What are the Differences Between Google Scholar, OneSearch and Bibliographic Databases When Looking for Journal Articles?

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Bibliographic databases contain references to journal articles (as well as sometimes other formats such as book chapters, etc.) with metadata that may typically include a title, authors, publisher, publication date and place, pages, abstract, index terms, etc. Examples: PubMed, Embase, Web of Science, Scopus, and PsycINFO.

When you search bibliographic databases you are actually searching for metadata, so you can specify what “field” you want your search terms to be found in (eg. title, abstract, author, journal, etc.). Available full text is linked to references/bibliographic records but cannot be searched.

Advantages to Searching Bibliographic Databases

  • The ability to use database functionality to do the best quality search and retrieve the best results. Such functionality in biomedical databases includes advanced functionality such as subject heading (e.g. MeSH) mapping.
  • Searching only essential information (title, abstract, author supplied keywords, index terms/subject headings) when searching on a topic may be a safeguard against overwhelming a searcher with an abundance of results.

    Unlike in bibliographic databases, searching for journal articles via OneSearch (the library’s catalog found on our homepage) or Google Scholar involves searching the full text of journal articles.

    Searching OneSearch
  • OneSearch is a library catalog plus, available at many Libraries, including MSK Library, that allows finding journals and other serials, books, etc., and also databases, newspapers, dissertations, and other print and electronic media typically available in the library’s catalog.
  • It also is able to search the full-text content of e-journals and e-books (owned/subscribed to by the Library) such as journal articles and book chapters.
  • Advanced Search also allows keyword searching in the Title, Author or Subject fields alone, as well as searching in Any Field, which includes the full text of an article.
  • OneSearch relies on keywords and phrases (using quotations), but does not include any mapping or controlled vocabulary.


Searching Google Scholar

  • In Google Scholar you can find journal articles by searching within their full text.
  • Google Scholar includes multidisciplinary content (e.g. Medicine, Physics, Computer Science, Humanities)
  • The articles come from journals that Google Scholar has authority to search regardless of and well beyond any institutional subscription.
  • You have poor control of how you design and execute the search and view your search results (no proper search tools, no abundance of limits and sorting options typical to bibliographic databases).
  • As you are searching the whole “universe” of journals with Google Scholar search, you will find articles that your institution does and does not have access to. An older MSK Library blog post explains how to get access to the full text or request the full text.

Disadvantages of Searching OneSearch and Google Scholar

-The lack of advanced search functionality. While essential search functionality is available (better in OneSearch, worse in Google Scholar) it is still not as advanced as in major biomedical databases.

-Searching the full text in either OneSearch or Google Scholar may end up in the overwhelming number of search results because you are searching the full text of journal articles.

Takeaways and Recommendations

  • Using OneSearch or Google Scholar involves searching the full text of journal articles.
  • To find journal articles, use bibliographic databases as the first choice; use OneSearch and Google Scholar as complementary to using bibliographic journal literature databases.
  • Searching OneSearch and GoogleScholar are most effective when the content being searched is likely not found in the title or the abstract, and thus searching full-text is required.

Non-Smokers At Higher Risk of Resistance to Lung Cancer Treatment, Low Risk of Secondary Cancers in Patients on CAR-T Cell Therapy And More

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  • Researchers from University College London, the Francis Crick Institute, AstraZeneca, etc., identified the genetic basis of an increased risk of resistance to treatment of non-small cell lung cancer (NSCLC) in non-smokers. The study was published in Nature Communications.
  • The Institute for Bioengineering of Catalonia (IBEC) study showed that different physical properties of colorectal cancer cells had different potential for cancer metastasis. The study was published in Nature Communications.
  • Researchers from Stanford University found that, contrary to an earlier FDA warning, the risk of secondary cancers in patients on CAR-T cell therapy is low. The study was published in the New England Journal of Medicine.
  • A study found that statins (in particular, pitavastatin), a class of drugs used to lower cholesterol, may be instrumental in suppressing chronic inflammation, a finding that could help prevent inflammatory-related cancers, e.g., pancreatic cancer. The study was published in Nature Communications.
  • “Father of tamoxifen”, Pharmacologist V. Craig Jordan, a professor of Breast and Medical Oncology and Molecular and Cellular Oncology at The University of Texas MD Anderson Cancer Center who discovered selective estrogen receptor modulators and developed breakthrough breast cancer treatment died last week.
  • Note: Kendra contributed this entry. The National Institutes of Health is developing a fairly simple (in today’s terms) model to predict whether a given cancer patient will likely respond to immunotherapy. This is a far cry from a large foundational model, but there’s still value in training simpler classifiers. The current state for predicting patient responses to immunotherapy is that one or both biomarkers approved by the FDA will be measured for a particular patient to help oncologists select the drugs most likely to work. 
    The model in beta right now is described in Nature Cancer (lead author Tian-Gen Chang). The model “makes predictions based on five clinical features that are routinely collected from patients: a patient’s age, cancer type, history of systemic therapy, blood albumin level, and blood neutrophil-to-lymphocyte ratio, a marker of inflammation. The model also considers tumor mutational burden.” The model has shown decent predictive ability and is available here: https://loris.ccr.cancer.gov.

ORCiD – SciENcv Integration: Another Reason to Register for an ORCiD iD

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Did you know that you can use the profile data that you have stored in your ORCiD profile to auto-populate your SciENcv?

Yes – the two tools have been integrated, meaning that you no longer must duplicate your effort to create a profile in each of these tools separately – you can save the information in your ORCiD profile and draw from it each time you need to create a new NIH biosketch. 

And this may soon matter more to NIH-funded researchers as using SciENcv to create NIH biosketches is likely to become mandatory for NIH grant submissions in about a year or so.

Here’s why:

Effective October 23, 2023, researchers are required to use the new SciENcv forms for submission to the NSF for grant applications: 
https://ncbiinsights.ncbi.nlm.nih.gov/2023/07/20/new-sciencv-biographical-sketch-coming/

NIH also has some related changes coming in 2025:
https://grants.nih.gov/policy/changes-coming-jan-2025/common-forms-for-bio-sketch.htm

“NIH is adopting the Biographical Sketch Common Form and the Current and Pending (Other) Support Common Form in 2025 as per the White House Office of Science and Technology Policy (OSTP) memorandum on Policy Regarding Use of Common Disclosure Forms for applications and Research Performance Progress Reports (RPPRs) submitted on or after May 2025.

The Common Forms represent a collaborative effort between Federal research agencies to ensure standard disclosure requirements as outlined in the National Security Presidential Memorandum – 33.”

What is SciENcv?

“Science Experts Network Curriculum Vitae (SciENcv) is an electronic system that helps you assemble professional information needed to apply for federal grant support.  

SciENcv helps you gather and compile information on expertise, employment, education, and professional accomplishments. You can use SciENcv to create and maintain financial documents and biographical sketches that are submitted as part of grant application packages.” 

What is ORCiD?

“ORCID provides a persistent digital identifier (an ORCID iD) that you own and control, and that distinguishes you from every other researcher. You can connect your iD with your professional information — affiliations, grants, publications, peer review, and more. You can use your iD to share your information with other systems, ensuring you get recognition for all your contributions, saving you time and hassle, and reducing the risk of errors.”

Learn more with these resources:

My NCBI Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2005-. SciENcv. 2013 Aug 12 [Updated 2024 May 21]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK154494/ 

Create your NIH or NSF Biosketch and other documents with SciENcv:
https://www.nlm.nih.gov/ncbi/workshops/2023-10_SciENcv/workshop-details.html 

SciENcv: Science Experts Network Curriculum Vitae (3:33 min)

SciENcv: Integrating with ORCID (3:43 min)

A Quick Tour of the ORCID Record (3:02 min)

Questions? Be sure to Ask Us at the MSK Library!