Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Jupyter for reproducible scientific workflows   Literate computing has emerged as an important tool for computational studies
and open science, with growing folklore of best practices. In this work, we
report two case studies - one in computational magnetism and another in
computational mathematics - where domain-specific software was exposed to the
Jupyter environment. This enables high-level control of simulations and
computation, interactive exploration of computational results, batch processing
on HPC resources, and reproducible workflow documentation in Jupyter notebooks.
In the first study, Ubermag drives existing computational micromagnetics
software through a domain-specific language embedded in Python. In the second
study, a dedicated Jupyter kernel interfaces with the GAP system for
computational discrete algebra and its dedicated programming language. In light
of these case studies, we discuss the benefits of this approach, including
progress toward more reproducible and reusable research results and outputs,
notably through the use of infrastructure such as JupyterHub and Binder.

22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th