As we begin a new year and celebrate the major milestone of more than 25,000 data packages published, it’s a great time to highlight the value for re-use of the scholarly resources that are openly available and licensed in Dryad.
So, which data packages published in 2018 have received the most downloads? Here are some at the top of the list.
Stafford et al (2018) Extreme diversity in the songs of Spitsbergen’s bowhead whales
Here’s a lovely example of “data” that can have uses well beyond research. We’d love to know what people might be doing with these audio files. Meditating to them? Incorporating them into musical compositions?
All about the data
It’s perhaps not surprising that Dryad data packages associated with Scientific Data get a lot of downloads, as they are a journal specifically for “descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data.” These three resources are proving especially popular:
- Bennett et al (2018) GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms
- Faraut et al (2018) Dataset of human medial temporal lobe single neuron activity during declarative memory encoding and recognition
- Kummu et al (2018) Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015
Avian functional traits
Storchová L, Hořák D (2018) Life-history characteristics of European birds
This is an example of a dataset compiled specifically for re-use. According to the authors, “Recently, functional aspects of avian diversity have been used frequently in comparative analyses as well as in community ecology studies; thus, open access to complete datasets of traits will be valuable.” To make the data as useful as possible, they included a broad spectrum of traits and provided the file in an accessible format: ASCII text, tab delimited, not compressed. Given the large number of downloads, it has indeed proven valuable!
Improving clinical research transparency
Kilicoglu et al (2018) Automatic recognition of self-acknowledged limitations in clinical research literature
Here’s another dataset created for the purpose of improving research — in this case, reporting of limitations in clinical studies. The machine-learning techniques tested here can be incorporated into the workflows of other projects, to support efforts in increasing transparency.
Huge thanks are due to researchers who take the time and effort to publish their data, to the journals who support them in doing so (including those highlighted above), and to the Dryad member organizations who make it all possible. Here’s to the next 25,000, and the millions of downloads they will produce!