tl;dr: So far, I can’t see any principal difference between our three kinds of intellectual output: software, data and texts. I admit I’m somewhat surprised that there appears to be a need to write this post in 2014. After all, this is not really the dawn of the digital age any more.
Reproducible research involves the careful, annotated preservation of data, analysis code, and associated files, such that statistical procedures, output, and published results can be directly and fully replicated. As the push for reproducible research has grown, the R community has responded with an increasingly large set of tools for engaging in reproducible research practices (see, for example, the ReproducibleResearch Task View on CRAN).
There is a great post by Jeni Tennison on the Open Data Institute blog entitled Five Stages of Data Grief. It resonates so much with my experience working with biodiversity data (such as building BioNames, or exploring data errors in GBIF) that I've decide to reproduce it here.
{.alignnone .size-full .wp-image-1393 loading=“lazy” decoding=“async” attachment-id=“1393” permalink=“https://jabberwocky.weecology.org/2014/02/13/ecodata-retriever-quickly-download-and-cleanup-ecological-data-so-you-can-get-back-to-doing-science/retriever_logo_wtext/” orig-file=“https://i0.wp.com/jabberwocky.weecology.org/wp-content/uploads/2014/02/retriever_logo_wtext.png?fit=515%2C234&ssl=1” orig-size=“515,234” comments-opened=“1”
A common way to model the relationships of data, information, knowledge, and wisdom is the DIKW pyramid. Starting with its base, each concept builds on top of the former. The gist is that we require data to form information † we use information to gain knowledge † and we use knowledge toward developing wisdom. This model is one doorway to understanding the relationship between these concepts.
Bryan Drew and colleagues have published a piece in PLoS Biology bemoaning the lack of databased phylogenies: This is an old problem (see for example "Towards a Taxonomically Intelligent Phylogenetic Database" doi:10.1038/npre.2007.1028.1), but alas the solution proposed by Drew et al. is also old: In my opinion, as soon as you start demanding people do something you've lost the argument, and you're relying on power ("you don't get to publish
A friend of mine once joked that doing ecological informatics meant working with data that was big enough that you couldn’t open it in an Excel spreadsheet.
This is a guest post by Elita Baldridge (@elitabaldridge), a graduate student in Ethan White’s lab in the Ecology Center at Utah State University. As a budding macroecologist, I have thought a lot about what skills I need to acquire during my Ph.D. This is my model of the four basic attributes for a macroecologist, although I think it is more generally applicable to many ecologists as well: Data
We had a great time at ESA this year and enjoyed getting to interact with lots of both old and new friends and colleagues. Since we’re pretty into open science here at Weecology, it’s probably not surprising that we have a lot of slides (and even scripts) from our many and varied talks and posters posted online, and we thought it might be helpful to aggregate them all in one place. Enjoy.
Slides and script from Ethan White’s Ignite talk on Big Data in Ecology from Sandra Chung and Jacquelyn Gill’s excellent ESA 2013 session on Sharing Makes Science Better.