Communicating UAF's Return on Investment in the Computational Sciences

Using graphic design and statistics to analyze and present complex fiscal information in an easily understood format, my research focuses on mastering the dynamic process of communicating scientifi c information. Required for this process is the ability to evaluate the specifi c needs of an organization, to gather requirements, criteria and constraints of the information to be communicated, and to develop expertise using the Excel, Adobe Photoshop, Illustrator, and InDesign software applications. This poster will demonstrate the application of this process using various techniques to visually demonstrate fi scal information to inform and educate University Leadership about resource use at the Arctic Region Supercomputing Center for the 2011 fiscal year

Towards decolonising computational sciences

This article sets out our perspective on how to begin the journey of decolonising computational fields, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour (WoC); and b) rejection of the idea that centering individual people is a solution to system-level problems. The longer we ignore these two steps, the more "our" academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fields' histories and heritage holds the key to avoiding mistakes of the past. For example, initiatives such as "diversity boards" can still be harmful because they superficially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the shoulders of many WoC's work, who have been paving the way, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences -- including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artificial intelligence. We aspire for these fields to progress away from their stagnant, sexist, and racist shared past into carving and maintaining an ecosystem where both a diverse demographics of researchers and scientific ideas that critically challenge the status quo are welcomed.Comment: A version of this work will appear in the Danish Journal of Women, Gender and Research (https://koensforskning.soc.ku.dk/english/kkof/) in December 202

Science in Computational Sciences

The existing theory in relation to science presents the physics as an ideal, although many sciences not approach the same, so that the current philosophy of science-Theory of Science- is not much help when it comes to analyze the computer science, an emerging field of knowledge that aims investigation of computers, which are included in the materialization of the ideas that try to structure the knowledge and information about the world. Computer Science is based on logic and mathematics, but both theoretical research methods and experimental follow patterns of classical scientific fields. Modeling and computer simulation, as a method, are specific to the discipline and will be further developed in the near future, not only applied to computers but also to other scientific fields. In this article it is analyze the aspects of science in computer science, is presenting an approach to the definition of science and the scientific method in general and describes the relationships between science, research, development and technology

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Towards decolonising computational sciences

This article sets out our perspective on how to begin the journey of decolonising computational fi elds, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour; and b) rejection of the idea that centring individual people is a solution to system-level problems. The longer we ignore these two steps, the more “our” academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fi elds’ histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as “diversity boards”, which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences — including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artifi cial intelligence. We aspire to progress away from these fi elds’ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurtures demographically diverse researchers and ideas that critically challenge the status quo

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology