|Workshop and location||Website||Students who have attended (and year)||Brief review of the course (< 100 words)||How many stars would you give this course (0 to 5 stars; 5 stars is the best)?|
|Stable Isotope Mixing Model Training Workshop, PR Statistics, Orford, Canada||https://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm04/||Brandon Gerig (2017)|
|Xenopus Bioinformatics Workshop, Woods Hole Marine Biological Lab, Woods Hole, MA||https://www.xenbase.org/entry/doNewsRead.do?id=573||Kyle Dubiak; Elizabeth Peuchen (2017)|
|Strategies and Techniques for analyzing Microbial Population Structures (STAMPS) Workshop, Woods Hole Marine Biological Lab, Woods Hole, MA||http://www.mbl.edu/education/courses/stamps/||Mauna Dasari (2017)||STAMPS is an interdisciplinary course aimed at bringing together a diverse set of microbiome researchers and software developers. The instructors did their best to match the course pupils’ levels, often starting with very basic introductions to concepts before ramping up into their tutorials. I would recommend this workshop to future students who have at least some theoretical background in the statistics most often used in microbial ecology, as well as at least a novice background in coding/familiarity with using Unix or R. Additionally, having your own data will allow the student to take full advantage of the instructors and TAs, especially during the open lab hours.||4 stars|
|Meaningful Modeling of Epidemiological Data, African Institute of Mathematical Sciences, South Africa||https://aims.ac.za/2018/06/08/clinic-on-meaningful-modeling-of-epidemiological-data/||Rachel Oidtman (2017)||If you have an epidemiological background and are looking for more training on the math side of things, this is a great oppportunity for that. Even coming from a more quantitative background, there was still the oppportunity to learn more about epidemiology. The networking aspect of things was unmatched and we still reconvene for food/drinks at conferences.||5 stars|
|Summer Institute for Statistical Genetics, University of Washington||https://www.biostat.washington.edu/suminst/sisg||Sage Davis (2017)||This course offers anywhere from beginner (intro to R, intro to stats) to advanced (machine learning, networks analysis) classes, and thus is made for all levels of people. Each class is intense and fast paced due to the short druation (2.5 days each) and thus some of the stuff can be rushed. Some classes are much more hands on compared to others which are much more lecture based. To make the most of your time I would suggest some proficiency in R prior to attending the courses. The way the class selections work you can choose to skip the intro classes (which typically happen week 1) and choose to partake in the more advanced classes week 2 and week 3. This course is heavily based on human data, however, so expect some modules/portions of courses to be irrelevant if that is not your focus. Great networking opportunity as many professors are invited from other universities and are up and coming within their field, and the large number of participants brings students, postdocs, and employees of wide range of experience and specialities.||4 stars|
|Environmental Genomics, MDI Biological Laboratory, Bar Harbor, ME||https://mdibl.org/course/environmental-genomics-2018/||Chissa Rivaldi (2017)||This week-long workshop started by doing an illumina library prep for an RNA-seq sequencing run(Daphnia model). MDI had an arrangement worked out with Jackson labs so we got the results of our sequencing within ~2 days. The rest was then computational - shell and R mostly. This was a great setup because the data was real and imperfect (as opposed to other workshops which tend to use a curated dataset to avoid problems during analysis) - very useful to prepare for an actual research project. There was also a lecture every night which focused on the theory behind our methods and several synthesis sections where we got to discuss our research with the instructors and TAs.||5 stars|
|Pharmacometric Analyses in Clinical Trials using R, NIH, Bethesda, MD||https://faes.org/biotech84||Marwa Asem (2018)||The main objective of the workshop is teaching how to utilize Rstudio software in performing
biostatistics analysis for biological and clinical data.
The key points I learned in this workshop are:
• How to use Rstudio to do coding, execute orders and create blots and graphs.
• The workshop gave an introduction about pharmacokinetics and pharmacodynamics and
clinical trials design.
• How to use different modeling and biostatistics tests to analyze data obtained from
patients and biological samples.
• During the workshop we analyzed several datasets obtained from clinical trials and
presented these data in final blots and graphs.
Rstudio is a very important biostatistics analysis tool in STEM and I strongly recommend this
workshop for students who are interested in using this software in data analysis.
|Data Exploration, Regression, GLM & GAM with introduction to R by Highland Statistics, Madeira, Portugal||http://www.courseportal.highstat.com/index.php/data-exploration-regression-glm-gam||Chelsea Weibel (2018)||This course served as a great introduction to regressions, GLMs, and GAMs in R.The course was taught with one or two lectures per day, followed by several case studies that the participants worked through over the course of the week. I would recommend this course for other students who have a little bit of familiarity with R, but are hoping to expand their coding skills, as well as their statistical knowledge.||4 stars|
|Isotopes in Spatial Ecology and Biogeochemistry course, University of Utah||https://itce.utah.edu/spatial.html||Keith O'Connor (2018)||This two-week long course utilizes R and ArcGIS to investigate stable isotopes over a large spatial scale. Each day consisted of two to three 1.5 hour lectures by different experts, followed by a 5-hour lab where we learned mapping in R, extracting data in ArcGIS, watershed modeling, and more. At the end of each of the two weeks, our small lab groups would present projects that we worked on throughout the week to our peers and experts. I highly recommend this course if you have a strong stable isotope background and at least intermediate knowledge of R and/or ArcGIS.||5 stars|
|Microbial diversity course, Woods Hole Marine Biological Lab, Woods Hole, MA||http://www.mbl.edu/education/courses/microbial-diversity/||Brittni Bertolet (2018)||This course is an extensive, hands-on course in environmental microbiology. Traditionally, the course topics included basics in cultivation for diverse microorganisms, experimental evolution, and microbial genetics, but has now expanded to include compuational genomics with a focus on metagenomic and transcriptomic analyses. The course was 6.5 weeks long and included an independent research component. I would recommend thic course for advanced graduate students and early career scientists, who are interested in intensive research training in microbiology.|
|Transposable element annotation, Physalia Courses, Berlin, Germany||https://www.physalia-courses.org/courses-workshops/course24/||Meredith Doellman (2018)|
|New Advances in Land Carbon Cycle Modeling Training Course, Northern Arizona University||https://events.nau.edu/event/symposium-new-advances-in-land-carbon-cycle-modeling/||Ann Raiho (2018)||The New Advances in Land Carbon Modeling Training Course taught students how to implement matrix models for land carbon balance equations. This technique helps improve land carbon modeling diagnostics and development by simplifying the equations. This course was taught using lectures, exercises, and individual projects. This course would be useful for students seeking to interact with top of the field carbon modeling scientists and/or develop their own land carbon cycle model. Knowing a coding language and having a strong background in land carbon modeling would be very important to a student taking this course.||4 stars|
|This website from NIH has a list of workshops they offer|