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1. Rus, V., Fancsali, S.E., Venugopal, D., Pavlik Jr., P., Graesser, A.C., Bowman, D., Ritter, and The LDI Team. (2021)

   Rus, V. (January 2021, Proceedings of The Second Workshop of the Learner Data Institute , The 14th International Conference on Educational Data Mining (EDM 2021))

   null (Ed.)

   This paper provides a progress report on the first 18 months of Phase 1, the conceptualization phase, of the Learner Data Institute (LDI; www.learnerdatainstitute.org). LDI is currently in Phase 1, the conceptualization phase, to be followed by Phase 2, the institute or convergence phase. The current 2-year conceptualization phase has two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering for the future institute, and (2) use the framework to provide prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. By targeting a critical mass of key challenges that are at a tipping point, LDI aims to start a chain reaction that will transform the whole learning ecosystem. We will emphasize here the key elements of the LDI science convergence framework that our team developed, implemented, and now is in the process of evaluating and refining. We highlight important outcomes of the convergence framework and related processes, including a 5-year plan for the institute phase and data-intensive prototype solutions to transform the learning ecosystem. 

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2. The Learner Data Institute: Emerging Science Convergence and Research Opportunities

   Rus, V.; Fancsali, S.E.; Pavlik, Jr.; Venugopal, D.; Ritter, S.; Bowman, D.; The LDI Team (July 2022, The Learner Data Institute: Emerging Science Convergence and Research Opportunities, Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022))

   This paper provides an update of the Learner Data Institute (LDI; www.learnerdatainstitute.org) which is now in its third year since conceptualization. Funded as a conceptualization project, the LDI’s first two years had two major goals: (1) develop, implement, evaluate, and refine a framework for data-intensive science and engineering and (2) use the framework to start developing prototype solutions, based on data, data science, and science convergence, to a number of core challenges in learning science and engineering. One major focus in the third, current year is synthesizing efforts from the first two years to identify new opportunities for future research by various mutual interest groups within LDI, which have focused on developing a particular prototype solution to one or more related core challenges in learning science and engineering. In addition to highlighting emerging data-intensive solutions and innovations from the LDI’s first two years, including places where LDI researchers have received additional funding for future research, we highlight here various core challenges our team has identified as being at a “tipping point.” Tipping point challenges are those for which timely investment in data-intensive approaches has the maximum potential for a transformative effect. 

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3. Ecosystem Genomics Convergence Institute Report 2024

   Jensen, Zoe; Ul_Alam, MD_Nafis; Arad, Neda; Beach, Christine; Cortez, Erica; Davis, Griffin; del_Aguila_Pasquel, Jhon; Mustri, Michael; Nepal, Sallu; Northing, Penelope; et al (August 2024, ecosystemgenomics.arizona.edu)

   Ingram, Heather; Arnold, Anne; Mook, Anne; Saleska, Scott (Ed.)

   From May 13-15, 2024, the University of Arizona Ecosystem Genomics Community, participated in the annual Convergence Institute. The Convergence Institute is a 3-day summit meeting that is equal parts science, training, inclusion, professional development, evaluation, and science communication. A student pre-session offers professional development on a variety of topics. Each year, participants hear from a rotating panel about the challenges of ecosystem genomics, then present and– depending on their cohort– receive feedback on their proposed or completed summer research experiences. Students who have completed their NRT requirements are invited to help lead sessions and introduce presentation themes. This report was written by students participating in the team skills and writing workshops presented during the pre-session by Dr. Anne Mook, Mook, a senior team scientist at the Institute for Research in the Social Sciences (IRISS) at Colorado State University. The report includes an executive summary, general components of the institute, objectives, 2024 institute overview, conclusions, a participant directory, organizers and panelist directory, presentation topics by theme and key takeaways, relevance of convergence research, future directions, defining and evaluating Ecosystem Genomics as an emerging field and Appendices. 

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4. Tracing the Movement of Knowledge across Vast Distances and Long Temporal Spans

   https://doi.org/10.3167/TRANS.2019.090106  

   Smith, Pamela H. (June 2019, Transfers)

   A research group at the Max Planck Institute for the History of Science on “Itineraries of Materials, Recipes, Techniques, and Knowledge in the Early Modern World” held a series of workshops (2014–2015) on the movement of knowledge(materials, techniques, objects) across Eurasia, resulting in an edited volume. Participants articulated a framework of “entangled itineraries,” “material complexes,” and “nodes of convergence” by which historians might follow routes ofknowledge-making extending over very long distances and/or great spans of time. The key concepts are (1) “material complex” denoting the constellation of substances, practices, techniques, beliefs, and values that accrete as knowledge around materials; (2) the “relational field,” the social, intellectual, economic, emotional domain formed by a “node of convergence”—often a hub of trade and exchange—within which a material complex crystalizes; and (3) “itineraries,” or the routes taken by materials through which they stabilize and/ or transform. 

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5. Software Training in HEP

   Sudhir Malik, Samuel Meehan (February 2021, ArXivorg)

   null (Ed.)

   Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020s this will only become increasingly relevant throughout this decade. Meeting this sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g. Unix, version control,C++, continuous integration). The second is knowledge of domain specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving more specialized techniques. These include parallel programming, machine learning and data science tools, and techniques to preserve software projects at all scales. This paper dis-cusses the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP). The program equips participants with an array of software skills that serve as ingredients from which solutions to the computing challenges of HEP can be formed. Beyond serving the community by ensuring that members are able to pursue research goals, this program serves individuals by providing intellectual capital and transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP 

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