What the course is

A practical, end-to-end course that turns a finished (or near-finished) research/data science project into a submission-ready manuscript + reproducibility package (code, data, environment) tailored to a target journal or conference.

Who it’s for

Graduate students, researchers, analysts, and data scientists who have results to publish (empirical study, predictive model, software/methods paper, dataset paper, systematic review, or evaluation).

Learning outcomes (you can assess these)

By the end, participants can:

• Map a project to the IMRaD structure (or alt formats for methods/software/dataset papers).

• Select an appropriate journal/venue and align scope, readership, word limits, and policies.

• Write clear Methods with enough detail for replication (data, code, parameters, hardware, seeds).

• Report statistics and model results correctly (effect sizes, CIs, uncertainty, validation, error analysis).

• Create publication-quality figures/tables and captions that stand alone.

• Package reproducible assets (Git repo, environment lockfile, README, codebook, data dictionary).

• Apply reporting guidelines (e.g., TRIPOD for prediction models; PRISMA for reviews; STROBE for observational; CONSORT for trials; FAIR data).