It seems like only yesterday that we started our internship. As our mentor (Megan, who interned last year) told us, 9 weeks goes by fast! She wasn’t kidding! Our final week helped us to wrap up some project elements while firming up other parts for future collaborations. Some compelling patterns Continue reading Week 9: Continued project developments and internship wrap-up
In the final week of our internship we’ll make separate blog posts and use our posts to both provide updates about our project and also reflect on the experience of our two month DataONE internship. In the final week of our project, we made additions and edits to the data Continue reading Week 9: Best practices and reflection
In our 8th week, we worked with project mentors to refine our systematic review database and analysis. The main question for our internship was whether a selection of data-aggregation studies could be repeated through repositories available in DataONE. During the preceding weeks we extracted dozens of pieces of data from Continue reading Week 8: Analysis, figures and data citation best practices
Welcome to the seventh installment of Project 2: Supporting Synthesis Science! First, I neglected to include in last week’s blog one of the nice plots Rob made to display the characteristics of our set of data-aggregation studies. This one is a map showing how the number of papers breaks down Continue reading Week 7: Modeling data-aggregation input data source and output information
In our sixth week of the synthesizing science project, we jumped into the analysis of data we’d extracted, corrected, and re-tooled over previous weeks. This process was aided by establishing a github repository for data and R scripts and by employing the very useful “googlesheets” R package (by Jenny Bryan) Continue reading Week 6: analysis of science synthesis data
In week 5, Rob and I shifted gears from data extraction to data analysis. Our final database has a total of 80 articles, 40 identified from the list of NCEAS-authored publications and 40 identified from our Web of Science search. In total, we extracted over 500 rows of data (i.e. Continue reading Week 5: Finalizing data extraction, writing, and analysis
Following our refinements to our database of data sources and the lessons of last week, we dove further into the pool of data-synthesis articles we identified previously from NCEAS and Web of Science. Data extraction is (probably) the part of a systematic review that takes the most effort. It is Continue reading Week 4 – Data extraction
Continuing with our collaboration and joint post from last week, our main goal for week #3 was the extraction of data source information from papers employing data syntheses. We anticipated the need to refine our database (e.g., fields and categories) along the way. In reviewing the abstracts of papers identified Continue reading Week 3 – Database refinement and data extraction
The majority of this week’s work was very collaborative, so today’s blog post is also a collaboration. Our main goals for Week #2 of our internship revolved around fine tuning our database for the systematic review. Last week, Rob’s blog post highlighted one way to reduce bias in a systematic Continue reading Week 2 – Revising database, outline methods, begin data extraction
Greetings, my name is Giancarlo Sadoti and I’m working collaboratively with Rob Crystal-Ornelas on Project #2: Supporting Synthesis Science with DataONE. Week 1 was a helpful and illuminating launch into the world of data syntheses literature! While I was familiar with Web of Science (WoS) for systematic searches of the literature, Continue reading Week 1: Search strategies for science syntheses