And that's it! To keep up to date with the latest developments, follow @eshackathon & @srtoolbox. Sorry if I've not acknowledged any of the creators, I tried to find everyone's profiles. Let me know what other tools are making your evidence synthesis research easier!

The {forester} #rstats package by @randyboyes makes forest plots look so good! Make a publication-quality forest plot with as much or as little information displayed on either side as you like.

One-stop shop for meta-analyses?? The {meta} #rstats package by the authors of 'Meta-Analysis with R' is all you need to analyse & visualise your meta-analytical data. The forest plot below is what comes straight out of the box, no customisation needed!

The {robvis} #rstats package (short for "risk of bias visualisation") & web app (mcguinlu.shinyapps.io/robvis…) by @mcguinlu takes the summary table from risk-of-bias assessments and produces customisable plots formatted according to the assessment tool used.

The {PRISMA2020} #rstats package & web app (estech.shinyapps.io/prisma_f…) by @nealhaddaway produces a high-quality flow diagram that conforms to the latest PRISMA standards. Data can be manually entered into the function, or imported using the template CSV file.

The {litsearchr} #rstats package by @ElizaGrames allows quick, objective, reproducible search strategy development using text-mining and keyword co-occurrence networks to identify important terms to include in a search strategy. Reduce time & bias in keyword selection!

Often with this type of research, you'll need to reverse engineer images of figures to extract the underlying data. WebPlotDigitizer (automeris.io/WebPlotDigitize…) is a semi-automated tool that makes this process extremely easy.