While conducting your systematic review, you will likely need to work with a large amount of data. You will need to extract data from relevant studies in order to examine and compare results. While the data is being extracted, it is very important to employ good data management practices. Proper data management should begin as soon as you start extracting data, and may even dictate which types of data you decide to retain.
The NYU Health Sciences Library has put together a short video about best data management practices - including some great examples of what not to do!
The video outlines four data management tips:
Systematic review management software tools are specifically tailored to the needs of systematic review teams. In addition to reference management, some of these tools can also help with data extraction, perform meta-analysis, track team progress, and facilitate communication between members. As indicated below, some of these tools are fee-based. You should also bear in mind that not every tool is appropriate for every kind of synthesis or review - be sure to choose the right fit for your project.
Once multiple team members have screened the entire list of references, you will be left with a core group of studies to be included in your review. The next step is to extract the data from each of the studies in order to synthesize their results. The extraction process should be tracked using a standardized data extraction form (see examples below). Data can also be coded for computer analysis. For more information about data extraction, check out this subject guide by the Himmelfarb Health Sciences Library at George Washington University: