Sysrev is an intelligent, collaborative platform for document review and data extraction? But what does that mean? What is the point of reviewing documents and extracting data?  Generally speaking, Sysrev serves two primary functions: Literature Reviews & Data Curation.

Literature reviews can be thought of as summaries of research. They look at all the relevant publications and attempt to generalize methods and findings, as well as identify gaps in research. Not just the domain of academia, literature reviews are also essential pieces of regulatory assessments and landscape analyses. The most rigorous of literature reviews is called a systematic review.

If you are interested in systematic reviews, our blog post "Supporting COVID Research" details how an international team used Sysrev (for free!) for three phases of their review (Screen, Extract, Assess) when researching for A Systematic Review of COVID-19 and Kidney Transplantation.

Another type of literature review is called a narrative review, in which the aim of the review is to simply gain a macro-level understanding of the subject matter. For these applications, Sysrev has machine-learning capabilities to help optimize the process.

In our narrative review of Mangiferin, we utilized Sysrev's machine learning to save time on the screening process. After manually reviewing 206/725 articles, we used Sysrev Inclusion Predictor Model to screen the remaining articles. You can learn more about this process in our blog post "Mangiferin Managed Review"

Data curation is when data is extracted for the purpose of building a standardized dataset, often for machine learning. We actually built Sysrev version 0.1 to help curate training data for our own predictive models. In the time since, we have added a number of features specifically for complex data extraction.

One of the biggest problems in data extraction tasks is simply not knowing how much or what types of data are in a given document. As an example, imagine you are interested extracting chemical toxicity testing data from scientific publications. How does one capture the variations between dose, species, and effect? Perhaps the salmonella responded at 10 micrograms but the e. coli responded at 15 micrograms.

If you think of document review from within a spreadsheet paradigm, then the purpose of document review is to create a row of data per document. However, as in the toxicity example above, there are instances where the data is too complex for a single row of data.

The available solutions are 1) add new columns for each set of data, 2) use delimiters, or 3) try and reduce the data to one row. Unfortunately, regardless of the method, there is a substantial risk of loss or confusion of information. For this reason, we developed Group Labels.

Group Labels facilitate the extraction of customizable tables of information from documents. The example below uses a PDF of a Toluene MSDS to showcase the feature's versatility. Here we created two Group Labels "Health Effects" and "LD50/LC50", each of which contain a combination of categorical and string labels.

Like many PDFs, an MSDS contains a lot of valuable information. Also like many PDFs, there is substantial variability from MSDS to MSDS. For example, while the Toluene MSDS contained 5 "Potential Health Effects", a different MSDS may have 7 or 3 or 12. By allowing users to build extendable tables, adding as many rows as needed, Group Labels are able to capture inter-document variations.

The second feature built specifically for complex data extraction is the ability to integrate and systematically parse complex data sources such as JSON or XML documents. These semi-structured documents contain semantic prose wrapped in a consistent hierarchy. The example below uses clinical trial data to show how Sysrev leverages the hierarchical structure to simplify the review process.

As you can see above, the raw JSON from contains many fields or elements, each of which contains a different type of information. While more information is generally better, the sheer amount of information actually makes it harder for reviewers to maintain speed and accuracy. By allowing the user to select which fields are rendered, Sysrev makes it easier for the reviewer to identify and extract the proper information. You can learn more about reviewing semi-structured documents here.

See examples of Literature Reviews and Data Curation on Sysrev

Sysrev is a powerful platform built on simple, versatile tools. Our aim is to allow ours users to review any document and extract any data. If you have a literature review or data extraction project, or even an idea to make Sysrev even better, please feel free to drop us a line at