The life sciences are erupting with data. Thanks to advancements in DNA sequencing technologies and the speed and capacity of computational algorithms, the generation of vast quantities of genomic and proteomic data is now commonplace and expected. However, analysis of this data is not keeping pace with its acquisition (storage space is yet another issue…). One limiting factor is that many biomedical scientists do not yet know how to access, much less use, the available analytical resources. This article describes a platform for multi-omic data analysis that is accessible, reproducible, and transparent, and recommends resources on how to use it.
In our continuous effort to support your research needs, HSLS is offering four new classes this spring covering: (1) introduction to mapping, (2) Python through Jupyter, (3) beginning command line for bioinformatics, and (4) options for bioinformatics analysis. Class descriptions and registration links are listed below.
Thursday, February 15, 2018, 11 a.m. – 1 p.m.; Registration required
Mapping is a great way to visualize and analyze information—and to tell stories. In this introductory workshop, you’ll learn the principles of mapmaking, understand how computers are used to plot addresses on a map, conduct basic spatial analysis, and update records in a database based on location. Along with a deeper appreciation for computers, this class will provide you with a solid foundation of mapping concepts and processes, and get you prepared to take your first computer-based mapping class. No computers will be used in this class.
PubMed Central (PMC) was established in 2000 as the National Library of Medicine’s full-text, journal article repository. Since 2005, PMC has also been the designated repository for papers submitted in accordance with the NIH Public Access Policy. Today, PMC serves as the full-text repository for papers across a variety of scientific disciplines that fall under a number of funding agencies’ public access policies.
“Open Data” is defined by SPARC (Scholarly Publishing and Academic Resources Coalition) as “research data that
- is freely available on the Internet;
- permits any user to download, copy, analyze, re-process, pass to software, or use for any other purpose; and
- is without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.”
The phrase “open data” first appeared in a PubMed article title in 2000, but it took another 13 years for an increase in publications. As we approach 2018, how do researchers now view open data? And most importantly, how does HSLS support health sciences researchers at Pitt?
As previously reported, HSLS hosted a National Center for Biotechnology Information (NCBI) Hackathon from September 25-27, 2017, in collaboration with numerous campus partners. The event took place in the Digital Scholarship Commons of the University Library System (ULS). HSLS, the Center for Research Computing (CRC), and the Department of Biomedical Informatics (DBMI) generously provided support for breakfasts. Computing Services and Systems Development (CSSD), the School of Computing and Information (SCI), and the CRC provided expert technical support.
Data journals are a means to share datasets and communicate detailed information about the methods and instrumentation used to acquire the data.
HSLS is pleased to announce that the National Center for Biotechnology Information (NCBI) Hackathon is coming to the University of Pittsburgh on September 25-27, 2017! HSLS is working with numerous groups across campus to organize this event, including the Center for Research Computing (CRC), Computing Services and Systems Development (CSSD), School of Computing and Information (SCI), and University Library System (ULS).
- Are you interested in automatic alerts for new datasets of interest?
- Do you need to download data for multiple genomes?
The Final Rule of the FDA Amendments Act of 2007 has updated registration and reporting requirements, effective January 18, 2017, with compliance mandated by April 18, 2017. The purpose of the final rule is to clarify the statutory language, expand the minimum reporting data set, and add critical details throughout the ClinicalTrials.gov record to improve effectiveness and compliance overall.
The week of February 13–17, 2017, is Love Your Data (LYD) week, a social media event designed to raise awareness about research data management, sharing, and preservation. This year’s theme is emphasizing data quality for researchers at any stage in their career. Each day of the week will focus on a different topic:
Join the librarians at the University of Pittsburgh in celebrating Love Your Data (LYD) week, a social media event designed to raise awareness about research data management, sharing, and preservation. During the week of February 13–17, 2017, practical tips, resources, and stories will be shared via Twitter (#LYD17 or #loveyourdata) and via in-person and online data classes offered at Pitt’s libraries. Mark your calendar and stay tuned for forthcoming details.
R is a programming language and software environment used for data analysis and/or visualizations. Below are several resources available to help you learn how to use R with your data.
Online training through lynda.pitt.edu (for Pitt users only)
The University provides access to online training via Lynda.com, which includes thousands of videos on topics such as Web design, video editing, Excel, PowerPoint, Photoshop, and more, including R.