SciViD: The Publisher of Video Science

OUR VISIONScholarly publishing remains essential for disseminating knowledge, but it faces persistent problems of cost, access, transparency and format that limit who can participate and how research is communicated. SciViD is being developed as a complementary publishing model that combines video presentation, AI-assisted production and an inclusive editorial framework, with the aim of addressing some of these problems.CHALLENGES IN SCHOLARLY PUBLISHINGCost and access. Subscription journals restrict access through paywalls, and many open-access journals charge article processing charges (APCs). Both can disadvantage early-career researchers, students and institutions with limited funding.Opaque peer review. Peer review varies in quality, can be affected by bias, and is rarely visible to readers.Unequal participation. Scholarly publishing is concentrated in a small number of regions, and researchers who are not native English speakers face additional barriers to publication.Limits of static formats. Text and static figures convey dynamic processes, experimental technique and simulations poorly, which can hinder reproduction and teaching.HOW SCIVID RESPONDSFree posting. Posting a video preprint on SciViD is free. Professional video production through SciViD Studio is a separate paid service.Transparent status. Every posted item is labelled "Preliminary · Not peer reviewed" and carries a version number, so readers can see which checks it has and has not passed.Multilingual access. SciViD is developing multilingual subtitles and AI-assisted translation to widen access for researchers and readers who are not native English speakers.Visual presentation of methods. Video papers present methods, simulations and devices visually rather than in text alone. This format is intended to support reproduction and to make published work usable for teaching.AI-assisted production. AI tools for narration, animation, editing and translation are intended to reduce the cost and effort of producing a video paper. All AI use must be disclosed, and AI-generated visuals must be labeled in accordance with SciViD’s Integrity Guide.Pathways for early-career researchers. Emerging Scholars gives senior high school and early undergraduate students a supervised route to posting their first research as a video preprint, with parent or guardian consent for authors under 18.

LIMITATIONS OF STATIC PUBLICATION FORMATSThe text-based research paper remains the principal record of scientific work, and its precision and archival stability are not in question. Text and static figures are, however, poorly suited to conveying certain kinds of information: practical technique, dynamic processes, and the course of an experiment or simulation over time. SciViD’s video papers are intended to address these limitations.1. Under-reported practical techniqueDetails that determine whether an experiment can be repeated, such as instrument settings, timing, sample handling and manual technique, are difficult to specify fully in prose. In the Reproducibility Project: Cancer Biology, none of the experiments selected for replication was described in enough detail to design a replication protocol without contacting the original authors. Incomplete methods reporting is one of several causes of replication failure, alongside publication bias, incomplete statistical reporting, and limited sharing of data and materials; visual documentation of technique addresses only the first of these.2. Dynamic processes reduced to snapshotsProcesses involving motion or transformation, such as fluid flow, self-assembly or the behavior of living cells, must be reconstructed by the reader from a sequence of still images. Research on instructional animation shows a moderate overall learning advantage of animation over static pictures, with larger effects for realistic, video-based material and for learning procedural skills. The benefit is not automatic: dynamic formats help mainly when they show change that static images cannot, and when pacing and signaling are well designed. These findings concern learning outcomes; whether they translate into higher replication success has not, to our knowledge, been tested directly.3. Barriers for readers outside the specialtyResearch papers are written for specialists, and their terminology and compressed style limit access for students, researchers from adjacent fields and non-native English speakers. Video papers that pair narration with visual explanation can lower some of these barriers, because people generally learn better from words and pictures together than from words alone.4. Video as a supplement rather than the recordMany journals already accept supplementary videos, and dedicated video journals exist. In conventional papers, however, video is usually supplementary material rather than the primary object of review and citation. SciViD treats the video preprint itself as the posted, versioned and citable record.THE PATH FORWARDText-based papers will remain the foundation of the scholarly record. Formats that show what text describes, including video papers, interactive simulations, and shared data and code, can complement them by making methods easier to see, evaluate and repeat.

Text-based papers remain the foundation of the scholarly record. SciViD is being developed as a complementary model that keeps the conventions of scholarly publishing while using video to show what text can only describe.1. Scholarly standards with video presentationPosting checklist. Every submission is screened against a posting checklist covering format, originality, references, permissions and consent. Posted items are not peer reviewed and are labelled accordingly.Persistent identifiers. With the authors' consent, SciViD plans to deposit selected items with Zenodo so that they receive a DOI.Scholarly structure. Video papers will follow familiar scholarly conventions: abstract, introduction, methods, results and discussion.Visual evidence. Video papers can show dynamic processes, narrated walkthroughs of experimental setups, and simulation output.The format is particularly suited to fields in which motion and transformation are central, such as microfluidics, nanotechnology, soft matter and experimental biology.2. AccessibilityMultilingual access. SciViD is developing multilingual subtitles and AI-assisted translation for readers who are not native English speakers.Accessible video. Captions, transcripts and audio description are needed for video papers to be accessible to readers with hearing or visual impairments; SciViD intends to provide them.Low-bandwidth access. Because video requires more bandwidth than text, SciViD aims to offer lower-resolution and downloadable versions for readers with limited connectivity.3. Education and student authorshipTeaching use. Video papers can be used in flipped classrooms and project-based courses.Student portfolios. Through Emerging Scholars, students can build a public portfolio of posted work in research and scientific communication.Independence of screening. Teachers and mentors may advise students while they prepare their work but have no role in screening their submissions.4. Methodological transparencyDirect observation. In experimental video papers, viewers can see recorded footage of instrument calibration, sample handling and troubleshooting rather than inferring these steps from written description. Recorded footage is distinguished from conceptual animation in accordance with SciViD’s Integrity Guide.Complement to written protocols. Such footage can complement written protocols, particularly for procedures that depend on manual technique.5. Machine-readable recordsTranscripts and metadata. SciViD plans to publish each video paper with a transcript and structured metadata, so that its content can be searched, indexed and processed by text-based tools.Interactive extensions. Authors can link an interactive lab to their posted item, and SciViD hosts such labs free of charge.6. Publishing costsFree posting. Posting is free. Professionally produced video papers are available separately through SciViD Studio.Institutional partnerships. SciViD welcomes partnerships with schools and universities that wish to use video papers in teaching and research training.7. Scholarly and public visibilityHosting and versions. Each posted item is hosted on SciViD with its version history; copies may also be shared on public video platforms to reach a wider audience.Discoverability. With DOIs and structured metadata in place, published video papers are intended to be findable through search engines and academic discovery tools.Multiple uses. A single video paper can serve as a citable research output, a teaching resource, and supporting material for funding applications and conference presentations.SciViD aims to combine established scholarly standards with visual communication, making experimental science easier to follow and authorship more accessible to early-career researchers.

SciViD welcomes original computational and theoretical research, including numerical simulation, analytical modeling and machine-learning models, as well as reviews of models and methods. Video allows authors to show how a model is set up, how it behaves as parameters change, and how its output compares with theory or experiment.PAPER CATEGORIES1. Numerical and analytical modelingOriginal research in which the authors’ own code or modeling software (for example MATLAB, COMSOL, ANSYS, LAMMPS or custom Python code) produces new results.Requirements: Describe the model and the numerical method. Report verification (for example convergence and mesh or time-step independence) and validation against analytical solutions or experimental data, where applicable. State the main assumptions and the sensitivity of the results to key parameters. Visuals presented as results must be generated from the simulation data, and all post-processing and rendering steps must be described.2. Machine-learning and predictive modelingOriginal research in which a trained model predicts physical, chemical or biological behavior (for example protein structure, material properties or climate patterns).Requirements: Describe the datasets and their provenance, the training procedure, how training and test data were separated, the validation metrics, and the comparison with baseline methods. Visuals showing the model’s predictions must be labeled “Model-Predicted Output” and kept distinct from illustrative material.3. Reviews of models and methodsEducational or review papers that explain existing models or theoretical frameworks.Requirements: Cite all original sources. Conceptual animations (for example made with Manim or Blender) and visual metaphors are welcome but must be labeled “Conceptual Animation” or “Illustrative Metaphor”.PROHIBITED PRACTICESPseudo-simulations: presenting AI-generated or hand-made animations as the output of a scientific solver or trained model.AI-altered output: applying AI upscaling, frame interpolation or generative enhancement to footage labeled as Simulation Output or Model-Predicted Output, since these create frames the model did not compute.Unlabeled or misleading visuals: animations that are not labeled as conceptual, or that suggest behavior not supported by the data or cited theory.Selective presentation: showing only favorable runs, parameter ranges or time windows without disclosing that others were performed.Unattributed work: using code, datasets or visual outputs created by others without citation or permission under their license.SUBMISSION REQUIREMENTSComputational setup: software and versions, governing equations, initial and boundary conditions, numerical method and discretization, solver settings and convergence criteria.Assumptions and limitations: the main simplifications in the model and the range of conditions in which the results are expected to hold.Visual labeling: every segment of the video must carry an on-screen label identifying it as Simulation Output, Model-Predicted Output, Conceptual Animation or Illustrative Metaphor, as applicable. Segments labeled as output must reflect the data accurately, with color scales and units shown.Code and data availability: a statement explaining where the code, input files and data can be obtained, or why they cannot be shared. Deposit in an archived repository with a DOI is strongly encouraged.AI Use Statement: as specified in SciViD’s Integrity Guide, covering any AI-generated or AI-enhanced narration, visuals or animation, included in the submission metadata and the video credits.Narrative walkthrough: the video should explain the purpose of the model, the choice of parameters, and the significance and limitations of the results.EXAMPLE TITLES (illustrative only; not published papers)“Self-Propelled Janus Micromotors Near Walls: A Brownian Dynamics Study”“Droplet Breakup in a Microfluidic T-Junction: Volume-of-Fluid Simulations Across Capillary Numbers”“Accretion Disk Instabilities Around a Black Hole: A Magnetohydrodynamic Simulation”“Predicting the Mechanical Properties of Nanoporous Materials with Machine-Learned Interatomic Potentials”“Molecular Dynamics of Lipid Bilayers: A Visual Review of Methods and Force Fields”Video lets readers see how a model behaves, not only what it predicts. Combined with transparent methods and shared code, it can make computational work easier to evaluate and to reproduce.

SCIVID INTEGRITY GUIDE: VISUAL INTEGRITY AND AI USESciViD video papers combine recorded data, model output and explanatory visuals. Viewers must always be able to tell which is which. This guide sets out how visuals must be labeled, how AI tools may be used, and what constitutes misconduct. It applies to all visuals, whether produced with AI tools, animation software or by hand.1. LABEL EVERY SEGMENT BY WHAT IT SHOWSEvery segment of a video paper must carry an on-screen label stating its status. The video description and transcript may add detail but do not replace the on-screen label.Recorded Data: footage, micrographs, sensor data or graphs recorded during the research.Simulation Output: results generated by a numerical or analytical model.Model-Predicted Output: predictions generated by a trained machine-learning model.Conceptual Animation: an explanatory animation that does not represent data.Illustrative Metaphor: a visual analogy used to explain a concept.Hypothetical Scenario: an imagined setup, process or outcome.2. REQUIREMENTS BY PAPER CATEGORYExperimental video papers present original experiments performed by the authors.Results must be shown as Recorded Data. Conceptual animations may supplement the data and must be labeled.Adjustments of brightness, contrast or color must be applied uniformly to the whole image or sequence and must not remove or add features.Scale bars, timestamps and playback speed (for time-lapse or slowed footage) must be shown.AI denoising, upscaling, frame interpolation, inpainting and generative enhancement must not be applied to Recorded Data. Transitions must not blend frames of data.AI may be used for titles, layout, narration, captions, translation and cleaning up narration audio.Computational papers present original numerical, analytical or machine-learning research.Model results must be labeled Simulation Output or Model-Predicted Output and must not be altered by AI upscaling, frame interpolation or generative enhancement.Detailed requirements are set out in Publishing Computational Models and Simulations.Review and conceptual papers synthesize existing literature.Authors may use diagrams, animations and visual metaphors, including AI-generated ones. All must be labeled and must not be presented as evidence.3. CORE PRINCIPLESNo fabrication. Regardless of the tool used, it is misconduct to create visuals that suggest data or trends not present in the data, to generate missing experimental steps or results, to present stock or third-party footage as original data, to alter published figures from other authors to support a different conclusion, or to show an imagined setup or result without the “Hypothetical Scenario” label.Consistency with sources and data. Illustrative visuals must be consistent with the data and cited sources. A process described as slow and gradual must not be animated as instantaneous.Accuracy over spectacle. Visuals must represent scale, timescale and structure faithfully. False color must be identified as such, and objects must not be given misleading appearance or behavior.Rights and consent. Authors must hold the rights to publish all content under SciViD’s license, including AI-generated material subject to the tool’s terms of use. Synthetic replicas of a real person’s face or voice require that person’s documented consent.Full disclosure. Every submission must include an AI Use Statement listing:Tools: each AI tool used, with its version where available;Scope: which segments and tasks involved AI, including data analysis, literature review, script drafting, narration, translation and visuals;Intent: whether AI was used to present authentic content, to create illustrative material, or to generate a model’s output.4. VERIFICATION AND CONSEQUENCESEditors may request raw footage, project files, simulation data or code at any stage. Violations may lead to rejection, correction or withdrawal, as set out in SciViD’s editorial policies.SUBMISSION CHECKLISTDoes every segment carry an on-screen label stating its status?Is all Recorded Data free of AI denoising, upscaling, interpolation and generative enhancement?Are scale bars, timestamps and playback speeds shown where relevant?Are all illustrative visuals consistent with the data and cited sources?Is the AI Use Statement included in the submission metadata and the video credits?Do I hold the rights and any consents needed to publish all content under SciViD’s license?SciViD’s rule: every visual must show what it claims to show and state what it is.Questions about visual integrity: editor@scivid.net

SciViD posts video preprints from researchers and, through Emerging Scholars, from students; neither pathway is peer reviewed. Generative AI tools are widely used to produce images, animation, narration and text. These standards set out how authors, reviewers and editors may use them. Labeling of visuals and the AI Use Statement are specified in SciViD’s Integrity Guide.1. DisclosureAll AI use must be disclosed in the AI Use Statement specified in the Integrity Guide and included in the submission metadata and the video credits.Example: “Conceptual animations in segments 3 and 5 were generated with an image-generation model and are labeled Conceptual Animation. Narration was synthesized from a script written by the authors. A large language model was used to edit the script for clarity; all scientific content was written and checked by the authors.”2. Authorship and accountabilityAI tools cannot be listed as authors.The research question, methods, data analysis and conclusions must be the work of the named authors, who are responsible for the accuracy of all content, including content produced with AI.Student authors must carry out and understand the work they present. Work substantially performed or written by a mentor may not be presented as the student’s own.Contributions from mentors, teachers and other collaborators, including those provided through paid programs, must be described in a contribution statement.AI must not be used to paraphrase the work of others without citation or to evade similarity checks.3. Privacy and consentPeople who can be identified in footage or audio must have given their consent; for people under 18, the consent of a parent or guardian is required.Identifiable footage, voice recordings or other personal data of other people, particularly minors, must not be uploaded to third-party AI tools without the consent of the people concerned.AI-generated content must not misrepresent or defame individuals or institutions.4. Content and safetySciViD does not publish content that promotes violence, hatred or illegal activity, or that depicts people or groups in a stereotyped or demeaning way.Videos of hazardous procedures must show appropriate safety measures. SciViD may decline content whose demonstration of methods poses a significant risk of misuse.Editors will screen submissions against these standards.5. Use of AI by reviewers and editorsReviewers and editors must not upload submitted manuscripts, videos, data or review reports to generative AI tools, as this breaches the confidentiality of review.SciViD will state in its editorial policies any AI tools it uses to screen submissions.6. Appropriate usesAI tools can help authors to:illustrate processes that cannot be filmed, such as molecular interactions or field distributions, using labeled conceptual animations;produce captions, subtitles and translations;improve the language of scripts written by the authors.AI must not determine research results, analyses or conclusions.7. School-aged authorsFor school-aged authors, these standards draw on the Australian Framework for Generative Artificial Intelligence in Schools, published by Australian Education Ministers in 2023 and reviewed annually, in particular its emphasis on fairness, privacy and the critical use of AI tools.8. ConsequencesViolations may lead to rejection, correction or retraction. In serious cases, SciViD may inform the author’s institution or, for school students, their school. Cases will be handled in line with established publication-ethics guidance, such as that of the Committee on Publication Ethics (COPE).

Dear colleagues, educators and students,For nearly two decades I have studied catalytic micro- and nanomotors, rolled-up nanomembranes and microfluidic systems, including postdoctoral research at the Max Planck Institute for Intelligent Systems in Stuttgart, a visiting appointment at Harvard University, and more than eight years as a tenured professor at Fudan University. As an author, a reviewer for more than 50 journals and a guest editor, I have repeatedly encountered the limits of text and static figures. The motion of a nanomotor or the timing of a microfluidic protocol could be obvious in the laboratory yet hard to convey in print, and students and colleagues sometimes misread procedures that looked clear on paper.I have also seen talented students, particularly those outside well-funded institutions or writing in a second language, struggle to publish their first results.SciViD is my response. Its aim is to keep the core standards of scholarly communication, including honest labelling, persistent identifiers and transparent ethics, while using video to show what text can only describe. It is intended to complement traditional journals, not to replace them, and to serve both research and teaching.We are building SciViD around three commitments:Integrity: clear posting standards, honest labelling and transparent rules for the use of AI.Inclusion: a supervised pathway for student authors and support for researchers who are not native English speakers.Impact: video papers that researchers can cite, teachers can use in class and students can learn from.SciViD is at an early stage and is now accepting submissions. I invite you to help build it as an author, editor or partner.Professor Alexander SolovevFounder and Editor-in-Chief, SciViD