About

Join The UW eScience Institute, UW Institute for Neuroengineering, iSchool DataLab, and CSE Interactive Data LabThis for their next Data Science Seminar with John Darrell Van Horn, M.Eng., Ph.D., associate professor of neurology, University of Southern California.

The seminar will be on Wednesday, Apr. 11, from 3:30 to 4:20 p.m., in the Physics/Astronomy Auditorium, room A102.

This seminar will be co-sponsored by the UW Institute for Neuroengineering.

View the flyer and RSVP here.

Abstract:

In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions. These methods are having significant impacts in diverse domains ranging from genomics, to health informatics, to environmental research, and beyond. The NIH’s Big Data to Knowledge (BD2K) Training Consortium, in particular, has worked to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem, giving them the ability to explore, prepare, analyze, visualize, and interpret Big Data.

To this end, the BD2K Training Coordinating Center (TCC) was funded to facilitate in-person and online learning, and to open the concepts of data science to the widest possible audience. In this presentation, I will describe the activities of the BD2K TCC, particularly the construction of the Educational Resource Discovery Index (ERuDIte). ERuDIte identifies, collects, describes, and organizes over 10,000 data science training resources, including: online data science materials from BD2K awardees; open online courses; and videos from scientific lectures and tutorials. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required.

In effect, data science is being used to inform training in data science where the so-called FAIR principles apply equally to these resources as well as to the datatypes and methods they describe. As a result, the work of the TCC has aimed to democratize novel insights and discoveries brought forth via large-scale data science training. This presentation will be of interest to anyone seeking to personalize their own data science education, craft unique online training curricula, and/or share their own online training content.

The NIH Director’s Early Independence Award supports exceptional investigators who wish to pursue independent research essentially directly after completion of their terminal doctoral/research degree or end of post-graduate clinical training, thereby forgoing the traditional post-doctoral training period and accelerating their entry into an independent research career. Applications are welcome from individuals of diverse backgrounds and perspectives and in any topic of relevance to the broad mission of NIH. The NIH Director’s Early Independence Award is a component of the High-Risk, High-Reward Research program of the NIH Common Fund.

The NIH Director’s Early Independence Award provides an opportunity for exceptional junior scientists to accelerate their entry into an independent research career by forgoing the traditional post-doctoral training period. Though most newly graduated doctoral-level researchers would benefit from post-doctoral training, a small number of outstanding junior investigators would benefit from skipping such training and launching essentially directly into an independent research career. For those select junior investigators who already have established a record of scientific innovation and research productivity and who have demonstrated unusual scientific vision and maturity, typical post-doctoral training would unnecessarily delay their entry into independent research. Also, importantly, the NIH Director’s Early Independence Award provides an opportunity for institutions to invigorate their research programs by bringing in the fresh scientific perspectives of the awardees that they host.

Time window for eligibility: Given the focus on early research independence, the receipt date of the terminal doctoral degree or end of post-graduate clinical training of the PD/PI must be between June 1, 2017 and September 30, 2019. The degree receipt date is that which appears on the official transcript for the degree. The end of post-graduate clinical training includes residency and fellowship periods. At the time of application, the PD/PI must not have served as a post-doctoral fellow following a previous (not the most recent) doctoral degree for more than twelve months.

At the time of award, either 1) the Early Independence investigator must have received a PhD, MD, DO, DC, DDS, DVM, OD, DPM, ScD, EngD, Dr PH, DNSc, ND (Doctor of Naturopathy), PharmD, DSW, PsyD, or equivalent doctoral degree from an accredited domestic or foreign institution (it is the responsibility of the sponsoring institution to determine if a foreign doctoral degree is equivalent), or 2) an authorized official of the degree-granting or training institution must certify that all degree requirements have been met and that the receipt date of the degree (as will appear on the transcript) will be before September 30, 2019; in addition, an authorized official of the host institution must certify that the PD/PI will be eligible to conduct independent research at the institution at the time of the project start date.

The Duke Network Analysis Center (DNAC) and the Duke Population Research Institute, with support from the  NICHD, will be hosting a third, week-long Social Networks and Health workshop from May 14 – 18, 2018.  Registration is now open at https://events.duke.edu/socialnetworks18.  Registration costs $150 for the week; please register by April 15, 2018.

The Social Networks and Health workshop will cover topics in social network analysis related to studying health behaviors, including:

• Data collection
• Ego network analysis
• Diffusion and peer influence
• Communities in networks
• Respondent-driven sampling
• Network visualizations
• Statistical Models for networks (ERGM, AMEN, SOAM)
• Agent-based modeling

The workshop will also contain a substantial lab component, which will give attendees an opportunity to learn how to use the R statistical computing language to analyze networks.  Last year’s presentations and labs are available online at https://dnac.ssri.duke.edu/social-networks-health-scholars-training-program.php.

The University of Washington’s eScience Institute is hosting a GeoHackweek, Sept 10 – 14, 2018. Join us for five days of tutorials, data exploration, software development and community networking, focused on open source tools to analyze and visualize geospatial data. Our event will include instructors from academia and industry across many different geospatial disciplines.

Please visit our website for details on how to apply.

Below you will find our listing of workshops for the quarter and some new data sources. We may add a few more courses later in the quarter depending on requests.We are developing a few new workshops given we have a set of new consultants this year with new programming talents but we will still continue to offer our old standby courses as well.

As always, registration is open and free to anyone in the UW community. Please let your colleagues, staff, and students know by sharing this newsletter. Individuals can subscribe to the newsletter here, and our newsletter archive is found here.

 

Short Course/Workshop Offerings Spring 2018 Quarter (listed in order of scheduled appearance)

Introduction to R using Rstudio

This class will teach you how to get started with R using the free integrated development environment called Rstudio. The course will cover the basic organization of R and RStudio, where to find good help references, and how to begin a basic analysis. This class is ideal for users who have little or no experience with R.

Instructor: Yunkang Yang, CSSCR Consultant
Date: Monday, 2 April 2018
Time: 10:30am – 11:30am
Place: Savery 117
Register here.

Introduction to GIS

This course will provide students with a broad overview of what geographic information systems (GISs) are and how social scientists can benefit from using them in their research. Students will explore basic GIS concepts through hands-on exercises using ArcGIS, a widely used GIS software package, as well as freely available data sets.

Instructor: Will Brown, CSSCR Consultant
Date: Thursday, 5 April 2018
Time: 9:30am – 10:30am
Place: Savery 121
Register here.

Introduction to SPSS

This courses introduces the SPSS statistical package including reading in datafiles as well as basic data management and introductory statistical procedures. Additional topics include computing and recoding variables and selecting and filtering cases.

Instructor: Aya Masilela, CSSCR Consultant
Date: Friday 6 April 2018
Time: 11:30am – 12:30pm
Place: Savery 117
Register here.

Introduction to R using Rstudio

This class will teach you how to get started with R using the free integrated development environment called Rstudio. The course will cover the basic organization of R and RStudio, where to find good help references, and how to begin a basic analysis. This class is ideal for users who have little or no experience with R.

Instructor: Jasmine Jiang, CSSCR Consultant
Date: Monday, 9 April 2018
Time: 3:30pm – 4:30pm
Place: Savery 121
Register here.

Data Wrangling in R

This course will cover some of R’s useful tools for data management and exploration. Most of class will be devoted to learning Hadley Wickham’s excellent “tidyr” and “dplyr” packages. Attendees are assumed to have basic familiarity with R.

Instructor: Stephanie Lee, CSSCR Consultant
Date: Thursday, 12 April 2018
Time: 9:30am – 10:30am
Place: Savery 117
Register here.

Introduction to SPSS

Description:

This courses introduces the SPSS statistical package including reading in datafiles as well as basic data management and introductory statistical procedures. Additional topics include computing and recoding variables and selecting and filtering cases.

Instructor: Galen Kerrick, CSSCR Consultant
Date: Thursday, 19 April 2018
Time: 1:30pm – 2:30pm
Place: Savery 121
Register here.

Introduction to Qualitative Research and ATLAS.ti

Description:

This course provides a brief, practical introduction to working in ATLAS.ti, covering basic terminology and functionality of the program. This will include importing text documents, coding and annotating documents, and exploring relationships through analysis and query tools. Time permitting, we may also briefly discuss best practices for data management. The course assumes no prior use of Atlas-ti.

Instructor: Will Brown, CSSCR Consultant
Date: Monday, 23 April 2018
Time: 2:30pm – 3:30pm
Place: Savery 121
Register here.

Introduction to STATA

Description:

This course will introduce you to the basic Stata statistical package including reading in STATA datasets, basic data manipulation in Stata, and common statistical procedures.

Instructor: Stephanie Lee, CSSCR Consultant
Date: Tuesday, 24 April 2018
Time: 9:30am – 10:30am
Place: Savery 117
Register here.

Using R for Analyses of Time Series Data

Description:

This class assumes some familiarity with R (at least some basics from prior CSSCR workshops) and will tackle problems of time series data set-up and some basic statistical procedures for analyzing data in this format.

Instructor: Jasmine Jiang, CSSCR Consultant
Date: Monday, 14 May 2018
Time: 3:30pm – 4:30pm
Place: Savery 121
Register here.

Making Maps with Tableau

This courses explores the use of Tableau’s mapping features. The course requires no prior experience though prior exposure to GIS is helpful.

Instructor: Aya Masilela, CSSCR Consultant
Date: Tuesday, 15 May 2018
Time: 9:30am – 10:30am
Place: Savery 117
Register here.

Intermediate SPSS

This courses builds from CSSCR’s introductory SPSS classes and includes additional data management topics as well as more statistical procedures.

Instructor: Gabby Gorsky, CSSCR Consultant
Date: Wednesday, 16 May 2018
Time: 2:00 pm to 3:00 pm
Place: Savery 117
Register here.

To register for the above classes, follow this link.