General

DCCP at MLA '19: Check out our Slides, Notes, Posters and More!

It was an eventful week in Chicago for MLA ‘19!

While we wish everyone was able to make it to the conference, we know that isn’t always possible, so we have uploaded all of the slides, posters, and notes related to the DCCP and our work. Below, we have listed a description of each presentation, the slides or poster, and a person to contact if you have any questions.

The DCCP Information Session

Kevin Read presenting at the DCCP Information Session at MLA ‘19

Kevin Read presenting at the DCCP Information Session at MLA ‘19

  • Provided information about what it means to join the DCCP, implementing the Data Catalog, and how different institutions are using the catalog for their specific needs

  • Link to slides

  • Link to notes

  • Contact: Kevin Read, DCCP Project Lead: kevin.read@med.nyu.edu

Paper presentation: From Conception to Action: Elevating Library Projects through Collaboration between Librarians and Developers

  • Demonstrates how developers and librarians have worked together on the Data Catalog, as well as other library projects and provides tips on how to improve developer and librarian collaborations

  • Link to the slides

  • Contact: Ian Lamb, Solutions Developer, ian.lamb@nyulangone.org

Paper presentation: Developing Workflows to Facilitate the Sharing of Electronic Health Record Data

  • Discusses how NYU created a process to include Electronic Health Record (EHR) data in the NYU Data Catalog. Outlines the workflow and provides example records for EHR data in the NYU Data Catalog

  • Link to the slides

  • Contact: Nicole Contaxis, NYU Data Catalog Coordinator: nicole.contaxis@nyulangone.org

Paper presentation: Creating Institution Specific Resources on Data Transfer and Data Sharing

  • Illustrates how NYU supplements their work on the NYU Data Catalog with ongoing projects to help researchers transfer and share their data while still being in compliance with national regulation, funder and publisher requirements, and institutional policy

  • Link to the slides

  • Contact: Nicole Contaxis, NYU Data Catalog Coordinator: nicole.contaxis@nyulangone.org

Poster: A Multisite Collaboration to Improve Data Curation and Discovery in Academic Health Sciences Centers

dccp_general_poster.jpg

  • Provided information on what the Data Catalog Collaboration is, what our goals are, and ways that the Data Catalog is used at participating institutions

  • Contact: Kevin Read, DCCP Project Lead: Kevin.Read@med.nyu.edu

  • Link to the poster

Poster: Outreach Strategies and Researchers’ Motivations for Sharing Data through a Data Catalog

dccp_outreach_poster.jpg
  • Demonstrated why researchers share data through the Data Catalog as well as the outreach strategies employed at different institutions in the DCCP

  • Link to the poster

  • Contact: Melissa A. Ratajeski, Pitt Data Catalog Lead and Coordinator of Data Services at the University of Pittsburgh Health Sciences Library System, mar@pitt.edu

Poster: Using the PubMed Central Data Availability Search Filter and an Institutional Data Catalog to Make Data more Discoverable

PMC_MLA19_Poster.jpg
  • Illustrates how NYU is using the PubMed Central (PMC) Data Availability Search filter to add new datasets to the NYU Data Catalog. Includes the workflow and an example record

  • Link to the poster

  • Contact: Nicole Contaxis, NYU Data Catalog Coordinator, nicole.contaxis@nyulangone.org

The DCCP at MLA 19: Interested in Data Discovery? Come to the Information Session on the Data Catalog Collaboration Project

For all librarians attending MLA in Chicago this year, the DCCP will be hosting another information session. This session is open for anyone to attend, and geared towards librarians who are:

  • Interested in making their institutional research data discoverable

  • Seeking ways to support researchers as they share and discover data

  • Interested in learning about how librarians from many different institutions collaborate to improve the discovery of their institutional data  

When? Sunday May 5 @ 12pm - 12:55pm

Where? Gold Coast Room (West Tower, Concourse/Bronze Level)

During the session we will help others understand what it takes to get a data catalog up and running with a plan for sustainability going forward, provide examples of the ways the DCCP works to index data at our respective institutions, outline the level of support and priorities offered by each DCCP institution, and describe the successes and challenges faced when working in the realm of data discovery.

We would love for the majority of this session to be discussion with members of the audience. So whether you are interested in implementing the data catalog locally, are working on your own data discovery efforts and want to share your thoughts, or are a librarian seeking to learn more about data discovery, we hope you will join us!


Learning from the Data Curation Network

Last week, I attended the Data Curation Network (DCN) Workshop at Johns Hopkins University in Baltimore, MD. The DCN is a consortium of libraries that work together to help curate research data in order to make that data more Findable, Accessible, Interoperable, and Reusable (FAIR).

Tweet from Cynthia Hudson-Vitale (Head of Research Informatics and Publishing at Penn State) about the workshop

Tweet from Cynthia Hudson-Vitale (Head of Research Informatics and Publishing at Penn State) about the workshop

The objectives of the DCN and the DCCP are closely aligned. With their hands on the data, DCN curators work with researchers to ensure that the data put into repositories is FAIR. The DCCP, on the other hand, works with researchers to make their data discoverable, even if that data is not in a repository. In other words, where the DCN deals with data curation as a whole, including cataloging the data, the DCCP focuses primarily on cataloging only. I attended the workshop not just to learn more about data curation myself but also to see what lessons the DCCP can learn from the DCN.

The DCN created and uses a workflow that they call CURATE. This workflow walks curators through the steps of making a dataset FAIR and provides checklists for each step to act as a guide. Because the DCCP works with data that is not necessarily in a repository, not all aspects of the workflow are applicable to our model. However, these checklists provide an excellent resource as we at the DCCP work to improve our data cataloging and provide guidance to new members.

We at the DCCP look forward to reviewing all the resources created by DCN members and working together to ensure that data is FAIR, no matter where it is stored.

Finding Data To Index: When the Data Availability Statement Leads Nowhere

This blog post is final part of a series on using the “has data avail” filter on PubMed Central (PMC) to identify a wide range of institutional datasets and what we at NYU learned about our institution’s data sharing practices from this exercise. To learn more about the background of this project and how we pulled the bibliographic data used, please refer to our first post. This blogpost is the last in the series and will discuss additional findings related to the bibliometric data we pulled from PMC.

Unsavory Researcher Behavior

When investigating Data Availability Statements (DAS), we learned about how researchers use repositories, use data that is available through application to a consortium, and make their data available in Supporting Information Files. Yet, we also found several examples of unsavory researcher behavior. Several authors listed the data as available in non-existent repositories. For example, on researcher stated that his data was available at an institutional data access point that does not exist. Other researchers listed the data as available on their lab websites, yet when librarians examined the lab website, there wasn’t any data available.

Uninformed Researcher Behavior

Additionally, other Data Availability Statements (DAS) seemed to demonstrate a lack of understanding on what constitutes “data” and what should be included in a statement. One statement reads, “No datasets were generated or analyzed during the current study,” even though the researchers took samples and analyzed them in the publication. Other DAS’s did not list enough information for a researcher to track down the data described. For example, one stated, “NLM has access to all the data and data are available upon request.” With so little information, it seems unlikely that the data could be located and re-used in a meaningful way.

What Librarians Can Do

While it may be easy to assume that all of these researchers are bad actors, it is also possible that the researchers require more guidance in order to write helpful and meaningful DAS’s. As librarians, we can advocate for better DAS’s by providing information on what the DAS is meant to accomplish - guide other researchers to the data for re-use or replications. While it could be helpful for librarians to develop templates, data varies immensely across disciplines and projects. Providing the logic of the DAS will allow researchers to extrapolate about what information is necessary within the boundaries of their project and their domain.

Identifying Significant Growth in Data Sharing: Results from the Annual NYU Data Catalog Contributor Survey

The NYU Data Catalog is designed to facilitate data sharing, and with data from our annual surveys in 2018 and 2019, we can now see growth in the number of researchers participating in the NYU Data Catalog and in the number of interactions researchers have around data sharing because of the NYU Data Catalog.

Of those researchers who responded to our 2019 survey (48.2% response rate), 46.3% were contacted at least once about data sharing and the NYU Data Catalog. This represents a marked increase in the percentage of researchers who reported being contacted in 2018 (27.8%). Furthermore, between 2018 and 2019, there was a 51% increase in the number of contributors to the data catalog.

Rubella research. Photograph from the National Library of Medicine Digital Collections, UID: 101541114. Available at: http://resource.nlm.nih.gov/101541114

Rubella research. Photograph from the National Library of Medicine Digital Collections, UID: 101541114. Available at: http://resource.nlm.nih.gov/101541114

Researchers that were surveyed either serve as local experts on external datasets, like the New York City Community Health Survey, or they have contributed research datasets that are a product of their original research, like Dr. Scott Sherman’s CHART New York Smoking-Cessation Interventions for Urban Hospital Patients Dataset. The annual NYU Data Catalog Contributor survey allows us to gain a better understanding of how researchers are using the catalog and sharing their data, thus providing a way to measure change in data sharing practices over time.

The annual surveys ask five questions:

  • Have you generated new datasets this year?

  • Are you willing to have the new datasets described in the NYU Data Catalog?

  • Are there any changes or modifications to the datasets already described in the NYU Data Catalog?

  • Please briefly describe those changes to your dataset(s).

  • How many times have you been contacted by people asking about a dataset in the NYU Data Catalog?

In a later blog post, we will discuss other data points and new questions that were added to the survey in 2019 to help us better understand researcher data sharing practices.

Cataloging Software and 3D Models in the Pitt Data Catalog

When the Health Sciences Library System at the University of Pittsburgh launched the Pitt Data Catalog last spring, we wanted to provide researchers with flexible options for advertising and sharing their data. Now that the catalog has grown to describe more than 20 Pitt-created datasets, that flexibility has led our collection development in surprising and exciting directions. We have recently added our first records describing software code and 3D models, all created by Dr. Charles C. Horn.

Dr. Horn is an associate professor of medicine who studies gut-brain communication, particularly via the vagus nerve. His research makes use of several open-source software packages, which he demonstrates in his paper (with David M. Rosenberg) “Neurophysiological analytics for all! Free open-source software tools for documenting, analyzing, visualizing, and sharing using electronic notebooks.” Electrophysiological data used to demonstrate the software tools are available in the publication’s data supplements and on Github, where Dr. Horn has also uploaded scripts and a Docker image containing tools to make neurophysiological data analysis easier. Pitt Data Catalog records linking to those software/data packages include:

Dr. Horn has also designed several printable 3D models for experimental apparatuses in electrophysiology. The files shared through the NIH 3D Print Exchange include printable files in a variety of formats, photos, and assembly instructions. The 3D model records in the Pitt Data Catalog are:

From a collections standpoint, expanding our catalog to include software and 3D models is a logical consequence of our mission to collect Pitt-authored data, especially in computational fields where relatively few data products fit the definition of a traditional “dataset.” So far, the DCCP’s metadata schema has proven flexible enough to accommodate these new entity types, but we may pursue some software-specific modifications if the need arises. Shortly after Pitt published these records, NYU added their own first software record, so this may be the beginning of a collaboration-wide trend or a new working group, similar to the DCCP Basic Science Working Group.

Data in the News: ProPublica and the U.S. Health and Retirement Study

As the year winds down and we all recover from the busy holiday season, ProPublica published an article on the ways in which employers push older U.S. workers out of their jobs. The article, “If You’re Over 50, Chances Are The Decision to Leave a Job Won’t be Yours,” by Peter Gosselin uses data from the U.S. Health and Retirement Study (HRS) from the University of Michigan. Gosselin refers to HRS as the “premier source of quantitative information about aging in America,” as it provides longitudinal data about 20,000 people in the United States from the age of 50 and older.

The NYU Data Catalog includes datasets collected outside of NYU (e.g. by the U.S. Census Bureau or by other universities) in order to help researchers locate datasets that they may not otherwise know about. The HRS is an one of the external datasets included in the NYU Data Catalog, and two faculty members act as local experts on the dataset for other researchers at NYU. While not all instances of the Data Catalog include local experts, at NYU we include information on researchers who have already worked on a dataset in order to encourage collaboration at the institution. Local experts are institutional researchers with experience using the dataset who agree to help guide researchers as they decide whether a dataset can answer their questions or provide meaningful information.

What the ProPublica article demonstrates (as well as the many articles in PubMed that feature the dataset) is that a single dataset can be used to investigate a wide variety of questions, if the analysis is done properly. For example, while Gosselin uses the dataset to investigate how U.S. workers are pushed out of their jobs and the financial ramifications of this practice, Virginia Chang, a researcher in the College of Global Public Health at NYU, has used it to investigate the effects of obesity on the survival rates of common acute illnesses.

The Data Catalog was designed to increase cross-disciplinary research and collaboration, and Gosselin’s article illustrates how research data can benefit the public when many people with different areas of expertise have access to it.

Data Catalog Collaboration Project receives CTSA Great Team Science Contest Award for Top Importance

what is team science?

Team science is a collaborative effort to address scientific challenges that leverage the strengths and expertise of professionals trained in different fields. One of the overarching goals of the Clinical and Translational Science Awards (CTSA) given to select institutions is to promote team science through establishing mechanisms by which biomedical researchers can collaborate, be trained in why team science is important, and develop evaluation measures to assess teamwork in biomedical research contexts.

about the award

Last week, the Data Catalog Collaboration Project (DCCP) found out that they had received an award from the CTSA Great Team Science Contest, which asked CTSA-funded hubs to submit examples of team science successes to be evaluated by a review panel and presented at the fall meeting. Each application was scored based on a number of categories: overall score, top importance, top innovation, top impact, among others. 170 applications were submitted, and the DCCP received the highest score for the Top Importance category. I was able to present the topic at the Fall CTSA Program Meeting where I could discuss the value of the data catalog approach to leaders in biomedical translational research. The people I spoke to were most interested in how the data catalog can help them make disparate, hard to find research datasets that are spread out and stored in various places across their institution more discoverable using a single system.

Expanding our reach beyond libraries

From our perspective, the most exciting part about receiving this award was that our approach of having libraries implement local data catalogs, establishing collaborations between librarians and developers to improve data discovery, fostering partnerships with our local institutional research initiatives, and making concerted efforts to reduce the barrier on the research community to share was seen as the most important project by a community that expands well beyond the realm of libraries. This is a considerable achievement because the other projects that were submitted were very strong in addressing a diverse range of team science initiatives. The DCCP has long been an advocate of ensuring that institutional research data is discoverable, available and usable regardless of where it is stored, and this award is an acknowledgement that the broader biomedical research community agrees.

The DCCP has grown to 8 libraries in total working to improve institutional data discovery, and this award can serve as evidence of its value to libraries or broader institutions interested in improving their data discovery needs. The DCCP members all provide a great service to their institution, and to the other libraries participating in this effort. If you are interested in being a part of this effort, please reach out to us.