Ten Simple Rules for Effective Computational Research

<p>Ten Simple Rules for Effective Computational Research</p

ATLaS: Assistant Software for Life Scientists to Use in Calculations of Buffer Solutions

Many solutions such as percentage, molar and buffer solutions are used in all experiments conducted in life science laboratories. Although the preparation of the solutions is not difficult, miscalculations that can be made during intensive laboratory work negatively affect the experimental results. In order for the experiments to work correctly, the solutions must be prepared completely correctly. In this project, a software, ATLaS (Assistant Toolkit for Laboratory Solutions), has been developed to eliminate solution errors arising from calculations. Python programming language was used in the development of ATLaS. Tkinter and Pandas libraries were used in the program. ATLaS contains five main modules (1) Percent Solutions, (2) Molar Solutions, (3) Acid-Base Solutions, (4) Buffer Solutions and (5) Unit Converter. Main modules have sub-functions within themselves. With PyInstaller, the software was converted into a stand-alone executable file. The source code of ATLaS is available at https://github.com/cugur1978/ATLaS

Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration

With an increase in subject knowledge expertise required to solve specific biological questions, experts from different fields need to collaborate to address increasingly complex issues. To successfully collaborate, everyone involved in the collaboration must take steps to "meet in the middle". We thus present a guide on truly cross-disciplinary work using bioimage analysis as a showcase, where it is required that the expertise of biologists, microscopists, data analysts, clinicians, engineers, and physicists meet. We discuss considerations and best practices from the perspective of both users and technology developers, while offering suggestions for working together productively and how this can be supported by institutes and funders. Although this guide uses bioimage analysis as an example, the guiding principles of these perspectives are widely applicable to other cross-disciplinary work

Diez reglas sencillas para una exitosa colaboración transdisciplinar

El presente artículo es la versión en castellano de la publicación: KNAPP, B.; BARDENET, R.; BERNABEU, M.O.; BORDAS, R.; BRUNA, M.; CALDERHEAD, B. ET AL. (2015) “Ten Simple Rules for a Successful Cross-Disciplinary Collaboration”. PLoS Comput Biol 11(4): e1004214, disponible en: https://doi.org/10.1371/journal.pcbi.1004214. La traducción, autorizada por la entidad editora, ha sido llevada a cabo por Ona Lorda Roure y Leila Adim, colaboradoras del Instituto de Investigación TransJus y supervisada por el Dr. Juli Ponce Solé, Director del TransJus. En la misma se han incluido algunas notas aclaratorias para el lector en español, así como bibliografía complementaria en español.[spa] En el auge de las colaboraciones interdisciplinarias entre los distintos campos científicos, la transdisciplinariedad se presenta como la clave para encontrar soluciones a una variedad de problemas globales. Este trabajo, situado en el marco de la biología informática, se centra en exponer una lista extensa de reglas y consejos útiles para lograr una exitosa sinergia entre los varios colaboradores de un proyecto transdisciplinar. Se trata, de hecho, de una guía que pretende dirigirse tanto a investigadores noveles como a aquellos investigadores consolidados que se adentran en un espacio transdisciplinar por primera vez. En particular, este trabajo expone los beneficios principales de establecer una colaboración transdisciplinar, así como los problemas que de ella puedan surgir.[cat] En l'auge de les col·laboracions interdisciplinàries entre els diferents camps científics, la transdisciplinarietat es presenta com la clau per trobar solucions a una varietat de problemes globals. Aquest treball, situat en el marc de la biologia informàtica, es centra en exposar una llista extensa de regles i consells útils per aconseguir una reeixida sinergia entre els varis col·laboradors d'un projecte transdisciplinar. Es tracta, de fet, d'una guia que pretén dirigir-se tant a recercadors novells com a aquells recercadors consolidats que s'endinsen en un espai transdisciplinar per primera vegada. En particular, aquest treball exposa els beneficis principals d'establir una col·laboració transdisciplinar, així com els problemes que d'ella puguin sorgir.[eng] At a time of increasing interdisciplinary collaboration between different scientific fields, cross-disciplinarity represents a key for finding solutions to a variety of global problems. This work, located within the framework of computer biology, focuses on exposing an extensive list of rules and useful tips to achieve a successful synergy among the various collaborators of a transdisciplinary project. It is, in fact, a guide aimed at addressing both first-time researchers and consolidated researchers who enter a transdisciplinary space for the first time. In particular, this work exposes the main benefits of establishing a cross-disciplinary collaboration, as well as the problems that may arise from it

Reproducibility in Research: Systems, Infrastructure, Culture

The reproduction and replication of research results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the challenges closely revolve around the ability to implement (and exploit) novel algorithms and models. Taking a new approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and transient project websites. Alongside this issue, benchmarking, and the lack of open, transparent and fair benchmark sets present another barrier to the verification and validation of claimed results. In this paper, we outline several recommendations to address these issues, driven by specific examples from a range of scientific domains. Based on these recommendations, we propose a high-level prototype open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new e-infrastructure for reproducible computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the quality and efficiency – and thus reproducibility – of scientific exploration.Royal Society UR

Improving functional magnetic resonance imaging reproducibility

BACKGROUND: The ability to replicate an entire experiment is crucial to the scientific method. With the development of more and more complex paradigms, and the variety of analysis techniques available, fMRI studies are becoming harder to reproduce. RESULTS: In this article, we aim to provide practical advice to fMRI researchers not versed in computing, in order to make studies more reproducible. All of these steps require researchers to move towards a more open science, in which all aspects of the experimental method are documented and shared. CONCLUSION: Only by sharing experiments, data, metadata, derived data and analysis workflows will neuroimaging establish itself as a true data science

Responsible modelling : unit testing for infectious disease epidemiology

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase uptake of this methodology in our field

Responsible modelling: Unit testing for infectious disease epidemiology

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field