This is a mostly self-contained research-oriented course designed for undergraduate students (but also extremely welcoming to graduate students) with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data. These often lie in overlaps of two or more of the following: Mathematics, Applied Mathematics, Computer Science, Electrical Engineering, Statistics, and / or Operations Research.

Think OS is an introduction to Operating Systems for programmers. In many computer science programs, Operating Systems is an advanced topic. By the time students take it, they usually know how to program in C, and they have probably taken a class in Computer Architecture. Usually the goal of the class is to expose students to the design and implementation of operating systems, with the implied assumption that some of them will do research in this area, or write part of an OS.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB® programming.

A key component of scientific communication is sufficient information for other researchers in the field to reproduce published findings. For computational and data-enabled research, this has often been interpreted to mean making available the raw data from which results were generated, the computer code that generated the findings, and any additional information needed such as workflows and input parameters. Many journals are revising author guidelines to include data and code availability. This work evaluates the effectiveness of journal policy that requires the data and code necessary for reproducibility be made available postpublication by the authors upon request. We assess the effectiveness of such a policy by (i) requesting data and code from authors and (ii) attempting replication of the published findings. We chose a random sample of 204 scientific papers published in the journal Science after the implementation of their policy in February 2011. We found that we were able to obtain artifacts from 44% of our sample and were able to reproduce the findings for 26%. We find this policy—author remission of data and code postpublication upon request—an improvement over no policy, but currently insufficient for reproducibility.

Instructional materials for the course "ENTC 2170: Computer Aided Design and Drafting" include videos, assignments, slides, and drawings on the following topics: engineering graphics and scales, orthographic views, isometric views, dimensioning, section views, AutoCAD, layers, colors, mirrors, fillet, arrays, chamfer, blocks, Fusion 360, sheet metal modeling, tracing, textures, lofting and more.

Secondary educators across Lebanon County, Pennsylvania developed lesson plans to integrate the Pennsylvania Career Education and Work Standards with the content they teach. This work was made possible through a partnership between the South Central PA Workforce Investment Board (SCPa Works) and Lancaster-Lebanon Intermediate Unit 13 (IU13) and was funded by a Teacher in the Workplace Grant Award from the Pennsylvania Department of Labor and Industry. This lesson plan was developed by one of the talented educators who participated in this project during the 2018-2019 school year.

Students learn about complex networks and how to use graphs to represent them. They also learn that graph theory is a useful part of mathematics for studying complex networks in diverse applications of science and engineering, including neural networks in the brain, biochemical reaction networks in cells, communication networks, such as the internet, and social networks. Students are also introduced to random processes on networks. An illustrative example shows how a random process can be used to represent the spread of an infectious disease, such as the flu, on a social network of students, and demonstrates how scientists and engineers use mathematics and computers to model and simulate random processes on complex networks for the purposes of learning more about our world and creating solutions to improve our health, happiness and safety.

This course examines wave equations for fluid and visco-elastic media, wave-theory formulations of acoustic source radiation and seismo-acoustic propagation in stratified ocean waveguides, and Wavenumber Integration and Normal Mode methods for propagation in plane-stratified media. Also covered are Seismo-Acoustic modeling of seabeds and ice covers, seismic interface and surface waves in a stratified seabed, Parabolic Equation and Coupled Mode approaches to propagation in range-dependent ocean waveguides, numerical modeling of target scattering and reverberation clutter in ocean waveguides, and ocean ambient noise modeling. Students develop propagation models using all the numerical approaches relevant to state-of-the-art acoustic research.
This course was originally offered in Course 13 (Department of Ocean Engineering) as 13.853. In 2005, ocean engineering subjects became part of Course 2 (Department of Mechanical Engineering), and this course was renumbered 2.068.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

In a new effort to make our research transparent and reproducible by others, we developed a workflow to run and share computational studies on the public cloud Microsoft Azure. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded “reproducibility package” that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions. Although cloud providers have improved their offerings, many researchers using high-performance computing (HPC) are still skeptical about cloud computing. Thus, we ran benchmarks for tightly coupled applications to confirm that the latest HPC nodes of Microsoft Azure are indeed a viable alternative to traditional on-site HPC clusters. We also show that cloud offerings are now adequate to complete computational fluid dynamics studies with in-house research software that uses parallel computing with GPUs. Finally, we share with the community what we have learned from nearly two years of using Azure cloud to enhance transparency and reproducibility in our computational simulations.

The software package known as SHAPE (Shape Software 521 Hidden Valley Road, Kingsport, TN 37663) provides an excellent method for accurately drawing crystals. The following three boxes describe the basic steps involved in using SHAPE. Refer to these instructions when completing the exercises.

(Note: this resource was added to OER Commons as part of a batch upload of over 2,200 records. If you notice an issue with the quality of the metadata, please let us know by using the 'report' button and we will flag it for consideration.)

Data Science and AI in Psychology is an interactive eTextbook that provides an introduction to data science, big data, and machine learning in psychology. It covers current trends in data science and big data in the field of psychology (Chapter 1), applications of AI in the field of psychology (Chapter 2), the psychology of data visualization (Chapter 3), data ethics (Chapter 4), an introduction to how machines learn (Chapter 5), a hands-on guide for reading and critiquing machine learning research articles that are relevant to psychological topics (Chapters 6 and 7), and an introduction to coding in Python (Chapter 8). This eTextbook also includes an introduction to ChatGPT and tips for using ChatGPT to assist with writing and coding without plagiarizing (Chapters 6 and 8). This is an interactive resource that provides students with opportunities to engage with their peers and develop critical thinking skills through problem-based, active learning.

Note: This webinar was presented in Spanish. The slides presented during this webinar can be found here:https://osf.io/6qnse/ The slides presented during this seminar can be found here: https://osf.io/6qnse/ Este seminario web se centrará en el estado de la ciencia abierta en América Latina, desde los esfuerzos de los investigadores individuales para abrir sus flujos de trabajo, herramientas para ayudar a los investigadores a ser abiertos y nuevas redes e iniciativas prometedoras en ciencia abierta. Ricardo Hartley (@ametodico) es profesor de metodología de la investigación de la Universidad Central de Chile, investigador en biología de la reproducción y en comunicación - valoración del conocimiento. Organizador de las OpenCon Santiago 2016 y 2017 y embajador COS. Erin McKiernan es profesora del Departamento de Física, Programa de Física Biomédica de la Universidad Nacional Autónoma de México. También es la fundadora del Why Open Research? proyecto, un sitio educativo para que los investigadores aprendan cómo compartir su trabajo, financiado en parte por la Fundación Shuttleworth. Fernan Federici Noe es profesor asistente e investigador de la Universidad Católica de Chile y fellow internacional del OpenPlant Synthetic Biology Center, University of Cambridge. Fernan es miembro del Global For Open Science Hardware (GOSH) y TECNOx (www.tecnox.org).

Teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. Combines lectures and hands-on experimental exercises; requires an independent experimental project. Some experience in programming desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments; a fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

This book provides students with an introduction to the skills needed to succeed in Data Science.

To address the cost of resources for exercise manuals in GIS, I have developed a series of class exercises that can be used with QGIS, a free and open source software.
In addition, some class activities address more general ideas associated with cartography, such as color theory. To make these resources fully accessible to students, I have recorded publicly available videos documenting how these class activities can be completed. There is one video for each classroom lesson in this course offering, and links to all of the videos on the website Vimeo are linked to.

DESCRIPTION
Develops skills needed to produce maps using ArcGIS Desktop software. Outlines cartographic principles and map use. Emphasis on mapping techniques within a GIS. Intended for students enrolled in GIS or UAS programs.

LEARNING OUTCOMES
1. Demonstrate knowledge and apply skills essential to the discipline.
2. Coordinate and manage the delivery of reliable, valid, GIS data including analysis and specialized user needs products.
3. Communicate effectively and appropriately within a professional setting in both written and oral form.
4. Utilize professional communication skills to achieve project outcomes in collaboration with GIS users across disciplines.
5. Research, interpret and apply data/information in the professional setting.
6. Develop and implement GIS services for users who may or may not be GIS knowledgeable.

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization.

You will learn to:

- Read CSV and Excel data into Julia
- Process data in Julia, that is, learn how to answer data questions
- Filter and subset data
- Handle missing data
- Join multiple data sources together
- Group and summarize data
- Export data out of Julia to CSV and Excel files
- Plot data with different Makie.jl backends
- Save visualizations in several formats such as PNG or PDF
- Use different plotting functions to make diverse data visualizations
- Customize visualizations with attributes
- Use and create new plotting themes
- Add LaTeX elements to plots
- Manipulate color and palettes
- Create complex figure layouts

This webinar will introduce the integration of JASP Statistical Software (https://jasp-stats.org/) with the Open Science Framework (OSF; https://osf.io). The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github, Mendeley, and now is integrated with JASP, to streamline workflows and increase efficiency.