SciViD: The Publisher of Video Science

The scientific publishing system, though historically essential for knowledge dissemination, faces structural barriers, slow timelines, and inequities that limit global participation. SciViD represents a timely and necessary evolution — integrating visual storytelling, AI-assisted workflows, and an inclusive editorial framework to build a more transparent, accessible, and sustainable knowledge ecosystem. CURRENT ISSUES IN ACADEMIC PUBLISHING 1. High Costs and Limited Access Many academic journals impose significant fees for both authors (APCs) and readers (paywalls). This creates systemic barriers for early-career researchers, students, and institutions in lower-income regions, concentrating scientific knowledge in well-funded circles. 2. Incentive Misalignment Academic evaluation systems often prioritize publication quantity over quality. This pressure leads to fragmented publications and redundant studies, while innovative, risky, or interdisciplinary ideas are frequently deprioritized. 3. Slow Publication Timelines It often takes months or years for papers to complete peer review and reach final publication. Time-sensitive discoveries — especially in health, climate, and emerging technologies—are delayed, hindering rapid scientific progress. 4. Opaque Peer Review Processes Traditional peer review can be inconsistent in quality and prone to bias. Excellent papers may be unfairly delayed, while the lack of transparent evaluation systems erodes trust in scholarly communication. 5. Underrepresentation of Global Voices Scientific publishing is heavily dominated by institutions in a few geographic regions. Consequently, local innovations and research from diverse linguistic or cultural backgrounds are often excluded from the global discourse. 6. Limitations of Static Formats Traditional publication formats relying solely on text and static figures are poorly suited for conveying complex experimental techniques, real-world applications, and digital simulations. This severely limits reproducibility and educational utility. HOW AI ENHANCES THE RESEARCH WORKFLOW 1. Literature Analysis and Gap Detection AI tools can process vast repositories of publications to identify emerging trends and suggest underexplored topics, supporting highly inclusive, data-informed research planning. 2. Editorial and Ethical Support Advanced language models can assist editorial teams by performing first-level quality checks, screening for plagiarism, and flagging potential inconsistencies, thereby strengthening the integrity of the review process. 3. Democratizing Scientific Communication AI-assisted writing and translation tools help non-native speakers polish manuscripts and disseminate knowledge in multiple languages, leveling the playing field in global scientific communication. 4. Advanced Visualization AI can assist researchers in generating data animations, digital twins, and graphical summaries to replace static figures, drastically improving cross-disciplinary understanding. HOW SCIVID PUBLISHER TRANSFORMS THE ECOSYSTEM SciViD offers a fundamentally new model for academic communication. By addressing the bottlenecks of traditional publishing, we focus on clarity, inclusivity, and methodological rigor. ✅ Accessible Publishing Pathways By integrating AI tools for structured narration, animation, and editing, researchers can produce rigorous video-based papers efficiently. This significantly lowers the prohibitive financial and technical barriers for early-career researchers and underfunded groups. ✅ Accelerated Dissemination Time-sensitive discoveries shouldn't be locked behind years of review cycles. By utilizing a digital-first format and a streamlined editorial workflow, SciViD accelerates the path from submission to publication without compromising academic rigor. ✅ Visualization of Complex Ideas SciViD requires authors to visually demonstrate their methods, simulations, and devices rather than merely describing them in technical prose. This method-centered approach dramatically improves reproducibility and turns every paper into an interactive educational resource. ✅ Global and Multilingual Accessibility Science is global, but publishing is often restricted by language. SciViD’s digital infrastructure supports multilingual subtitles and AI-assisted translations, empowering non-English-speaking communities to seamlessly access and contribute to global research. ✅ Empowering Emerging Scholars We believe rigorous scientific training should start early. Through dedicated mentorship tracks and the Emerging Scholars Collection, SciViD provides supervised, peer-guided pathways for high-achieving high school and university students to publish verifiable, methodologically sound research. ✅ Transparent and Ethical AI Use SciViD enforces strict transparency requirements. AI contributions in data analysis, literature review, and visual generation must be explicitly declared. We champion the responsible and ethical integration of AI into scholarly work, with editors and reviewers assessing both originality and execution. ✅ Sustainable Open Access Moving away from extractive publishing models that rely on exorbitant article processing charges (APCs), SciViD is committed to developing sustainable, community-driven frameworks. We ensure that impactful visual science remains open, equitable, and accessible to everyone. Comparative Summary https://static.wixstatic.com/media/60245b_954101e54b80495b9b2e22b8cd2bc504~mv2.png

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. Below, we outline the key limitations of static publishing formats in the context of contemporary science and education. 1. The Reproducibility Gap and Loss of "Embodied" Knowledge A persistent flaw in text-based literature is the under-communication of practical laboratory techniques. Details critical for successful replication — such as instrument calibration, procedural timing, manual dexterity, and environmental conditions — are notoriously difficult to codify in prose. This loss of tacit, visual knowledge directly exacerbates the global reproducibility crisis, as attempts to replicate "complete" written protocols frequently fail in practice. 2. Inadequate Representation of Dynamic Phenomena Scientific processes involving movement, transformation, or spatial complexity — such as fluidic behavior, nanoscale self-assembly, or living cellular systems—are inherently multidimensional. Static images and written descriptions force readers to mentally reconstruct complex temporal processes from 2D snapshots, leading to cognitive overload, misinterpretation, and a severe loss of mechanistic nuance. 3. Cognitive Barriers and Epistemic Exclusion Traditional papers are written for narrow disciplinary peers, assuming a high baseline of domain-specific jargon. This creates severe linguistic and cognitive barriers for high school and undergraduate students, researchers entering adjacent fields, and non-native English speakers. Furthermore, relying exclusively on linguistic-symbolic processing systematically disadvantages visual and neurodiverse learners, while the rigid linear format strips the narrative of the exploratory, human nature of scientific discovery. 4. Systemic Inequities: High Costs and Slow Timelines The traditional peer-reviewed publishing system is slow (often taking 6–12 months) and prohibitively expensive, with Article Processing Charges (APCs) frequently costing between $2,000 and $4,000. With content locked behind paywalls or requiring massive upfront fees, the system privileges well-funded institutions. This perpetuates epistemic inequality, restricting access and contribution for the Global South, public educational institutions, and independent innovators. 5. Incompatibility with AI and Modern Knowledge Systems Text-based PDFs are static and non-interactive by design. They lack the structured metadata, multimodal annotations, and semantic uniformity required by modern AI-driven platforms. Next-generation educational ecosystems—including adaptive AI tutors, semantic scientific search, and augmented reality (AR) labs—rely on transcribable speech, embedded time-series data, and interactive video demonstrations to effectively parse, personalize, and enhance learning. THE PATH FORWARD 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. To address these cognitive, practical, and systemic limitations, there is a pressing need for complementary publication formats—including video papers, interactive simulations, and multimodal digital repositories—that ensure science remains transparent, highly reproducible, and universally accessible.

Text-based scientific papers continue to serve as the cornerstone for archival precision. However, in an evolving digital landscape, the limitations of static formats in conveying complex experimental processes and ensuring reproducibility have become increasingly apparent. SciViD 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 while embracing the full expressive capacity of video: • Editorial Standards: A developing peer-review and editorial framework ensures scientific validity and methodological clarity. • Persistent Identification: Planned DOI registration through Crossref provides stable citation and future indexing in academic databases. • Structured Scholarship: Submissions maintain compatibility with scholarly standards (Abstract, Introduction, Methods, Results, etc.). • Visual Evidence: SciViD enables visual demonstrations of dynamic processes, narrated walkthroughs of experimental setups, and high-fidelity simulations. This hybrid format is essential for fields where movement and transformation are central, such as Microfluidics, Nanotechnology, Quantum Physics, and Experimental Biology. 2. Universal Accessibility and Multilingual Inclusivity SciViD is designed to break long-standing barriers to scientific entry: • Global Reach: Support for multilingual subtitles and AI-assisted translation makes high-level research accessible to non-native English speakers. • Inclusive Design: Multimodal presentation (visual + auditory) supports diverse learning styles, including neurodiverse individuals and those with visual or auditory impairments. • Digital Literacy: The platform is optimized for mobile and web integration, ensuring that scientific knowledge reaches researchers and students regardless of their geographic location. 3. Pedagogical Optimization and Student Empowerment Unlike traditional journals, SciViD is natively designed with educational utility in mind. Through the Emerging Scholars Collection, students transition from passive consumers to active contributors: • Project-Based Learning: The video format aligns perfectly with flipped classrooms and advanced digital curricula. • Portfolio Development: Students gain verifiable skills in scientific communication and multimedia production, building a portfolio of peer-guided work suitable for top-tier university applications. • Active Mentorship: Educators and schools can act as co-reviewers, fostering a collaborative environment for early-career academic development. 4. Methodological Transparency and Instrument Literacy SciViD fills a critical gap in traditional literature by enabling the visual documentation of experimental protocols: • Real-Time Observation: Viewers can observe instrument calibration, sample handling, and troubleshooting steps as they happen. • Reproducibility: Providing visual "tacit knowledge" offers superior training value compared to static text, which is vital for highly specialized or safety-sensitive procedures in chemistry and engineering. 5. AI-Native Architecture and Future Integration Each SciViD submission is built for compatibility with the next generation of knowledge systems: • Semantic Search: Content is optimized for AI transcription and segmentation, allowing it to be indexed within intelligent tutoring systems and knowledge graphs. • Structured Metadata: Deep metadata tagging allows for precise concept-based retrieval, moving beyond simple keyword searches. • Simulation Extensions: The platform is designed to eventually support AR/VR overlays and physics-based simulations, positioning SciViD at the frontier of AI-enhanced education. 6. Ethical and Sustainable Publishing Economics SciViD pioneers an equitable model that addresses the high costs associated with traditional open-access publishing: • Low Barriers: By utilizing AI-assisted workflows, SciViD aims to minimize Article Processing Charges (APCs), ensuring that underfunded researchers and students can publish without prohibitive fees. • Value Recognition: We are developing sustainable frameworks to ensure that contributors—authors, reviewers, and institutions—are fairly recognized for the long-term educational impact of their work. • Institutional Partnership: Schools and universities can integrate SciViD into their research ecosystems, fostering an inclusive and sustainable model for knowledge sharing. 7. Hybrid Scholarly and Public Visibility SciViD maximizes both academic legitimacy and public impact: • Dual Presence: Articles are designed to reside within both scholarly archives and professional video platforms to maximize reach. • Discoverability: Structured metadata and DOI integration ensure that work is findable by both search engines and academic discovery tools like Google Scholar. • Cross-Channel Impact: A single SciViD publication can simultaneously serve as a formal research artifact, a classroom resource, and a high-impact presentation for funding or conferences. SciViD is more than a platform; it is a new academic paradigm. By merging scholarly standards with visual communication, we enable authentic engagement with experimental science, equitable authorship for the next generation, and an ethical model for the digital age.

SciViD provides two distinct pathways tailored to the author’s academic goals and experience level. Both pathways prioritize visual clarity and methodological transparency, but they differ in their review depth and institutional positioning. 1. The Emerging Scholars Collection (Editorial Review + DOI) Purpose: Designed for high-achieving high school and undergraduate students, science clubs, and early-career researchers building an international academic portfolio. • Focus: Mentorship, structured scientific thinking, and the creation of a verifiable digital artifact for university applications (e.g., Common App, UCAS). • Review Process: Undergoes Supervised Editorial Review. Each submission is reviewed by SciViD PhD mentors for clarity, ethical AI use, and methodological soundess. It is an instructional review process designed to elevate the student’s work to professional standards. • DOI & Indexing: Includes a permanent DOI (via Crossref) and a dedicated metadata page. This ensures the work is a permanent, citable part of the student's academic record. • Recognition: Authors are credited as Junior Research Scholars. • Best for: Students aiming for Tier-1 university admissions who need a high-impact, verifiable research project. 2. The Flagship Journal (Formal Peer Review + DOI) Purpose: Designed for senior researchers, PhD candidates, and professors seeking formal academic recognition and contribution to the global body of scientific knowledge. • Focus: Original research findings, comprehensive literature reviews, and high-fidelity experimental simulations. • Review Process: Undergoes Formal Peer Review. Submissions are evaluated through a structured editorial workflow and, as our network expands, independent peer assessment to ensure scientific validity and impact. • DOI & Indexing: Includes a permanent DOI and standardized metadata optimized for discovery in academic databases and library systems. • Recognition: Published as a formal Peer-Reviewed Video Article. • Best for: Professional scientists and educators aiming to publish credible, high-visibility video articles that address the reproducibility crisis in their fields. SUMMARY OF KEY DIFFERENCES AN INTEGRATED ECOSYSTEM By offering these two pathways, SciViD creates a seamless bridge between science education and professional publishing. We empower the next generation of scientists to publish ethically and visibly, while providing senior researchers with a powerful new medium for transparent, method-centered communication.

Yes. SciViD actively encourages the publication of original computational modeling, numerical analysis, and simulation research. In an era where theoretical and in-silico investigations are central to scientific progress, we provide a high-visibility platform to showcase the "how" and "why" of complex digital experiments. ACCEPTABLE TYPES OF MODELING & SIMULATION PAPERS 1. Original Research Simulations These are papers based on original numerical investigations where the author’s code or modeling environment (e.g., MATLAB, COMSOL, Python, ANSYS, LAMMPS) produced novel data. • Requirement: Authors must explain the methodology, input parameters, and validation process. Visualizations should be derived directly from simulation outputs. 2. AI-Driven & Predictive Modeling Papers utilizing machine learning or neural networks to predict phenomena (e.g., protein folding, material behavior, or climate patterns). • Requirement: Authors must present the datasets, training methodology, and validation metrics. Visualizations may include 3D renders or time-lapse animations but must be clearly disclosed as AI-generated. 3. Methodological & Conceptual Reviews Educational or review papers that explain existing models or complex theoretical frameworks. • Requirement: While the author may use conceptual animations (e.g., Manim, Blender) or metaphoric visuals to aid understanding, all original sources must be cited, and visuals must be labeled as "conceptual representations." ACADEMIC INTEGRITY & PROHIBITED PRACTICES To maintain the highest standards of scientific communication, SciViD strictly prohibits • Pseudo-Simulations: Presenting AI-generated animations or "faked" visuals as if they were generated by a scientific solver when they were not. • Misleading Visuals: Animations that suggest experimental behavior without being grounded in underlying data or cited theory. • Unattributed IP: Utilizing simulation code, datasets, or visual outputs created by others without proper citation or legal authorization. SUBMISSION REQUIREMENTS To ensure a Modeling or Simulation paper meets SciViD standards, the following must be included: • Methodological Transparency: Clear description of the software, coding environment, governing equations, boundary conditions, and computational setup. • Visual Integrity: Visuals must accurately reflect simulation data. Every frame must be labeled to distinguish between Empirical Simulation Output and Conceptual Animation. • Open Science (Recommended): We strongly encourage the sharing of source code, configuration files, or links to public repositories (e.g., GitHub) to support reproducibility. • Mandatory AI Disclosure: If any part of the video—including narration, background visuals, or specific animations—was generated or enhanced by AI, this must be explicitly stated in the submission metadata and the video credits. • Educational Narrative: The video should not only present data but also provide a narrative "walkthrough" explaining the significance of the model, the logic of the parameters, and the broader context of the results. EXAMPLE TITLES IN THE SIMULATION TRACK • “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” SciViD transforms static code into dynamic, understandable science. Whether you are modeling fluid flows or training neural networks, we ensure your work is not just recorded, but truly observed and reproduced by the global community.

At SciViD, we bridge the gap between rigorous scholarship and immersive storytelling. Because we host both Experimental Video Papers (empirical data) and Review/Conceptual Video Papers (literature synthesis), we must maintain a "zero-distort" policy. This guide ensures that AI-generated media enhances understanding without compromising academic integrity or misleading the scientific community. One Platform, Two Visual Standards 1. Experimental Video Papers Definition: Original research documenting empirical experiments performed by the author(s). • Requirement: Must center on authentic video footage, micrographs, sensor data, or graphs recorded during the actual research process. • Role of AI: Limited to cosmetic enhancements (e.g., UI layout, titling, background denoising, or transition effects). • 🚫 Strictly Prohibited: Using AI to "fill in" missing experimental steps, simulating results that weren't captured, or using AI-generated stock footage to represent original data. 2. Review & Conceptual Video Papers Definition: Educational or syntheses based on existing peer-reviewed literature. • Requirement: Authors are encouraged to use AI-generated diagrams, whiteboard animations, and visual metaphors to explain complex concepts. • Role of AI: High creative freedom to aid pedagogy. • Requirement for Transparency: Every AI-generated asset must be clearly labeled as "Conceptual Animation" or "Illustrative Metaphor." It must never be presented as empirical evidence. The 6 Core Principles of AI Integrity 1. Explicitly Distinguish Fact from Illustration Never leave the viewer guessing. If a visual is an AI-generated artist’s interpretation rather than raw data, it must be declared via on-screen labels, audio cues, or the video description. 2. Zero Tolerance for Data Fabrication AI should never be used to "hallucinate" results. Even in educational reviews, it is misconduct to: • Create visuals suggesting false data trends. • Modify published figures from other authors to support a different conclusion. • Show imagined experimental setups without clear "Hypothetical" labeling. 3. The Full "Tool-Stack" Disclosure Transparency is the new currency of trust. Every SciViD submission must include an AI Use Statement detailing: • The Tools: (e.g., Midjourney, Sora, ElevenLabs, Pika). • The Scope: Which parts are AI (e.g., "Narrative voiceover," "Conceptual protein-folding animation"). • The Intent: Whether the AI was used for data visualization or purely for aesthetic engagement. 4. Respect for Intellectual Property (IP) AI is not a license to bypass copyright. Do not use AI to replicate proprietary instrument designs, copyrighted figures from other journals, or trademarks without legal authorization. SciViD supports ethical, original creation. 5. Precision Over Sensationalism Science is spectacular enough on its own—don't let AI turn it into sci-fi. Avoid "clickbait" visuals: cells shouldn't look sentient, and black holes shouldn't appear inside test tubes. Focus on clarity and accuracy, not cinematic exaggeration. 6. Mandatory Alignment with Peer-Reviewed Text Every AI-generated visual in a Review Video must strictly correlate with the cited scientific sources. If the literature says a reaction is "slow and subtle," the AI animation shouldn't show it as an "instant explosion." SciViD’s Golden Rule AI visuals must illuminate, not manipulate. They are tools for enhancing human understanding, never for simulating reality without full disclosure. Submission Checklist: AI Visual Integrity • [ ] Is every AI-generated asset clearly labeled as "Conceptual" or "Illustrative"? • [ ] Have I ensured no AI was used to fabricate or approximate experimental outcomes? • [ ] Is the AI Use Statement included in the credits and metadata? • [ ] Do all AI visuals align strictly with the underlying peer-reviewed text? • [ ] Have I verified that no AI content violates third-party copyrights or trademarks? Questions regarding visual ethics? Reach out to our editorial team at editor@scivid.ai.(mailto:editor@scivid.ai)

SciViD is a pioneering video-based academic publisher that empowers students and researchers to transform their scientific work into engaging, peer-reviewed video papers. As generative AI becomes a staple in creative workflows—from generating 3D animations to synthetic narration—it is critical to maintain a foundation of responsibility, transparency, and ethics. This guide outlines SciViD’s standards for using generative AI tools—such as text-to-image (DALL·E, Midjourney), whiteboard animation (InstaDoodle), and text-to-video (Pika, Runway, Sora). Our policies align with international best practices and national education policies, including the Australian Framework for Generative AI in Schools (2023). SciViD Ethical Guidelines on Generative AI 1. Mandatory Declaration and Disclosure Transparency is the bedrock of academic trust. All submissions must explicitly identify which components were generated or assisted by AI. • Scope of Disclosure: This includes AI-generated imagery, animations, synthetic voiceovers, automated script polishing, and translations. • The AI Use Statement: Every video must include a clear disclosure in the credits or metadata. Example: "Visuals created via Midjourney; Narration assisted by ElevenLabs Voice AI; Script authored by the researcher with structural polishing via Claude 3.5." 2. Authorship and Intellectual Contribution AI is a tool — not an author. While AI can assist with production, the core intellectual "soul" of the paper — the hypothesis, experimental method, data analysis, and original conclusions—must be the work of the named human authors. • Student Autonomy: Students must remain the primary architects of their reasoning and narratives. • Attribution: All mentors, teachers, or collaborators who assisted in the AI workflow must be transparently acknowledged. 3. Privacy, Consent, and Copyright Integrity SciViD maintains a strict policy regarding the "likeness" and "property" of others. We do not accept: • Deepfakes: AI-generated replicas of real people without explicit, documented consent. • Copyright Infringement: Content generated using protected data without the legal right to reproduce it. • Misrepresentation: AI-generated voices or images used to defame or misidentify individuals or institutions. 4. Safety and Bias Mitigation All AI-generated content undergoes ethical screening. We prohibit: • Inappropriate Content: Visuals depicting violence, hate speech, or illegal activities. • Algorithmic Bias: Stereotypical or biased depictions of race, gender, culture, or religion. • Misinformation: AI-generated "hallucinations" presented as scientific facts or verified data. 5. Support, Don't Replace, Scientific Thinking AI should function as your lab assistant, not your lead scientist. SciViD encourages generative tools to: • Visualize the Invisible: Animate molecular interactions, cellular dynamics, or quantum states. • Enhance Accessibility: Simplify complex concepts for public engagement. • Improve Literacy: Assist non-native speakers in refining their scientific communication. • Limit: Do not let AI dictate your experimental results or research direction. Alignment with National Frameworks For school-aged authors, SciViD operates in strict accordance with the Australian Framework for Generative AI in Schools, prioritizing: • Equity of Access: Ensuring AI tools are used to bridge, not widen, the digital divide. • Data Privacy: Protecting student identity and intellectual property from exploitative data harvesting. • Critical AI Literacy: Teaching students to question the outputs, biases, and limitations of the tools they use. The SciViD Integrity Agreement Why SciViD Champions Ethical AI We believe that AI offers an incredible opportunity to democratize scientific publishing and make learning more visual for the next generation. However, that potential is only realized when paired with rigorous ethical standards. Academic Integrity + AI Literacy = Future-Ready Scholars. Questions regarding your AI workflow? Reach out to our editorial board at editor@scivid.ai.(mailto:editor@scivid.ai)

Dear Colleagues, Educators, and Students, As a researcher and innovator working at the intersection of nanotechnology, microfluidics, and interdisciplinary science, I have spent the past two decades navigating the structural limitations of traditional academic publishing—both as an author and a mentor. My journey has taken me through world-renowned institutions, including Harvard University, the Max Planck Institute, and Fudan University. These experiences led me to a pivotal realization: while science is becoming increasingly visual, dynamic, and collaborative, 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 frequently struggled to convey core discoveries through words and 2D images alone. I witnessed firsthand how students and colleagues could misinterpret protocols that were crystal clear in the laboratory but poorly communicated in print. More importantly, I have mentored many brilliant young scientists whose ideas deserved global attention but were sidelined by institutional gatekeeping, high journal fees, or linguistic bias. They could demonstrate the science, but they could not easily publish it. SciViD was created to change this. Our vision is to build a new kind of platform: one that retains the essential rigors of peer review and DOI indexing, but harnesses the expressive power of video to make science truly understandable and reproducible. Whether it is an Emerging Scholar presenting their first experiment, or a global expert visualizing quantum concepts through animation, SciViD invites contributions that show science rather than just describe it. We have built SciViD around three core principles: • Integrity: Maintaining strict editorial standards and transparent ethics in every publication. • Inclusion: Empowering youth authorship and non-native English speakers through AI-enhanced accessibility. • Impact: Creating a sustainable ecosystem where contributors and institutions are recognized for their educational and scientific value. Our goal is not to replace traditional journals, but to complement them — bridging the gap between formal research and global learning. SciViD is a tool for educational transformation: enabling teachers to use verified video-based materials, empowering students to build an international academic portfolio early in their careers, and allowing institutions to adopt multimedia content for enrichment. In the long term, I envision SciViD as a living archive of scientific practice — where motion, method, and meaning converge. It is a platform where science is no longer locked behind PDFs and paywalls, but shared through voice, visuals, and verifiable experimentation. I warmly invite you to join us in reshaping how the world learns, shares, and publishes science. Professor Alexander A. Solovev Founding Director, NanoTRIZ Innovation Institute Founder, SciViD – The Publisher of Video Science