Topic |
Activity |
Social Warm-Up |
Discussion Topic |
Extra |
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| Week 01: Introduction Part 1: Welcome to DART | Log into Thinkific and explore what you find there. Make sure you can do the following two things which are necessary for full participation in the DART program:
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Introduce yourself in your community’s forum. Who are you and why did you sign up for the DART program? | Find a module in your pathway that you are particularly excited about and share why it is exciting. Note that not everyone in your community is on the same pathway, so you might be excited about a module that someone else doesn't have assigned. Post your own answer on your Community of Practice forum and respond to someone else’s post. | Planning: If you want to meet synchronously with members of your Community of Practice, start to plan that now. What are good times to meet? |
| Week 02: Data Use Best Practices Part 1: FAIR Data | What makes data FAIR? Take a look at this discussion of FAIR data practices and take the quiz at the bottom. | Is it "the data is" or "the data are," i.e. is the word data singular or plural? And does your belief match the way the words naturally come out of your mouth in conversation? | Share a way in which you have benefited by working with FAIR data, or a time in which you would have benefited had a data set followed the rules of FAIR. Have others experienced similar situations? | Explore Further: For a much more in-depth discussion of FAIR data, check out the FAIR Data 101 course offered by the Australian Research Data Commons. |
| Week 03: Data Use Best Practices Part 2: Reproducibility | Take the PLOS reproducibility assessment to determine how reproducible your research is. Watch this short video (under 5 minutes) about the frustrations that result when data is not managed with reproducibility in mind. You may have already seen this video in DART's module on reproducibility. | Find and post a cartoon about data. You might already have a favorite, or you can search the internet for “data cartoons.” Many of the DART team’s favorites are by Randall Munroe of xkcd.com. | What did you learn about how reproducible your research is? Are there things you want to start implementing or things that you have already implemented to make your research reproducible? See what others have already done and ask for advice. | Explore Further: Learn about the Center for Open Science’s Reproducibility Project: Cancer Biology. |
| Week 04: Data Use Best Practices Part 3: NIH Data Sharing Rules | How well do you know the new 2023 NIH data sharing rules? Read through the FAQs to get an overview of what these new rules mean for you and your work. | Give a six word description of what you do. This could be silly or serious, and certainly won’t be long enough to get into much detail. Examples might be:
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Pick something new you learned from the NIH data sharing rules and share it with your community of practice. Does your lab have a plan in place? Are there practices that will have to change to comply with these new policies? | |
| Week 05: Learning How to Learn Data Science Part 1: Getting Help and Support | It is extremely common to come across words and acronyms that are brand new to you when learning or practicing data science. Do a web search for something you have come across in a module or in your work that is new to you. If you don't have one handy, try JSON. We are intentionally not giving you any context about "JSON." | What webpage cheat sheets do you keep coming back to? Or do you have any phrases or commands that you do a web search on every time you need to use them? | There is a lot of information on the internet and web searches can be a very effective way to learn. However different sites and platforms have different tones and expectations. Stack Exchange, for example, has abundant information and a very active community of users answering questions, but can also be an unfriendly place. Think about the search results you got in the activity when discussing the following questions:
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Explore Further: Read this article on Medium about how to ask for programming help in public forums. The author is making an effort to be warm and welcoming to new programmers while providing useful advice. Do you find they achieved that goal? |
| Week 06: Learning How to Learn Data Science Part 2: Getting Stuck | Attempt to open the attached mystery file and see what it contains. Does it open when you double click on it? Does right-clicking give you a list of possible applications to open it with? A basic text editor like NotePad (Microsoft) or TextEdit (Apple) will be able to open this file. What, if anything, can you learn from looking at the file? | Describe a time someone helped you get unstuck with your work. | Describe what feelings you have when you look at this file. Were you able to extract any information at all from it? What would you do if you had to tell someone what this file contains? | |
| Week 07: Learning How to Learn Data Science Part 3: Getting Unstuck | The file we gave you last week isn’t meant to be viewed as a text file, it is actually a geojson file. There are ways to use programming languages like R or Python to open it and get a better idea of what it contains. There are also online tools that will display the data inside it in a more useful way. Use the power of the internet to figure out how to better display mystery_file.geojson and determine what it contains. If you get stuck, ask for help in the community discussion. | Share your favorite (or least favorite, or most common) error message. What do you do when it appears? | How were you able to open the file? What did it contain? If you got stuck at any point, how did you get past that? When you did open it, did you have any feelings of excitement, accomplishment, or joy? | |
| Week 08: Learning How to Learn Data Science Part 4: Processing the Emotional Side of Learning | This week’s activity is to fill out the 🔴mid-program survey🔴. You can fill out the survey either before or after participating in this week’s discussion. | Social Warm-Up: One method programmers use a lot, called "rubber ducking," involves explaining what they are trying to do in plain language to a colleague, or if a colleague is not available, an inanimate object like a rubber duck. What qualities (personality/catchphrase/outfit/etc) would you want your rubber duck to reflect back to you? | Reflect on the last few weeks' activities where you were repeatedly asked to try things you didn’t know how to do.
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Planning: If you want to meet synchronously with members of your Community of Practice during the second half of the program, start to plan that now. What are good times to meet? |
| Week 09: Version Control Part 1: The Problem of Version Control | Read the article Excuse me, do you have a moment to talk about version control? by Jenny Bryan. While there may be some sections that don’t apply to you directly (for example if you don’t use R), the overall message is extremely important. | Share the "best" bad file name you have come across (or perhaps used yourself). These might be files like
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How do you keep track of changes in your files over time? What works well, and what problems have you encountered? | |
| Week 10: Version Control Part 2: Introduction to GitHub | If you don’t already have one, create a GitHub account at github.com. This can be a place for you to put some of your public work as part of your public portfolio. | What is the silliest/weirdest/best user name you have had or seen that you are willing to share? (Keep it work-appropriate, please.) | Share your GitHub handle with other members of your community of practice. If you have a pre-existing account, tell your community about what you have been using it for, but we expect that most of them will be empty because you just created them this week. | |
| Week 11: Version Control Part 3: Open Source Projects | A project is open source if all of the code and files used to make it are publicly available and are free from most intellectual property restrictions. In other words, you can change and reuse open source code in your own project without getting special permission. There are a great many open source projects on GitHub, some maintained by large groups of collaborators. Use GitHub’s search capabilities - you can search by topic, author, or project name if you happen to know it - to find a repository that is interesting to you. Read its README file to learn more about it and explore the files it contains. | What is a technical term in your field that sounds like something different (or confusing or weird) to people outside of your subfield or community? For example GitHub users talk about "forking" a repository, which means making a copy of it that they can then use and modify however they like. | Share a link to the GitHub repository (or repo) you found interesting and give a little information about what it contains. Some questions to consider answering include, but are not limited to:
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| Week 12: Working with Data Part 1: Locating Public Data | Find an open data repository for your country, state / territory, or city. Search for "[region name] open data." Explore what datasets are available. Examples of open data repositories include:
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Share a data visualization you have particularly enjoyed. It might be from a news article, scientific journal, really anywhere as long as it is public. Or share a bad data visualization and explain why it is so bad. Many examples are available at https://badvisualisations.tumblr.com/. | Share a dataset you encountered in this week’s activity. Where and how did you find it? What makes it particularly interesting or useful to you? Is there anything you are excited to do with this dataset in the future? | Explore Further: Search for interesting public datasets on kaggle.com, a site with over 50,000 public datasets. |
| Week 13: Working with Data Part 2: Exploring Data | Explore a public data set. You can use yours or someone else's from last week or find a new one, just don't use one from your own research that needs to be kept private because we are asking you to share it. See if you can figure out:
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Share a story of missing or wrong data. It could be something minor, or a major problem like the California man who got the license plate NULL for his car. | Share what you learned about your dataset. What programs, languages, or tools did you use to explore it? Did you get stuck at any point? Were you able to figure out if there was missing data? | Explore Further: The questions we asked in this week’s activity are examples of metadata. Read this short article about what metadata is and why it is important. |
| Week 14: Working with Data Part 3: Visualizing Data | Try to create a visualization of some of the data in your dataset from last week. While not everyone had R or Python in their pathway through the modules, if you did, try to create your visualization using one of those. You will most likely get error messages as you attempt to create a visualization. When you get an error message, share your error message with the group and see if anyone else got stuck at a similar spot. | What have you used in the past to create data visualizations? Are those visualizations easy to make and update? Do they look good and convey information effectively? | If you are able to create a visualization without encountering any error messages or difficulty running code, post your visualization and code and respond to others' posts about where they got stuck. What was challenging about creating, or attempting to create, your visualization? | Explore Further: Different members of your community of practice probably created their visualizations using different programming languages. No programming language is inherently better or worse than any other. Check out this talk by Gabriele S Hayden on the cultural meaning of programming languages. |
| Week 15: Wrapping Up Part 1: Reflecting on the DART Program | Take the Post-Program Survey. You will be getting a link to take this survey in a separate email. Whether you did every module on your pathway, never once interacted with the program, or were somewhere in between, your survey responses are crucial data for our research! | Find and post a cartoon that speaks to your experience with the program or with coding so far. | Reflect on your last 15 weeks as a community. What activities, discussions, or modules were most helpful to you? Have you changed anything about how you do your work over the course of this program? Or how you think about data? | |
| Week 16: Wrapping Up Part 2: Beyond the DART Program | Add your recent skills acquisition to your CV. If you didn't do the Post-Program Survey yet, do it now to get some sample language for your CV! You can also:
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Share your contact information and ways your community can follow you in the future e.g. ORCID, social media, etc. | Would you like to continue this community of practice in some way after the DART Program concludes? If so, now is the time to formulate a plan for ongoing communication. |