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Spyder is a popular scientific environment and IDE among researchers, engineers and data analysts. Over the past few years, a common request from its users has been to support remote and cloud-based workflows, with data and code on high performance computing (HPC) clusters or cloud providers like AWS, GCP and DigitalOcean. Adding such a feature would open up many new research possibilities by enabling the scientific community to tackle data and compute-intensive programming tasks from the ease and efficiency of their local development environment. When users cannot connect to a server through a web browser or want to use an IDE for its reproducibility/reusuability features, they have two options to work remotely. They can either edit and execute their files in a terminal, which is not user-friendly; or start their IDE on the server and display it locally, which is resource-intensive on the remote machine and slow on the local one. Furthermore, scientists typically find it difficult to sync files to the local machine and/or version control their code on the remote one, so remote changes are often lost, out of sync or difficult to integrate with code developed locally. To address this situation, we are adding and enhancing support for local installations of Spyder to sync files and run code on remote compute servers. That will allow users to develop code from their local machine with a desktop IDE and the full computing power of a remote system at their fingertips, while efficiently using server resources. The first step in that direction is to enhance the existing remote code execution in Spyder. The current workflow for that is error-prone and quite cumbersome. It requires users to install the Spyder-Kernels package on the remote machine, manually start a kernel on it and provide the kernel connection file to the local machine. Since Spyder does not currently have a way to store or identify state changes, users will have to repeat this costly setup procedure if there are any configuration modifications on the remote machine. The poster details the plans to fully automate this process so that Spyder can start and connect to a remote kernel automatically, with the user only needing to provide the SSH credentials and some remote environment details. It presents our rationale to make Spyder an IDE with first-class support for remote development, then it will then showcase the new functionality described above, and finally outline our next steps to further improve this workflow for users., Funded by a Chan Zuckerberg Initiative Essential Open Source Software for Science grant - Cycle 5

In 2021, Trusted CI is conducting our focused "annual challenge" on the security (sometimes called "assurance") of software used by scientific computing and cyberinfrastructure. The goal of this year-long project, involving seven Trusted CI members, is to broadly improve the robustness of software used in scientific computing with respect to security. During the first part of the year, Trusted CI interviewed creators of scientific software and released a findings report based on those conversations. Part of that effort focused on identifying gaps in the software security of the projects and analyzing what barriers prevented them from being addressed. This guide is a direct result of those findings and attempts to begin bridging those gaps by providing concrete advice for anyone involved in developing or managing software for scientific projects. It is our hope that this effort will help scientific software projects better understand and ameliorate some of the most important gaps in the security of scientific software, and also to help policymakers understand those gaps so they can better understand the need for committing resources to improving the state of scientific software security. Ultimately, we hope that the effort will support scientific discovery itself by shedding light on the risks incurred in creating and using scientific software., This document is a product of Trusted CI. Trusted CI is supported by the National Science Foundation under Grant #1920430. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Background: Scientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer. Scope and approach: This paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community. Key findings and conclusions: The paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.

Getting scientific software installed correctly and ensuring it performs well has been a ubiquitous problem for several decades now, which is compounded currently by the changing landscape of computational science with the (re-)emergence of different microprocessor families, and the expansion to additional scientific domains like artificial intelligence and next-generation sequencing. The European Environment for Scientific Software Installations (EESSI) project aims to provide a ready-to-use stack of scientific software installations that can be leveraged easily on a variety of platforms, ranging from personal workstations to cloud environments and supercomputer infrastructure, without making compromises with respect to performance. In this article, we provide a detailed overview of the project, highlight potential use cases, and demonstrate that the performance of the provided scientific software installations can be competitive with system-specific installations., Software : practice & experience, 53 (1), ISSN:0038-0644, ISSN:1097-024X

Maintaining a web server is complex, expensive, and probably unnecessary for many purposes. A Google Colab notebook provides an alternative that may speed up scientific software’s dissemination and upkeep.

Author(s): Evans, TM; Siegel, A; Draeger, EW; Deslippe, J; Francois, MM; Germann, TC; Hart, WE; Martin, DF | Abstract: The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages—C, C++, and Fortran—and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.

An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, websites, and code comments) and the lack of structured metadata to describe it. As a result, researchers have to manually inspect existing tools to understand their differences and incorporate them into their work. This approach scales poorly with the number of publications and tools made available every year. In this paper we address these issues by introducing a framework for automatically extracting scientific software metadata from its documentation (in particular, their readme files); a methodology for structuring the extracted metadata in a Knowledge Graph (KG) of scientific software; and an exploitation framework for browsing and comparing the contents of the generated KG. We demonstrate our approach by creating a KG with metadata from over 10,000 scientific software entries from public code repositories.

The scientific software community's lack of computer science background and software engineering training takes a toll on scientists who need to develop software. We built a taxonomy of 2,110 reported problems and grouped them into three major axes: technical-related, socialrelated, and scientific-related problems.

Let us review the main elements of strategies, which we identified in the development of high energy physics simulations. Based on exemplary Monte Carlo simulation projects, several conjectures for the general aspects of work on scientific software are presented, including openness to change, robustness against external changes, modular development processes, and the constant evolution of tests. The analyzed projects were developed over more than 30 years, much longer than the typical lifetime of computer industry software. Some general observations are drawn together with quantitative evaluation on how the quality of software, measured mainly by the precision of the simulation, grows together with the complexity of software and tests. We find these observations interesting, even if attempts to generalize may remain to a degree speculative, as every researcher is tuning that to the specific needs of their own application. In the future, this can be validated, adjusted, or extended by the experiences gained in other projects.

Testing helps assure software quality by executing program and uncovering bugs. Scientific software developers often find it challenging to carry out systematic and automated testing due to reasons like inherent model uncertainties and complex floating point computations. We report in this paper a manual analysis of the unit tests written by the developers of the Storm Water Management Model (SWMM). The results show that the 1,458 SWMM tests have a 54.0% code coverage and a 82.4% user manual coverage. We also observe a “getter-setter-getter” testing pattern from the SWMM unit tests. Based on these results, we offer insights to improve test development and coverage.

Raw Data and Software replication package for the 2022 SBST workshop paper entitled "A Comparative Evaluation on the Quality of Manual and Automatic Test Case Generation Techniques for Scientific Software - A Case Study of a Python Project for Material Science Workflows". Additonally contains the graphics used in the paper in higher resolution.

Scientific software is all too often cumbersome to install and execute. The work described in a journal article is backed by code in a repository, with a set of instructions to install and execute. Often, after following these instructions, one is still unable to execute the software. Missing or incompatible dependencies, ineffective interfaces or lack of documentation are some of the reasons that frustrate users and may choose to not use some software altogether. We found our software tools exhibiting some of these pitfalls, so we started a journey towards more effective distribution of our bio2Byte Tools (b2BTools). We deployed them to a web server, which allowed their execution via web interface or REST-API. This approach carries some limitations, explained in board “web solution”. In this work, we present the deployment of our tools as a python package, which can be executed locally, and circumvents the limitations that our web solution has. Our experience is explained as a use case of how we believe scientific software should be published to increase its impact and integration in pipelines., Presented in the conference "Applied Bioinformatics in Life Sciences (4th edition)" during the second poster session on March 11th 2022, {"references":["Kagami, L. P., Orlando, G., Raimondi, D., Ancien, F., Dixit, B., Gavaldá-García, J., Ramasamy, P., Roca-Martínez, J., Tzavella, K., & Vranken, W. (2021). b2bTools: online predictions for protein biophysical features and their conservation. Nucleic Acids Research, 1. https://doi.org/10.1093/nar/gkab425"]}

These are the slides of a talk given on January 17th 2022, for the EEG at the TU Vienna. The talk is subtitled 'Tools and best practices of scientific software development' and roughly covers the following three sections: * git * GitHub * General python best practices

Due to their widespread adoption, frequent use, and diverse sensor capabilities, smartphones have become a powerful tool for academic studies focused on sampling human behaviour. While packing many technological advances, the need for researchers to develop their own software packages in order to run smartphone-based studies has resulted in a clear barrier to entry for researchers without the financial means, time, or technical knowledge required to overcome this technical barrier. We present AWARE-Light, a new smartphone application for data collection from study participants, which is accompanied by a website that provides any researcher the possibility to easily configure their own study. To highlight the possibilities of our tool, we present a research scenario on digital phenotyping for mental health. Furthermore, we describe the methodological configuration possibilities offered by our tool, and complement the technical configuration possibilities with recommendations from the existing literature.

In a talk given to theWorkshop on the Science of Scientific-Software Development and Use (12-15 Dec 2021; virtual), I present some results from a study of scientific software development with James Howison and Caifan Du. I provide a narrative from our data and our taxonomies of 1) grant-funded scientific software development organizations and 2) transitions that those organizations can make to pursue sustainability for theirsoftware. The goal of this talk is to give audience members(many of whom were unfamiliar with this style of research)a description of how data collection and analysis proceededas well as an argument for the value of this kind of research. I relate the narrative and taxonomiesto theoretical concepts from sociological literature to show invisible work in scientific peer production. I mention ongoing efforts and relevant literature to show that the visibility of scientific software work is changing but that the audience has opportunity to shape that process for their benefit. By establishing desired metrics and working with scholars of science, the scientific software community can directly affect the research conducted about their own work. Social studies of science can both inform software work and, as I have demonstrated, help argue for its valuation. Initial study results presented here have been published in prior workshop papers; journal article is in progress. Talk script is in slide notes. All illustrations created by the author (contact author if you'd like individual images or Illustrator files for your own use). References in final slide.

This document presents a framework for testing and validating ExPaNDS services against reference data sets. The process is demonstrated for the case of Jupyter notebook type services.

HUBzero has different subsystems for managing and launching the execution of software tools. These subsystems enable the setup and installation of software tools to be used by the end user community via the web browser easily. Earlier subsystems provided the capability to interact with Graphical tools via VNC or submitted compute jobs to HPC clusters. This tutorial will introduce and discuss the new SimTools, which provides a pluggable framework to package and launch rich interactive web applications by leveraging Jupyter notebooks. In addition, SimTool services and requirements, all the data they generate are accessible and queryable. In this tutorial, we will walk attendees through how SimTools operate within the nanoHUB.org gateway. In a 90-minute session designed for beginners, attendees will create an account on nanoHUB to complete hands-on activities during the tutorial. Some experience with Python is preferred.

Sustainable scientific software needs a strong collaboration framework to ensure continuity by passing on the tools, skills and knowledge needed to the next generation. The COVID-19 pandemic triggered the unexpected effect of accelerating the development of remote platforms and tools to open up collaborations to a wider global community. In this article we outline the elements needed for such a framework, such as education, tools and community building, and discuss the current advances in technology with a nod to the future.

Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific computing. In this survey paper, we examine a modest set of HPC scientific simulation applications that are already using cutting-edge HPC platforms. Several have been in existence for a decade or more. Our objective in this survey is twofold: first, to understand the landscape of scientific computing on HPC platforms in order to distill the currently scattered knowledge about software practices that have helped both developer and software productivity, and second, to understand the kind of tools and methodologies that need attention for continued productivity.

espanolLa mayoria de los textos de Geometria Diferencial elemental aborda el estudio del aparato de Frenet-Serret de curvas espaciales a partir de una parametrizacion de las mismas. Sin embargo, es posible calcular el aparato de Frenet-Serret de curvas espaciales dadas como la interseccion de dos superficies expresadas en forma cartesiana. En estetrabajo se presenta el nuevo paquete, FSAparatus, codificado en el lenguaje del software cientifico Mathematica v. 11.2. Las salidas obtenidas son enteramente compatibles con los comandos propios del Mathematica. Este paquete podria constituirse en una herramienta en el proceso de ense˜nanza aprendizaje del curso geometria diferencial de pre-grado. EnglishMost elementary Differential Geometry texts address the study of Frenet-Serret apparatus of spatial curves from a parameterization thereof. However, it is possible to calculate the Frenet-Serret apparatus of given spatial curves as the intersection of two surfaces expressed in Cartesian form. In this work the new package, FSAparatus, coded in the language of the scientific software is presented Mathematica v. 11.2. The outputs obtained are entirely compatible with the own commands of Mathematica. This package could become a tool in the process of Teaching course learning differential geometry of undergraduate.

In this paper we present research on improving the resilience of the execution of scientific software, an increasingly important concern in High Performance Computing (HPC). We build on an existing high-level abstraction framework, the Oxford Parallel library for Structured meshes (OPS), developed for the solution of multi-block structured mesh-based applications, and implement an algorithm in the library to carry out checkpointing automatically, without the intervention of the user. The target applications are a hydrodynamics benchmark application from the Mantevo Suite, CloverLeaf 3D, the sparse linear solver proxy application TeaLeaf, and the OpenSBLI compressible Navier–Stokes direct numerical simulation (DNS) solver. We present (1) the basic algorithm that OPS relies on to determine the optimal checkpoint in terms of size and location, (2) improvements that supply additional information to improve the decision, (3) techniques that reduce the cost of writing the checkpoints to non-volatile storage, (4) a performance analysis of the developed techniques on a single workstation and on several supercomputers, including ORNL’s Titan. Our results demonstrate the utility of the high-level abstractions approach in automating the checkpointing process and show that performance is comparable to, or better than the reference in all cases.

This report is the working document for the Research Software definition workshop held on February 23rd2021 as part of the FAIR4RS working group, subgroup 3 activities. The workshop wasdivided into 3 themes: 1. The concept of an "inclusive" vs. "exclusive" definition 2.Softwareexamples to complete report's case study 3. The "chicken or the egg dilemma" ofsoftwarein a scholarly infrastructure

Software is essential in modern research; it plays vital roles at multiple stages of the research lifecycle. The term Research Software is widely used in the academic community but, what do we mean when we use these terms? Software and research? When you think of software, you may think of a digital object that is executed on a machine. Yet software is more than just this, it is a complex and evolving artifact. It may be a concept or a project designed to solve a puzzle by a team or a community that develops its functionalities and algorithms, which might not be digital objects. Furthermore, the software artifacts are digital objects, e.g., executables and source code files for different environments. These digital artifacts, which are used in a scholarly setting, might be important in the research process, but should all these be considered Research Software? This report is the result of a discussion examining the scope of the community definition of the FAIR principles for Research Software as part of the work in the FAIR for Research Software working group (FAIR4RS). We aim to clarify the scope of the FAIR principles by identifying which software artifacts the FAIR principles should apply to. This discussion portrayed a complex landscape of software uses in research and existing definitions that can help to better understand the complexity of different types of software in academia. Finally we determine the scope of the FAIR4RS with a short and concise definition of Research Software as a separate metaphor of software in research.

Product line engineering emerged from a fruitful interaction of applied research in academia, industry research, and software engineering practice. SPLC was created as the primary venue to exchange ideas on this emerging topic and integrate the communities. Yet, today, SPLC is mostly regarded as an academic conference with little industry participation. Since a strong integration of academia and industry is often seen positive, here, we try to better understand motivations for practitioners to visit academic conferences like SPLC and the impact this has on such conferences. This analysis is based on nine systematic interviews with practitioners and researchers, who have been members of the SPLC community and other leading software engineering communities for a long time. Our preliminary results clarify the relevance and interest of practitioners and researchers to exchange knowledge and learn when attending scientific software engineering conferences such as SPLC. Yet, the results also highlight the differences between the goals of industry and academic conference participants, which often lead to inefficiencies and even barriers for constructive interaction at scientific conferences such as SPLC. We use this as a basis for pointing out further discussion points, both from the perspective of the interviewees as well as the authors.

[EN] Scientific software is a fundamental player in modern science, participating in all stages of scientific knowledge production. Software occasionally supports the development of trivial tasks, while at other instances it determines procedures, methods, protocols, results, or conclusions related with the scientific work. The growing relevance of scientific software as a research product with value of its own has triggered the development of quantitative science studies of scientific software. The main objective of this study is to illustrate a link-based webometric approach to characterize the online mentions to scientific software across different analytical frameworks. To do this, the bibliometric software VOSviewer is used as a case study. Considering VOSviewer's official website as a baseline, online mentions to this website were counted in three different analytical frameworks: academic literature via Google Scholar (988 mentioning publications), webpages via Majestic (1,330 mentioning websites), and tweets via Twitter (267 mentioning tweets). Google scholar mentions shows how VOSviewer is used as a research resource, whilst mentions in webpages and tweets show the interest on VOSviewer's website from an informational and a conversational point of view. Results evidence that URL mentions can be used to gather all sorts of online impacts related to non-traditional research objects, like software, thus expanding the analytical scientometric toolset by incorporating a novel digital dimension., RC was partially funded by the South African DST-NRF Center of Excellence in Scientometrics and Science, Technology, and Innovation Policy (SciSTIP).

This paper focuses on a simple, lightweight model with tools and practices to help small scientific software teamsintroduce structure and process to their management efforts. We emphasize a handful of tools and practices we have found to be broadly useful.This model has been used and evolved as part of the author’s student research efforts at St. John’s University. The model, tools and practices described here can be useful for large teams, too. Often large teams are composed of small teams, so these practices can be applied at the small team level and applied similarly to cross-team leadership as a group. Furthermore, team policy becomes even more important on large teams, as do lightweight communication strategies such as project Kanban boards where the status of activities can be referenced asynchronously via online tools.

Science is similar to a game, as both involve rules-based participation in search of optimal outcomes. Supported by key texts in history and philosophy of science, the authors propose a game-based model for understanding scientific inquiry and practice, particularly through computational resources. They conclude that this model creates space for more speculative and reflective approaches to scientific practice and can contribute to the design and development of better scientific software, simulation and visualization.

Discussions of how the version control tool, Git, was introduced to technical teams at Sandia National Laboratories via structured training courses; a history of the motivation and approaches used to create and present training content.

Discussions of how the version control tool, Git, was introduced to technical teams at Sandia National Laboratories via structured training courses; a history of the motivation and approaches used to create and present training content.

To make a developer truly productive, that person must also be happy. This paper describes 10 practices to help make remote workers productive and happy.

A discussion of what enables the success of a remote software engineer team member, in particular for research software development.

A discussion of software development practices that are useful for medium-sized scientific software development teams.

We describe a use-case for balancing the demands of commercial intellectual property and open-source in a collaborative scientific software project.

Our function-as-a-service (FaaS) framework transformed end users' questions into automated tests for scientific software. Our case study illustrates the FaaSification of scientific software testing and the importance of value-driven evaluations by focusing on real-world defect detection.

Scientific software can be used for decades and is constantly evolving. Recently, metamorphic testing, a property-based testing technique, has shown to be effective in testing scientific software, and the necessary properties are expressed as metamorphic relations. However, the development of metamorphic relations is difficult: it requires considerable practical expertise for the software tester. In this article, we report our experience of uncovering metamorphic relations from a user forum's questions of the United States Environmental Protection Agency's Storm Water Management Model (SWMM). Our study not only illustrates a wealth of end users’ expertise in interpreting software results, but also demonstrates the usefulness of classifying the user-oriented metamorphic relations into a nominal, ordinal, and functional hierarchy mainly from the software output perspective.

Metadata that describes scientific software in standard ways ??? in particular citation metadata such as title, authors, publication year, and venue ??? is essential for proper software citation implementation. The metadata should be generated by the software author, ...

Metamorphic testing uncovers defects by checking whether a relation holds among multiple software executions. These relations are known as metamorphic relations (MRs). For scientific software operating in a large multi-parameter input space, identifying MRs that determine the simultaneous changes among multiple variables is challenging. In this poster, we propose a fully automatic approach to classifying input and output variables from scientific software’s user manual, mining these variables’ associations from the user forum to generate MRs, and validating the MRs with existing regression tests. Preliminary results of our end-to-end MR support for the Storm Water Management Model (SWMM) are reported.

The proposed workflow increases the quality of research software in Computational Science and Engineering (CSE) by applying established software engineering practices extended with CSE-specific testing, visualization, and periodical cross-linking of scientific software, reports/publications, and datasets.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

v1.1Presentation on Best Practices for Scientific Software for the Winter 2021Reading, Writing, Reviewing, Experimental Design and Reproducibility Bi 254course. v1.0Presentation on Best Practices for Scientific Softwarefor the Winter 2020Reading, Writing, Reviewing, Experimental Design and Reproducibility Bi 253course.

We introduce Sterling, a web-based visualization tool that provides interactive views of relational models and allows users to create custom visualizations using modern JavaScript libraries like D3 and Cytoscape. We outline its design goals and architecture, and describe custom visualizations developed with Sterling that enable verification studies of scientific software used in production. While development is driven primarily by the Alloy community, other relational modeling languages are accommodated by Sterling’s data agnostic architecture.

Scientific software from all areas of scientific research is pivotal to obtaining novel insights. Yet the coding standards adherence of scientific software is rarely assessed, even though it might lead to incorrect scientific results in the worst case. Therefore, we have developed an open source tool and benchmark called , that provides a relative software coding standards adherence ranking of 48 computational tools from diverse research areas. can be used in the review process of software papers and to inform the scientific software selection process.

Understanding which input and output variables are related to each other is important for metamorphic testing, a simple and effective approach for testing scientific software. We report in this paper a quantitative analysis of input/output (I/O) associations based on co-occurrence statistics of the user manual, as well as association rule mining of a user forum, of the Storm Water Management Model (SWMM). The results show a positive correlation of the identified I/O pairs, and further reveal the complementary aspects of the user manual and user forum in supporting scientific software engineering tasks.

International audience; The development of scientific software relies on the collaboration of various stakeholders for the scientific computing and software engineering activities. Computer languages have an impact on both activities and related concerns, as well as on the engineering principles required to ensure the development of reliable scientific software. The more general-purpose the language is-with low-level, computing-related, system abstractions-the more flexibility it will provide, but also the more rigorous engineering principles and Validation & Verification (V&V) activities it will require from the language user. In this paper, we investigate the different levels of abstraction, linked to the diverse artifacts of the scientific software development process, a software language can propose, and the V&V facilities associated to the corresponding level of abstraction the language can provide to the user. We aim to raise awareness among scientists, engineers and language providers on their shared responsibility in developing reliable scientific software.

The paper presents a comparative analysis of the current instrumental software for studying models of controlled dynamic systems using artificial intelligence. We pay special attention to the open and free software use, as well as some aspects of this software use in the development of the author’s software package for modeling of controlled dynamic systems. The results of computational experiments based on the specified software package are presented. We propose a formalized description of a generalized model of a nonlinear dynamic switchable system. Based on the implementation of the neural network switching generation algorithm and the neural network training algorithm, we present a graphical interpretation of the trajectories in a model example. During the experiments, the correctness of the algorithms was checked, taking into account the relevant requirements. The obtained results can be used in the problems of synthesis and analysis of controlled dynamic transport systems models, in the problems of computer modeling using scientific software, as well as in the development of algorithms and programs based on artificial intelligence methods #CSOC1120.

The goal of the proposed talk is to present the publication Gomez-Diaz T. and Recio T., On the evaluation of research software: the CDUR procedure, [version 2 ; peer review: 2 approved] 26 Nov 2019. F1000Research 2019, 8:1353, https://f1000research.com/articles/8-1353 Background: Evaluation of the quality of research software is a challenging and relevant issue, still not sufficiently addressed by the scientific community. Methods: Our contribution begins by defining, precisely but widely enough, the notions of research software and of its authors followed by a study of the evaluation issues, as the basis for the proposition of a sound assessment protocol: the CDUR procedure. Results: CDUR comprises four steps introduced as follows: Citation, to deal with correct RS identification, Dissemination, to measure good dissemination practices, Use, devoted to the evaluation of usability aspects, and Research, to assess the impact of the scientific work. Conclusions: Some conclusions and recommendations are finally included. The evaluation of research is the keystone to boost the evolution of the Open Science policies and practices. It is as well our belief that research software evaluation is a fundamental step to induce better research software practices and, thus, a step towards more efficient science.