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.

Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding. The persistence of poor methods results partly from incentives that favour them, leading to the natural selection of bad science. This dynamic requires no conscious strategizing—no deliberate cheating nor loafing—by scientists, only that publication is a principal factor for career advancement. Some normative methods of analysis have almost certainly been selected to further publication instead of discovery. In order to improve the culture of science, a shift must be made away from correcting misunderstandings and towards rewarding understanding. We support this argument with empirical evidence and computational modelling. We first present a 60-year meta-analysis of statistical power in the behavioural sciences and show that power has not improved despite repeated demonstrations of the necessity of increasing power. To demonstrate the logical consequences of structural incentives, we then present a dynamic model of scientific communities in which competing laboratories investigate novel or previously published hypotheses using culturally transmitted research methods. As in the real world, successful labs produce more ‘progeny,’ such that their methods are more often copied and their students are more likely to start labs of their own. Selection for high output leads to poorer methods and increasingly high false discovery rates. We additionally show that replication slows but does not stop the process of methodological deterioration. Improving the quality of research requires change at the institutional level.

The developments in ICT over the past two decades possibly represent the greatest opportunities for and challenges to the formal educational process. This article inevitably takes a fairly conservative view since it assumes that schools and teachers will continue to operate in the medium term in the same way they do now, however it provides a comprehensive review of the skills required of the trainee teacher preparing to go into schools where the provision and use of computers and other ICT resources can be highly variable between different institutions. Detailed insight is given regarding the training processes operating within particular institutions and downloads are provided of teaching materials used - to provide stimulating ideas that may be adapted (or rejected) for use elsewhere. A wide range of references is given for reflecting on, accessing and evaluating the use of electronic whiteboards, PowerPoint, spreadsheets and modelling, datalogging and using the internet.

ESCOLAR aims to offer ALL elementary and middle-school students the opportunity to learn science within an engaging online environment.

This course and its materials follow the CompTIA Linux+ curriculum and prepares students for that certification.

By the end of this course participants will…Understand how local phenomena interact with the Next Generation Science Standards, climate change, ecosystems, and people in a community.Experience how local phenomena and field investigations can build scientific understanding.

This course surveys canonical and recent theories and methods in science studies. We will organize our discussions around the concept of “reproduction,” referring variously to:

Scientific reproduction (how results are replicated in lab, field, disciplinary contexts)
Social reproduction (how social knowledge and relations are regenerated over time)
Biological reproduction (how organic substance is managed in the genetic age)
Electronic reproduction (how information is reassembled in techniques of transcription, simulation, computation).

Examining intersections and disruptions of these genres of reproduction, we seek to map relations among our social, biological, and electronic lives.

Scientific data and tools should, as much as possible, be free as in beer and free as in freedom. The vast majority of science today is paid for by taxpayer-funded grants; at the same time, the incredible successes of science are strong evidence for the benefit of collaboration in knowledgable pursuits. Within the scientific academy, sharing of expertise, data, tools, etc. is prolific, but only recently with the rise of the Open Access movement has this sharing come to embrace the public. Even though most research data is never shared, both the public and even scientists in their own fields are often unaware of just much data, tools, and other resources are made freely available for analysis! This list is a small attempt at bringing light to data repositories and computational science tools that are often siloed according to each scientific discipline, in the hopes of spurring along both public and professional contributions to science.

This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science (https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf) as our guide for course recommendation.

It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet (linked in resource) to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.

Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

The Data Science curriculum assumes the student has taken high school math and statistics.

Noted scientist Stephen Wolfram shares his perspective of how the unexpected results of simple computer experiments have forced him to consider a whole new way of looking at processes in our universe. (86 minutes)

CVPR 2012 tutorial
Graph Cut based Optimisation for Computer Vision

Partial differential equations are paramount in mathematical modelling with applications in engineering and science. The book starts with a crash course on partial differential equations in order to familiarize the reader with fundamental properties such as existence, uniqueness and possibly existing maximum principles. The main topic of the book entails the description of classical numerical methods that are used to approximate the solution of partial differential equations. The focus is on discretization methods such as the finite difference, finite volume and finite element method. The manuscript also makes a short excursion to the solution of large sets of (non)linear algebraic equations that result after application of discretization method to partial differential equations. The book treats the construction of such discretization methods, as well as some error analysis, where it is noted that the error analysis for the finite element method is merely descriptive, rather than rigorous from a mathematical point of view. The last chapters focus on time integration issues for classical time-dependent partial differential equations. After reading the book, the reader should be able to derive finite element methods, to implement the methods and to judge whether the obtained approximations are consistent with the solution to the partial differential equations. The reader will also obtain these skills for the other classical discretization methods. Acquiring such fundamental knowledge will allow the reader to continue studying more advanced methods like meshfree methods, discontinuous Galerkin methods and spectral methods for the approximation of solutions to partial differential equations.

During this activity, the students will use drag and drop computer code to create an interactive ecological energy pyramid model that shows how the 10% law applies to the energy available at each trophic level. As part of the hour of code, students can use this activity to participate in the Hour of Code week during their biology class. This lesson plan results from a collaboration between the Alabama State Department of Education and ASTA.

Brain-Computer Interface (BCI) enables direct communication between the brain and an external device. BCI systems have become a popular area of study in recent years. These technologies can be utilized in various ways to assist people with disabilities and healthy individuals. Regarding substantial BCI advancements, we can say that these systems are on the verge of commercialization. This review has considered current trends in BCI research on inclusive education to assist students with disabilities in achieving improved learning outcomes for all students in an inclusive environment.

This course helps students develop computational programming skills and gain experience with computational tools to be used in the solution of engineering problems. Topics include: Introduction to Computing, Basic Matlab commands, Arrays: one-dimensional and multi-dimensional, Flow control, Selective execution, Repetitive execution and iterations, Input and Output, Modular Programming: Functions, Plotting, and Advanced data types.

Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals.

In this lesson students develop a preliminary definition of a computer. To begin the lesson, the class will brainstorm possible definitions for a computer and place the results of this brainstorm on the board. Next, students will work in groups to sort pictures into “is a computer” or “is not a computer” on poster paper. Groups will place their posters around the room and briefly explain their motivations for choosing some of their most difficult categorizations. The teacher will then introduce a definition of the computer and allow students to revise their posters according to the new definition.

Introduction to computer programming within a numerical computing environment (MATLAB or similar) including types of data representation, graphical display of data, and development of modular programs with application to engineering analysis and problem solving.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Survival is a complex energy-balancing act that involves a number of cell signaling pathways. One important player in this act is the protein mTORC1. Previous studies have shown that inhibiting mTORC1 is beneficial for health—and lifespan—serving as one promising way to fight cancer, for example. Now, a new computational model could reveal important details about key players in aging. This model captures the dynamic of known key players in the aging process, such as AMPK, mTORC1, and SIRT signaling pathways. Model simulations indicated that PRAS40 can be considered as another mTORC1 inhibitor. This access point is clinically important, as it provides a way of suppressing mTORC1 with the optimum dose of the inhibitor rapamycin, which at high doses and long exposure is known to cause problems such as insulin resistance. Results suggest that Sestrin2 is another potential candidate that can inhibit mTORC1..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This course is an introduction to principles and techniques of visual communication, and provides opportunities for science and engineering majors to acquire practical skills in the visual computer arts, in a studio environment. Students will learn how to create graphics for print and web, animations, and interactive media, and how to use these techniques to effectively communicate scientific and engineering concepts for learning and teaching. This class involves three hands-on creative projects, which will be presented in class.