Scientific films
SciViD Studio provides free templates and tools for making your own films about your research, which you run and render on your own computer. SciViD does not produce, render or review films.
Films are optional. Making one is not required for posting and has no effect on editorial screening or posting decisions.
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SciViD Author Resources · Guide
A guide for students and researchers
Most errors in video preprints are made before the first frame is rendered: in the choice of model, the parameters and the interpretation. A polished animation does not correct a wrong model; it makes the error more convincing. This guide lists the mistakes we see most often, how to detect and fix them, and where to ask for help.
It is written for students and early-career researchers at every stage — high school, undergraduate and graduate — who are preparing a computational, modeling or experimental video preprint. Experienced researchers can use Sections 5 and 7 as a checklist.
Every result shown in a video preprint should carry one of three status labels:
| Label | Meaning | Minimum evidence |
|---|---|---|
| Benchmarked | The method reproduces an independent reference, and the result is converged | Benchmark comparison, convergence or repeatability test, documented source data |
| Simplified estimate | An order-of-magnitude or reduced-model result with stated assumptions | Assumptions listed, validity range stated, no claim beyond that range |
| Conceptual | An illustration of an idea, mechanism or scenario | Clearly marked as illustrative; not used as evidence |
These labels describe individual results. For interactive labs, use the Quantitative and Illustrative model labels in Publishing Computational Models and Simulations; for video segments, use the on-screen labels in the Integrity Guide. Item-level labels used on SciViD, such as “Numerical results only” or “Model simulation, not experimental data,” still apply.
A preliminary, non-peer-reviewed preprint is acceptable. A preliminary result presented as a validated one is not.
The most frequent single error is applying a model outside the regime in which it is valid. Check each item below before you produce any figure.
| Mistake | How to detect it | How to handle it |
|---|---|---|
| Model used outside its regime (e.g., continuum flow where the gas is rarefied, inertia included at very low Reynolds number, Brownian motion neglected for sub-micrometer particles) | Compute the relevant dimensionless numbers (Reynolds, Knudsen, Péclet, Biot) for your parameters | State the regime explicitly; choose a model valid in it; cite the source that defines the validity range |
| Unit and dimension errors (mixing µm and m, °C and K, per-minute and per-second rates) | Check that every equation is dimensionally consistent; run one case by hand | Work in SI units internally; convert only for display; label every axis and value with units |
| Wrong or unstated boundary and initial conditions | Ask: what happens at each wall, inlet, outlet and at t = 0? | Write them out in the methods; justify each against the physical system |
| No comparison with a known solution | Ask: has this code ever reproduced a result I did not produce myself? | Reproduce an analytical limit or a published benchmark first; show the comparison |
| No convergence test | Results change when the mesh, time step or sample size is refined | Refine at least twice; report the change in the key quantity; use the converged setting |
| AI-generated code accepted without checking | The author cannot explain each line or each parameter | Read and comment the code; test it on a case with a known answer; record what the AI tool produced in the Claim & AI-use log |
| Parameter values without a source | A value appears in a table or narration with no reference | Give a reference, a measurement or an explicit assumption for every parameter |
| Too few samples for stochastic results | Brownian or Monte Carlo results vary noticeably between runs | Run multiple independent realizations; report the mean and spread |
If you cannot complete the second column for your project, ask your mentor or supervisor before continuing.
A conclusion must not be stronger than the evidence behind it. The wording of the narration is part of the scientific claim.
| Mistake | Example | Correct handling |
|---|---|---|
| Overclaiming | “This device could revolutionize drug delivery” from a single simulation | State what was shown, under which assumptions, and what remains untested |
| Correlation presented as mechanism | Two quantities change together, so one is said to cause the other | Say “is associated with”; propose the mechanism as a hypothesis and state how to test it |
| Estimate presented as validated | An order-of-magnitude force estimate narrated as a measured result | Use the correct status label in the text, on screen and in the narration |
| Missing uncertainty | A single number with no error bar or range | Report the spread across runs, parameter sensitivity, or the range of validity |
| Selected parameters | Only the parameter set that “works” is shown | Show the parameter sweep, including cases where the effect disappears |
| Novelty not checked | “For the first time” without a literature search | Search the literature; cite prior work; describe precisely what is new |
| Extrapolation beyond the model | A result at the micrometer scale is narrated as applying to the human body | Restrict claims to the scale and conditions modeled |
A useful test: could a skeptical expert agree with every sentence of your narration after seeing only your data? If not, rewrite the sentence.
Viewers believe what they see. In a video, the boundary between illustration and evidence must be visible on screen, not only in the text.
| Mistake | Why it matters | Correct handling |
|---|---|---|
| Conceptual animation mistaken for evidence | A 3D render or generative-video shot looks like a result | Mark such shots on screen as “Conceptual illustration”; keep data-derived visuals visually distinct |
| Generative video showing physically impossible behavior | AI video tools do not obey physics; motion, scale and timing may be wrong | Use generative footage only for non-evidentiary context; never to show a result |
| Plots without units, scale bars or error bars | The viewer cannot judge magnitude or reliability | Label axes with quantity and SI unit; add scale bars to images; show uncertainty |
| Misleading color maps | Rainbow-type maps create false boundaries and are hard to read for colorblind viewers | Use perceptually uniform maps (e.g., viridis, cividis); show the color bar with units |
| Truncated or inconsistent axes | Small differences look large; panels cannot be compared | Start axes at zero where magnitudes are compared; use the same scale across panels |
| Narration diverging from the data | The voiceover states a value or trend the figure does not show | Script the narration from the results file; check every number spoken against the source |
| Time and length scales not shown | Slowed or accelerated motion, or magnified objects, mislead viewers | Display time stamps, playback speed and scale bars |
| Too much on screen at once | Viewers cannot follow equations, plots and narration simultaneously | One idea per scene; give equations time to be read |
These errors pass a casual review because the output looks plausible. They are most common in AI-assisted projects.
A technically correct preprint can still be unacceptable if authorship, disclosure or rights are handled badly.
Complete every item before submitting. An item you cannot tick should be stated as a limitation in the preprint.
Ask early: a question about the model in week one costs far less than a correction after the video is rendered. Go to people first, then to the references below.
| Topic | Resource | What it gives you |
|---|---|---|
| Reproducible computation | Sandve et al. (2013), Ten Simple Rules for Reproducible Computational Research, PLOS Comput. Biol. 9, e1003285 | Practical rules: track how each result was produced, record versions and random seeds, store the raw data behind plots |
| Figures | Rougier, Droettboom and Bourne (2014), Ten Simple Rules for Better Figures, PLOS Comput. Biol. 10, e1003833 | Designing figures for a given audience, message and medium |
| Color maps | Crameri, Shephard and Heron (2020), The misuse of colour in science communication, Nat. Commun. 11, 5444 | Why rainbow and red–green maps distort data; how to choose accessible, perceptually uniform maps |
| Units | BIPM, The International System of Units (SI Brochure, 9th edition) | Authoritative definitions and rules for writing SI units and quantities |
| AI and authorship | COPE position statement: Authorship and AI tools | Why AI tools cannot be authors and what authors must disclose |
| Coding skills | Software Carpentry lessons | Free introductory lessons on the Unix shell, Git, and Python or R |
| Archiving data and code | Zenodo | A free general-purpose repository operated by CERN that assigns a DOI to each deposit |
When asking for help, send a minimal example: the smallest piece of code or the single equation that shows the problem, the result you expected, and the result you obtained.
