Here is a list of programming projects and brief summaries that may be relevant to future research endeavors. Each is linked to the full write-ups:

  • For an ASN blog post, I used Python to semi-automate an analysis of NHANES data for the percent of the population below Dietary Reference Intakes for 21 micronutrients.
  • Analyzed NHANES data to estimate the percent of the population meeting dietary guidelines.
  • An analysis of Prop37 (GMO) activism on twitter: Collected about 250,000 tweets over the election period by writing a Python program and explored about 55,000 in R. Plotted tweet frequency, locations, descriptives, top people tweeting, top tweets, top links, word associations, sentiment analysis. Concluded that people tweeting about genetically modified food put out poor information and link to poor sources.
  • Analysis of the annual Academy of Nutrition and Dietetics’ Food & Nutrition Conference & Expo: Collected about 14,500 tweets and analyzed in R. Plotted top people tweeting, descriptives, top tweets, plotted locations, plotted tweet frequency, most frequent terms, word associations, and sentiment analysis.
  • Analysis of FNCE 2013, compared to 2012.
  • Analysis of Experimental Biology 2013 tweets: Collected about 5,500 tweets with the #EB2013 tag and performed similar analyses as above. Also wrote code to extract photos from the conference and upload them to a tumblr here.
  • Analysis of Experimental Biology 2014 tweets, compared to 2013. Featured in ASBMB Today.
  • Analyzed 1,402 nutrition Experimental Biology poster abstracts and made some visuals (Python, R, Gephi).
  • 2012: A year of nutrition according to twitter: Downloaded a year of tweets from trusted sources, plotted frequency, and ran analysis of top links.
  • 2013: What to eat according to twitter: wrote Python scripts to scrape USDA database and create a list of foods, then did word matching against over 400,000 tweets to see what foods are talked about most. Also ran top foods through nutrient analysis and created weighted average food for each group of people (also using Python).
  • 2014: Mining Twitter for Popular Nutrition & Cancer Myths. Analysis and write-up for the AICR: collected over 420,000 tweets with “cancer” over a couple weeks and analyzed top sources and word associations.
  • A twitter bot to monitor for scholarly article links and check for pull papers [now disabled]: I wrote a bot that scans for links, checks if it is a link to pubmed or a non-pubmed journal link, searches Google Scholar to see if the full text is freely available, and if it is tweets back a link.  It can also accept queries, and it searches Google Scholar and returns the first result if there is a full text. I also added the ability to connect to Mendeley (a reference manager) and push abstracts and PDFs to the user’s library through twitter.
  • Nutrition Research Trends: Wrote javascript in Google Apps Script that tracks a number of nutrition-related paper counts by querying pubmed for paper counts. I plan to write more complex methods to explore nutrition trends, including things like journal impact factors and alternative metrics, term frequencies and associations, and more. As an example, I wrote a very rudimentary app using‘s API on Google App Engine here that pulls scores of each paper from the latest issue of AJCN. I am very interested in trying to quantify trends and identify changing research areas and/or popular papers.
    • Reproduced for the American Institute for Cancer Research for cancer trends here (2013) and here (2014).
  • Lazy Scholar: a Google Chrome extension (javascript/jquery, html) to make checking for an indexed full text of papers through Google Scholar much easier (and so much more now- see
  • Wrote a Python program to download and count the words and references in all of my articles on this website, which I plan to further develop to analyze nutritional science-blogging trends. (As of 2012): Since 2009, my total article count is 160, averaging 859 words per article covering more than 150 references. Nearly all of these posts are strictly discussing research papers. This is a demonstration of my passion for nutrition research and writing practice.

As a testament to credibility, here are some notable achievements that science blogging has accomplished for me:

  • Blog post cited in the peer-reviewed paper “Members’ Attitudes Toward Corporate Sponsorship of the Academy of Nutrition and Dietetics” by Reitshamer, Schrier, Herbold, and Metallinos-Katsaras (2012). doi: 10.1080/19320248.2012.704748
  • Contributed data for peer-reviewed paper “Research Blogs and the Discussion of Scholarly Information” by Shema, Bar-Ilan, and Thelwall (2012). doi: 10.1371/journal.pone.0035869
  • Invited guest article for the American Institute for Cancer Research (link)
  • American Society for Nutrition student blogger, 2013-2014.
  • ASN Experimental Biology 2014 meeting blogger.
  • Several posts published at, a group blog on agricultural science.
  • One post co-written with Travis Saunders at debunking an absurd article about a  “Chocolate Milk Diet” (link)
  • Post at Food Product Design (link)
  • Guest post at Weighty Matters (link)

Here is a sampling of some of my personal favorite posts.