​The activity
​Our session, Building Science with LEGO®: Why Details Matter, introduced students to the importance of research transparency, reproducibility, and scientific methodology through collaborative LEGO®-based activities

​Why details matter
Participants worked together to explore how small details in research design, methods, materials, and documentation can influence the reliability and reproducibility of scientific findings. Using LEGO® as a creative and accessible medium, abstract concepts were made tangible and engaging, helping students connect fundamental research principles to broader scientific challenges

​Looking ahead
​We greatly enjoyed welcoming these young scientists to the COSSEE space and hope they left feeling inspired by the possibilities that scientific research offers. We also appreciated the opportunity to share why transparency and reproducibility are essential for building trustworthy science.

​I quote the exact explanation of each term but give my own examples relevant to the COSSEE Lab.
​

• Intradisciplinary: “Working within a single discipline”
  • Example: Two ecologists are working together in designing and collecting data examining the life history of the Blue Tit.

• Crossdisciplinary: “Viewing one discipline from the perspective of another.”
  • Example: The ecologists recruit a statistician to consult, to ensure that the statistical model they select is optimal for their design.

• Multidisciplinary: “Working together with people from different disciplines, each drawing on their disciplinary knowledge.”
  • Example: An evolutionary biologist forms a team with the ecologists and statistician, to examine how ecological conditions drive changes in the life history of the Blue Tit.

• Interdisciplinary: “Integrating knowledge and methods from different disciplines, using a real synthesis of approaches.”
  • Example:  Using knowledge gained from the team, they joined forces with local historians with expertise in the local community’s interactions with the bird to create one unified approach to creating conservation efforts and a roadmap.

• Transdisciplinary: “Creating a unity of intellectual frameworks beyond the disciplinary perspectives.”
  • Examples: Based on the success of the implementation of the conservation roadmap- this becomes a transdisciplinary conservation effort.

There have been excellent visualizations of these concepts, with other examples provided,  Archontia Manolakelli offers both on a fantastic blogpost: https://www.archpsych.co.uk/post/disciplinarities-definitions. There has also been scholarly literature on working across these travels (Transdisciplinary- Harris et al., 2024; Interdisciplinary - Nancarrow et al., 2013; Multidisciplinary; Ooi et al., 2023 as examples of the examination or use of the approach). However, this is a blogpost, and now that you are hopefully familiar with the different definitions of research across boundaries and have had the opportunity to check out some of the research on it, we can move to the tips that I found useful in my own disciplinary travels.
​
Tip 1: Figure out where you and your research are positioned in the continuum
The position of your research and yourself as a researcher will change over time. It will fluctuate across subprojects and different dynamics of collaborations, or might fit in between two definitions. Questions that can help you reflect are as follows: 

• what disciplinary lens are you bringing to this blogpost? 
• what brought you to this COSSEE blogpost - the lens of open science, or ecology and evolution? 

Reflecting on such matters can be considered as “positionality”, which is frequently considered in qualitative research (Bourke, 2014).
​
Tip 2: Read widely, read what you’re interested in and what you don’t yet quite understand.
I originally encountered Stembers’ definition (1991) of disciplinary boundaries through Tarlo & Tucker's (2019) excellent introduction to an event bringing together academics and artists to explore the disciplinary boundaries and “explore ideas around place, ecology and environment” (2019, p.19) through a conference, exhibition and manuscript. Yet the reason I read this text in the first place, was because it was in the journal “Green Letters” dedicated to studies of empirical ecocriticism - a field which brings together the humanities and ecology. It was an interesting way of bridging the discipline of my last role in empirical literary studies and now biological sciences (as recommended by my former PI Dr. Ailise Bulfin). I didn’t know it then, but it would become a foundational text for defining collaborations, which is a key part of my proposed PhD thesis. 

Tip 3: Respect other disciplines, even if you don’t understand or are interested in them.
Speaking of reading things you are interested in that will lay dormant in your mind until the right time (like the sword in the stone), there will be disciplines that do not interest you neither professionally nor personally. I will not disrespect any discipline by naming the ones I’m apathetic to publicly, as to another scholar they are the source of passion, and passion is infectious. Thus, it is important to respect other disciplines, as you may end up working with someone nestled within them or have a history in them.

Tip 4: Learn the language of different disciplines. 
Although all disciplines are connected to some degree in the pursuit of knowledge and truth - even ones using similar methodological frameworks have different “accents” to the same language. For instance, although statistical models are used both in psychology as well as biology there are stark differences in the design they are applied to. This is rendered visible in the differences in terminology; the predictor versus the independent variable for the effect of interest, the response versus the dependent variable for what is being effected, respectively. 

Tip 5: The sunk cost fallacy is just a fallacy.
No knowledge is ever wasted or rendered obsolete. If you become an expert in something or advance your career in a certain field, you are not obligated to remain there. You can move away, and the knowledge will move with you and be transformed in new disciplines and new domains.

References:

Bourke, B. (2014). Positionality: Reflecting on the research process. The qualitative report, 19(33), 1-9.

Harris, F., Lyon, F., Sioen, G. B., & Ebi, K. L. (2024). Working with the tensions of transdisciplinary research: a review and agenda for the future of knowledge co-production in the Anthropocene. Global Sustainability, 7, e13.

Jensenius, A. R. (2022).Sound actions: Conceptualizing musical instruments. MIT Press.

Nancarrow, S. A., Booth, A., Ariss, S., Smith, T., Enderby, P., & Roots, A. (2013). Ten principles of good interdisciplinary team work. Human resources for Health, 11(1), 19.

Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., ... & Wong, L. W. (2025). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 65(1), 76-107.

Tarlo, H., & Tucker, J. (2019). Cross Multi Inter Trans: a contextual introduction. Green Letters, 23(3), 219–230. https://doi.org/10.1080/14688417.2019.1682347

Why does this relationship, which should be one of partnership and construction, generate so much frustration? A very common mistake is that most advisors start advising without defining clear rules. Graduate programs usually provide bureaucratic guidelines on enrollment and norms for structuring dissertations and theses, but they fail to explain conduct, duties, and how advising should occur on a day-to-day basis. As a result, without prior formal preparation, many advisors act intuitively, guided by trial and error or past experiences. Without clarity of roles, students do not know what to expect, and advisors do not know the limits of their actions, weakening both the quality of the research and the relationship itself.

But how do we solve this in practice? The solution is not just to "talk more," but to formalize what is left unsaid.

The Problem: The Expectations Vacuum
Most problems arise because each side—students and advisors—assumes that the other knows what to do. On one side, advisors expect autonomy. On the other hand, students expect direction. This lack of harmony is not just a detail, as academic dialogue is influenced by affective, professional, and institutional aspects that shape all scientific production (Viana, 2008). When these expectations do not match, the result is frustration and a direct impact on well-being, with moderate levels of depression and anxiety reported in the academic environment (Pinzón et al., 2020).

The Solution: The "Supervisor-Student Agreement"
To generate real value in your research routine and mitigate this mismatch, I suggest using a practical tool: an expectations agreement. It is not a legal contract, but an alignment of conduct and a "record of intentions".

Here are the main points this agreement should cover for your research to flow:

1. Meeting Cadence: Do not leave it at "we'll keep in touch". Define the frequency and who is responsible for the agenda. Modern advising requires the supervisor to ensure specific training and bridge the gap between research and teaching (Nóbrega, 2018).
2. Writing Feedback: How much time does the supervisor need to read a chapter or a manuscript you are writing?. Defining this prevents the student's research from stalling. A student's self-efficacy and academic literacy depend directly on this articulated support (Magalhães, 2024).
3. Data Ownership and Authorship: Who owns the data after graduation?. How will the order of authorship be decided?. Discussing this early avoids ethical conflicts and headaches later on.
4. Autonomy vs. Supervision: Does the student need a step-by-step guide or do they prefer freedom?. Aligning management styles prevents feelings of abandonment or micromanagement (Lopes et al., 2020).

Why Does This Change the Game?
When you put these expectations on paper, you remove the emotional weight of demands. The relationship between supervisor and student is the foundation of the scientific production process (Lopes et al., 2020). Professionalizing it through an expectations agreement can help separate success from burnout.

Golden Tip: Supervisors, offer this model to the students in your lab. If you are a student, take the initiative and schedule a meeting with your advisor to discuss this agreement. This demonstrates maturity and commitment to the work.

Graduate school is challenging enough. Having a map of how you and your mentor will work together is a good first step toward making the advising process profitable. Don’t forget that you can—and should—revisit and discuss these expectations as often as necessary. Over a long PhD process, many understandings may change as needs and maturity levels evolve.

The idea of discussing this agreement came from my own experience in my PhD. I had the chance to use an expectations model with my supervisor, and while our relationship was already very good, having that document certainly helped organize our interactions over nearly four years of research.

I will attach a file to this post with the expectations agreement model I edited based on what I used. While this model generally works for graduate students, it can be edited to mediate relationships with post-docs, supervisors, or even lab staff. I'm also including some references to studies investigating the advisor-advisee relationship. I hope this helps! If you need anything, feel free to message me.

References

• Lopes, E. F. B. et al. (2020). A relação entre orientador e orientando no processo de produção científica. Brazilian Journal of Development.
• Magalhães, L. (2024). Você já leu o meu trabalho? Interrogando expectativas e realidades na relação entre discentes e docentes, na pós-graduação brasileira. Movimento.
• Nóbrega, M. H. (2018). Orientandos e Orientadores no Século XXI: desafios da pós-graduação. Educação & Realidade.
• Pinzón, J. H. et al. (2020). Barreiras à Carreira e Saúde Mental de Estudantes de Pós-graduação. Artigo jul.-dez. 2020.
• Viana, C. M. Q. Q. (2008). A relação orientador-orientando na pós-graduação stricto sensu. Linhas Críticas.

​

DOI Manager

Once files are stored cleanly, the next weak point is metadata—especially DOIs. In theory, every modern academic paper has a DOI. In practice, many Zotero entries are missing them or contain malformed ones, depending on how the paper was imported.
This matters a lot more than it seems. The DOI is the unique identifier that allows Zotero to fetch authoritative metadata from CrossRef and other services. It’s also the most reliable way to detect duplicates.
The DOI Manager (https://github.com/bwiernik/zotero-shortdoi) plugin scans your library and:

• finds missing DOIs;
• validates existing ones;
• corrects errors.

Running it periodically keeps the library's “identity layer” clean. Once DOIs are in place, Zotero can reliably refresh metadata for entries that were imported in messy ways. Think of it as repairing the genetic code of your bibliography.

​Zoplicate: controlling duplicates before they spread

In any large library, duplicates are inevitable. You save the same paper from different sources. One entry has a DOI; the other doesn’t. Left unchecked, duplicates accumulate and slowly pollute the library.

Zoplicate scans the library for similar entries and groups potential duplicates. You can then merge them intelligently, preserving:

• PDFs;
• notes;
• annotations.

The result is one clean canonical entry per paper. Zoplicate (https://github.com/ChenglongMa/zoplicate) can run automatically and merge duplicates, e.g., giving precedence to the newer or older version.

​This is how an informal hands-on an impromptu social activity turned into an unexpectedly thought-provoking catalyst. A diverse group of researchers - from graduate students to seasoned professors - were handed pens and sheets of paper with innocent-looking ghost outlines. The task: write down the things in science that frighten you.
I repeated this activity during a virtual social event hosted by the Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology (SORTEE). Instead of paper, participants filled a shared Zoom whiteboard with their spooky scientific worries.

​So, what did people write?

Here’s an unordered and far-from-exhaustive list:

Reviewer #2,
impact factors,
discrimination,
pseudoreplication,
academic hierarchy,
APCs,
publication bias,
the Gollum effect,
predatory journals,
Type I error,
lack of funding,
lack of academic positions,
high expenses,
meaningless reviewer comments,
“data upon request,”
unequal access to education,
exclusion,
governmental intrusion,
monocultures,
the Matthew effect,
p-hacking,
exploitation of undergrads,
authorship conflicts,
elitism,
cherry-picking results,
scientific colonialism,
pyramid-scheme dynamics,
plastic waste,
isolation,
groupthink,
lack of collaboration,
socioeconomic bias,
replication crisis,
ghost authorship,
weak study validation,
missing data,
publisher monopolies,
small sample sizes,
inequitable funding and facility access,
AI-generated papers,
boys’ clubs,
opaque methods,
devaluation of Indigenous perspectives,
citing Darwin for everything,
partial code,
research waste

… and so on …

​You can see many of these fears in the pictures - one from the in-person exercise with pens and paper, and one from the virtual Zoom whiteboard.

What about you?

Do any of these scientific “ghosts” haunt you as well?

And perhaps the bigger question…

are we still managing to have a wicked good time in science?

The second part of the trip was in the Rockies in Alberta. Driving from Edmonton to the Rockies is quite a contrast, from flat plains closer to Edmonton you arrive at massive mountainous rock formations. As Lorenzo mentioned in his blog, every turn on Highway 93 had a breathtaking view. At one corner, a large black bear walked right in front of our car before sitting down to eat berries and flowers by the roadside, with no care for us or the queue of cars building behind us.

Maligne lake and somewhere along AB-93
​​

Prior to going to Canada, doing my best to prepare like a true tourist, I read up on anything bear related and I read many times the saying "if it's brown lay down, if it's black fight back, if it's white goodnight". But I soon realised that black bears can have very different fur colours, and even grizzly (“brown”) bears can be quite blond. So, I guess that expression isn’t too helpful, and perhaps these common names should be revised. It was also cool to see the bear-safe bins, as I had read about how they were engineered to balance the human-bear trade-off.  
​
All in all, it was a special trip, and I feel very fortunate to have had the opportunity to visit UoA and these places. Saying goodbye to Shinichi and fellow lab members in Edmonton was bittersweet, but it’s a normal part of how things move on. Back in Sydney, there have also been many farewells over the past months, with colleagues wrapping up their contracts and starting new roles. I’ll be the last to finish up in the Sydney branch, so for now I’ll be wrapping up my thesis, appreciating these experiences, and getting ready for the next chapter.

​What is sampling variance?
Sampling variance is a fundamental concept in statistics, yet it is often misinterpreted in meta-analysis. When researchers talk about “sampling variance,” they usually mean the variance associated with effect size estimates from primary studies. However, the term is broader: sampling variance exists whenever we estimate any parameter; an effect size, an overall mean effect, a regression coefficient, or even a variance component such as the between-study variance (tau^2). In essence, sampling variance reflects the uncertainty that arises from random sampling. It quantifies how much an estimate would fluctuate if we were to repeat the study many times under identical conditions.

Sampling distribution and standard Error
To understand sampling variance, we first need to understand the sampling distribution. Imagine conducting a study with a sample size 𝑛 to estimate an effect size, such as the standardized mean difference (SMD) between treatment and control groups. If you could repeat this experiment many times (each time drawing a new random sample from the same population), you would obtain a distribution of SMD estimates. This is the sampling distribution of the SMD.

​The standard error (SE) is the standard deviation of this sampling distribution. It quantifies the precision of your estimate: smaller SEs imply greater precision. The sampling variance is simply the square of the standard error (SE^2). In practice, we rarely know the true effect, so we rely on the standard error (and thus sampling variance) to gauge how much our estimate might vary due to random sampling.

In meta-analysis, sampling variance arises in several contexts:
  (1) effect size estimates from primary studies,
  (2) overall mean effect from an intercept-only meta-analytic model,
  (3) regression coefficients from a meta-regression, and
  (4) variance components (e.g., tau^2, the between-study variance).
​

Deriving sampling variance: “ideal” vs. “practical” approaches
Ideally, to calculate the sampling variance of an effect size estimate, you would repeat a study with the same sample size (n) many times, compute the effect size each time, and then calculate the standard deviation of the resulting sampling distribution.

For example, to estimate the sampling variance of an SMD, you would:
   (i) Conduct the study multiple times with sample size (n).
   (ii) Compute the SMD for each study.
   (iii) Form the sampling distribution of SMDs.
   (iv) Calculate its standard deviation (the standard error) and square it to get the sampling variance.

Similarly, for the overall mean effect in a meta-analysis, you would:
   (i) Draw Randomly sample sets of studies many times.
   (ii) Fit an intercept-only meta-analytic model to each sample to estimate the overall mean effect.
   (iii) Form the sampling distribution of these mean effects.
   (iv) Calculate its standard deviation and square it.

This conceptual exercise shows what sampling variance means, but of course, repeating studies thousands of times is impractical.

One of the highlights for me was reconnecting with familiar faces from previous SETAC conferences and meeting new colleagues working across a wide spectrum of pollutants and approaches - ranging from lab toxicology and field ecology to modelling and policy translation. There is something special about seeing so many different perspectives converge on a shared goal to reduce pollution.
 
After the conference we took the opportunity to explore some of the local sights and enjoyed availing of the local cafes, restaurants and pubs. All of which had their own unique vibe and character which was super cool and interesting to see. Although the week flew by, we are left grateful and inspired. We are already looking forward to staying in touch with the SETAC community and to future opportunities - whether at the next conference or in collaborative projects that keep the momentum going.