April 2018 Issue

Sharing research data can bring many benefits, including greater visibility for data creators, a more transparent research process, and opportunities to identify potential collaborators. But what about datasets that are stored on a lab server instead of in a data repository, or that should only be shared with vetted researchers? The Pitt Data Catalog is a new platform at HSLS designed to help Pitt health sciences researchers share and discover their otherwise hard-to-find datasets, while keeping ultimate control over the data in researchers’ hands.

“The Pitt data catalog has the potential to improve research collaborations and accelerate the impact of research being conducted in the schools of the health sciences. I strongly encourage each researcher to work with HSLS to make your datasets discoverable through the catalog in accordance with the FAIR Data Principles: Making Data Findable, Accessible, Interoperable and Reusable.” Dr. Arthur Levine, Senior Vice Chancellor for the Health Sciences

Unlike data repositories like Dryad or Zenodo, the Pitt Data Catalog does not host any data files. Instead, each dataset included in the catalog is described in a metadata record that includes information about the dataset’s authors, subject domain, and data creation process, as well as instructions for accessing the dataset itself and links to associated publications. Some data catalog entries describe publicly-available datasets, so their records link directly to the data in a repository. Other entries that describe privately-held datasets may direct a visitor to e-mail the corresponding author, or link to a data-access application form. Each record is created in collaboration with the researcher to ensure accurate and comprehensive information.

If you have datasets you would like to have described in the Pitt Data Catalog, please contact the HSLS Data Services team at HSLSDATA@pitt.edu or through our dataset inclusion form. We’ll schedule an in-person or phone consultation to learn more about your datasets and discuss the most appropriate terminology to describe your data. After we create a draft of your dataset’s record, we’ll send it to you for final approval. If you have updates after the record is published, just contact us to make changes; we may also contact you to make sure our information is still current.

HSLS Data Services staff are happy to give demonstrations for individual health sciences researchers, departments, or labs. If you would like to investigate whether the Pitt Data Catalog would be a good match for your datasets, please reach out and we will gladly explore its possibilities with you.

The University of Pittsburgh, Health Sciences Library System, is a member of the Data Catalog Collaboration Project and has customized this data discovery tool in part with Federal funds from the National Library of Medicine, National Institutes of Health, Department of Health and Human Services, under cooperative agreement number UG4LM012342 with the University of Pittsburgh, Health Sciences Library System. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

~Helenmary Sheridan

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.

Galaxy is a community-supported platform that provides access to over 5,500 tools for a multitude of analytical needs, in categories such as variant analysis, imaging, and statistics. Its components include the Galaxy Software Framework and the Public Galaxy Service. The software framework is an open-source, web-based application that functions as an intermediary between researchers without informatics expertise and the computational infrastructure that runs and stores the analyses. The public service includes the main instance, which is an installation of the Galaxy software combined with many tools and data, as well as over 80 public servers. Some of these servers are even domain-specific (ImmPort Galaxy, focusing on flow cytometry analysis) or tool-publishing (MBAC Metabiome Server, simplifying the control, usage, access, and analysis of microbiome, metabalome, and immunome data). Local institutional instances are also possible; the University of Pittsburgh has a Galaxy server hosted by the Center for Research Computing.

The scale of Galaxy is initially a bit daunting. Fortunately, there are numerous resources to help researchers navigate the analytical possibilities. Everything to get you started is at galaxyproject.org, including Galaxy 101, dataset collections, interactive tours, and a growing collection of tutorials developed and maintained by the worldwide Galaxy community and Galaxy Training Network.

The HSLS Molecular Biology Information Service can also assist you with using Galaxy for your research. During the spring 2018 semester we are introducing two hands-on workshops that will teach the basics of Galaxy including (1) interface navigation and interaction and (2) how to create, modify, and extract workflows.

• ChIP-Seq & Galaxy (April 13)
• RNA-Seq & Galaxy (April 27)

To learn more, read the bioRxiv article on “Community-Driven Data Analysis Training for Biology” or contact the HSLS Molecular Biology Information Service.

~Carrie Iwema