1,721,018 research outputs found
Data Scavenger Hunts: Learning About Data Together
No full textData exploration and visualization are a highly accessible gateway activity to learning data science. In this talk, we discuss our experience with "Data Scavenger Hunts" using web apps to democratize data science and make it accessible to a wide variety of audiences. In order to acheive this, we have developed an R package called `burro` that can enable public datasets to be explored together via a sharable web app. In this talk, we talk about our experience with using data scavenger hunts to teach each other interesting things about data. In particular, we share our experiences with exploring the NHANES (National Health Nutirition Examination Survey) data and the insights we have taught each other. We show that this guided and communal data exploration leads to increased confidence and curiosity about data science in Biodata-Club, our learning community. `burro` apps can be deployed by anyone to start conversations about data
laderast/clusteringLecture: Initial Release
No full textInteractive Slides for Explaining Aspects of Clustering Algorithm
laderast/flowDashboard: 0.1.0 (initial release)
No full text<p>This is the initial release of flowDashboard.</p>
laderast/flowDashboard: Official Release
No full text<p>This is the official pre-release for the <code>flowDashboard</code> package. The code is functional and utilizes data objects to auto-build the dashboards.</p>
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
surrogateMutation: v1.0
No full text<p>Current version of Surrogate Mutation finding R package.</p>
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