Streamlining Literature Reviews with the Researcher Workspace: A Step-by-Step Guide

In the vast sea of scientific literature, finding the needle in the haystack can be a daunting task. Iris.ai Researcher Workspace is here to simplify and streamline your literature reviews, offering a suite of powerful tools designed to make your research more efficient and effective. In this step-by-step guide, we’ll explore best practices for three common use cases: “searching from millions of documents to find fewer than 10 000 relevant articles”,”searching from less than 10,000 documents to find less than 150 relevant articles”, “searching for unknowns, when you or the field is new”.

Searching from Millions of Documents 

Start by Creating a Manageable Dataset

The journey begins with the creation of a dataset in your Iris.ai account. Whether you’re starting with PubMed, Open Access, or another extensive source, the process remains consistent. The primary objective is to narrow down the initial list to below 10,000 articles. Employ a combination of filters such as limiting results to a relevant year range, filtering by repository, and utilizing the Analyze tool   for Topic and Word analysis. Selecting and including/excluding main topics and using word analysis further refines your dataset, allowing you to focus on relevant information.

Refining with a New Dataset

Once your dataset is below 10,000 articles, save it as a new dataset. This step is crucial, as it enables a more granular analysis on a smaller dataset, providing results tailored to your specific field of interest.

Searching from Less than 10,000 Documents

Creating a Content Collection

For a smaller dataset, your options for building a content collection include the flexibility of uploading documents, employing broad-term filters, or crafting Explore maps. The objective here is not to accumulate a large volume of documents, but rather to curate a focused collection with intent. Aim for precision over quantity, ensuring that the collection remains manageable, making analysis and subsequent steps more effective and targeted.

Choosing Your Search Strategy

The process of choosing a search strategy is pivotal to the success of your literature review. It involves experimenting with various approaches to pinpoint the one that aligns best with your research goals. Three distinct strategies cater to different preferences and requirements:

Increasingly Focused Analysis

Begin by applying the Analyze tool to your dataset, delving into both the main keywords, concepts and the topics derived. Choose topics and terms for inclusion and exclusion, refining your results. A manual “sanity check” ensures your reading list is significantly reduced, potentially by 50%. Save this dataset as a new, smaller set and repeat the analysis. The subsequent iterations allow for an increasingly focused selection, tailoring your research to the most pertinent articles.

Venn Diagram of Contexts

This unique filter in the Researcher Workspace offers a potent tool for scenarios where precise key terms might be elusive. It allows you to use your own description of what you’re seeking and performs text similarity matching with all the documents in your dataset. The resulting contextual similarity provides a more comprehensive understanding. Employ an iterative approach by adding each context filter, sorting the list based on context filter scores, and adjusting as necessary. Applying multiple context filters creates a sort of venn diagram of contexts, offering a nuanced perspective.

Mix and Match

This strategy is characterized by its flexibility. Smart filters can be combined in various ways, allowing you to create a new, smaller dataset based on a blend of analysis and context filters. This amalgamation provides a customized approach to your research process. Feel free to delete articles from your dataset at any point during manual reviews, maintaining control over the relevance and coherence of your reading list.

Searching for Unknowns – When You or the Field is New

If you’re new to research or a specific field, the Explore tool   with a snowballing search approach is recommended. Start by creating visual maps based on a problem statement or article link. Dive into main topics, read headlines, and bookmark interesting articles. Select the most interesting articles, and use these to build new maps – alternatively, make some modifications to your original input statement based on what you learn in the process.

Our research shows that by building 15-18 maps over a couple of hours, you will get a sufficient overview over the topic, find more spot-on papers, and be able to draw superior conclusions than if you had spent the same amount of time with a regular search engine. 

Then you can use the merging feature to combine all your new datasets together and use other strategies mentioned above to narrow down your articles to a more manageable, focused and relevant reading list. 

Conclusions

In conclusion, the Researcher Workspace offers a transformative solution for navigating the vast realm of scientific literature. With a systematic approach for both extensive and focused datasets, the platform enables researchers to efficiently refine their searches, utilizing tools such as Explore, Analyze, Filter, Summarize, Extract and Chat. The emphasis on creating manageable datasets, employing various filters, and utilizing powerful analysis and contextual tools ensures a targeted and precise approach. The guide introduces three distinct search strategies, providing researchers with versatile tools to curate relevant reading lists. Whether for seasoned researchers refining their focus or newcomers exploring the unknown, the Researcher Workspace stands as a comprehensive ally, revolutionizing knowledge management in the world of research.

Share your recommendations, and together, let’s enhance the way we navigate the world of research. Happy exploring!