In the previous video we looked at the basics of operating systems. …
In the previous video we looked at the basics of operating systems. In this video we take a look at the current operating systems available to consumer. In addition to looking at Windows, we also cover the current Mac OS X, Linux and Mobile Operating systems.
Links from video: http://www.ubuntu.com/ http://knopper.net/knoppix/index-en.html
Our last video in the Computer Software lesson, part of our Introduction …
Our last video in the Computer Software lesson, part of our Introduction to Computers curriculum.
In this video we look at the types of application users can find. We talk about business software and personal software. We also give our 3-5 to picks for viewers to check out.
The text, labs, and review questions in this book are designed as …
The text, labs, and review questions in this book are designed as an introduction to the applied topic of computer security (cybersecurity). With these resources students will learn ways of preventing, identifying, understanding, and recovering from attacks against computer systems. This text also presents the evolution of computer security, the main threats, attacks and mechanisms, applied computer operation and security protocols, main data transmission and storage protection methods, cryptography, network systems availability, recovery, and business continuation procedures.
Video 4 in our Introduction to the CompTIA A+ Certification Exam looks …
Video 4 in our Introduction to the CompTIA A+ Certification Exam looks at the tools the computer technician should have in their bag in order to get the job done. We look at hardware tools for computer repair and software tools for computer repair.
This webinar (recorded Sept. 27, 2017) introduces how to connect other services …
This webinar (recorded Sept. 27, 2017) introduces how to connect other services as add-ons to projects on the Open Science Framework (OSF; https://osf.io). Connecting services to your OSF projects via add-ons enables you to pull together the different parts of your research efforts without having to switch away from tools and workflows you wish to continue using. 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 and Mendeley, to streamline workflows and increase efficiency.
This video will go over three issues that can arise when scientific …
This video will go over three issues that can arise when scientific studies have low statistical power. All materials shown in the video, as well as the content from our other videos, can be found here: https://osf.io/7gqsi/
Using multi-media and basic atmospheric physics, students investigate contrails and chemtrails. Students …
Using multi-media and basic atmospheric physics, students investigate contrails and chemtrails. Students apply previous learning about pseudoscience, and couple that with an understanding of the atmosphere to reach a decision about chemtrails. Students write an analysis paper on the issue of chemtrails, followed by a decision paper about whether society should be concerned about chemtrails.
The arrow of time, from origin of the universe to the present …
The arrow of time, from origin of the universe to the present and beyond spans several major epochs throughout all of history. Cosmic evolution is the study of the many varied changes in the assembly and composition of energy, matter and life in the thinning and cooling of the universe.
This survey instrument is designed to understand how creators use (or decide …
This survey instrument is designed to understand how creators use (or decide not to use) Creative Commons licenses. The target survey participants are people who create materials for documentation, maintenance, instruction, learning, and/or revitalization of Indigenous, minority, endangered, and/or low-resourced languages. Part 1 of this survey is designed to learn about the digital creation formats and sharing practices in this specific community of practice. Parts 2 through 4 of this survey contain general questions intended to elicit information about research participants’ knowledge and awareness of Creative Commons (CC) licenses; understanding of how to apply CC licenses to their digital creations; interest in and experience with applying CC licenses to their digital creations; motivations for and barriers to applying CC licenses to their digital creations; and understanding of how to adapt or reuse digital creations licensed with CC licenses.
This survey can be easily adapted for use in other communities of practice by editing some of the questions and multiple-choice responses.
If you wish to read this survey and background information about it, please start with the file ReadMe-CCLuseSurvey.pdf.
The file Creative_Commons_License_Use_Survey.qsf is a Qualtrics Survey File that can be used to recreate this survey in your own instance of the Qualtrics software.
The file Creative_Commons_License_Use_Survey.pdf is a PDF of the survey. This version includes Creative Commons graphics that are used in the survey questions. The file ReadMe-CCLuseSurvey.pdf does not include the graphics.
Abstract Training materials. The DATUM for Health training programme covers both generic …
Abstract Training materials. The DATUM for Health training programme covers both generic and discipline-specific issues, focusing on the management of qualitative, unstructured data, and is suitable for students at any stage of their PhD. It aims to provide students with the knowledge to manage their research data at every stage in the data lifecycle, from creation to final storage or destruction. They learn how to use their data more effectively and efficiently, how to store and destroy it securely, and how to make it available to a wider audience to increase its use, value and impact.
Python is a general purpose programming language that is useful for writing …
Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in one and a half days (~ 10 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.
Data Carpentry lesson from Ecology curriculum to learn how to analyse and …
Data Carpentry lesson from Ecology curriculum to learn how to analyse and visualise ecological data in R. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.
Python is a general purpose programming language that is useful for writing …
Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.
The Biology Semester-long Course was developed and piloted at the University of …
The Biology Semester-long Course was developed and piloted at the University of Florida in Fall 2015. Course materials include readings, lectures, exercises, and assignments that expand on the material presented at workshops focusing on SQL and R.
A part of the data workflow is preparing the data for analysis. …
A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identified and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis. OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand.
Databases are useful for both storing and using data effectively. Using a …
Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.
This is an alpha lesson to teach Data Management with SQL for …
This is an alpha lesson to teach Data Management with SQL for Social Scientists, We welcome and criticism, or error; and will take your feedback into account to improve both the presentation and the content. Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.
Good data organization is the foundation of any research project. Most researchers …
Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.
Data Carpentry lesson to learn how to use command-line tools to perform …
Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation. A lot of genomics analysis is done using command-line tools for three reasons: 1) you will often be working with a large number of files, and working through the command-line rather than through a graphical user interface (GUI) allows you to automate repetitive tasks, 2) you will often need more compute power than is available on your personal computer, and connecting to and interacting with remote computers requires a command-line interface, and 3) you will often need to customize your analyses, and command-line tools often enable more customization than the corresponding GUI tools (if in fact a GUI tool even exists). In a previous lesson, you learned how to use the bash shell to interact with your computer through a command line interface. In this lesson, you will be applying this new knowledge to carry out a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population. We will be starting with a set of sequenced reads (.fastq files), performing some quality control steps, aligning those reads to a reference genome, and ending by identifying and visualizing variations among these samples. As you progress through this lesson, keep in mind that, even if you aren’t going to be doing this same workflow in your research, you will be learning some very important lessons about using command-line bioinformatic tools. What you learn here will enable you to use a variety of bioinformatic tools with confidence and greatly enhance your research efficiency and productivity.
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