Journal of Statistical Software
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| Journal of Statistical Software | |
|---|---|
| Title | Journal of Statistical Software |
| Abbreviation | JSS |
| Discipline | Statistics |
| Editor | -- |
| Publisher | -- |
| Country | -- |
| History | 1996–present |
| Frequency | Quarterly |
| Issn | -- |
Journal of Statistical Software is a peer-reviewed open-access periodical focusing on the development, implementation, and dissemination of software for statistical analysis. It publishes articles that document software packages, describe algorithms, and present reproducible computational studies, attracting contributors and readers from the communities behind R (programming language), Python (programming language), Julia (programming language), SAS, Stata, and MATLAB. The journal serves as a bridge among projects such as CRAN, Bioconductor, NumPy, SciPy, and Pandas (software), and audiences associated with institutions like University of California, Berkeley, Stanford University, Massachusetts Institute of Technology, Harvard University, and University of Oxford.
History
The journal was founded in the mid-1990s during a period of rapid expansion in computational resources exemplified by initiatives at Bell Labs, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, European Organization for Nuclear Research, and CERN. Early volumes documented software developments connected to projects at AT&T Labs, Bellcore, IBM Research, Microsoft Research, and Apple Inc. and reflected methodological advances linked to work from Johns Hopkins University, Columbia University, University of Washington, University of Pennsylvania, and Princeton University. Over time, contributions came from researchers associated with funding agencies and programs like the National Science Foundation, European Research Council, Wellcome Trust, National Institutes of Health, and DARPA. The journal’s trajectory parallels milestones in reproducible research discussed at conferences such as the Joint Statistical Meetings, UseR!, Conference on Neural Information Processing Systems, International Conference on Machine Learning, and International Conference on Data Mining.
Scope and Content
Articles span software descriptions, methodological papers demonstrating implementations, benchmark studies, and vignettes tied to packages hosted on repositories like GitHub, Bitbucket, GitLab, CRAN, and Bioconductor. Typical topics intersect with work from labs and groups such as Tidyverse, RStudio, scikit-learn, TensorFlow, PyTorch, and Keras (software), and reference algorithms originating in research by authors from Carnegie Mellon University, ETH Zurich, University of Cambridge, Imperial College London, and National University of Singapore. Manuscripts frequently link to datasets curated by organizations like UCI Machine Learning Repository, Kaggle, OpenML, Data.gov, and European Data Portal. The journal publishes practical tutorials and case studies that echo pedagogical efforts at Coursera, edX, Udacity, MIT OpenCourseWare, and HarvardX.
Editorial Board and Peer Review
The editorial structure comprises an editor-in-chief supported by associate editors, statistical reviewers, and software reviewers drawn from departments at Yale University, University of Chicago, University of Toronto, McGill University, and Australian National University. Peer review practices align with standards promoted by organizations such as the Committee on Publication Ethics, International Committee of Medical Journal Editors, CrossRef, and ORCID integration efforts. Reviewers often include contributors known for work at Google Research, Facebook AI Research, DeepMind, Amazon Web Services, and IBM Watson, and editors recruit expertise from specialty groups like the International Statistical Institute, Royal Statistical Society, Institute of Mathematical Statistics, and American Statistical Association.
Publication and Access Model
The journal operates an open-access model facilitating free distribution of articles consistent with initiatives by SPARC, Directory of Open Access Journals, Public Library of Science, and mandates from funders such as the Wellcome Trust and Horizon 2020. Authors commonly deposit code and supplementary materials in archives such as Zenodo, Figshare, Dryad (repository), and Code Ocean, and link persistent identifiers through DOI registration agencies. The platform for manuscript submission and editorial management leverages systems akin to Open Journal Systems and integrates metadata standards used by CrossRef, DataCite, and ORCID.
Abstracting and Indexing
The journal is indexed in bibliographic services comparable to Scopus, Web of Science, MathSciNet, Zentralblatt MATH, and INSPEC. Abstracting and indexing enhance discoverability alongside aggregators and libraries such as PubMed Central, Google Scholar, JSTOR, EBSCOhost, and ProQuest. Citation tracking and altmetrics connect content to profiles maintained on ResearchGate, Academia.edu, ORCID, and institutional repositories hosted by universities like University of Michigan, Cornell University, University of California, Los Angeles, and University of Texas at Austin.
Impact and Reception
The journal has influenced software dissemination practices in communities surrounding R (programming language), Bioconductor, scikit-learn, Stan (software), and WinBUGS, informing adoption by practitioners at companies such as Google, Microsoft, Amazon, Facebook, and IBM. Scholarly impact is reflected in citations from articles published in outlets including Journal of the American Statistical Association, Annals of Statistics, Biometrika, Nature Methods, and IEEE Transactions on Pattern Analysis and Machine Intelligence. The publication is referenced in policy and methodology documents from bodies like the National Academies of Sciences, Engineering, and Medicine, European Commission, World Health Organization, and Organisation for Economic Co-operation and Development for its role in promoting reproducible computational research.
Category:Statistical journals