Looking before leaping: Creating a software registry

What lessons can be learned from examining numerous efforts to create a repository or directory of scientist-written software for a discipline? Astronomy has seen a number of efforts to build such a resource, one of which is the Astrophysics Source Code Library (ASCL). The ASCL (ascl.net) was founded in 1999, had a period of dormancy, and was restarted in 2010. When taking over responsibility for the ASCL in 2010, the new editor sought to answer the opening question, hoping this would better inform the work to be done. We also provide specific steps the ASCL is taking to try to improve code sharing and discovery in astronomy and share recent improvements to the resource.Comment: 11 pages; submission for WSSSPE2. Revised after review for publication in the Journal of Open Research Softwar

It's your software! Get it cited the way you want!

Are others using software you've written in their research and citing it as you want it to be cited? Software can be cited in different ways, some good, and some not good at all for tracking and counting citations in indexers such as ADS and Clarivate's Web of Science. Generally, these resources need to match citations to resources, such as journal articles or software records, they ingest. This presentation covered common reasons as to why a code might not be cited well (in a trackable/countable way), which citation methods are trackable, how to specify this information for your software, and where this information should be placed. It also covered standard software metadata files, how to create them, and how to use them. Creating a metadata file, such as a CITATION.cff or codemeta.json, and adding it to the root of your code repo is easy to do with the ASCL's metadata file creation overlay, and will help out anyone wanting to give you credit for your computational method, whether it's a huge carefully-written and tested package, or a short quick-and-dirty-but-oh-so-useful code.Comment: 2 figures, 1 tabl

Using the Astrophysics Source Code Library: Find, cite, download, parse, study, and submit

The Astrophysics Source Code Library (ASCL) contains 3000 metadata records about astrophysics research software and serves primarily as a registry of software, though it also can and does accept code deposit. Though the ASCL was started in 1999, many astronomers, especially those new to the field, are not very familiar with it. This hands-on virtual tutorial was geared to new users of the resource to teach them how to use the ASCL, with a focus on finding software and information about software not only in this resource, but also by using Google and NASA's Astrophysics Data System (ADS). With computational methods so important to research, finding these methods is useful for examining (for transparency) and possibly reusing the software (for reproducibility or to enable new research). Metadata about software is useful for, for example, knowing how to cite software when it is used for research and studying trends in the computational landscape. Though the tutorial was primarily aimed at new users, advanced users were also likely to learn something new.Comment: 4 figure

Realism in Selected Works of William Dean Howells

William Dean Howells, poet, novelist and critic, and one of the most prolific American writers of the latter nineteenth century, set sail on the literary seas during the period of vast economic, social and political reforms in America. The master craftsmen of the industrial East were giving way to mass production in the The gay West, where individual miners, trappers, and settlers could once strike it rich, was now being taken over by big operators and gigantic companies. As Paul Revere, the master silversmith, was the symbol of the old East, and Jim Bridger, the footloose scout, a symbol of the old West, so the big men of this new America were Andrew Carnegie, who made steel, John D. Rockefeller, who pumped petroleum, Commodore Vanderbilt, who owned railroads, and J. P. Morgan, whose money power extended into a dozen different industries. The country had gone big business, and by 1880, there was an inevitable reaction against the optimism and hope of the Early West. Farmers had settled too far out on the prairie, on land that was too dry for farming, and drought was beginning to drive them back. They were aware that most of the good western land was already taken up and most of the rich minerals were now owned by Companies. There was no longer the same chance of a fresh start which the West had offered for almost a century. Now there was no farther west to which they could move, no more get-rich-quick possibilities, no pot of gold behind the rainbow. Westerners began to realize that they had to fight it out where they were. So it was that late in the century the West was losing its symbolic quality as an American symbol of opportunity, and becoming a world of hard facts. And out of this setting, realism came to literature. Realism— a feeling on the part of an author that the thing worth writing about was the real thing. Let\u27s forget the misty and glamourous past, people were saying. Let\u27s forget old optimism and hopefulness. Let\u27s face things as they are. Let\u27s quit writing about men who never existed—the ideal men, the unbeatable Americans, the Paul Bunyans, the great heroes. Let\u27s write about the ordinary man, the average man, the little man. Our government, our business, our economic lives aren\u27t perfect, let\u27s show them as they are. That was the spirit in which Howells began to write fiction—realistic fiction.1 1Rewey B.Inglis, et, al., Adventures in American Literature (New York: Harcourt, Brace and Company, 1949), pp. 724-725

Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective

Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and autonomous-vehicle navigation. Can machine learning enable similar leaps in the natural and social sciences? This is certainly the expectation in many scientific fields and recent years have seen a plethora of applications of non-linear models to a wide range of datasets. However, flexible non-linear solutions will not always improve upon manually adding transforms and interactions between variables to linear regression models. We discuss how to recognize this before constructing a data-driven model and how such analysis can help us move to intrinsically interpretable regression models. Furthermore, for a variety of applications in the natural and social sciences we demonstrate why improvements may be seen with more complex regression models and why they may not

Quantum Mechanically Derived Biomolecular Force Fields

Molecular mechanics force fields are used to understand and predict a wide range of biological phenomena. However, current biomolecular force fields assume that parameters must be fit to the properties of small molecules and subsequently transferred to model large proteins. Here, we look to challenge this assumption and create a new class of QUantum mechanical BEspoke (QUBE) biomolecular force fields. QUBE is based around the use of atoms-in-molecule electron density partitioning to derive the non-bonded component of the force field. This thesis focusses on the derivation and validation of compatible bonded parameters that enable QUBE to be used in protein modelling. Whilst parametrizing the bond and angle components of the new force fields, the inade- quacy of current parametrization schemes became apparent. This led to the development of a new bond and angle parametrization method that relies on only the quantum mechanical Hessian of a molecule. The new method resulted in the accurate recreation of the normal modes for a set of small molecules, heterocyclic molecules, dipeptides and a large osmium containing complex. The new method had an overall error in the normal mode frequency recreation of 6.3%, which is below that of the popular force field OPLS (7.4%). Torsional parameters were also calculated for our protein force field and the conformational preferences of peptides and proteins were subsequently tested. Comparable accuracy to standard transferable force fields was achieved for simulations of short peptides, and this was demonstrated by the simulations’ J coupling errors, rotamer populations and backbone distributions. The J coupling errors remained at an acceptable level for protein simulations of ubiquitin and GB3, and two of the five proteins tested retained their experimental structure well during the MD simulations. In certain regions, particularly those with no clear secondary structure or a turn, three of the proteins exhibited some deviations from the experimental structure as the simulations progressed. However, given that this is the first generation of our QUBE force field, with future version envisaged, we view the results as promising. Additionally, improvements to the electrostatic potential of system-specific small molecule force fields were investigated. A new method was developed to add off centre point charges. The extra charges led to a reduction in the error of an atom’s electrostatic potential of 65.8%, as well as improvements to the free energy of hydration, for a benchmark set of molecules. The methods and software developed in this thesis have the potential to improve the accuracy and accessibility of force field derivation, particularly for applications in biomolecular modelling.EPSR