| Scientific Writing Video Library | |
| Title (time) | Goals |
| Intro to Science Writing (14:09) | ■Learn what you'll get from this course |
| Expectations for your Lab Book (19:50) | ■Understand why scientists keep lab notebooks; how to keep a good one |
| Model Development & Experiment Planning: | |
| The Model Development Process (12:59) | ■How to successfully go from observations, to questions, to hypotheses, to experiments |
| Experimental Design 1: Core Principles (13:24) | ■Tell different types of scientific studies apart; list key properties of good experiments; identify sources of bias impacting experiments; understand and distinguish between 2 different meanings of "control"; identify types of variability and distinguish from bias |
| Experimental Design 2: Types of Variables (7:09) | ■How to classify variables (types; subtypes; components) and how each type is used in experiments; conventions for displaying variables in graphs |
| Experimental Design 3: Putting it Together (11:32) | ■How to go from a model to good, specific, concrete experiments that you can do to make a scientific advance, and why do these steps in order |
| Learning R: | |
| R, Introduced (13 min) | ■Understand what R is and why it's useful |
| R Interactive, Part 1 (3 min) | ■How to use the command line: Line-by-line hands-on practice with operating R |
| R Interactive, Part 2 (6 min) | ■Understanding the key moving parts that make R work |
| R Interactive, Part 3 (11 min) | ■How complex/multidimensional sets of information are organized and accessed in R (matrices, data frames); how to pull out elements and how to apply functions and operators to them |
| R Interactive: Bonus Tips (5 min) | ■Getting text to wrap so it doesn't run off the page; shortcuts to run code faster; how comments work and why to use them; using scripts to write and execute code |
| Intermediate R Skills Video (27 min) | ■What packages are and how to load them; how to quickly and easily get your data into R; understanding why R does different things to different types of information; avoiding common data import pitfalls; developing good coding, project, and script organization habits to make your life MUCH easier in the long run; basics on how to troubleshoot when you run into problems; orientation to R's help documentation; what you must send me when you ask for help |
| Basic Stats: | |
| Part 1: Center and Spread (11:15) | ■Understand what summary statistics are and how they are structured and calculated, including functions in R; difference between means and medians |
| Part 2: Shapes of Data Distributions (06:00) | ■Names for different data distribution shapes; how to plot them in R; how the data distribution impacts measures of center and spread; what it actually means for data to be "skewed" and why you must use that word carefully |
| Part 3: Boxplots (16:52) | ■Going beyond just the range: Methods to summarize and portray the spread of data in a compact format; how to read boxplots; why they are more accurate and information-rich than barplots; how to generate them in R; why you need to define your plot elements in figure legends |
| Part 4: The Standard Deviation as a Measure of Spread (08:24) | ■More statistically advanced methods for characterizing data distributions: definition and calculation of variance |
| Part 5: Selecting the Appropriate Plot Type (08:13) | ■Decision tables for going from data to the correct type of plot |
| Part 6: Populations vs. Samples (04:16) | ■How and when is it appropriate to generalize beyond your research findings to the world at large? And where do statistics fit in? |
| Part 7: Fundamentals of creating plots in R (19:50) | ■Refresher on how to make boxplots, plus scatterplots and line plots |
| Stats Part II - comparing 2 groups: | |
| Hypothesis Testing & P values (13:11) | |
| The t Statistic (12:25) | |
| Finer Details of the t-test (11:40) | |
| Understanding P Values (21:09) | |
| Doing a t test in R (13:12) | |
| Stats Part III - comparing multiple groups: | |
| ANOVA #1: Intro to multiple comparisons (9.5 min) | |
| ANOVA #2: Post hoc tests (9 min) | |
| ANOVA #3: Two way ANOVA (11.5 min) | |
| ANOVA #4: ANOVA & post hoc tests in R (6.5 min) | |
| ANOVA #5: two way ANOVA in R (4 min) | |
| General Resources | |
| Making a Data Figure (9:02) | ■Data are at the heart of scientific papers. They contain a ton of details displayed in a compact, precise format. How to get all the information in quickly and accurately. |
| Writing Workflow | ■Scientific papers aren't written from start to end. What's the most efficient way to put them together, and why? |
| What are primary research articles and how to find them | ■Understand how primary research articles get published and what differentiates primary research articles from their sneaky cousins |
| Using Zotero to manage references (OPTIONAL BUT VERY HELPFUL!) | ■Save yourself a lot of tedious typing for citations: Use a reference manager instead. Applies to non-science courses, too. |