SciViD Emerging Scholars: Student Video Preprints
(Open Call, 2026-2027)

SciViD

SciViD Emerging Scholars is a collection of video preprints by high school students (normally grades 10–12; grade 9 with a teacher’s or mentor’s recommendation), undergraduate and graduate students, and early-career researchers. Students post a short manuscript with a video abstract narrated in a student author’s own voice or with a disclosed synthetic voice and, where relevant, an interactive dashboard or virtual lab built from their own data or model. Each item is screened by the editors against a posting checklist and signed off by a teacher or mentor, but it is not peer reviewed. Submissions are accepted on a rolling basis.

I. Why post on SciViD

A public, dated and versioned record of your own work with a SciViD identifier, labeled “Preliminary · Not peer reviewed”.
Practice in the full research cycle: a testable question, verified literature, sound methods, honest reporting, and clear communication in writing and on video.
Responsible use of AI: every item includes an AI-use statement.
Posting is free. A DOI is optional: authors may deposit a posted item in a repository such as Zenodo and link it, and for selected items SciViD may assist by prior agreement. SciViD does not arrange a DOI for work with an author under 18.

A preprint is not a peer-reviewed publication and does not guarantee admission, awards or later journal publication.

SciViD Studio tools are free, and posting never requires payment.

II. Eligibility, authorship and consent

High school students (normally grades 10–12; grade 9 with a teacher’s or mentor’s recommendation), undergraduate and graduate students, and early-career researchers, individually or in teams (normally up to four authors; larger teams with the editors’ approval).
Students are the authors. Teachers and mentors are named in the acknowledgements with their role; a mentor who made a substantial contribution (idea, model, analysis) may be a co-author, with the role disclosed.
A teacher or mentor signs off on each submission, confirming the students’ own contribution, ethical compliance, that models were compared with known results, and that claims match the raw data.
For students under 18, written consent from the student and a parent or guardian is required, covering posting, the CC BY 4.0 license and the student’s voice in the video abstract. No photos or personal contact details are published. A parent or guardian can ask for a preprint to be withdrawn with the student’s personal details removed, or to be removed completely. A Zenodo DOI record, if created, is permanent and the student’s name may remain in its metadata.

III. Categories

Reviews: a critical synthesis with a literature map and DOI-verified references
Computational and modeling studies, validated against known results
Lab protocols and technical notes: materials, safety notes, step-by-step procedure, representative data with uncertainty, troubleshooting
Preliminary results from computational, simulation or experimental work, with assumptions, limitations and next steps stated
Short communications: a brief report of one finding, observation or method
Original research with the students’ own data

Every project needs a clear research question, the authors’ own analysis, data or model, and at least one figure or table produced by the authors. Research with human participants requires school or IRB approval. Laboratory work must take place under the supervision and safety rules of a school or host lab.

IV. What to submit

A short manuscript (typically 2–6 pages): title, author names and school or institution; a structured abstract (150 words maximum); introduction, methods, results and discussion (adapted for reviews); references checked by DOI; a contribution statement; and an AI-use statement.
A video abstract (1–3 minutes) narrated in a student author’s own voice or with a synthetic voice; any synthetic or professional voice must be disclosed. Showing faces is not required; other people’s faces are not shown; any AI-generated voice or footage must be disclosed.
Optional: an interactive dashboard or virtual lab built from the project’s own data or model, labeled “Model simulation, not experimental data” where it shows simulations.
Figures: your own, or created anew from data or equations with sources cited. Figures from CC BY-licensed work may be reused with attribution; any other reused figure requires written permission, obtained by the authors.

V. How posting works

Screening: format, plagiarism check, consent forms, figure permissions, and the minimum requirements in Section III.
Checklist check: references are checked by DOI, and the mentor’s sign-off confirms that models were compared with known results and claims with the raw data. Projects mentored by SciViD’s founder, including through the NanoTRIZ Research Scholar Program, are decided by an unconflicted editor, or an independent moderator appointed for that submission, with no NanoTRIZ role. If independent screening cannot be arranged, posting is deferred. The relationship is disclosed on the posted item.
Revision: authors respond to the screening feedback. Projects that do not meet the requirements are not posted.
Posting: the preprint appears with its SciViD identifier, version number and status labels. Later versions are added with a dated note of changes, and earlier versions stay available.

VI. Free workshop for schools: doing a real research project and using AI honestly

SciViD offers a free 30-minute online workshop for high schools, colleges and science clubs. It covers choosing a testable question, checking the literature by DOI, choosing a sound method, reporting results and limitations honestly, and disclosing any use of AI. Posting a finished project on SciViD is optional and free, is decided only against the posting checklist, and requires parent or guardian consent for students under 18.

VII. Contact

Submissions: open the submission form with the button below. For each author under 18, upload the SciViD consent form signed by the student and a parent or guardian; we then email the parent or guardian to confirm before posting.

Questions and workshop requests: editor@scivid.net

Open the submission form

Latest Video Preprints

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Once posts are published, you’ll see them here.

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

SciViD Emerging Scholars: Student Video Preprints
(Open Call, 2026-2027)

Latest Video Preprints

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

SciViD Emerging Scholars: Student Video Preprints (Open Call, 2026-2027)

Latest 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 you

The student and the work

The video abstract

Your consent

SciViD Emerging Scholars: Student Video Preprints
(Open Call, 2026-2027)