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Every Overtone Interviews episode follows a consistent interview methodology, utilizing the same set of questions in the same order, regardless of the guest or format. We publish this protocol for two reasons: to ensure prospective guests understand precisely what they're agreeing to before they accept an invitation, and to maintain the integrity of the series' goal — if we're exploring the drift of scientific language, it is essential that our approach remains transparent and consistent, rather than being adjusted quietly from episode to episode. This transparency also aligns with our commitment to sharing academic research insights. Every guest reviews this page before consenting to an interview.

A Published Feature under your name that you approve before it goes live — easy to share on LinkedIn or X, with a natural link from your lab page or CV. This serves as a citable record capturing your academic research insights at this point in your career, highlighting not just what you eventually published, but also how you discuss your work. Practicing the interview methodology to explain findings to those outside your subfield is a valuable skill that manifests in many areas, including job talks, grant panels, and media requests. This is low-stakes practice supported by an editor, rather than addressing a cold audience. You'll receive professional feedback on your message and, after our discussion, we're happy to continue the dialogue — whether it’s about how you’re presenting this work to funders, your department, or a broader audience if that adds value for you. Much of what we discuss may not make it into an episode, but that conversation is invaluable. You'll gain early access to a growing network, joining the founding cohort of this series and Overtone Analytics’ broader research community, with the first opportunity for a follow-up interview if your work progresses. There’s no cost or catch — it's unpaid, but the only requirement is your time and your words. You’ll review the draft before publication and can request changes to ensure your voice is accurately represented.

Published alongside every episode, we provide key academic research insights, including: 

- Name, current role, institution 

- Field / therapeutic area 

- Career stage 

- Stage of the work discussed (unpublished, preprint, under review, published) 

- The finding, in one sentence, in the guest's own words 

- Self-rated confidence in the finding at the time they first described it internally (1–5) 

- Self-rated confidence in the finding today (1–5) 

This format not only emphasizes our interview methodology but also highlights the potential for scientific language drift as research evolves over time.

Origin and first framing

Take us back to the moment you first thought "this might be something." What's the first sentence you used to describe it — to your PI, in a lab notebook, in a lab meeting? How did you convey the significance of the finding the first time you had to write it down formally? Do you still have that original language? In reviewing those early academic research insights, is there a word or phrase from that initial description you wouldn't use today? What has changed since then?

Bench to publish

What evolved between how you described this internally and how it reads in the submitted (or published) manuscript? Where did the pressure to sharpen, soften, or reframe the claim originate — a co-author, a reviewer, a PI, a funder, or your own second-guessing? Was there a specific comment, edit, or conversation that altered how you told the story? What got cut from the narrative along the way, and why?

Calibration and language

Which word or claim in the final paper are you most confident still holds up? Are there any words or claims that no longer align with your story—would you change anything in hindsight? How do you personally navigate the use of scientific language drift, deciding between hedged language ("suggests," "may," "is consistent with") and more assertive claims? If you had unlimited space, what caveat would you reintroduce that didn't make the final cut?

Translation and forward look

What needs to be true for this finding to have clinical or commercial implications? What do you think this finding could become that you wouldn't yet say in a paper? What's the most likely way someone outside your subfield could misapply or over-read this result? If someone revisits this paper in five years, what do you think will still hold up — and what might appear overstated, or understated?

Reflection and advice

What's one thing you wish someone had shared with you about scientific writing before your first submission? What advice would you provide to an early-career scientist trying to describe a finding honestly without underselling it, while ensuring the integrity of their academic research insights?

Protocol updated October 2026: one question added and one widened under Translation and forward look. Guests interviewed before this date were not asked them.

If we talk again after the work has advanced, we can consider several academic research insights: How does the finding read to you now compared with when we first talked? What's different between where you thought this would go and where it actually went? Did the scientific language drift in the eventual publication, patent, or trial protocol match what you expected from our initial discussion on interview methodology?

We also track a short set of internal, non-public fields for each interview—funding mechanism, model system, and a few language-calibration ratings—to support our own academic research insights into how scientific language drift occurs in scientific communication. These details aren't published with individual episodes; when we share findings from that research, it will be in aggregate or de-identified form. This research includes developing our own methods and tools for analyzing scientific language.

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