CATSUP Examples

3/15/2024: What to Do and What Not to Do During Meetings

This week, the Shiu Lab focused on how efficient meetings should be run. Two types of meetings, synchronous and asynchronous, can be useful depending on the context and if a conversation between members of the meeting is required. In addition, the importance of a list of goals for each meeting, possible short pre-meeting reading, and guided discussion are all keys to a efficent and productive meeting.

12/8/2023: Project Time Management: Strategies, Tips & Tools

Time management is an important part of project management in any field. Referencing an article about time management the conversation revolved around how to best organize projects and meet goals while managing their own projects, research, and collaborations.

12/1/2023: Why is China’s high-quality research footprint becoming more introverted?

Today the Shiu lab discussed international collaboration using a recent trend discussed in Nature. The discussion revolved around the future of international collaboration, as well as how US researchers can continue to do innovative research amidst a changing research landscape.

9/15/2023: The Difference between Productivity and Performance

The lab talked about optimizing productivity and how, when marketing yourself, the focus is often on previous performance. The discussion covered what this meant for future job opportunities and how to show productivity as a graduate student does not always lead to high performance-based measures due to setbacks in research.

3/31/2023: How Nancy Hopkins and her tape measure revealed the extent of sexism in science

Sexism in academia can take nondescript forms, such as the amount of lab space a female presenting scientist may have in comparison to a male presenting scientist, as Nancy Hopkins discovered at MIT. This week we talked about the realities of women in academia and science, as well as the barriers that different groups face when pursuing a career in science.

3/10/2023: ChatGPT passing USMLE shines a spotlight on the flaws of medical education & How To Make Sure You Don’t Lose Your Job To Artificial Intelligence!

It is a double feature this week as ChatGTP takes center stage. Recently ChatGTP has passed important exams for specialized professions. The future of education and how to adapt to a world post ChatGPT was discussed. We also addressed what we have already done and can continue to do to ensure our future job stability beyond what the second article of the week mentioned.

3/3/2023: Transparent AI Challenges and Its Solutions | Ultimate Guide

The future of AI and science continues to be a theme this semester as we discuss the definition of transparent AI. We even talked about how some articles like the one we read may one day be written by AI.

2/24/2023: The Great Automatic Grammatizator by Roald Dahl (1954)

ChatGTP’s recent public release has caused people to question the future of writing. Roald Dahl addressed a very similar issue in 1954 when he released a short story. This meeting was an opportunity to reflect on the future of AI, how research will change, and the importance of preventing the spread of misinformation.

2/9/2023: “Respect them,” says He Jiankui, creator of world’s first gene-edited humans

This week’s article addresses a controvery from 2018 involving gene edited humans. We discussed the experiment’s ethics, how researchers who cross ethical lines should be reprimanded, and how this might apply to us as a plant biology lab.

1/27/2023: Scientists rise up against statistical significance

A strong understanding of statistical significance is essential if a scientist is to interpret statistical significance, but this week’s article discusses how statistical significance may be misunderstood. We discussed different types of measurements that could be used to analyze and explain results, but found p-values are very useful if used correctly.

12/09/2022: 11 Characteristics of Self-Actualized People

Self-Actualization is achieved when a person has realized their full potential according to this week’s article. We focused on how self-actualization may look different to various people and how we define self-actualization for ourselves.

12/02/2022: Meet this super-spotter of duplicated images in science papers

Recent discussions of data alteration and fabrication brought this week’s article to the forefront. We talked about the reasons data is altered and fabricated as well as how to be vigilant, such as by referencing papers that have undergone extensive peer review.

11/11/2022: Could AI help you to write your next paper?

The rise of ChatGTP and other similar technology, the future landscape of scientific research is changing. This week, our lab discussed the ethics of using these new technologies, authorship when utilizing ChatGTP, and the future of LLMs.

10/14/2022: Balance is needed when discussing academic careers

Academia can be rewarding and difficult, but so are positions outside of academia. This week we focused on how to create work-life balance in an academic space, if it is possible, and how to fairly critique work-life balance in academia.

9/23/2022: Climate Change Worsens Most Infectious Diseases

The discussion for this week revolved around how climate change facilitates transmission of infectious diseases. We further talked about the implications of infectious diseases worsening and how this may be applied to plants.

9/9/2022: Why Did Ferns Persist When All Other Plants Perished?

This article discusses how different researchers have approached researching why specific plants, such as ferns, have survived ancient extreme environmental events and why that research is difficult, but important. We discussed the many variables that make biology data challenging to explain as well as the benefits to this difficult research.

8/19/2022: Best Bugs: How E. coli Evolves into a Stinkbug Symbiont

A study found that E. coli can mutate to become beneficial over many generations in stinkbugs, working as a symbiont. We discussed the study setup and what makes this a compelling experiment.

6/3/2022: A Major Science Journal Publisher Adds A Weird Notice to Every Paper. What’s Behind This?

Today we discussed publishing practices and the movement of publishers to remain neutral in global conflicts. We further discussed publishing at large and how to choose a journal to submit a paper to.

5/6/2022: Can Alondra Nelson Remake the Government’s Approach to Science and Tech?

Alondra Nelson, a sociologist, is the new director of the White House’s Office of Science and Technology Policy. Our meeting today was based off an article about her and her new position where she may be able to enact change in the executive branch’s relationship with science. Using her as a model, we postulated how we, as scientists and scientists-in-training, can address our research to those who may not interface with our research questions very often or use different jargon. We talked about the changes in science and ethical implications of inappropriate behavior in the workplace and how we can mitigate those effects through positive treatment of those we work with. Our lab hopes to increase outreach toward different groups in order to educate others in what we research and why.

4/22/2022 Day 8: Data transformation - Skewness, normalization and much more

In our research, we may conduct transformations on our data, however we must understand why data transformation is important and under what circumstances it is appropriate. Our article this week focuses on how data might be transformed and why. Sometimes a large range of values between different columns can cause values in each column to be weighted differently. When your data is transformed, it can help appropriately scale data and mitigate issues that may arise from skewedness and attribute aggregation. For normalization we can use the min-max or Z score techniques, where the min-max moves values toward the mean of the data and the Z score maintains impact of outliers. To handle issues with skewedness, there are multiple approaches that may be used: cube root and logarithmic transformations may be used for both positively and negatively skewed data, but due to mathematical limitations, square root transformation should only be applied to positively skewed data, whereas square transformation is used on negatively skewed data. Each of us works with different datasets, so we all recognize the importance of addressing different reasons for data transformation and the types of data transformation we may use.

4/8/2022 Data Science Mistakes to Avoid: Data Leakage

Data leakage can happen when you have information about testing data in your training data or when your model has been trained using information that is not available after it is pushed to production according to an article in Toward Data Science. This article focuses on making a machine learning model as accurate as possible. To prevent data leakage, this article suggests things like “using a sliding window to split time series data” and not to randomly split groups because the model might learn off of data that may not be accurate to the overall pattern. Outside of academia, when deploying our trained model, it might need to be retrained when presented with new information. A way to test this is to create a “challenger model” and test both the previous and challenger models on the challenger model test set. However, we must ensure the previous model’s training set does not contain data in the challenger’s test set. To prevent data leakage, some typical best practices are: splitting your data immediately, using cross-validation, being skeptical of high performance, using pipelines such as those in scikit-learn, and ensuring that features correlate with the target (what you want to predict). We have our ML_Pipeline where we do our best to follow best practices that avoid data leakage, but also like to remind ourselves that there is always room to improve on our systems.

3/25/2022 Broken bread - avert global wheat crisis caused by invasion of Ukraine

This week we recognize a major global event, the invasion of Ukraine by Russia. The article we read was one that Nature published about the impact this invasion has on the global wheat supply. Since Ukraine and Russia contribute about 11% of the world’s calories and 1/3 of the world’s wheat supply, as stated in the article, the invasion of Ukraine by Russia has caused a disruption to the global food supply, especially for wheat. As plant scientists and scientists-in-training, we understand it is important for us to educate ourselves about the scientific landscape. This example has shown how humans must ensure that we are taking steps to prevent crisis. To do so, the article mentioned that we could to expand world wheat production, help farmers gain access to best growing practices, create new flour blends, use monitoring systems for analysis on crop production and optimization of crop fields, study genomics to track pests and plant pathogens, and further invest in agriculture policy and science to support women farmers in rural areas. This article brings attention to potential paths for our research and how we can increase global food security.

3/11/2022 Scientists want to create a library of every sound in the ocean

We are 2 months into the spring semester and our CATSUP meetings have focused on improving our research or coding skills, but we must remember why we are all in this lab in the first place: we love science! This week’s article from Science we learned about how a group of scientists want to create a library, named “GLUBS”, which could eventually collect all underwater sounds. Their goal is to track changing marine ecosystems using this sound library. This article highlights something we love about AI: the diversity and creative ways that we can use different AI approaches to solve problems. A goal they have is to create an app that can be used by citizens to upload/identify sounds collected. Through teamwork, this library will be used to teach an AI to “learn” what the sounds are and identify unknown sound sources in the ocean.

2/25/2022 Python Code Quality: Tools & Best Practices

In the Shiu lab, we have both experienced and novice coders, but we know that everyone can always benefit from reviewing best coding practices. So, this Friday, we talked about the article Python Code Quality: Tools & Best Practices. While we talked about how to make code easier to read, like by having docstrings, and maintain/extend individually, we also recognize the importance of utilizing tools that already exist. This lead us to the conversation about if we should use linters. Some linters, such as Pylint will tell you what is wrong, whereas other linters, like Black, will automatically format your python document. The 2 main categories of linters, logical linters and stylistic linters. Logical linters will bring attention to coding errors, while stylistic linters will point out the code not following typical stylistic patterns. All-in-all, it is a personal preference how a person prepares their coding environment, the Shiu Lab does want to ensure we are all doing our best to follow the same docstring format and general stylistic formatting.

1/28/2022 Logical Fallacies

Logical fallacies are an issue that scientists need to look out for, as we discussed this week during CATSUP, using the article Logical Fallacies from Purdue University’s Online Writing Lab. As scientists-in-training, the graduate and undergraduate students in our lab work to avoid illegitimate arguments and faulty reasoning. Those in the lab with doctorates also recognized that throughout our careers, we will always need to consciously look for fallacies in our own and others arguments to ensure rigorous scientific research. Many of the fallacies are tied to oversimplifying an argument, which we worry will happen when we attempt to make our arguments as clear and concise as possible. We need to balance the nuances of our research with making our inherently multi-disciplinary research accessible to all groups who may look to access it.

1/13/2022 How scientists fool themselves – and how they can stop

In CATSUP this week we discussed the article How scientists fool themselves – and how they can stop by Regina Nuzzo and how our lab can best combat our own cognitive biases to conduct more robust and ethical science. We addressed “The Texas sharpshooter” cognitive trap, where someone produces multiple results and finds a pattern that may not be accurate. Sometimes a person has a tendency to pick the data that most agrees with their hypothesis or is interesting. A part of the article we thought would be a good way to combat this issue is by having “rivals”. In our lab try to create a collaborative environment where we work together to address problems that are related to multiple areas of our research. However, this collaborative environment includes questioning the reasoning of our collaborators and respectively creating counterpoints to address holes in our research results and methods.