It is a pleasure to welcome you to the inaugural issue of the Bulletin of Computer and Data Sciences (BCDS), a scholar-led venue dedicated to rigorous research, reproducible results, and impactful applications across computing and data-driven inquiry. Our mission is to advance knowledge that matters and to make that knowledge usable by researchers, practitioners, educators, and policy makers.

Computing and data pervade every discipline—from the design of reliable systems and trustworthy AI to evidence-based public policy and climate modeling—yet the literature is often split along artificial boundaries between computer science and data sciences, theoretical elegance and practical relevance, or novelty and careful replication. BCDS is designed to bridge these divides and to welcome work that pushes the frontier, consolidates it, or makes it accessible and reusable.

The journal’s scope spans core computer sciences (including algorithms, theory, programming languages, software engineering, systems, networking, operating systems, security, privacy, and human–computer interaction); data and AI (including machine learning, data mining, causal inference, statistics for data sciences, information retrieval, natural language processing, computer vision, and responsible, robust, and sustainable AI); data systems (including databases, data engineering, streaming, storage, scalable analytics, visualization, and knowledge graphs); applied and interdisciplinary directions (from bio/health informatics and cyber-physical systems to geospatial analytics, computational social science, fintech, and climate and sustainability informatics); and work on practice and education, such as large-scale deployments, industry case studies, reproducibility reports, and computing and data-science pedagogy.

To build a cumulative scientific record, BCDS publishes research articles reporting original theoretical, methodological, systems, or empirical contributions; brief communications that share timely results or compact insights; surveys and syntheses that distill mature areas or emerging threads with balance and clarity; data, software, and benchmark notes that document artifacts of community value; registered reports and replication studies that strengthen methodological rigor and reduce publication bias; and application and practice papers that document real-world deployments, including negative or mixed outcomes when they teach general lessons.

Our editorial standards rest on four commitments presented here as principles rather than checklists. First, rigor comes before hype: claims must be supported by careful theory, principled methodology, or transparent evidence, and empirical work should report effect sizes and uncertainty, ablations, sensitivity or stress tests, and comparisons against strong baselines. Second, reproducibility and reusability are expected: authors should include clear data and code availability statements and, when direct release is constrained by privacy or intellectual-property restrictions, provide synthetic or masked data, detailed protocols, and environment specifications—such as containers and commit hashes—sufficient for independent verification. Third, research must be responsible: papers should document relevant ethics approvals, articulate potential risks and mitigations, and follow community norms for disclosure in security research while engaging with topics such as fairness, accountability, transparency, safety, and sustainability. Fourth, peer review should be constructive and fair: BCDS uses double-anonymous review by default to reduce bias and offers an open-review option, with the emphasis on reviews that help authors improve their work.

We recognize that timeliness is part of impact. Our process emphasizes early triage, fast yet thorough reviews, and decisions that come with actionable feedback. When revisions are requested, editors work with authors to converge efficiently without compromising quality.

Excellence in computing and data science is global. BCDS is committed to geographic, institutional, and demographic inclusion across authors, reviewers, and editors. We actively encourage submissions from early-career researchers and from communities under-represented in the field, and we welcome interdisciplinary collaborations that connect computing with domain expertise.

To mark our launch, we especially invite submissions to collections on trustworthy, safe, and responsible AI; data-centric AI and evaluation, including dataset quality and benchmark science; efficient and sustainable computing, from algorithms to carbon-aware systems; edge, embedded, and real-time intelligence for resource-constrained and cyber-physical environments; and open knowledge and reproducibility, including replications, negative results with general lessons, and reusable toolchains. Proposals for guest-edited special issues are welcome and should outline the theme, its societal or technical impact, the target communities, and plans for inclusive outreach.

BCDS is a community project, and there are many ways to participate. Authors can help by framing clear problem statements, limitations, and artifact instructions, and by including preregistrations or ethics approvals where appropriate. Reviewers can build a culture of rigorous, respectful, and useful feedback that explains why a paper is strong or needs work. Practitioners and policy makers can shape research agendas by sharing real-world problems and lessons learned through translational papers and field reports.

Launching a journal is an invitation to write, review, debate, and build together. With the Bulletin of Computer and Data Sciences, we aim to create a venue where methodological excellence meets practical impact; where negative results are learned from rather than hidden; and where open artifacts turn ideas into foundations others can stand on. On behalf of the founding editorial team, thank you for reading, contributing, and reviewing. We look forward to the scholarship you will entrust to BCDS—and to the communities it will inform and empower.