Term project for B8800 section#
Warning
The term project is only for those students enrolled in the course as EAS B8800. For those of you enrolled in EAS 48800 or SUS 7300B, this is not required.
Overview: data analysis project on a climate dynamics topic of your choice#
In a paper and accompanying in-class presentation focusing on some topic within the broad umbrella of climate science, you will access a dataset, perform some analyses on that dataset, read some relevant peer-reviewed articles, and synthesize your findings from your reading and your data analysis.
The topic needs to be (at least predominantly) hard science as opposed to, say, climate policy, climate justice, climate economics, etc.
This should be about something that you particularly want to learn more about. That said, for inspiration some example potential topics are:
Polar-amplified warming
Climate sensitivity vs. Earth-system sensitivity
The Atlantic Meridional Overturning Circulation and climate
How has the Montreal Protocol influenced global temperatures?
How do aerosols influence deep convective clouds?
Physically, how would Solar Radiation Management geoengineering work?
Role of land-use and land-cover change in climate change
Atmospheric circulations on other terrestrial planets
How ocean biological processes are incorporated into climate models
Expectations#
Length#
The final submitted paper should be ~10-15 double-spaced pages, including figures and the bibliography.
The abstract should not exceed roughly 250 words. The abstract should introduce the scientific question you’re exploring, motivate it, and briefly summarize the paper’s key findings, and possibly conclude with some outlook for future work etc. No more, no less.
The 10-15 page range is only a guideline: more important than the exact length is how clearly you convey your scientific story. If you can do that in, say, 8 pages, that’s great. (As the adage goes, “Sorry I wrote you a long [term paper]; I didn’t have time to write you a shorter one.”)
Or, especially if you include multiple large figures, perhaps you’ll end up needing, say, 18 pages.
Citations and bibliography#
You must include in-line citations for every nontrivial factual claim made in your paper that isn’t based on your own data analysis. Even if multiple consecutive sentences are derived from the same single source, that source should be cited in each individual sentence, not just the last one.
What is a “nontrivial factual claim?” A useful rule of thumb is the following: would a typical adult be able to find the information and understand it via a few minutes of Googling? If yes, it doesn’t need a citation. So, for example, the radius of Earth does not require a citation. But if not, it does require a citation. So, for example, the role Earth’s radius plays in the atmospheric angular momentum distribution would require one or more citations.
When in doubt, include a citation—much better to add a citation that could have been done without than to leave out a citation that really should have been there. This is because omitting needed citations can give the impression of academic disintegrity, which is very serious indeed. By comparison, excess citations are just moderately distracting/grating for the reader.
You must include a bibliography after the main text, comprising every paper that is cited in the main text and no others.
There is no particular required formatting style—MLM, APA, etc.—for the in-line citations or bibliography. Pick one, use it consistently, and follow the same two rules:
The bibliography entries must be complete, meaning they include each of the following: article title, publication, volume, issue, page numbers, DOI, and publication year or date. E.g. “Jane Doe, “Numerical investigation of XYZ.” Journal of ABC, Volume 32, Issue 1, 1023-1033. doi:10.1077/jabc-123.”
The in-line citations should include the last name(s) of the author(s) and the year of publication, e.g. “Doe 2019”, “Lopez and Doe 1994”, or, if there are more than two authors, ``Lopez et al 1959’’.
The bibliography entries of the final submitted paper should not include the annotations you generated earlier on in the semester.
Figures and tables#
In addition to the figures you generate yourself, you are welcome to include figures or tables taken from the papers you cite. Simply include in the figure caption language along the lines of “Reproduction of Fig.~X from Martinez et al. 2020.” Likewise for tables.
Every included figure or table should be numbered, captioned, and explicitly referenced in the text at least once, even if the reference is as brief as “…blah blah (Figure 1)”.
Formatting#
In addition to being double-spaced, keep all other formatting (font, margins, etc.) standard-ish. I’d rather have it go to e.g. a 16th page with ample margins and a 11 or 12-pt font rather than it being squished into 15 pages.
Precision and concreteness#
As much as possible, avoid generic statements like “X influences Y”, where “influence” could alternatively be “effects”, “modifies” “changes” etc. Precisely how does X influence Y? In what sign, and if possible quantitatively by how much? Via what physical (or other) mechanism?
A useful rule of thumb here is: what/when/where/why/how/how much. Each one should be answered.
Avoid puff#
“Puff” is a phrase I’m borrowing from the late author David Foster Wallace. It denotes words, clauses, or sometimes whole sentences that are intended to make the writing seem more important than it actually is. From DFW himself, describing one particular puff word, utilize (emphasis mine):
Utilize: A noxious puff-word. Since it does nothing that good old use doesn’t do, its extra letters and syllables don’t make a writer seem smarter; rather, using utilize makes you seem either like a pompous twit or like someone so insecure that she’ll use pointlessly big words in an attempt to look sophisticated. […] What’s worth remembering about puff-words is something that good writing teachers spend a lot of time drumming into undergrads: “formal writing” does not mean gratuitously fancy writing; it means clean, clear, maximally considerate writing.
Warning: LLMs such as ChatGPT tend to generate tons of puff.
Editorializing#
It’s ok and even encoraged to give, as part of your Concluding section, some brief and narrowly scoped remarks regarding what you believe is important moving forward given what you learned from the papers you reviewed. But keep it to that, and avoid absolutist language: (e.g. “we must”, “imperative”, etc.). The job of this paper is to present some nontrivial data analysis on a given topic and put it in context of the broader state of understanding. Nothing more, nothing less.
Grading#
(credit: copied nearly verbatim from *Teaching Statistics: A Bag of Tricks by Andrew Gelman and Deborah Nolan)
Rubric#
The table below is a competency matrix for this report. The first column describes each critical task for the assignment, and the 2nd, 3rd, and 4th columns respectively describe what work in that task would constitute Needing Improvement, Basic Competency, and Surpassed Expectations.
Critical task |
Needs Improvement |
Basic |
Surpassed |
|---|---|---|---|
Computation. Perform computations necessary for the data analysis. |
Computations contain errors |
Difficult to determine accuracy of computations |
Computations correct and clear |
Analysis. Choose and carry out analysis appropriate for data and context. |
Choice of analysis is overly simplistic, irrelevant, inappropriate for the data, or missing key component. |
Analysis appropriate, but incomplete and important features and assumptions not made explicit. |
Analysis appropriate, complete, advanced, relevant, and informative. |
Synthesis. Identify key features of the analysis, and interpret results in context. |
Conclusions are missing, incorrect, or not made bade on analysis |
Conclusions reasonable, but partially correct or partially complete. |
Relevant conclusions explicitly connected to analysis and context. |
Visual. Communicate findings graphically clearly, precisely, and concisely. |
Inappropriate choice of plots; poorly labeled plots; plots missing |
Plots convey information corretly but lack context for interpretation |
Plots convey information correctly with adequate and appropriate reference information |
Written. Communicate findings in writing clearly, precisely, and concisely |
Explanation is illogical, incorrect, or incoherent |
Explanation is partially correct but incomplete or unconvincing. |
Explanation is correct, complete, and convincing. |
Assigning points#
Basic competency in all five categories results in 80 points. Three points are added for each task in the Surpassed category, for up to three tasks. If a fourth task meets Surpassed, add an additional five points. If a fifth task meets Surpassed, an additional six points.
Similarly, three points are deducted for each competency in the Needs Improvement category, for up to three tasks. If a fourth task meets Needs Improvement, deduct an additional five points. If a fifth task meets Needs Improvement, deduct an additional six points.
As such, the maximum possible score is 100, and the minimum possible score is 60.
Final presentation#
The final presentation will be in the style of a standard talk at an academic conference within the Earth sciences.
Each student will present a “conference-style” oral presentation to the class summarizing their final project. “Conference style” means that it follows the format of a standard oral presentation typical of major conferences in Earth Sciences such as the American Geophysical Union Fall Meeting and the American Meteorological Society Annual Meeting.
Deadlines#
You must email your final slides to the professor before 1:30pm on the day of your presentation.
The professor will download the submitted slides to his computer the morning before class, and everyone will use the same computer to present (rather than each person trying to connect their own computer to the A/V system one after the other).
Format#
Conference style means the following:
Total duration: 12 minutes
Presentation: 10 minutes
Questions from the audience: 2 minutes
(The more standard conference length is 15 minutes, 12 for the talk and then 3 for Q&A, but to get the whole class in we have to do a shorter style. Also, since COVID, more conferences are doing shorter talks for various reasons.)
Presentation requirements#
You must include slides. These can be in Powerpoint, Keynote, Google Slides, or anything else. You will submit these and they will be evaluated on their own in addition to your actual delivery of the presentation. Guidelines below offer recommendations, but ultimately there are no hard requirements on the actual content of your slides.
Instructions for how to submit your slides has been posted to the course Blackboard.
Guidelines#
Note
Everything in this section is meant to be helpful, but none of it is strictly required. You can deviate from e.g. the slide template if you feel like your presentation will be better served by a different structure.
Presentation scope#
Whereas the written report for this project is meant to be fairly exhaustive, where you document all the important analyses that you performed, an oral presentation has to be more targeted. Eight minutes will fly by! So ask yourself: if you had to pick just one thing that you want to convey about your project, what would it be? Then build your talk around that.
Narrative#
Human beings are storytellers. We understand and retain things best when they are presented as a coherent narrative, with a beginning, middle, and end. (Actually, we retain them best of all when they are put to music, but I won’t ask you to sing your presentation.)
This approach of creating a narrative can be contrasted with what’s unfortunately more typical in scientific presentations (and teaching writ large): a “data dump” listing one fact after the other without linking things together.
So before you start hacking away at slides, a useful first step is to write down the thesis statement of your presentation: what’s the 1 sentence summary of what you want to convey? From there, you can craft a narrative with an intro that motivates that problem (“beginning”), a main, middle section that actually conveys it (“middle”), and an end that synthesizes the individual things you presented (“end”) and leaves the audience wanting more.
Presentation structure#
Tell the audience what you’re going to say; say it; then tell them what you’ve said.
—Dale Carnegie
A good rule of thumb is 1 minute per slide on average over your whole presentation. So for a 10 minute talk (not 12! the last 2 are for Q&A), you should aim for 8-10 slides. This suggests the following template, in the case of just 8 slides:
Title slide: who are you, what’s the overall topic you’ll be presenting
Motivation: what is the big-picture topic you’re addressing, and why is it important and/or interesting? (I.e. why should we care?)
Introducing your project (what, in broad strokes, did you do to address the topic?) and talk outline (“tell the audience what you’re going to say”): \(\leq\)1 sentence summary of each of your \(\leq\)3 main points. (In a longer talk, these would be split into separate slides.)
Main point 1
More on main point 1
Main point 2
More on main point 2 (or maybe a 3rd main point if you have it.)
Recap (“tell them what you’ve said”) and Discussion (Where is this going / could this go from here? What are the implications?) (In a longer talk, these would be split into separate slides.)
Slides#
Some guidelines:
Less is more.
Make each slide do one thing, not multiple things.
Make each slide’s title a complete sentence that summarizes the main point you want the slide to convey.
Some text is helpful, but usually people include too much. Boil it down to the essentials.
Plots: describe in words every single image and table you include. This is for accessibility, but also because almost always audience members can’t tell as fast as you think they can what it is you’re showing.
Make all text, plot labels, plotted symbols, and images big enough that everyone in the room can read them.
Give your slides some breathing room: a slide that’s totally full with text, multiple plots, etc. is overwhelming and results in less information being effectively conveyed than if you had less.
Related, dispense with “slidejunk”: you don’t need slide numbers, logos, the date, etc. on every slide. (Except for a logo of your institution on the title slide, you don’t even need these anywhere!)
If the professor’s own slides for this class fail to meet these recommendations sometimes, well, “Do as I say, not as I do” ;)
Delivering the presentation#
Try not to worry! Public speaking can be intimidating, but especially in this setting everyone, the professor and the other students, are there to support you and learn from you.
Practice the talk at least once ahead of time with a timer. Make sure that you’re within the time limit. Nobody likes a talk that goes way beyond its allotted time; it’s rude to the audience and the other presenters.
Don’t be afraid of silence. For the audience, it’s actually a huge relief when a speaker takes a few seconds between slides or to take a sip of water. It helps the audience take a second to gather their thoughts.
Answering questions#
It can be helpful for everybody, yourself included, to repeat the question back to the person in your own words for two reasons: (1) you make sure everyone in the audience heard it. (2) You make sure that you interpreted the question correctly.
Once you’ve confirmed you understand what they’re asking, take a second (or a few)! There’s no need to answer as soon as the last word is out of their lips.
If you don’t know the answer to a question, that’s OK! Take a few seconds to think hard about it, and then just give it your best shot.
Grading#
Your presentation will be graded based on the following:
Narrative quality: do you tell a single, coherent “scientific story” with a clear beginning, middle, and end? Or do you try to pack in too many different things?
Science quality: are the arguments, calculations, and plots presented valid? Or do they include errors or other problems?
Slide quality: does each slide convey a message in service of your story? Is there enough text, plots, etc. on each slide to convey that message? Is there too much on each slide for the audience to digest? Are the fonts big enough?
Length: did you complete your slides within the 8 minute time limit? Or did you go over? (To ensure we get through them all, you’ll get 2-minute and 1-minute warnings, and at the 8 minute mark I’ll ask you to wrap up essentially right away, regardless of how far you’ve gotten.)
Answering questions: do you make a good-faith attempt to understand and address each question? Do your answers cohere with what you presented?
Your grade will be based on a rubric nearly identical to that for the final report provided above, but using each of the five categories immediately above under “Your presentation.”
Logistics#
How to submit#
My preference is for you to submit the final paper and intermediate steps as LaTeX files, shared with me via Overleaf.
That said, I will also accept Word, Pages, or PDF files. You can email them as attachments or share them with me via Google Drive…just use the Google Drive associated with your CUNY “citymail” email account, and share it with my email address that’s on the syllabus.
Timeline#
(early in the semester): Discuss and agree on a suitable topic w/ the Prof.
By Friday, April 25th: Submit one plot or other form of data analysis on the dataset you’ve identified. (This can be extremely simple…the point is mostly to ensure you’ve found a dataset and have successfully managed to access it and at least begin working with it.)
By Friday, May 2nd: Submit draft title and annotated list of 3 papers you will read and incorporate into your own paper. Annotations = 1-2 sentences explaining why you’ve chosen the paper.
By Friday, May 9th: Submit draft versions of paper abstract and at least 5 figure panels or tables (that you generated), each one with a caption that fully describes it.
Wednesday, May 14th: in-class final presentations
Sunday, May 18th (by 11:59pm): Final paper submission deadline