Get Ready to Harmonize with Overtone Analytics

Every Overtone Interviews episode uses the same set of questions, asked in the same order, regardless of guest or format. We publish the protocol here for two reasons: so prospective guests know exactly what they're agreeing to before they say yes, and so the series stays honest to its own premise — if we're studying how scientific language drifts, our own method needs to be consistent and visible, not something we quietly adjust episode to episode.

Every guest sees this page before agreeing to an interview.

• A Published Feature under your name that you approve before it goes live — easy to share on LinkedIn or X, and a natural link from your lab page or CV. A citable record of how you talked about your work at this point in your career, not just what you eventually published.
• Practice explaining a finding to people outside your subfield is a skill that shows up everywhere — job talks, grant panels, media requests. This is low-stakes practice with an editor on your side, not a cold audience.
• Professional feedback on your message. Once we've talked, we're glad to keep talking — about how you're describing this work to funders, your department, or a broader audience, if that's useful to you. Plenty of what we discuss never makes it into an episode; that conversation is worth having on its own.
• Early access to a growing network. Guests become part of the founding cohort of this series and of Overtone Analytics' broader research community, with first opportunity for a follow-up interview if and when the work moves forward.
• No cost, no catch. Unpaid, but nothing is asked of you beyond your time and your words. You see the draft before anything publishes and can request changes to your own material.

Published alongside every episode:

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• Stage of the work discussed (unpublished, preprint, under review, published)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

1. 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?
2. How did you describe the significance of the finding the first time you had to write it down formally? Do you still have that original language?
3. Looking back, is there a word or phrase from that early description you wouldn't use today? What changed?

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

1. Which word or claim in the final paper are you most confident still holds up? Are there any words or claims that no longer support the story--would you change anything in hindsight?
2. How do you personally decide between hedged language ("suggests," "may," "is consistent with") and more assertive claims?
3. If you had unlimited space, what caveat would you add back in that didn't make the final cut?

1. What has to be true for this finding to matter clinically or commercially?
2. What's the most likely way someone outside your subfield could over-read or misapply this result?
3. If someone reads this paper again in five years, what do you think will have held up — and what might look overstated?

1. What's one thing you wish someone had told you about scientific writing before your first submission?
2. What would you tell an early-career scientist trying to describe a finding honestly without underselling it?

If we talk again after the work has advanced, we add:

• 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 language in the eventual publication, patent, or trial protocol match what you expected when we first spoke?

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 research into how scientific communication evolves. These aren't published with individual episodes; when we share findings from that research, it will be in aggregate or de-identified form. Guests can see exactly what this covers in the [Interview Release & Consent Form].

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