All posts (Page 129 of 130)
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.rstudio.com/ropensci/usgs_app/. We were directed to use one or more of their web services, including mashing up with other web services. Of the USGS web services, we only used ITIS, but included 4 web services from other providers.
...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. Thus, we are sharing use cases on our blog.
...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. Many providers use OAI-PMH, including DataCite, Dryad, and PubMed.
...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. If any researchers were interesting is using the suite of tools we’ve built so far, we offered to help them through the technical challenges they might encounter. We’re excited to report that we’ll be working closely with Simon Queenborough and Julien Colomb on this effort. Below are brief summaries of their work. Stay tuned for updates....
We have been writing code for R packages for a couple years, so it is time to take a look back at the data. What data you ask? The commits data from GitHub ~ data that records who did what and when.
Using the Github commits API we can gather data on who commited code to a Github repository, and when they did it. Then we can visualize this hitorical record.
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