Introduction to Researcher Workspace
We at Iris.ai are on the edge of our seats with excitement with the upcoming launch of the Researcher Workspace! Why, you ask? The new tool suite is content focused, and allows you to follow your own workflow and processes. It can easily be adapted to your organization’s needs. The Researcher Workspace includes the modules: Search, Filter, Analyze, Extract, Summarize, Automate, Report. In this blog post we want to give you an overview of the new tool.
What differentiates the Researcher Workspace from our current tools is that there is no structured process that you must follow. You can upload your data from any source to your dashboard and decide what you want to do with it. Some of the ways you’ll be able to input information will range from using external URLs, cloud drives, open-source repositories to using files on your own device.
Once you have added documents that you want to investigate further, your main point of interaction with the data will be the dataset preview. It will allow for document preview so you know what you are playing with and the outputs from the other tools will be accessible in this one unifying place.
Upon selecting at least one dataset, you will be able to search for literature by giving the system a self-written text or a link to a research paper – much like our existing Explore tool. As a result, you will get a list/map of relevant papers including their relevance scores. The results will automatically create a new dataset with an automatic context filter related to the input. This tool is great when you don’t really know what you are looking for, allowing you to do a broad search on a topic.
After your dataset is created, you can use our word and topic analysis tool to filter through your results – similarly to our existing Focus tool, but more flexible. You can use generated words and topics as inclusion/exclusion criteria on your dataset so you can filter through it.
You can also filter your data using metadata filters for (1) publication dates and (2) add/remove repositories or (3) create filters using context descriptions based on your free-text. The context filter is saved automatically, so you can use it later on a different dataset. Context filters are especially useful when you are trying to filter something that cannot be described with one word but rather by using one or multiple descriptions.
You can as well automatically extract the data from selected documents into a tabular format, which has been pre-approved and made available by Iris. This feature allows the extraction of different types of data possibly corresponding to the different type of document selected for the extraction.
The Iris.ai Workspace comes with a configurable summarization engine. You can quickly get summaries of the articles and check if there are any novel and relevant findings in them. So you can skim a lot of content faster. The engine can rapidly produce summaries of one or multiple abstracts or of one or multiple full text documents. The summarization that Iris.ai uses is abstractive which captures the context of summarized text. The generated summary contains new phrases and sentences that may not appear in the source text.
If you want to document your search, we can prepare customized automatic report generation for you. The report can contain the number of searches, most used filters and more.
Every research process is a little different, and your Research Workspace will enable any workflow. In the future blog posts we will share with you more details, so stay tuned!
Interested in a demo? Get in touch!