OntoSoft

Gil, Yolanda; Ratnakar, Varun; Garijo, Daniel

doi:10.1145/2815833.2816955

Cited by 33 publications

(9 citation statements)

References 4 publications

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“…Activities and projects of this international community include: Force11 Software Citation Working group published draft Software Citation Principles for broad adoption among the various stakeholder communities. (Smith et al, ). WSSSPE has run a series of workshop providing a forum for problems relating to the role of software in research including credit ( http://wssspe.researchcomputing.org.uk/) (Katz et al, ; Katz, Choi, Niemeyer, et al, ; Katz, Choi, Wilkins‐Diehr, et al, ). The Software Sustainability Institute (SSI) provides guidelines on how to cite software http://software.ac.uk/so‐exactly‐what‐software‐did‐you‐use. Zenodo ( https://zenodo.org/) provides an online repository for software and its metadata and can provide DOIs. OntoSoft ( http://www.ontosoft.org/) facilitates capture of metadata and software publication providing training that supports software citation and credit (Gil et al, ). …”

Section: Discussion Of Open Issuesmentioning

confidence: 99%

Software and the Scientist: Coding and Citation Practices in Geodynamics

Hwang

Fish

Soito

et al. 2017

Earth and Space Science

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In geodynamics as in other scientific areas, computation has become a core component of research, complementing field observation, laboratory analysis, experiment, and theory. Computational tools for data analysis, mapping, visualization, modeling, and simulation are essential for all aspects of the scientific workflow. Specialized scientific software is often developed by geodynamicists for their own use, and this effort represents a distinctive intellectual contribution. Drawing on a geodynamics community that focuses on developing and disseminating scientific software, we assess the current practices of software development and attribution, as well as attitudes about the need and best practices for software citation. We analyzed publications by participants in the Computational Infrastructure for Geodynamics and conducted mixed method surveys of the solid earth geophysics community. From this we learned that coding skills are typically learned informally. Participants considered good code as trusted, reusable, readable, and not overly complex and considered a good coder as one that participates in the community in an open and reasonable manor contributing to both long-and short-term community projects. Participants strongly supported citing software reflected by the high rate a software package was named in the literature and the high rate of citations in the references. However, lacking are clear instructions from developers on how to cite and education of users on what to cite. In addition, citations did not always lead to discoverability of the resource. A unique identifier to the software package itself, community education, and citation tools would contribute to better attribution practices.

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Section: Discussion Of Open Issuesmentioning

confidence: 99%

Software and the Scientist: Coding and Citation Practices in Geodynamics

Hwang

Fish

Soito

et al. 2017

Earth and Space Science

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“…Adding visual elements to code that could then be used to generate visualizations on the fly might be a next step. Efforts to standardize metadata for model description move in this direction (Gil, Ratnakar, & Garijo, 2015), but they do not necessarily provide easy access to model structural components, nor are they searchable and visual.…”

Section: Automating Visual Representation Of Model Structurementioning

confidence: 99%

Visualization and Eco-hydrologic Models: Opening the black box

Tague

Frew

2020

Preprint

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Earth system models synthesize the science of interactions among multiple biophysical and, increasingly, human processes across a wide range of scales. Ecohydrologic models are a subset of earth system models that focus particularly on the complex interactions between ecosystem processes and the storage and flux of water. Ecohydrologic models often focus at scales where direct observations occur: plots, hillslopes, streams, and watersheds, as well as where land and resource management decisions are implemented. These models complement field-based and data-driven science by combining theory and data to create virtual laboratories. Ecohydrologic models are tools that managers can use to ask "what if" questions and domain scientists can use to explore the implications of new theory or measurements. Recent decades have seen substantial advances in ecohydrologic models, building on both new domain science and advances in software engineering and data availability. The increasing sophistication of ecohydrologic models however, presents a barrier to their widespread use and credibility. Because they are "black boxes," what the models actually do is rarely clear even to those who design and use them and this opacity leads to mistrust and complicates the interpretation of model results. For models to effectively advance our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. In this paper, we outline a framework for increasing the usefulness of ecohydrologic models through better visualization. We outline four complementary approaches, ranging from simple best practices that leverage existing technologies, to ideas that would engage novel software engineering and cutting edge human-computer interface design. Our goal is to open the ecohydrologic model black box in ways that will engage multiple audiences, from novices to model developers, and support learning, new discovery, and environmental problem solving.

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“…Gil et al . [], for example, have developed OntoSoft for the geoscience community; a metadata repository and ontology to describe software metadata. The development of code metadata, and consistent use of such a repository, while more challenging than development of metadata standards for data, will greatly facilitate the process of code identification and reuse, and through broad community engagement, lead the way toward the development of more formal ontologies for specific components of hydrological software, which will greatly improve model interoperability [see Elag and Goodall , ].…”

Section: Towards Reproducible Computational Hydrologymentioning

confidence: 99%

Most computational hydrology is not reproducible, so is it really science?

et al. 2016

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Reproducibility is a foundational principle in scientific research. Yet in computational hydrology the code and data that actually produces published results are not regularly made available, inhibiting the ability of the community to reproduce and verify previous findings. In order to overcome this problem we recommend that reuseable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that we can verify previous findings, and build directly from previous work. In cases where reproducing large‐scale hydrologic studies is computationally very expensive and time‐consuming, new processes are required to ensure scientific rigor. Such changes will strongly improve the transparency of hydrological research, and thus provide a more credible foundation for scientific advancement and policy support.

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OntoSoft

Cited by 33 publications

References 4 publications

Software and the Scientist: Coding and Citation Practices in Geodynamics

Software and the Scientist: Coding and Citation Practices in Geodynamics

Visualization and Eco-hydrologic Models: Opening the black box

Most computational hydrology is not reproducible, so is it really science?

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