scholarly journals Towards Replication in Computational Cognitive Modeling: A Machine Learning Perspective

2019 ◽  
Author(s):  
Chris Emmery ◽  
Ákos Kádár ◽  
Travis J. Wiltshire ◽  
Andrew T Hendrickson
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Computational Modeling ◽  
Cultural Change ◽  
Cognitive Modeling ◽  
Open Science ◽  
Robust Modeling ◽  
Downstream Effects ◽  
Computational Cognitive Modeling ◽  
Modeling Practices

The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on `Robust modeling in cognitive science', we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.

scholarly journals Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective

2019 ◽  
Vol 2 (3-4) ◽  
pp. 242-246 ◽  
Author(s):  
Chris Emmery ◽  
Ákos Kádár ◽  
Travis J. Wiltshire ◽  
Andrew T. Hendrickson
Keyword(s):  
Machine Learning ◽  
Cognitive Modeling ◽  
Computational Cognitive Modeling

scholarly journals A New Science for Future

2021 ◽  
Author(s):  
Simon David Hirsbrunner
Keyword(s):  
Computational Modeling ◽  
Computer Modeling ◽  
Digital Technology ◽  
Science Communication ◽  
Open Science ◽  
Climate Impact ◽  
Climate Risks ◽  
New Science ◽  
Modeling Practices ◽  
Climate Impact Research

Building on concepts from Science & Technology Studies, Simon David Hirsbrunner investigates practices and infrastructures of computer modeling and science communication in climate impact research. The book characterizes how scientists calculate future climate risks in computer models and scenarios, but also how they circulate their insights and make them accessible and comprehensible to others. By discussing elements such as infrastructures, visualizations, models, software and data, the chapters show how computational modeling practices are currently changing in light of digital transformations and expectations for an open science. A number of inventive research devices are proposed to capture both the fluidity and viscosity of contemporary digital technology.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals How To Publish: Preparing Manuscripts for Clarity, Transparency, Scholarly Integrity, and Success

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 858-858
Author(s):  
Suzanne Meeks
Keyword(s):  
Best Practices ◽  
Round Table ◽  
Open Science ◽  
High Impact ◽  
Publication Ethics ◽  
Scholarly Work ◽  
Scholarly Writing ◽  
Annual Symposium ◽  
The Right

Abstract The GSA publications team sponsors this annual symposium to assist prospective authors to successfully publish their gerontological scholarship in GSA’s high impact and influential journals. The first part of the session will include five brief presentations from the Editors-in-chief of Journals of Gerontology-Series B, Social and Psychological Sciences, The Gerontologist, and Innovation in Aging, plus one of GSA’s managing editors. We will integrate practical tips with principles of publication ethics and scholarly integrity. The topics will be as follows: (1) preparing your manuscript, including how to choose the right journal; (2) strong and ethical scholarly writing for multidisciplinary audiences; (3) transparency, documentation, and Open Science; (4) successfully responding to reviews; and (5) working with Scholar One. Following these presentations, we will hold round table discussions with editors from the GSA journals portfolio. At these roundtables, editors will answer questions related to the podium presentations and other questions specific to each journal. Intended audiences include emerging and international scholars, and authors interested in learning more about best practices and tips for getting their scholarly work published.

scholarly journals Best practices in machine learning for chemistry

2021 ◽  
Vol 13 (6) ◽  
pp. 505-508
Author(s):  
Nongnuch Artrith ◽  
Keith T. Butler ◽  
François-Xavier Coudert ◽  
Seungwu Han ◽  
Olexandr Isayev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Best Practices

scholarly journals WORCS: A workflow for open reproducible code in science

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Caspar J. Van Lissa ◽  
Andreas M. Brandmaier ◽  
Loek Brinkman ◽  
Anna-Lena Lamprecht ◽  
Aaron Peikert ◽  
...  
Keyword(s):  
Best Practices ◽  
Source Code ◽  
R Package ◽  
Open Science ◽  
Research Projects ◽  
Tabular Data ◽  
Step Procedure ◽  
Starting Point ◽  
Conducting Research ◽  
And Training

Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

scholarly journals Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Hae Deok Jung ◽  
Yoo Jin Sung ◽  
Hyun Uk Kim
Keyword(s):  
Machine Learning ◽  
Computational Modeling ◽  
Cancer Treatment ◽  
Effective Treatment ◽  
Cancer Cells ◽  
Computational Models ◽  
Omics Data ◽  
Drug Resistant ◽  
Learning Models ◽  
Machine Learning Models

Chemotherapy is a mainstream cancer treatment, but has a constant challenge of drug resistance, which consequently leads to poor prognosis in cancer treatment. For better understanding and effective treatment of drug-resistant cancer cells, omics approaches have been widely conducted in various forms. A notable use of omics data beyond routine data mining is to use them for computational modeling that allows generating useful predictions, such as drug responses and prognostic biomarkers. In particular, an increasing volume of omics data has facilitated the development of machine learning models. In this mini review, we highlight recent studies on the use of multi-omics data for studying drug-resistant cancer cells. We put a particular focus on studies that use computational models to characterize drug-resistant cancer cells, and to predict biomarkers and/or drug responses. Computational models covered in this mini review include network-based models, machine learning models and genome-scale metabolic models. We also provide perspectives on future research opportunities for combating drug-resistant cancer cells.

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