Professor James Stacey Taylor of the College of New Jersey discusses the contributions of philosopher and economist Adam Smith to the Scottish Enlightenment. Smith is best remembered as the father of modern economics, but he also made important contributions to philosophy in his book "The Theory of Moral Sentiments".

Professor James Stacey Taylor of the College of New Jersey discusses the contributions of philosopher, historian, and economist David Hume to the Scottish Enlightenment, with a particular focus on sentimentalist philosophy.

Professor James Stacey Taylor of the College of New Jersey discusses the contributions of philosopher Francis Hutcheson to the Scottish Enlightenment, especially his contributions to the sentimentalist approach to morality.

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

In the fall of 2021, students in Pseudoscience courses started creating this open educational resource (OER), which has been built upon by subsequent classes. Our intention is to create a free textbook for this course that might also be used by students of critical thinking elsewhere and of all ages, whether in a classroom or not. Our growing, interactive textbook employs the Paul-Elder Model and other critical-thinking resources, and is freely available to all, learners and educators alike.

The topic of pseudoscience offers a rewarding way for students to learn the value of thinking critically, even as they get to argue things, like Flat Earth Theory and astrology, that may seem trivial at first. At a time when truth is understood as largely subjective, we have, not surprisingly, seen a resurgence in the popularity of pseudosciences and conspiracy theories, which many consider to hold significant truth value, just as valid as physical evidence. It is our aim here to demonstrate the reasoned analysis process — weighing truth, belief, opinion, and fact — so that others may be able to replicate this process and reason through their own questions about vaccines, extra-terrestrials, genetic modification, or the first people to arrive in the Americas.

Replication is the cornerstone of a cumulative science. However, new tools and technologies, massive amounts of data, interdisciplinary approaches, and the complexity of the questions being asked are complicating replication efforts, as are increased pressures on scientists to advance their research. As full replication of studies on independently collected data is often not feasible, there has recently been a call for reproducible research as an attainable minimum standard for assessing the value of scientific claims. This requires that papers in experimental science describe the results and provide a sufficiently clear protocol to allow successful repetition and extension of analyses based on original data. The importance of replication and reproducibility has recently been exemplified through studies showing that scientific papers commonly leave out experimental details essential for reproduction, studies showing difficulties with replicating published experimental results, an increase in retracted papers, and through a high number of failing clinical trials. This has led to discussions on how individual researchers, institutions, funding bodies, and journals can establish routines that increase transparency and reproducibility. In order to foster such aspects, it has been suggested that the scientific community needs to develop a “culture of reproducibility” for computational science, and to require it for published claims. We want to emphasize that reproducibility is not only a moral responsibility with respect to the scientific field, but that a lack of reproducibility can also be a burden for you as an individual researcher. As an example, a good practice of reproducibility is necessary in order to allow previously developed methodology to be effectively applied on new data, or to allow reuse of code and results for new projects. In other words, good habits of reproducibility may actually turn out to be a time-saver in the longer run. We further note that reproducibility is just as much about the habits that ensure reproducible research as the technologies that can make these processes efficient and realistic. Each of the following ten rules captures a specific aspect of reproducibility, and discusses what is needed in terms of information handling and tracking of procedures. If you are taking a bare-bones approach to bioinformatics analysis, i.e., running various custom scripts from the command line, you will probably need to handle each rule explicitly. If you are instead performing your analyses through an integrated framework (such as GenePattern, Galaxy, LONI pipeline, or Taverna), the system may already provide full or partial support for most of the rules. What is needed on your part is then merely the knowledge of how to exploit these existing possibilities.

James Powell, Dylan Johnson, Timothy Mandzyuk, Daniel Waybright, Alex Shocklee, and Nicholas Taylor (Los Alamos National Laboratory) present 'A keyphrase suggestion engine for semi-automated document characterization' during the Short Talk and Demo session at the Fantastic Futures ai4LAM 2023 annual... This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.

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