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.

​​I also learned a lot about Edmonton thanks to a memorable chat with my Uber driver, Nwabueze. He’s a Nigerian guy, a little older than me, who picked me up from the airport. After the usual chit-chat, he told me how he left Nigeria six years ago, not because things were bad, but because he wanted to open his mind and challenge himself. Among many interesting stories, he explained to me how incredibly cold it gets here in winter, how diesel fuel requires antifreeze additives during the colder months, and why so many windshields are cracked (spoiler: they put rocks on the roads to improve traction on ice). “It’s tough during winter,” he said. I believe him.

The University of Alberta was good, although very quiet. After those three weeks of work, my girlfriend and I set off on a road trip from Edmonton to the Rockies (Jasper and Banff NPs), down to Vancouver and Vancouver Island, and then back. About 4,500 km through the wilderness of western Canada.

The Rockies are absolutely stunning. Nature, landscapes, and alpine lakes that really take your breath away. Sadly, a large portion of Jasper burned last summer in a devastating wildfire. Driving and walking through the scorched land, where everything felt lifeless and silent, was surreal. A local in Jasper told us the cause of the fire is still uncertain, possibly a cigarette, lightning, or a mix of both. We spent three days there and hiked a couple of beautiful trails in the areas that hadn't burned. We saw squirrels, chipmunks, and even wild goats.

From Jasper, we took Highway 93 south toward Banff. That drive alone is worth the trip. The beauty is hard to put into words, so here’s a picture to help you imagine it.
​

We were lucky enough to spot a couple of American black bears along the way. It was amazing to see them roaming freely in their natural environment, minding their own business. In Banff, we stayed five days and visited the iconic Lake Louise and Moraine Lake. We also ventured into the nearby Yoho and Glacier National Parks. There, we saw elk and what I think was a marmot (but don’t quote me on that).

We did several incredible hikes, always carrying a bell to make noise (so grizzly bears know you're around) and a can of bear spray, which is mandatory for many trails. Unfortunately, or maybe fortunately, we didn’t see any grizzlies. I guess the bell worked.
​
After immersing ourselves in the mountains, we headed to Vancouver and Vancouver Island. We had a great impression of Vancouver: good vibes, tasty food, pretty skylines, and hidden gems tucked around the city. To reach Vancouver Island, we took a car ferry that crosses over in a couple of hours. Tofino was the highlight, a little village full of personality.

If I had to point out one downside to the trip, it would be the food. In Canada, decent quality food, what you'd consider average elsewhere, comes at a steep price. Many people resort to fast food a few times a week, and overall, Canada doesn’t really shine in the culinary department. Not that it was a surprise, considering some of the national staples are ketchup Lay’s chips and poutine (French fries with cheese curds and gravy).
​
So now it’s time to fly back to Sydney, slightly tired, definitely inspired, and maybe still craving real food. Canada surprised me in many ways, some good, some weird, and all worth it. I’m already looking forward to my next adventure (New Zealand in about 20 days!), but for now, I’ll just sit here with my Tim Hortons coffee and say: thanks, Canada. It was a wild ride.
​

Visualising layers of meta-data in meta-analyses

What counts as “data” or “meta-data” depends on the context (see my diagram above). In a primary empirical study, the data might consist of field or lab measurements of things, humans, or systems, while the meta-data includes descriptions of the variables in that spreadsheet (black parts of the diagram above). 

But once a primary study is published (or shared), it gains another layer of meta-data: title, abstract, publication date, author names, affiliations, etc. This is the meta-data librarians and other information specialists work with (green parts of the diagram above).

In a secondary study, such as a meta-analysis or systematic review, you typically compile not only data of primary studies (selected results and their descriptors), but also some of their meta-data (e.g., study-level characteristics such as study reference, title, authors, journal, DOI, etc.), and then also generate new data for your synthesis (e.g., recalculated effect sizes). The resulting dataset is a layered mix of data and meta-data from different sources and levels.

What to do in practice

In practice, for a meta-analysis (and systematic reviews or other secondary studies) use terminology consistently in the context of your study: call your dataset "data", and description of your dataset "meta-data" (purple/plum, NOT pink, parts of the diagram above). You can still acknowledge that your data contains some meta-data from underlying primary studies (e.g. information describing the publications).
 
Why it matters

Conceptual complexity—and the commonly inconsistent use of terminology—may partially explain why appropriate meta-data is often missing or poorly documented in shared datasets from meta-analyses (and various types of systematic reviews). When people are asked to share meta-data—but they think this is just their dataset (data)—they only share the dataset, without description of all variables (meta-data). But without complete and well-structured meta-data (the descriptions of data), it becomes difficult to interpret the dataset (the data), let alone reuse it or reproduce the analyses. Transparent and clear meta-data (descriptions of data = dataset) is crucial for making meta-analyses truly open and reusable.
 
NOTE:
You can find earlier blog posts from my “Around meta-analysis” series archived on my personal website.