This is a guest post by Class-Thido Pfaff We here present the BEFdata R package as part of the rOpenSci project. It is an API package that combines the strengths of the BEFdata portal in handling small, complex datasets with the powerful statics package R. The portal itself is free software as well and can be found here. The BEFdata platforms support interdisciplinary data sharing and harmonisation of distributed research projects collaborating with each other.

Many US federal agencies are now running app competitions to highlight their web services (see here), and hopefully get people to build cool stuff using government data (see Data.gov for more). See here for a nice list of the US government’s web services. One of these agencies was the United States Geological Survey (USGS). They opened up an app competition and [we won best overall app! Check out our app called TaxaViewer here: http://glimmer.

Real use cases from people using our software are awesome. They are important for many reasons: 1) They make the code more useable because we may change code to make the interace and output easier to understand; 2) They may highlight bugs in our code; and 3) They show us what functions users care the most about (if we can assume number of questions equates to use). If someone has a question, others are likely to have the same, or a similar question.

Scholarly metadata - the meta-information surrounding articles - can be super useful. Although metadata does not contain the full content of articles, it contains a lot of useful information, including title, authors, abstract, URL to the article, etc. One of the largest sources of metadata is provided via the Open Archives Initiative Protocol for Metadata Harvesting or OAI-PMH. Many publishers, provide their metadata through their own endpoint, and implement the standard OAI-PMH methods: GetRecord, Identify, ListIdentifiers, ListMetadataFormats, ListRecords, and ListSets.

At rOpenSci we’re very passionate about engaging with our community and getting more people on board with open science and open data. There are many challenges to be overcome before this practice becomes mainstream. Even when researchers see the value in engaging more openly, the learning curve associated with various aspects of the workflow can seem daunting. To identify some of these challenges and barriers, we launched an open science challenge at the start of the year.