleges and Universities (HBCUs), Minority Serving Institutions (MSIs), Tribal colleges, and higher education institutions in states designated by the NSF as eligible for funding via the Experimental Program to Stimulate Competitive Research (EPSCoR). Jetstream will be a novel distributed cyberinfrastructure, with production components in Indiana and Texas. In particular, Jetstream will deliver virtual Linux desktops to tablet devices and PDAs with reasonable responsiveness running over cellular networks. This paper will discuss design and application plans for Jetstream as a novel Distributed CyberInfrastructure system for research education.

Brian joined OCLC Research in 1996. Since that time, he has worked on projects in many areas, including digital preservation, cooperative print management, and data-mining of bibliographic resources. He was a co-founder of the working group that developed the PREMIS Data Dictionary for preservation metadata, and served as co-chair of a US National Science Foundation blue-ribbon task force on economically sustainable digital preservation. Brian's academic background is in economics; he has a Ph.D. in agricultural economics. Brian's current research interests include stewardship of the evolving scholarly record, analysis of collective collections, and the system-wide organization of library resources.

SESYNC - National Socio-Environmental Synthesis Center, US National Science Foundation (Grant # DBI-1052875); USDA Forest Service Urban Forestry South; Resources For the Future

Nell’attuale fase di evoluzione del capitalismo, caratterizzata dal ruolocrescente della conoscenza come risorsa strategica per la crescita e la com-petitivita, l’efficacia dei sistemi scientifici costituisce un fattore fondamenta-le per il benessere delle nazioni. In particolare, la struttura istituzionale e leprestazioni di un sistema scientifico influiscono sulle capacita di un paesedi sviluppare ed utilizzare le nuove tecnologie derivanti direttamente da co-noscenze e da competenze accademiche (Guerrieri, 1999; Pavitt, Patel1999; Whitley, 2003), come nei settori dell’informatica e delle biotecnologie(Mansfield, 1995; McKelvey, 1996; Narin

Over the last decade, the United States paleontological collections community has invested heavily in the digitization of specimen-based data, including over 10 million USD funded through the National Science Foundation’s Advancing Digitization of Biodiversity Collections program. Fossil specimen data—9.0 million records and counting (Global Biodiversity Information Facility 2024)—are now accessible on open science platforms such as the Global Biodiversity Information Facility (GBIF). However, the full potential of this data is far from realized due to fundamental challenges associated with mobilization, discoverability, and interoperability of paleontological information within the existing cyberinfrastructure landscape and data pipelines. Additionally, it can be difficult for individuals with varying expertise to develop a comprehensive understanding of the existing landscape due to its breadth and complexity. Here, we present preliminary results from a project aiming to explore how we might address these problems. Funding from the US National Science Foundation (NSF) to the University of Colorado Museum of Natural History, Smithsonian National Museum of Natural History, and Arizona State University will result in, among other products, an “ecosystem map” for the paleontological collections community. This map will be an information-rich visualization of entities (e.g. concepts, systems, platforms, mechanisms, drivers, tools, documentation, data, standards, people, organizations) operating in, intersecting with, or existing in parallel to our domain. We are inspired and informed by similar efforts to map the biodiversity informatics landscape (Bingham et al. 2017) and the research infrastructure landscape (Distributed System of Scientific Collections 2024), as well as by many ongoing metadata cataloging projects, e.g. re3data and the Global Registry of Scientific Collections (GRSciColl). Our strategy for developing this ecosystem map is to model the existing information and systems landscape by characterizing entities, e.g. potentially in a graph database as nodes with relationships to other nodes. The ecosystem map will enable us to provide guidance for communities working across different sectors of the landscape, promoting a shared understanding of the ecosystem that everyone works in together. We can also use the map to identify points of entry and engagement at various stages of the paleontological data process, and to engage diverse members within the paleontological community. We see three primary user types for this map: people new(er) to the community, people with expertise in a subset of the community, and people working to integrate initiatives and systems across communities. Each of these user types needs tailored access to the ecosystem map and its community knowledge. By promoting shared knowledge with the map, users will be able to identify their own space within the ecosystem and the connections or partnerships that they can utilize to expand their knowledge or resources, relieving the burden on any single individual to hold a comprehensive understanding. For example, the flow of taxonomic information between publications, collections, digital resources, and biodiversity aggregators is not straightforward or easy to understand. A person with expertise in collections care may want to use the ecosystem map to understand why taxonomic identifications associated with their specimen occurrence records are showing up incorrectly when published to GBIF. We envision that our final ecosystem map will visualize the flow of taxonomic information and how it is used to interpret specimen occurrence data, thereby highlighting to this user where problems may be happening and whom to ask for help in addressing them (Fig. 1). Ultimately, development of this map will allow us to identify mobilization pathways for paleontological data, highlight core cyberinfrastructure resources, define cyberinfrastructure gaps, strategize future partnerships, promote shared knowledge, and engage a broader array of expertise in the process. Contributing domain-based evidence FAIRly*2 requires expertise that bridges the content (e.g. paleontology) and the mechanics (e.g. informatics). By centering the role of humans in open science cyberinfrastructure throughout our process, we hope to develop systems that create and sustain such expertise.