The Importance of Advanced Literature Queries for Refining Search Results
Before commencing any research project it is essential to define a clear research question that will guide your search strategy.
The following are some of the more common challenges as a result of broad, unfocused searches:
- Information Overload: A broad search strategy leading to an overwhelming amount of data
- Lack of Precision: Search Strategy results are either too general or unrelated to the specific research objective
- Time-Consumption: Filtering through irrelevant information and decreasing research productivity
- Difficulty in Identifying Key Sources: Important studies or credible sources may be buried under less relevant results
- Ambiguity and Misrepresentation: The more vague the search strategy, the more likely it is to lead to misleading conclusions or misinterpretation of data.
- Reduced Credibility: A lack of focus can lead to unreliable or non peer-reviewed sources
The Power of Advanced Literature Queries
An advanced literature query within the research context refers to a highly structured search strategy to retrieve precise and relevant academic or scientific information from research databases.
These go beyond typical literature queries as they often are related to a well-defined research question and robust search strategy. Importantly, they also are able to leverage strategies such as the utilization of Boolean Operators, Nesting, Phrase Searching and truncation techniques to refine results and minimize irrelevant information.
Boolean Operators
Boolean Operators follow Boolean Logic. Which involve words and symbols such as AND, OR, and NOT that either allow for an expansion or narrowing of search parameters through a database.
The following are instances of how Boolean Operators can tailor search results.
- AND: This is typically used to combine search terms, ensuring that all terms appear in the results.
- For example, “Kidney Disease AND Diabetes”
- OR: Makes the search strategy broader by including results that contain any of the terms of synonyms connected by “OR”.
- For example, “kidney disease OR renal diseases”.
- NOT: Makes the search strategy narrower by excluding specific terms from the search results.
- For example, “kidney disease NOT chronic” will include results of papers mentioning kidney disease but not including chronic staging.
Nesting
Nesting involves the use of parentheses () in search queries in order to clarify relationships between terms, isolate parts of a query, and prioritize the order of search.
Nesting Example: (Kidney Disease AND Diabetes) NOT chronic
Should generate search results related to the relationship between Acute Kidney Disease and Diabetes.
Phrase Searching:
Phrase searching is another popular strategy. It involves adding quotation marks around keywords to narrow searches in databases and ensure they find exact phrases.
Phrase searching example: “Kidney Disease and Diabetes” will generate results that have Kidney Disease and diabetes in the same sentence.
Truncation techniques
Truncation techniques are used to broaden a search to find variations of a word by using the asterisk symbol to replace the word’s endings. This is often useful if a word has both singular and plural variations, or has different endings.
Truncation technique example: Treatment for a diabetic child*
The word Child has variations such as children, childrens.
How InnScience make query generation simple
Typically, the research team is responsible for query generation. However, since this process can be time-consuming, many have turned to Artificial Intelligence (AI) as a solution.
InnScience can automatically generate queries based on information about the research project which is inputted into the system, whether that be a research question or a brief description.
For example, the following information was inputted into the InnScience Platform:
Title: Empowering Patients and Caregivers to Use AI and Computer Vision for Wound Monitoring: A Feasibility Study
Description: Patients remotely monitor their own chronic wounds such as diabetic ulcers, venous ulcers, pressure injuries, and post-surgical wounds using AI/CV technology.
Figure 1 showcases the amount of search results generated based on the queries created by InnScience. Figure 2 compares the queries generated by InnScience vs. ChatGPT.
Figure 1: A screenshot from the InnScience platform using DataScope displaying 258 search results returned using the boolean query generated by InnScience's DataScope.
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Figure 2: A screenshot showing all of the queries generated by InnScience's Datascope (Left). Compared to the queries generated by ChatGPT (right) when prompted.
As shown here, ChatGPT broke down the research question and description into key components, resulting in the generation of eight distinct search strategies. This does not aid in research efficiency as it requires the researcher to input the queries multiple times into the preferred databases.
As illustrated on the left side of Figure 2, InnScience's AI software effectively integrates all relevant queries into a single search strategy, optimizing research efficiency and reducing search time.
Conclusion
InnScience's DataScope AI is a trusted, cutting-edge tool designed to support every stage of the research process. Rigorously tested and proven to accelerate research efficiency, it empowers researchers to streamline their workflows with confidence.
As part of InnScience's commitment to innovation, DataScope AI reinforces its mission to be the leading platform for groundbreaking research and seamless collaboration.