dead data tell no tales

@cjlortie

you will have to reuse your data

planning promotes reproducibility

https://dmptool.org to begin your game/journey

try a data management planning tool

Michener & Jones 2012

there is no perfect experiment

Ruxton & Colegrave 2016

there are no perfect data

data vary in class and structure

QA/QC

Cai & Zhu 2015

no one set of criterianeed fit all ecological data

but practical principlescan be used as a guide

Pipino et al. 2002

QA/QC

a practical guide to QA/QC for ecological data

increasingly adopt #rstats & #tidyverse workflows

Tip #1. Pilot data & meta-data

build tidy data & do data by design

rnorm(n = 10, mean = 39.74, sd = 25.09)

Tip #2. Use social coding for QA/QC

(at least) two-player mode

Tip #3. Check #rstats for data tools

there is a package for that (at least two)i.e. like ‘cheat codes’ to get you there sooner

Maia et al. 2013

pavo

biogeo for occurrence data

Robertson et al. 2016

codyn for community dynamic metrics with taxize to check names

codyn::check_multispp(), check_names(), check_sppvar()taxize::gnr_resolve()

use R Markdown + GitHub for versioned reviews & data cleaning

Tip #4. Version & annotate your data cleaning

Tip #5. Check classes of vectors/variables

str(), unique(), nrow(), tibble()

Tip #6. Decide what is a true zero

Martin et al. 2005

is.na(), data[!is.na(data$x), ]

Tip #7. Pre-print your data

publish sooner

the reproducibility crisis in science needs to end. today.

avoid a ‘game-over’ effect before the reuse even begins.

better data. better reproducibility.

nom nom