We are pretty excited about what modern technology can do for science and in particular the potential for increasingly rapid sharing of, and collaboration on, data and ideas. It’s the big picture that explains why we like to blog, tweet, publish data and code, and we’ve benefited greatly from others who do the same. So, when we saw this great talk by Michael Nielsen about Open Science, we just had to share.

In a recent blog post, Heather Piwowar, in discussing the advantages of citing datasets in the reference list of the article, said “No journals have standardized on this approach so far”. However, Pensoft Journals, a publisher that specializes in publishing biodiversity and biological systematics papers, and that has taken the lead in promoting the publication of datasets with DOIs, has exactly such a policy.

The meaning of the word “dataset” is ambiguous, changing with context.

The DataCite Metadata Kernel version 2.0 [1] specifies the minimal metadata, and optional metadata, that should accompany a DataCite DOI for the identification of a published data entity. Within the Metadata Kernel document there is an XML mapping of these metadata terms, using DCMI Metadata Terms, and an example encoded in XML.

In addition to using CiTO and CiTO4Data to describe relationships of relevance to data entities, as discussed in the previous blog post, FaBiO, the FRBR aligned Bibliographic Ontology described elsewhere, another member of the suite of SPAR (Semantic Publishing and Referencing) Ontologies, also has a number of classes and properties specifically designed for addressing data, software, metadata and other non-bibliographic entities.

There is an excellent post on open science, prestige economies, and the social web over at Marciovm’s posterous*. For those of you who aren’t insanely nerdy** GitHub is… well… let’s just call it a very impressive collaborative tool for developing and sharing software***. But don’t worry, you don’t need to spend your days tied to a computer or have any interest in writing your own software to enjoy gems like: Thanks to Carl Boettiger

An increasingly large number of folks doing research in ecology and other biological disciplines spend a substantial portion of their time writing computer programs to analyze data and simulate the outcomes of biological models. However, most ecologists have little formal training in software development¹. A recent survey suggests that we are not only;

Interest in archiving data and data publication is growing, as evidenced by projects such as Dryad, and earlier tools such as TreeBASE. But I can't help wondering whether this is a little misguided. I think the issues are granularity and reuse. Taking the second issue first, how much re-use do data sets get? I suspect the answer is "not much". I think there are two clear use cases, repeatability of a study, and benchmarks.

The Plant List (http://www.theplantlist.org/) has been released today, complete with glowing press releases. The list includes some 1,040,426 names. I eagerly looked for the Download button, but none is to be found. You can grab download individual search results (say, at family level), but not the whole data set.

The Ecological Database Toolkit Large amounts of ecological and environmental data are becoming increasingly available due to initiatives sponsoring the collection of large-scale data and efforts to increase the publication of already collected datasets. As a result, ecology is entering an era where progress will be increasingly limited by the speed at which we can organize and analyze data.