SciViD: The Publisher of Video Science

SciVid offers two main publication pathways: SciVid Video Papers (with internal editorial review) DOI Video Papers (with full external peer review—either research or review) Here is how they differ: 1. SciVid Video Paper (Internal Editorial Review) Purpose: Designed primarily for school projects, first-time authors, and early research experiences. Review Process: Undergoes a light internal editorial check for clarity, structure, and appropriate content—not a full scientific peer review. Publication: Published on the SciVid platform and YouTube, with a certificate of publication. Monetization: Eligible for YouTube royalty payments after reaching defined thresholds. DOI/Indexing: Does not receive a DOI, and is not indexed in Google Scholar or formal academic databases. Upgradability: Can be upgraded within 6 months to a peer-reviewed DOI paper after revisions and full review. ✔️ Best for: High school students, science clubs, early research experiences, showcasing creativity and structured thinking. 2. DOI Video Papers (Full Peer Review) Includes two types: Review Video Paper (DOI) – Literature-based Research Video Paper (DOI) – Original experimental work Purpose: Created for authors seeking formal academic recognition, global visibility, and citation through DOI registration. Review Process: Undergoes a rigorous peer review: Content accuracy for review papers Experimental validity for research papers Publication: Also published on SciVid and YouTube, but with: DOI (via CrossRef) Dedicated metadata page Planned indexing in Google Scholar and DOAJ Monetization: Eligible for royalties and citation tracking. ✔️ Best for: University-level students, teachers, young scholars, and research groups aiming to publish credible scientific video articles with academic value. Summary of Key Differences By offering both flexible and formal pathways, SciVid empowers all levels of contributors — from high school students to early-career researchers — to publish video-based science ethically and visibly.

CURRENT ISSUES IN SCIENTIFIC, RESEARCH, AND ACADEMIC PUBLISHING SYSTEMS The scientific publishing system, though historically essential for knowledge dissemination, is increasingly outdated in the face of digital, visual, and AI-driven possibilities. Structural barriers, slow timelines, and inequities are stalling progress and limiting participation. AI-enhanced platforms like SciVid represent a timely and necessary evolution—one that democratizes authorship, speeds up discovery, enhances educational utility, and opens the door for a more transparent, inclusive, and sustainable knowledge ecosystem. 1. High Costs and Limited Access Issue: Many academic journals impose significant fees for both authors (to publish) and readers (to access content). Impact: This creates barriers for early-career researchers, students, independent scientists, and institutions in lower-income regions. Scientific knowledge becomes concentrated in well-funded circles. Data Point: Open science advocates and global initiatives have repeatedly emphasized the need for equitable access to knowledge. 2. Incentive Misalignment – "Publish or Perish" Issue: Academic evaluation systems often prioritize quantity over quality, pressuring researchers to produce numerous publications regardless of significance. Impact: This leads to fragmented publications, superficial research, redundant studies, and gaming of citation metrics. Innovative, risky, or interdisciplinary ideas are deprioritized. Scientific Impact: Long-term, this may erode trust and slow progress in truly novel discoveries. 3. Slow Publication Timelines Issue: It often takes months—or even years—for papers to go through peer review, revision, and final publication. Impact: Time-sensitive discoveries, such as those in health, climate, and emerging technologies, cannot be shared or acted upon quickly. Young researchers may miss grant or job deadlines. 4. Opaque and Inconsistent Peer Review Issue: Peer review is mostly anonymous, inconsistent in quality, and prone to bias or conflicts of interest. Impact: Excellent papers may be unfairly rejected or delayed. Conversely, flawed research can be accepted due to personal networks or lack of rigorous evaluation. Emerging Concern: Calls for transparent and accountable peer review systems have been growing globally. 5. Underrepresentation of Global Voices Issue: Scientific publishing is dominated by institutions in a few regions, leading to underrepresentation of research from diverse linguistic, geographic, or cultural backgrounds. Impact: Local innovations, traditional knowledge, or regional solutions are often excluded from global discourse. 6. Lack of Support for Multimedia or Practical Learning Issue: Traditional publication formats (text + static figures) are poorly suited for conveying experimental techniques, simulations, or real-world applications. Impact: Reproducibility suffers. Education and interdisciplinary engagement are limited, as younger generations and cross-field researchers increasingly prefer visual and interactive formats. HOW AI CAN HELP ADDRESS THESE ISSUES 1. Literature Analysis and Research Gap Detection AI tools can process millions of publications, identify emerging trends, suggest underexplored topics, and match collaborators based on skills and equipment. This accelerates the ideation process and supports more inclusive, data-informed research planning. 2. Automated Support for Editorial Work Language models can perform first-level quality checks, suggest improvements, and detect ethical issues or plagiarism. Peer review processes can be assisted with consistency scoring, relevance evaluation, and flagging of potential bias. 3. Democratizing Writing and Translation AI-assisted writing helps non-native speakers prepare and polish manuscripts, improving fairness in scientific communication. AI translation allows wider dissemination of knowledge in multiple languages without added human cost. 4. Simulation and Visualization Enhancement AI can generate interactive simulations, data animations, and graphical summaries to accompany or replace static figures. Enhances understanding, reproducibility, and interdisciplinary access. HOW SCIVID PUBLISHER CAN TRANSFORM PUBLISHING SciVid offers a fundamentally new model for academic publishing through AI-assisted, video-based, and royalty-sharing systems. Its innovations address the above problems with a focus on speed, inclusivity, clarity, and fairness. ✅ Low Barrier to Publication Video-based papers can be created and submitted with minimal cost using AI tools for narration, animation, and editing. Early-career researchers, students, and underfunded groups can publish without needing thousands in fees. ✅ Faster Review and Dissemination A streamlined editorial process and optional open peer review track enable rapid publication within weeks, not months. Video papers can be immediately used for outreach, collaboration, and funding presentations. ✅ Visualization of Complex Ideas Experiments, methods, simulations, and devices can be visually demonstrated rather than described in technical prose. This improves educational outcomes and enhances reproducibility. ✅ Global and Multilingual Accessibility SciVid supports multilingual subtitles and AI-generated translations, allowing broader access and participation from non-English-speaking communities. ✅ Student Publishing and Mentorship Through programs like the Young Academics Video Paper Mentorship, SciVid supports high school and university students in publishing real academic work in video format. Educators and schools can act as co-reviewers and share in revenue or recognition. ✅ Transparent and Ethical AI Use AI contributions are explicitly declared. Editors and reviewers assess the originality, accuracy, and educational value of both content and video execution. Encourages responsible integration of AI into scholarly work. ✅ Revenue Sharing and Sustainability SciVid is designed to reward contributors — authors, reviewers, institutions, and educators—through royalties from views, subscriptions, and licensing. This transforms the publishing model from extractive to participatory. Comparative Summary

Advantages of SciVid – The Journal of Video Science Text-based scientific papers continue to serve as the cornerstone for archival precision and formal citation. However, in the evolving digital landscape, their limitations in conveying experimental processes, engaging diverse learners, and ensuring reproducibility have become increasingly apparent. SciVid – The Journal of Video Science offers a complementary, future-oriented publishing model that preserves academic rigor while unlocking new modes of scientific expression, dissemination, and participation. 1. Scientific Rigor with Multimedia Integration SciVid preserves the core principles of scholarly publishing: Peer review ensures scientific validity and editorial quality. DOI assignment provides persistent citation and indexing in academic databases. Structured sections (Abstract, Introduction, Methods, Results, etc.) maintain compatibility with scholarly standards. But unlike traditional formats, SciVid also embraces the full expressive capacity of video: Visual demonstrations of dynamic processes. Narrated explanations of complex theories. On-camera walkthroughs of experimental setups and instruments. Integration of graphs, animations, and screen captures. This hybrid format not only supports academic publication but enhances clarity, reproducibility, and knowledge transfer — particularly in fields such as: Nanotechnology (e.g., self-assembly, dynamic interactions), Quantum biology (e.g., coherence phenomena, entangled biosystems), Fluid mechanics (e.g., vortex shedding, microdroplet flows), Experimental physics and photonics (e.g., laser systems, wavefront manipulation). 2. True Accessibility and Multilingual Inclusivity SciVid breaks longstanding accessibility barriers: Multilingual video papers, with optional subtitles, voiceovers, and AI translation tools, make content understandable globally. Support for non-native English speakers, neurodiverse learners, and individuals with visual/auditory impairments through multimodal presentation. Adaptive design for integration into mobile, web, and AI-powered educational platforms. This inclusivity expands the reach of science to previously underrepresented groups, democratizing both access and authorship in global STEM communities. 3. Education-Ready, Pedagogically Optimized Content Unlike traditional publications, SciVid is designed with educational utility in mind: Video format aligns with flipped classrooms, digital curricula, and project-based learning. Teachers can embed video papers into lessons or assign them as primary learning materials. Students can create, narrate, and publish their own research projects under supervision, gaining skills in scientific communication, multimedia production, and collaborative publishing. This transforms students from passive consumers into active contributors to science, fostering critical thinking, creativity, and a portfolio of work suitable for university applications or early-career academic development. 4. Real-World Experimentation and Instrument Literacy SciVid fills a major gap in traditional literature by enabling the visual documentation of experimental protocols: Viewers can observe instrument calibration, sample handling, troubleshooting steps, and reaction dynamics in real time. This provides far superior training value compared to static text or diagrams, especially in highly specialized or safety-sensitive procedures. For fields such as microfluidics, nanomaterial fabrication, or optomechanics, where hands-on understanding is critical, SciVid becomes a unique instructional resource. 5. AI-Compatibility and Integration with Emerging Technologies Each SciVid submission is natively compatible with: AI transcription, indexing, and segmentation, enabling integration into intelligent tutoring systems, knowledge graphs, and semantic search engines. Metadata tagging for topic, field, author, institution, and concept-based retrieval. Augmented and virtual reality extensions, enabling future overlays of real-world lab scenes and interactive 3D models. This positions SciVid at the frontier of AI-enhanced education, where machine-readable, multimodal content is essential. 6. Royalties and Recognition for Authors, Teachers, and Institutions SciVid pioneers a transparent revenue-sharing model: Authors receive royalties from YouTube monetization, institutional licenses, and educational partnerships. Schools and universities can earn funding from hosting student contributors or integrating SciVid into curricula. Lifetime royalty tracking ensures that contributions remain valuable and recognized long after publication. This addresses the exploitative economics of traditional publishing (e.g., $3000–$4000 open-access fees) by offering an inclusive, low-cost, and revenue-generating alternative. 7. Global Visibility with Permanent Scholarly Status Each SciVid paper: Is published both on academic repositories and public video platforms (e.g., YouTube, Vimeo). Receives a DOI, ensuring citation and discoverability in academic databases and Google Scholar. Can be embedded in library systems, digital classrooms, and scientific archives worldwide. This dual strategy maximizes both academic legitimacy and public visibility, allowing a single work to reach journals, classrooms, conferences, and policy discussions simultaneously. SciVid is not merely a video journal — it is a new academic paradigm. By merging scholarly standards with visual publishing, it enables: Authentic engagement with experimental science. Equitable authorship for students and educators. New revenue models aligned with open access and digital transformation. In a time when science must be both rigorous and reachable, SciVid offers a scalable, ethical, and transformative model for the next generation of global STEM communication.

Despite their longstanding role as the dominant medium of academic communication, traditional text-based scientific papers exhibit fundamental limitations that constrain the accessibility, reproducibility, educational value, and interdisciplinary applicability of scientific knowledge. These issues are increasingly incompatible with the demands of modern research ecosystems, digital pedagogy, and AI-enhanced knowledge dissemination. Below, we outline and analyze the key limitations of static text-based publishing formats in the context of contemporary science and education. While traditional scientific papers continue to play a foundational role in academic discourse, their static, monomodal format is increasingly misaligned with the complex, dynamic, and interdisciplinary nature of 21st-century science. They are poorly suited for conveying experimental nuance, engaging diverse audiences, or enabling AI-assisted learning. In the era of digital transformation, there is a pressing need for complementary publication formats—including video papers, interactive simulations, and multimodal repositories—that address the cognitive, practical, and systemic limitations of the text-only model. 1. Inadequate Representation of Dynamic, Spatial, and Temporal Phenomena Scientific phenomena that involve movement, transformation, or spatial complexity—such as fluidic behavior, self-assembly of nanostructures, molecular kinetics, oscillatory chemical reactions, or living systems—are inherently time-dependent and multidimensional. Static images and written descriptions cannot adequately capture these dynamics. Readers are often left to mentally reconstruct processes based on snapshots and approximations, leading to cognitive overload, misinterpretation, or loss of mechanistic nuance. Examples: Microfluidic mixing patterns or flow instabilities. Growth of nanowires or membranes under strain. Photonic or plasmonic response of materials under pulsed laser illumination. 2. Inaccessibility for Students, Interdisciplinary Learners, and Non-Native English Speakers Scientific papers are written for peers within a specific discipline and assume a high baseline of domain-specific knowledge and terminology. This creates cognitive and linguistic barriers for: Students at the high school or undergraduate level. Researchers entering adjacent or cross-disciplinary fields. Non-native English speakers in the global South and emerging economies. The opacity of specialized jargon, compressed logic, and abstract prose severely limits the educational utility of traditional articles and excludes wide segments of potential learners and contributors. 3. Poor Conveyance of Experimental Methods and Instrumental Literacy A persistent gap in the literature lies in the undercommunication of practical laboratory techniques. Details critical for successful replication—such as instrument calibration, procedural timing, manual dexterity, environmental conditions, and tacit knowledge—are either omitted or insufficiently described. This renders many methods effectively irreproducible, especially for early-career researchers or institutions without direct mentorship access. Examples: Sample positioning in confocal microscopy. Electrode placement in electrochemical measurements. Precise timing and environmental modulation in biological assays. 4. Undermining of Scientific Reproducibility The global reproducibility crisis is exacerbated by the limitations of text in conveying context-sensitive procedures. Subtle variations in angle, concentration gradients, reagent freshness, or ambient humidity can radically alter outcomes, yet these factors are difficult to codify in prose. As a result, even when protocols are "complete" on paper, attempts at replication frequently fail due to the loss of embodied or visual knowledge. 5. Cognitive Rigidity and Limited Engagement for Diverse Learning Styles Text-based communication relies almost exclusively on linguistic-symbolic processing, which only benefits a subset of learners. Visual, auditory, kinesthetic, and neurodiverse individuals are systematically disadvantaged by the absence of: Visual storytelling. Auditory cues and narrative scaffolding. Interactive simulations or gesture-based explanations. This restricts comprehension, retention, and long-term application, particularly in educational and outreach contexts. 6. Lack of Narrative Structure and Emotional Engagement The rigid linear format (Introduction → Methods → Results → Discussion) common to journal papers disconnects the reader from the exploratory nature of science. It presents discovery as a cold, retrospective account devoid of emotion, uncertainty, or creative intuition—elements essential for inspiring students, non-specialists, and the general public. In contrast, multimodal media such as scientific videos or simulations can reconstruct the real-time intellectual journey of a researcher, highlighting failures, breakthroughs, and serendipity. 7. Static, Non-Interactive Format Incompatible with Modern Knowledge Systems Text-based PDFs are static and non-interactive by design. They cannot accommodate: Real-time manipulation of 3D molecular or simulation data. Replays of key procedural steps. Layered annotations, branching explanations, or learner-driven exploration. This makes them poorly suited for emerging education paradigms, including flipped classrooms, augmented reality (AR) labs, and interactive AI tutors. 8. High Cost, Long Timelines, and Inequitable Dissemination The traditional peer-reviewed publishing system is slow (6–12 months), expensive (often $2,000–4,000 for open-access), and exclusionary. Most papers are locked behind paywalls, with access restricted to well-funded institutions. This perpetuates epistemic inequality by privileging research from the Global North and restricting access for low-income countries, public schools, and independent innovators. 9. Incompatibility with AI-Powered Education and Scientific Search Text-based articles lack structured metadata, multimodal annotations, or semantic uniformity, which limits their usability in AI-driven platforms. Modern educational ecosystems—including adaptive learning, AI tutors, and scientific recommender systems—thrive on content that includes: Transcribable speech. Video demonstrations. Embedded time-series and contextual metadata. Such systems cannot effectively parse, personalize, or enhance traditional prose-only articles.

SciVid Integrity Guide: Preventing Academic Misconduct, Ensuring Trust, and Supporting Scientific Education Purpose of This Guide As SciVid allows both video research papers (based on real experiments) and video review papers (based on literature and conceptual visualization), we must ensure that the use of AI-generated media never misrepresents, fabricates, or distorts scientific understanding. This guide explains how to use AI visuals ethically and clearly—without breaking academic integrity standards. Two Types of SciVid Submissions and Their Visual Rules 1. Experimental Video Papers ➡️ What It Is: Videos documenting original experiments performed by the author(s). ➡️ Visual Content Requirement: Must include real video footage, photos, or graphs directly recorded, measured, or produced during the research process. AI tools may only be used for minor graphical enhancement (e.g., layout, titles, or background animations), but not to simulate or fabricate experimental data. ⚠️ Forbidden: Simulating experimental outcomes with AI images or stock footage and presenting them as real. Replacing missing experiments with AI approximations. Editing images to overstate results (e.g., false color overlays implying higher accuracy). 2. Review or Conceptual Video Papers ➡️ What It Is: Educational videos explaining a scientific topic, based on peer-reviewed literature. ➡️ Visual Content Allowed: AI-generated illustrations, diagrams, whiteboard animations, and visual metaphors are encouraged—as long as they are labeled clearly as conceptual or illustrative. These visuals should not claim to depict original experimental data or mislead viewers into believing that a visualization represents real-world results. ✅ Good Practice: "This image was generated using MidJourney to visually represent the concept of protein folding described in the reviewed paper [DOI]. It is not based on actual molecular simulations." 6 Core Principles to Avoid Misconduct When Using AI in SciVid Papers 1. Distinguish Fact from Illustration Always make it clear whether a visual: Represents real experimental data Is an artist’s conceptual interpretation Is generated by AI for educational enhancement 🔹 Use on-screen labels, audio disclaimers, or video descriptions. 2. Do Not Use AI to Fabricate or Falsify Results Even in review or educational work, it's misconduct to: Create visuals that suggest false data trends Modify published figures without permission or attribution Show imagined experimental setups without clear disclosure 🔒 Always cite the source, or declare when the content is speculative or hypothetical. 3. Provide Full AI Use Disclosure Every SciVid video must include an “AI Use Statement”, declaring: Which tools were used (e.g., DALL·E, ElevenLabs, Pika, RunwayML) What parts were AI-generated (images, voiceovers, visuals) Whether visuals are based on real data or for conceptual support ✍️ This maintains trust and aligns with the principles of transparency in scholarly publishing. 4. Respect Intellectual Property Never use AI to: Replicate figures from published articles without proper permissions Copy trademarks, logos, or known commercial designs Reconstruct real people, labs, or instruments without rights 📄 SciVid supports copyright-respecting educational reuse. Seek permission if needed, or generate alternative illustrations. 5. Avoid Emotional or Sensational Visuals AI-generated visuals should not: Exaggerate phenomena (e.g., making cells look sentient) Depict violent, catastrophic, or unrealistic outcomes to attract attention Include misleading cues (e.g., lightning bolts in neurons, black holes inside molecules) 🎓 Focus on clarity, accuracy, and educational value—not entertainment or clickbait. 6. Always Align with Peer-Reviewed Text All AI visuals in review videos must support the written scientific sources being discussed. 🔎 Do not introduce new speculative content unless it is clearly labeled as a hypothesis or conceptual analogy. Summary: SciVid’s Golden Rule AI visuals must illuminate—not manipulate. They are tools for enhancing understanding, never for simulating reality without disclosure. Submission Checklist: AI Visual Integrity Before submitting your SciVid paper, ask: ✅ Is every AI image clearly labeled or explained? ✅ Did I avoid using AI to fabricate or simulate experimental results? ✅ Did I declare all AI tools used? ✅ Are all visuals aligned with the cited peer-reviewed sources? ✅ Have I ensured no content violates copyright or depicts false data? 📨 For any doubts, contact SciVid’s editorial team at editor@scividjournal.com

Why This Guide Matters SciVid is a pioneering video-based academic journal that empowers students and researchers to turn their scientific work into engaging, peer-reviewed video papers. Many contributors now use AI tools to generate graphics, animations, and even narration. While these tools can enhance creativity and accessibility, they also require responsible, transparent, and ethical use. This guide outlines SciVid’s ethical standards and provides clear instructions for using generative AI tools—like text-to-image (e.g. DALL·E, MidJourney), whiteboard animation (e.g. InstaDoodle), and text-to-video (e.g. Pika, RunwayML)—in line with academic integrity and national education policies, including the Australian Framework for Generative AI in Schools (2023). SciVid Ethical Guidelines on Using AI Tools in Video Papers 1. Declare and Disclose All AI-Generated Content Transparency is essential. All video papers submitted to SciVid must clearly identify which parts of the content were generated with the help of AI tools. This includes: AI-generated images and animations AI-assisted narration or video production Any automated writing assistance or translation Include an “AI Use Statement” in the video or its accompanying metadata. Example: “Images were created using MidJourney; narration was assisted by ElevenLabs voice AI; script written by the author.” 2. Ensure the Originality and Intellectual Contribution of the Author AI is a tool—not an author. While AI can assist with visualization or production, the core intellectual content—the scientific idea, hypothesis, research method, and explanation—must be original and created by the named authors. Plagiarism, fabrication, and AI-generated fake content are not tolerated. Students must be the authors of their script, reasoning, and conclusions. Teachers, mentors, or collaborators should be acknowledged transparently. 3. Respect Privacy, Consent, and Copyright SciVid does not accept any content that includes: AI-generated replicas or deepfakes of real people without their explicit consent Images, voices, or likenesses that may misrepresent or defame others AI-generated content using copyrighted input data without rights to reproduce it When using free or commercial AI tools, ensure you have the right to publish the output in a public academic platform like SciVid. 4. Avoid Harmful, Inappropriate, or Biased Content We prohibit submission of AI-generated visuals or videos that depict: Violence, hate speech, or discrimination Misinformation or conspiracy theories Biased depictions of gender, race, or culture Content that violates the emotional or psychological safety of others All video papers undergo ethical screening prior to publication. 5. Use AI Tools to Support, Not Replace, Scientific Thinking AI should enhance your scientific expression—not replace your research. SciVid encourages the use of generative tools to: Visualize molecules, cells, and systems Animate complex processes Simplify explanations for public engagement However, critical thinking, scientific reasoning, and originality must remain central. 6. Align With School, University, and National Frameworks For school-aged authors, SciVid aligns with the Australian Framework for Generative AI in Schools, which emphasizes: Equity and access Digital and AI literacy Student data privacy Teaching students the risks and biases of generative AI Teachers and schools are encouraged to support student contributions and may act as co-mentors or reviewers. 7. Maintain Full Academic Integrity By submitting to SciVid, every author agrees to uphold these standards: Honest attribution of contributions No plagiarism (including AI plagiarism) Fair collaboration with mentors, co-authors, or institutions Responsibility for all content, including that created with AI tools Violations of these rules may result in retraction, bans from future publication, and notification of affiliated institutions. Why SciVid Encourages Ethical AI Use AI offers incredible potential to democratize scientific publishing, make learning more visual, and engage younger generations in research. But its power must be used responsibly. SciVid’s vision is to build an inclusive, ethical, and transparent global community of next-generation scientists and storytellers. We believe that academic integrity + AI literacy = future-ready scholars. 📩 Questions or Concerns? Reach out to our editorial board at editor@scividjournal.com

Dear Colleagues and Students, As a researcher, educator, and innovator at the intersection of nanotechnology, microfluidics, and interdisciplinary science, I have spent the past two decades navigating the limitations of traditional academic publishing — both as an author and a mentor. My journey has taken me through world-renowned institutions such as Harvard University, the Max Planck Institute, and Fudan University, and now into a new phase as the Founder of the NanoTRIZ Innovation Institute and Editor-in-Chief of SciVid – The Publisher of Video Science. The creation of SciVid stems from a simple but urgent realization: today’s science is increasingly visual, dynamic, and collaborative, yet the way we share it remains static, text-heavy, and often inaccessible. In my own research—particularly with nanoscale engines, fluidic systems, and self-assembling materials — I often struggled to convey core discoveries through words and static images alone. I witnessed firsthand how students and even senior colleagues misunderstood or misapplied protocols that were clear in the lab but poorly communicated in print. More importantly, I have mentored many brilliant young scientists, especially from underrepresented regions and non-English-speaking backgrounds, whose ideas and experiments deserved global attention. Yet, their work was consistently sidelined by barriers: high journal fees, institutional gatekeeping, and linguistic bias. They could demonstrate the science—but could not publish it. SciVid was created to change this. It is my vision of a new kind of journal: one that retains peer review, DOI indexing, and academic integrity, but harnesses the expressive power of video to make science understandable, reproducible, and accessible to all. Whether it is a high school student presenting their first experiment, a graduate researcher documenting a novel microfluidic method, or a global expert visualizing quantum concepts through animation, SciVid invites contributions that teach, inspire, and show science — rather than just describe it. We are building SciVid around three core principles: Integrity, through a rigorous editorial process and transparent ethics; Inclusion, through multilingual publishing, AI-enhanced accessibility, and youth authorship; Impact, through royalty models, institutional licensing, and real-world integration in STEM education. Our goal is not to replace traditional journals, but to complement and extend them — bridging the gap between formal research and global learning. SciVid is also a tool for educational transformation: enabling teachers to use peer-reviewed, video-based materials; empowering students to publish early; and allowing institutions to adopt multimedia research content for enrichment. In the long term, I envision SciVid as a living archive of scientific practice — where motion, method, and meaning converge. A platform where science is no longer locked in PDFs and paywalls, but shared through voice, visuals, and verifiable experimentation. As we grow, I warmly invite students, educators, researchers, and institutions to join us in reshaping how the world learns, shares, and publishes science. Sincerely, Professor Alexander A. Solovev Founder, NanoTRIZ Innovation Institute Editor-in-Chief, SciVid – The Journal of Video Science

Yes. SciVid encourages the publication of original computational modeling and simulation research as video papers, provided that the methods, data, and visualizations are transparent, reproducible, and aligned with academic integrity standards. SciVid recognizes the increasing importance of theoretical modeling, computer simulations, AI-based predictions, and numerical analysis in modern science and engineering. These types of research are fully eligible for publication in SciVid—whether based on physical modeling, agent-based systems, finite element analysis, Monte Carlo simulations, molecular dynamics, or machine learning. Acceptable Types of Modeling & Simulation SciVid Papers ✅ 1. Original Research Video Papers Based on Simulations You conducted a numerical/computational investigation. Your code, algorithm, or modeling environment (e.g., MATLAB, COMSOL, Python, ANSYS, LAMMPS) produced novel data. You explain your method, input parameters, results, and validation process. You visualize your results using real simulation outputs, animations, or graphical representations. Example: A SciVid paper presenting a fluid flow simulation through porous materials using COMSOL Multiphysics with on-screen narration of equations, boundary conditions, and mesh analysis. ✅ 2. AI-Generated Simulation-Based Research You used AI or machine learning to predict phenomena (e.g., protein folding, battery behavior, material design). You present datasets, training methodology, validation, and limitations. Visualizations may include 3D renders, time-lapse animations, or simplified educational graphics, clearly disclosed. Important: If you use AI to simulate outcomes, you must state this clearly and not present them as real-world experimental data unless validated. ✅ 3. Review or Conceptual Papers Explaining Models You did not run the simulations yourself but are explaining others' models. You use AI or standard tools to create animations or metaphoric visuals. You must reference all original sources and clarify that visuals are conceptual representations, not empirical results. 🚫 What is Not Allowed Fake Simulations: AI-generated videos or animations presented as if they were run in a scientific simulator when they were not. Misleading Visuals: Animations suggesting experimental or real-world behavior without data or source reference. Unattributed Models: Using simulations created by others without citing them or securing permission. What Must Be Included in a Modeling SciVid Paper To publish a modeling or simulation-based video paper in SciVid, authors should follow these key requirements: Clear Methodology: The paper must describe the modeling tools used (such as software or coding environments), the equations and assumptions involved, and the computational setup or parameters. Visual Integrity: All visualizations should accurately reflect the actual simulation outputs whenever possible. Every visual must be clearly labeled to distinguish between real results and conceptual illustrations. Code and Parameter Transparency: While not mandatory, it is strongly encouraged to share code, configuration files, or access to data repositories to support reproducibility and community trust. AI Use Statement: If any part of the video—including visuals, narration, or animations—was created or enhanced using AI tools, this must be explicitly disclosed in the submission. Peer-Reviewed Foundation: The simulation should be grounded in legitimate science. This means the video must be based on either the author’s own peer-reviewed research, a submitted manuscript under review, or a simulation process that is thoroughly documented and reproducible. Educational Narrative: The video should not only present data but also explain the purpose and process of the simulation—what is being modeled, why it is significant, how it was done, and what the results mean in a broader context. Example Titles of Accepted Modeling Papers in SciVid “Visualizing Magnetic Field Lines in Tokamak Reactors Using Python and OpenGL” “Simulating Urban Heat Islands: A GIS and Agent-Based Modeling Approach” “Modeling Quantum Tunneling in Nanowires with Time-Dependent Schrödinger Equation” “Predicting Drug-Target Interactions with a Deep Learning Framework (TensorFlow-based)” “Educational Review: Molecular Dynamics of Lipid Bilayers Visualized with LAMMPS and VMD” Summary Yes, modeling and simulation research is welcome at SciVid. The only requirement is to maintain academic honesty, clarity of method, proper disclosure of visualizations, and respect for the audience’s understanding. Use real outputs where possible, disclose conceptual content where necessary, and always align with best practices in scientific publishing.