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.

If you’ve every worked with scientific data, your own or someone elses, you know that you can end up spending a lot of time just cleaning up the data and getting it in a state that makes it ready for analysis. This involves everything from cleaning up non-standard nulls values to completely restructuring the data so that tools like R, Python, and database management systems (e.g., MS Access, PostgreSQL) know how to work with them.

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 Statistics Math

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. Slides are also archived on figshare. 1.  I’m here to talk to you about the use of big data in ecology and to help motivate a lot of the great tools and approaches that other folks will talk about later in the session. 2.

The recently released development draft for SHared Access Research Ecosystem (SHARE), authored by the Association of American Universities (AAU), the Association of Public and Land-grant Universities (APLU) and the Association of Research Libraries (ARL) in response to the OSTP memo on public access to federally funded research in the US sounds a lot like the library-based publishing system I’ve been perpetually arguing for.