One of the major goals of the Tropini Lab is that all Team Members feel welcome, respected and heard. Not only is this the right thing to do, but it also facilitates learning and discovery, and it's the basis of a cohesive team. With the knowledge that different people learn in different ways, and that treating students equitably does not mean to treat everyone equally, our team strives to represent different cultures, perspectives and modes of learning. There are three main initiatives that are at the heart of ensuring equity and inclusion in the Tropini Lab: our Lab Code of Conduct, our Yearly Lab Feedback Review and our yearly Individual Review of Team Members.
Beyond our actions within the lab, to reduce the effects of intrinsic bias, reference letters written by Carolina refer to the candidates by their initials only, and use gender neutral pronouns. The lab also actively contributes to outreach events aimed at fostering the involvement of underrepresented minorities in Science and Engineering, such as through the involvement in summer camps for Indigenous high school students.

Behavioural Conduct

Scientific Conduct

• Be Professional. Professional can mean a lot of things to a lot of people. Here's what it means in the Tropini lab.
  1. Safety first. We follow all safety procedures, always!
  2. Respect. We treat everyone, their experiments, equipment, ideas and the lab as a whole with respect, as if they were our own.
  3. Punctuality. We respect each other's time and schedules. It's not always possible to make the expected time, when that happens, we let the other person know as soon as we can so that they can also accommodate their schedule.


• Always Learn. It's why we're here! This lab is meant to help you become the best scientist you can be; that means learning. We learn from helping each other, troubleshooting, asking questions and always being curious. All questions are good questions, all experiences teach.


• We are a team. Everyone contributes, lends a hand, and takes responsibility not just for themselves but for each other, that's being a good lab citizen!


• Make Mistakes.. They're inevitable and everyone makes them and there is no reason to be afraid of making them - it's what you do with them that counts. Take responsibility when mistakes happen, not to be reprimanded, but so that we can get to the good stuff: learning from them, and teaching each other about them. This is the only way to improve systems and processes in the lab. Help those who make mistakes and always ask for guidance and assistance when you've made one of your own.


• Keep a balanced schedule. Being productive is more important than putting more hours in. Be available during core hours so that we can all learn from and support each other. Hard work that comes at the cost of personal health and well-being, is bad work. This is a marathon, not a sprint.


• Cite your sources. NEVER use content from outside sources (including your own) without properly citing them. We don't just respect the work that happens inside our lab, we respect it all. Especially that which informs and provides a foundation we build upon.


• Record everything. Keep meticulous records of your work and ALWAYS back up your data. Incomplete or spotty science is bad science, and we do good science in the Tropini Lab. Complete data is essential for audits and investigations. Incomplete data or poor data storage can destroy both your reputation as a scientist and that of the lab. If you don't have a way to back up your data, let us know and the lab will provide a solution. Note that your lab notebook belongs to the University so everything should be stored online on Notion (take snapshots of the physical lab notebook, embed excel spreadsheets etc.) except when working with de-identified patient data in which case we need to use another system.


• Never Manipulate or Cherry Pick Data. Remember we care about the truth, even if it runs counter to a desired result. The Tropini Lab has ZERO TOLERANCE for falsification. We will not, under any circumstance, compromise our scientific integrity. There is a fine line between removal of outliers and cherry-picking. Come talk to Carolina if you have concerns about mis-representing your data. Please also refer to UBC Responsible Research.


• See Something Say Something. If you witness unethical behaviour or scientific misconduct, report it. Tampering with experiments, data or analyses is inadmissable. This is part of being a good lab citizen. We take responsibility for everyone, every experiment, and the lab culture as a whole. Scholarly-integrity "UBC researchers are personally and directly responsible for the intellectual and ethical quality of their work and must ensure their work meets the requirements of all applicable funding agreements, policies, standards, laws and regulations. If you direct and supervise researchers, you share in this responsibility."


• Be aware of unconscious bias in research. You don't want to fall victim of research stress and do something unethical. If you, or one of your teammates, is feeling stressed about research, progress, unethical actions, anything at all, come speak to Carolina.


• Reproducibility and high quality data is at the core of our lab. For each publication the lab produces, we publish the raw data, code, and input required to generate each figure. It's every author's responsibility to ensure that a third party can run that code to reproduce a published figure. Published code must be commented and clearly laid out, and will be checked by an independent lab member prior to each publication. Raw data must be clearly labeled and accompanied by complete metadata.


• Respect safety protocols and individual risk assessments. Learn the safety protocols and procedures we are required to follow in the lab. This includes both general lab safety as well as pandemic-specific protocols. Not everyone sees risk the same as you - respect their boundaries, and expect them to respect yours.


• Ask for, give and accept help when needed. Productive struggle is important for developing learning resilience and a deep-focus mindset. It's important to test yourself and try to go further, but don't take this too far. Never be afraid to ask for help. There is no judgment in asking for it, and none in needing it, especially since getting stuck is a big part of the scientific process. Once you've tried Google, relevant literature, and your own thinking, don't hesitate to use your team, leaders included. Learning to judge whether to push yourself to figure something out or if it's time to ask for help is a wildly important skill, and it's one you can practice here.


• Respect each other's time. If you need something from a teammate, always consider their schedules. Everyone has their own deadlines, tasks, and goals, so beware the shoulder tap; interruption can impact everything from workflow to mood. Whenever possible, arrange a meeting at a convenient time—that's when to ask for help. If it's a bigger ask like feedback or text review, always give at least one week of lead time. If you are asking for help be clear about when you need it by and whether it is urgent. Be conscious that people may feel compelled to take action right away. Ask people what they prefer (meetings, emails, when etc.).


• Provide constructive, direct and kind feedback. The goal of feedback is to help your fellow team member improve, not to make them feel bad. Consider postponing providing feedback with other lab members when you are tired or otherwise emotionally activated or drained. It might be a good idea to reread that late-night email in the morning before you send it. Before giving feedback always ask yourself these questions:
  1. What's motivating you to give feedback? Get crystal clear with yourself about your motivations and do not act unless it is out of a genuine desire to help.
  2. Am I annoyed, frustrated, angry? If yes, take time to cool down before broaching the subject.
  3. Is it constructive? For something to be constructive, it has to be useful, helpful, and actually improve whatever it is that requires critique. If you aren't sure, then you haven't clarified your feedback enough to give it. Regroup and make certain what you have to say ticks those three boxes.
  4. Is this the right time? Maybe they're in an emotional place, maybe you are, maybe it's three minutes out from the end of the day. There's a time and a place for effective feedback and these aren't that.


• Feedback is a gift. Remember that the feedback you receive in the Tropini lab is only ever meant to help you learn, grow and become a better scientist. We want to grow together, and that means that feedback cuts both ways, whether you're in a leadership position or you're a high school student with no previous lab experience, we can all learn from each other. So be open to learning, and get excited about being wrong; it's just another opportunity to expand your knowledge and skill set.


• Thread carefully when using AI.
  DO:
  - Use AI for improving word/sentence/paragraph construction
  - Provide text to the chatbot and ask for improvement.
  - Always doublecheck that the AI go the intended meaning out.
  - As time goes on, try to learn so that you can reduce your use to just specific sentences.
  - When you use AI, acknowledge its use and the reason for that use (e.g., improving language construction).
  - Use ChatBots for learning about new topics
  - Always double check sources (independently, as currently citations are not great).
  - Use ChatBots to do minor editorial work (e.g., change the pronouns in a reference letter, organize a list better etc)
  - Use AI for data exploration. Check your work and make your own graphs.
  - VALIDATE the AI findings/suggestions with independent methods.
  - Share with the lab new uses, and check in as to whether they may cross an ethical line. This is a new technology and a moving target, discussing it together will help us move faster while reducing the risk of mistakes.
  DON’T (not a comprehensive list):
  - Get AI to write new text for you or to define your analysis or project. Use AI as a aid, not as the sole source of information.
  - Never get the AI to do the writing first and then you edit. Rather, write first then use AI to refine.