Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study

Boscoe, Bernadette M.; Pasquetto, Irene V.; Golshan, Milena S.; Borgman, Christine L.

doi:10.1109/jcdl.2017.7991618

Cited by 116 publications

(63 citation statements)

References 9 publications

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“…On the NetLogo platform, [Kravari and Bassiliades 2015], was designed the model. The NetLogo platform allows running parallel experiments, is easy integration with the R software [Maronna et al 2019], and can be run on Jupyter Notebook [Randles et al 2017]. The Jupyter Notebook ran the experiment.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Classifying Organizational Structures on Targets in the Cooperative Target Observation

Silva¹,

Araújo²,

Raimundo³

et al. 2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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This paper proposes an approach to classify the organizational structure of a group of moving target agents that are continuously monitored by a smaller group of moving observer agents. The approach considers that the group of target agents can organize themselves according to eight different paradigms. The agents communicate through the exchange of messages whose contents are speech-act performative. We evaluate the approach considering seven techniques to solve the problem of classifying the group of target agents' organizational structure. The results show that the approach is promising, as it obtained a good performance, measured during experiments using agent-based simulations.

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Section: Experiments and Resultsmentioning

confidence: 99%

Classifying Organizational Structures on Targets in the Cooperative Target Observation

Silva¹,

Araújo²,

Raimundo³

et al. 2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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“…Sixth and last, we made general note of the authors' expressed reasons for referencing Jupyter notebooks. The reasons were too complex to be classified reliably, as we determined in an earlier pilot study [21].…”

Section: Methodsmentioning

confidence: 99%

Jupyter Notebooks as Discovery Mechanisms for Open Science: Citation Practices in the Astronomy Community

Wofford¹,

Boscoe²,

Borgman³

et al. 2020

Comput. Sci. Eng.

Self Cite

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Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter notebooks aggregate data, software, and other objects, they may facilitate or hinder citation, credit, and access to data and software. We report on a study of references to Jupyter notebooks in astronomy over a 5-year period (2014-2018). References increased rapidly, but fewer than half of the references led to Jupyter notebooks that could be located and opened. Jupyter notebooks appear better suited to supporting the research process than to providing access to research objects. We recommend that authors cite individual data and software objects, and that they stabilize any notebooks cited in publications. Publishers should increase the number of citations allowed in papers and employ descriptive metadata-rich citation styles that facilitate credit and discovery.

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“…Each program is available on github (barricklab-atjhu/Ising_programs) as a .py file that can be run directly in a terminal command line or in an IDE such as Anaconda (2016). In addition, the programs are combined in an interactive python notebook (.ipynb; Perez and Granger, 2007) that can be run in Jupyter (Randles et al, 2017). The format of input and output files.…”

Section: Repeat Proteinsmentioning

confidence: 99%

A collection of programs for one-dimensional Ising analysis of linear repeat proteins with point substitutions

Marold

Sforza

Geiger-Schuller

et al. 2020

Preprint

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A collection of programs is presented to analyze the thermodynamics of folding of linear repeat proteins using a 1D Ising model to determine intrinsic folding and interfacial coupling free energies. Expressions for folding transitions are generated for a series of constructs with different repeat numbers and are globally fitted to transitions for these constructs. These programs are designed to analyze Ising parameters for capped homopolymeric consensus repeat constructs as well as heteropolymeric constructs that contain point substitutions, providing a rigorous framework for analysis of the effects of mutation on intrinsic and directional (i.e., N-versus C-terminal) interfacial coupling freeenergies. A bootstrap analysis is provided to estimate parameter uncertainty as well as correlations among fitted parameters. Rigorous statistical analysis is essential for interpreting fits using the complex models required for Ising analysis of repeat proteins, especially heteropolymeric repeat proteins. Programs described here are available at https://github.com/barricklab-at-jhu/Ising_programs. 3

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Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study

Cited by 116 publications

References 9 publications

Classifying Organizational Structures on Targets in the Cooperative Target Observation

Classifying Organizational Structures on Targets in the Cooperative Target Observation

Jupyter Notebooks as Discovery Mechanisms for Open Science: Citation Practices in the Astronomy Community

A collection of programs for one-dimensional Ising analysis of linear repeat proteins with point substitutions

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