nanoHUB.org: Experiences and Challenges in Software Sustainability for a Large Scientific Community

Zentner, Lynn; Zentner, Michael; Farnsworth, Victoria; McLennan, Michael; Madhavan, Krishna; Klimeck, Gerhard

doi:10.5334/jors.bd

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…To test our hypotheses, we chose nanoHUB, a digital platform in the interdisciplinary field of nanoscience and nanotechnology. NanoHUB was launched as part of the NSF-funded Network of Computational Nanotechnology at Purdue University [28]. The tools on the platform are web-based software programs (web applets) which allow users to conduct simulations or complex calculations for research but also educational purposes.…”

Section: Case Setting and Datamentioning

confidence: 99%

The Fluidity of an Individual’s Core-Periphery Position in Digital Knowledge Fields

Schecter¹,

Brunswicker²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The literature on digital knowledge fields suggests that knowledge coproducers are embedded in a coreperiphery social structure. This structure engenders an individual-level tension: be in the core where there is support for successful knowledge integration or be in the periphery where one can work outside of peer pressure. In this paper, we focus on the fluidity of coreperiphery structures. We study the case of nanoHUB, a digital knowledge field of nanoscience and engineering. We analyze 17,821 contributions made by 251 knowledge producers who coproduce 609 scientific simulation tools over a nearly ten-year period, encompassing over six million lines of code. We find that knowledge producers seek to resolve the coreperiphery tension by moving towards and then away from the temporal core. Additionally, we find that proximity to the temporal core at the point of the knowledge production has a curvilinear relationship with code produced.

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Section: Case Setting and Datamentioning

confidence: 99%

The Fluidity of an Individual’s Core-Periphery Position in Digital Knowledge Fields

Schecter¹,

Brunswicker²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…nanoHUB [94] is a repository of nanotechnology models and software. HUBzero [49] is the framework underlying nanoHUB, which has been used to develop repositories in many other domains.…”

Section: Model Repositoriesmentioning

confidence: 99%

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Gil

Garijo

Khider

et al. 2021

ACM Trans. Interact. Intell. Syst.

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Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

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“…Zentner et al [69] describes experiences and challenges with the large nanoHUB.org community, where community is defined as a "body of persons of common and especially professional interests scattered through a larger society." Support is challenging because of the diversity of viewpoints and needs.…”

Section: Communitiesmentioning

confidence: 99%

Summary of the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1)

Katz,

Choi,

Lapp

et al. 2014

Preprint

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Challenges related to development, deployment, and maintenance of reusable software for science are becoming a growing concern. Many scientists' research increasingly depends on the quality and availability of software upon which their works are built. To highlight some of these issues and share experiences, the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1) was held in November 2013 in conjunction with the SC13 Conference. The workshop featured keynote presentations and a large number (54) of solicited extended abstracts that were grouped into three themes and presented via panels. A set of collaborative notes of the presentations and discussion was taken during the workshop.Unique perspectives were captured about issues such as comprehensive documentation, development and deployment practices, software licenses and career paths for developers. Attribution systems that account for evidence of software contribution and impact were also discussed. These include mechanisms such as Digital Object Identifiers, publication of "software papers", and the use of online systems, for example source code repositories like GitHub.This paper summarizes the issues and shared experiences that were discussed, including crosscutting issues and use cases. It joins a nascent literature seeking to understand what drives software work in science, and how it is impacted by the reward systems of science. These incentives can determine the extent to which developers are motivated to build software for the long-term, for the use of others, and whether to work collaboratively or separately. It also explores community building, leadership, and dynamics in relation to successful scientific software.

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nanoHUB.org: Experiences and Challenges in Software Sustainability for a Large Scientific Community

Cited by 8 publications

References 8 publications

The Fluidity of an Individual’s Core-Periphery Position in Digital Knowledge Fields

The Fluidity of an Individual’s Core-Periphery Position in Digital Knowledge Fields

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Summary of the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1)

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