Link Roundup November 2017

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In this series, members of the RDS team share links to research data related stories, resources, and news that caught their eye each month. Feel free to share your favorite stories with us on Twitter @UWMadRschSvcs!

Ann Engler

A new tool called Seek & Blastn is being developed to find errors in research involving nucleotide sequences.

Tracking university students’ library usage can help improve services, but it raises important privacy concerns. Read how some universities are using library data here.

Looking for tips on organizing data in spreadsheets? This article may help.

OCLC Research is working on a project to examine RDM at four research universities.


Cameron Cook

Digital Preservation Coalition released “a ‘Bit List’ of Digitally Endangered Species” categorizing the risk levels of different types of digital materials.

twarc, a command line tool for archiving Twitter JSON, now is able to output CSV files.

Interested in working with your data in R? Check out the Programming Historian lessons on “R Basics with Tabular Data” and “Data Wrangling and Management in R“.

Tool: CodingBat Python

Information adapted from CodingBat Python website.

What is CodingBat Python?

CodingBat Python is a website that offers Python coding problems you can work through for practice (it also offers Java problems). It was created by Nick Parlante, a computer science lecturer at Stanford. It’s geared towards beginners, although some knowledge of Python is required. The website notes that these problems are the sort you’d encounter in a first or second computer science course.


November 2017 Brown Bag: Morton Ann Gernsbacher

The Rebecca J. Holz Series in Research Data Management is a monthly lecture series hosted during the spring and fall academic semesters. Research Data Services invites speakers from a variety of disciplines to talk about their research or involvement with data.

On November 15, Morton Ann Gernsbacher, Vilas Research Professor and Sir Frederic Bartlett Professor at UW-Madison, gave her talk titled “Benefits of Open Data and Open Stimuli”. Her slides are embedded below. There is a growing trend among scientists to ensure their research is reproducible by increasing its transparency, and Professor Gernsbacher described four ways researchers can do this: preregistering the study, providing open materials, sharing open data, and supporting open access.


Job Opening: Data Science Facilitator at UW-Madison

Information from the position listing on the Jobs at UW site.

UW Madison’s new Data Sciences Hub (DS Hub) is seeking a Data Science Facilitator! See below for the position summary. To view more information about the position as well as requirements and qualifications, visit the listing on the Jobs at UW site.

Position summary: 

The Data Science Hub at the Wisconsin Institute for Discovery (WID) provides a focal point for programs dedicated to research and application of modern techniques to the management, storage, and analysis of complex data sets. The Data Science Hub (DS Hub) is seeking an individual to advance the research activities of faculty members, students, and staff in a broad range of scholarly disciplines that rely on data science methods. The Data Science Facilitator will consult with researchers on campus to recommend appropriate solutions to data science problems impeding their research. The successful candidate will gain a wide range of skills at this job and will have the opportunity to work with experts in a range of research areas and data-centric technologies through Data Science Hub partnerships.

Additional Information:

This position will work closely with personnel at the Data Science Hub and its on-campus partners, which include but are not limited to: the Advanced Computing Initiative, the Bioinformatics Resource Center, the Biometry program, the Center for High Throughput Computing, the Center for Predictive Computational Phenotyping, the Humanities Research Bridge, Research Data Services, the Social Sciences Computing Cooperative, and many others.

Documenting DH: Christina Koch

Written by Laura Schmidt

Documenting DH is a project from the Digital Humanities Research Network (DHRN). It consists of a series of audio interviews with various humanities scholars and students around the University of Wisconsin-Madison campus. Each interviewee is given a chance to talk about how they view data, work with data, manage data, or teach data to others. Most recently, we interviewed Christina Koch, the Research Computing Facilitator at the University of Wisconsin’s Center for High Throughput Computing. Mainly working with scientists, she works with scholars who work with large-scale computational projects. Her interview is now accessible on the DHRN website.

Link Roundup October 2017

In this series, members of the RDS team share links to research data related stories, resources, and news that caught their eye each month. Feel free to share your favorite stories with us on Twitter @UWMadRschSvcs!

Ann Engler

Giving careful consideration to the structure of your Excel spreadsheet can save you headaches later on.

Should data scientists have a formal code of conduct? What would it include, how would it be enforced, could it keep up with rapidly evolving technology? See the discussion here.

Here‘s an article about how LOD is helping art history research of Florentine painters.’s blog has an ongoing series called “Humans of Data“. It’s presented as an art piece and features the international data research community thoughts on what they do and why. It’s an interesting read!


Cameron Cook

For those interested in Data Science and who like using R, there’s a free book “R for Data Science” available.

If you haven’t had a chance to attend a Data Carpentry event, here’s another great, easy to follow lesson on using OpenRefine by Miriam Posner.

If you’d like to improve your data visualization, watch “Tips for Presenting Data Effectively” by Stephanie Evergreen.


Tool: ATLAS.ti

Information adapted from ATLAS.ti website

What is ATLAS.ti?

ATLAS.ti is a software workbench that helps you perform qualitative analysis on large amounts of text, graphics, audio, or video. ATLAS.ti supports a wide range of data formats, including most common text formats (including .txt, .doc., .docx, and .pdf), “dozens” of graphic and audio formats including .wav and .mp3, and many common video formats. You can also import data from Twitter or Evernote, surveys, or a reference manager.