Category Archives: Resource Highlights

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Tenzing – A Tool for Capturing Author Affiliation and Authorship Contribution Data

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As team science and research articles with multiple authors (not to mention the “hyperauthorship” of “big team” science) have become the norm in the biomedical sciences, tools that help with tedious tasks (like keeping track of the credentials, affiliation and contact information for an unwieldy number of author contributors) are increasingly being developed and adopted by researchers eager for a simplified process.

In a previous blog post, the National Cancer Institute’s AuthorArranger tool was discussed. Developed in 2018, the AuthorArranger helps researchers collect the key author and affiliation details needed for manuscript submission. Although this tool is a huge time-saver and likely greatly increases the accuracy of the title page author information details transmitted to journal publishers, it could be even more useful if it collected some additional author data points (for example, ORCiD and individual author funding information).

In comes Tenzing, a more recent tool that elevates authorship data collection further, as it is “a web-based app that makes it easier for researchers to indicate who did what in their manuscripts”. Tenzing leverages the standardized CRediT (the Contributor Roles Taxonomy) system to collect details regarding how authors in different roles contributed to the project, in addition to, author contact, affiliation, ORCiD, and funding information.

To learn more about these tools and why they are so useful, see:

Questions? Ask Us at the MSK Library!  

JBI SUMARI Systematic Review Software

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The MSK Library supports researchers working on systematic review (SR) projects with a variety of resources and services and training opportunities. An additional SR tool option available to MSK users, that is included as part of the library’s subscription to the Joanna Briggs Institute (JBI) EBP Database (OVID), is an “EBP Tool” called JBI SUMARI.

According to the vendor:

This comprehensive software suite has been developed to assist users on developing, conducting and reporting on systematic reviews of evidence related to the feasibility, appropriateness, meaningfulness and effectiveness of health care interventions or professional activities.  

Similar to Covidence (another systematic review project management software tool currently available to MSK researchers), JBI SUMARI includes functionality for: Project Management, Protocol Builder, Import of Studies, Study Screening, Assessment of Risk of Bias, Data Extraction, Data Synthesis, and Report Writing. To learn more about what you can do with JBI SUMARI and how it can be used in conjunction with Covidence (to make the most of certain functionality that may be missing in one or the other of these tools), be sure to read this published review:

Piper C. System for the Unified Management, Assessment, and Review of Information (SUMARI). J Med Libr Assoc. 2019 Oct;107(4):634–6. doi: 10.5195/jmla.2019.790. Epub 2019 Oct 1. PMCID: PMC6774554. 

The JBI SUMARI Knowledge Base is also a great place to look for guidance on using this tool. Two areas worth noting where JBI SUMARI differentiates itself from Covidence are with its built-in Protocol Builder capabilities and its Synthesis functionality – features that are currently not available in Covidence.

Questions? Ask Us at the MSK Library!  

Proximity Search Functionality Added to PubMed

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With over 35 million records indexed in PubMed, finding exactly the information you need in an efficient way can often prove challenging for many searchers. To help with this, NLM recently added a new search capability to the PubMed search interface called “proximity searching”. In a nutshell, proximity searching is when a search interface allows the user to look for records containing two different search terms of interest, while specifying how far part these two terms can be from one another in the title and/or abstract of the citation record.

This relational specificity allows the searcher to conduct a broader search (with more search results returned) than they would if they were phrase searching. A proximity search would also return a narrower (smaller) set of results than if the two search terms were being picked up by the search engine having appeared anywhere in the text, regardless of the distance between each other.

This ability to increase the precision of search results is what makes proximity searching a useful capability to have in the PubMed search interface toolbox. For detailed instructions and screenshots illustrating how PubMed’s proximity searching works, be sure to check out the following links:

Questions?

Be sure to Ask Us or attend an upcoming MSK Library PubMed training session.