Information For Librarians

BJEDIS (Brazilian Journal of Experimental Design, Data Analysis and Inferential Statistics) is an open access, peer-reviewed journal that promotes methodological rigor and innovation in the fields of experimental design, statistical inference, and data-driven research. The journal also emphasizes methodological contributions that support decision-making under uncertainty and the responsible use of data in complex, real-world contexts. The journal is particularly relevant for academic institutions, research centers, and libraries that support interdisciplinary studies in science, engineering, education, social sciences, and health, as well as public and institutional environments where evidence-based decisions are critical.

We encourage librarians to include BJEDIS in their electronic journal holdings and digital catalogues, as all content is freely available under an open access license, with no subscription or access fees. This open model facilitates broad dissemination of reproducible research, applied statistical methodologies, and decision-oriented analytical frameworks. Articles are published with persistent identifiers (DOI), ensuring stable and reliable citation and archiving.

BJEDIS supports metadata harvesting via OAI-PMH and follows best practices in scholarly communication, including ethical publishing standards, ORCID integration, and support for bibliographic tools such as Zotero and Mendeley. The journal also values transparency in data provenance, uncertainty reporting, and methodological documentation, aligning with contemporary open science principles.

For institutional repositories and discovery services, BJEDIS provides full-text access, rich metadata, and citation export formats. In addition, BJEDIS welcomes works that rely on synthetic, anonymized, or privacy-preserving data representations, provided that their statistical validity is clearly documented. We also welcome collaboration with library science professionals to advance open science, digital preservation, and responsible research metrics, particularly in support of interdisciplinary and decision-centric research ecosystems.