About SciViD: How Video Preprints Work

SciViD

SciViD is an open-access platform for video preprints. Each item presents methods, data and simulations in structured visual form within a conventional scholarly format, to make research easier to understand, evaluate and reproduce. SciViD is at an early stage; its aim is to become an established venue where students and researchers can share preliminary work openly and safely. Submissions are open. Posted items are screened by the editors but are not peer reviewed.

SciViD combines three established developments in research communication: open preprints, short video abstracts and interactive models. We think that combining them makes research easier to understand and reuse, and we are testing this with student and researcher submissions.

I. What a video preprint contains

A short manuscript: abstract, introduction, methods, results, discussion and references.
A video abstract of 1 to 3 minutes, narrated in an author’s own voice or with a synthetic voice; any synthetic or professional voice is disclosed.
Where relevant, an interactive lab or simulation that readers can explore.

The video abstract is recorded by the authors themselves; slides with narration, an author on camera or footage of the experiment are all acceptable, and posting is free. Format and quality criteria are set out in the Author Guidelines.

Video preprint and optional film. A video preprint is the manuscript with its 1 to 3 minute video abstract. Authors may also link a longer film that they made themselves, for example with SciViD Studio templates; whether a film is made has no effect on posting decisions.

II. Types of submission

Experimental. Original experiments performed by the authors, including preliminary results and short communications.
Computational and theoretical. Numerical, analytical or machine-learning studies, which also follow the requirements for computational models and simulations.
Protocols and methods. A real, step-by-step video of an experimental procedure recorded by the authors, with materials, settings, safety precautions and expected results.
Reviews and perspectives. Syntheses of existing literature that cite all original sources.

Requirements for each type are set out in the Author Guidelines.

III. How posting works

Free. Posting a video preprint costs nothing.
Screened, not peer reviewed. Editors check scope, completeness, scholarly presentation, attribution, permissions, consent and visual labeling. Screening does not certify scientific accuracy, novelty or validity; authors remain responsible for their work.
Independent decisions. Posting decisions do not depend on payment or mentorship.
Labeled and versioned. Every posted item is marked “Preliminary · Not peer reviewed”, carries a SciViD identifier and version number, and is licensed by its authors under CC BY 4.0, except for third-party material marked otherwise.
Two series. Researchers submit to the Research series through Submit a Preprint. High school, undergraduate and graduate students submit to the Emerging Scholars series; graduate students may choose either, and authors under 18 must use Emerging Scholars, with the consent of a parent or guardian.

IV. What distinguishes SciViD

Methods shown, not only described. Authors show experimental procedures, data acquisition and simulations, which helps others understand and reproduce them.
Labeled visuals. Every segment of a video states on screen whether it shows recorded data, model output or an illustration, as set out in the Integrity Guide.
Disclosed AI use. Every submission includes an AI Use Statement listing the tools used, the tasks they were used for and their purpose.
A route for students. Emerging Scholars lets students post their own work, with a contribution statement that discloses help from mentors and teachers.
Scope. The natural sciences, engineering and research methods; current subject areas are listed under Collections.

V. Identifiers and discoverability

Optional DOIs. A DOI is not required for posting. Authors may deposit their work in a repository and provide the DOI for linking. SciViD may assist selected authors by prior agreement and with the consent of all authors; this is not automatic or guaranteed. SciViD does not arrange a DOI for an item with an author under 18. External repositories govern their own deposits and versions.
Metadata. The platform is being developed with structured bibliographic metadata to support indexing by academic search tools.

VI. Who SciViD is for

Researchers who want to communicate methods and results clearly and transparently.
Educators looking for visual teaching material on experimental methods, protocols and complex concepts.
Students developing research and science communication skills.
Institutions and libraries exploring open-access and multimedia publishing.

VII. SciViD Studio

SciViD Studio is a self-service workspace in which users create their own scientific films and remain fully responsible for accuracy, sources, permissions and the final edit. It provides free templates for Manim, Blender and Python, which users run and render on their own computers; SciViD offers no production or rendering service. Using Studio is not required for posting and has no effect on screening decisions.

VIII. Governance and editorial standards

SciViD is operated by SciViD Pty Ltd, Brisbane, Australia, and was founded by Prof. Alexander A. Solovev, who also mentors students through the NanoTRIZ Research Scholar Program. SciViD Studio, which provides free templates, is operated by SciViD Pty Ltd; the NanoTRIZ Research Scholar Program is run by NanoTRIZ Innovation Institute, a separate business owned by the founder. Posting decisions on work he has mentored or authored are made by an unconflicted editor or independent moderator with no NanoTRIZ role, and the relationship is disclosed on the posted item. If independent screening cannot be arranged, posting is deferred. Screening, conflicts of interest, versions and withdrawal, and how to report concerns are set out on the Editorial page.

IX. Policies and contact

Policies: Author Guidelines · Integrity Guide · Publishing Computational Models and Simulations · Editorial board and policies.
Contact: questions about submissions, SciViD Studio, school workshops and partnerships: editor@scivid.net

Submit a Preprint

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

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

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

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

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

SciViD

About SciViD: How Video Preprints Work

Common Mistakes in Video Preprints

1. Purpose and scope

Result status labels

2. Common mistakes in modeling and computation

3. Common mistakes in interpretation and claims

4. Common mistakes in visualization and video

5. Less obvious mistakes

6. Integrity, authorship and AI disclosure

7. Pre-submission checklist

Model and computation

Results and claims

Video

Integrity and reproducibility

8. Where to find help

About SciViD: How Video Preprints Work

1. Purpose and scope

Result status labels

2. Common mistakes in modeling and computation

3. Common mistakes in interpretation and claims

4. Common mistakes in visualization and video

5. Less obvious mistakes

6. Integrity, authorship and AI disclosure

7. Pre-submission checklist

Model and computation

Results and claims

Video

Integrity and reproducibility

8. Where to find help

About you

The student and the work

The video abstract

Your consent