In this first video Filipa explains how Lateral, a software to analyse and find important information in documents, can help in her literature review. The classic struggles conducting literature reviews are:
Lateral is a tool to eliminate these struggles, and Filipa will show you how in the following videos.
Step one is to get started, by simply creating a project and uploading documents.
Tableview setup: In this second video, we see how Lateral functions as a table view. Filipa dives into adding the concepts (which are the columns) where she will save the text snippets found in the documents (which are the rows), in order to organise everything. In this example, what she wants to explore is the evidence of presence of social withdrawal in:
which are also the created concepts.
In order to fill out the cells in the table view, you can use the Lateral features SuperSearch and DocumentViewer to create/select your snippets (the “cells” in the TableView).
SuperSearch: Filipa starts using the feature SuperSearch, allowing her to search keywords across all uploaded documents at once. She searches through the documents and after finding text snippets that are interesting to her literature review, she labels them under the relevant concept for a later referencing.
DocumentViewer: After using SuperSearch, Filipa dives into each document with the DocumentViewer feature. Here, she is able to simply highlight sections of the text within the document, and save them under the relevant concept similar as in SuperSearch.
In this third video, Filipa shows how the machine learning aspects of Lateral can help find new relevant text snippets that could have been missed if not using the software. This can also be done in both SuperSearch and DocumentViewer.
SuperSearch: She uses the feature SuperSearch, and searches the keywords “social”, “deficits”, “withdrawal”. Having found a text snippet on the topic of schizophrenia and saved it in a concept-on-the-go, she then directs her attention to the SimilarSnippets section which are Lateral’s model suggestions that are not solely based on keyword match, but rather on text snippets previously selected. In this instance, she finds a new relevant finding regarding social deficits in Schizophrenia from a different document.
DocumentViewer: Filipa then dives into the DocumentViewer, searching again for the evidence of presence of social withdrawal in Schizophrenia, looking for text snippets for this concept, and finds in suggested snippets another useful snippet. She also finds a reference to a relevant article which she saves with the comment "check this paper later".
Exporting: After having found snippets of text relevant to her literature review topics, Filipa then goes back to the table view for an overview of all text snippets now saved under each concept. She then exports the findings into a Word and Excel, giving her all the snippets she found related to the concepts in an easily shareable format.
If you see benefits in eliminating the following classic struggles in research:
then get access to the Lateral software by entering your email below, and get started today!