Therapeutics Initiative Methods Speaker Series: Keep a human in the machine and other lessons learned from deploying and maintaining Colandr

Colandr is a web-based, open access platform for conducting evidence reviews using machine learning and natural language processing algorithms which work in the background to learn what is relevant to each review and suggest more relevant citations and likely classifications, respectively. Colandr can be used by collaborative teams of any size and provides an organizational structure to manage information throughout the entire evidence review process.

Initially created as a DataKind DataCorps project, the impetus to develop Colandr emerged after a Science for People and Nature Partnership (SNAPP) Working Group on Evidence-Based Conservation led by Madeleine McKinnon, finally wrapped up a systematic map project on the evidence of links between conservation and human well-being. After nearly 2.5 years, the team had screened 35,000 citations, read over 3,000 full-text articles, and extracted data from over 1,000 articles. Systematic evidence syntheses are conducted using an established on a peer-reviewed a priori protocol – ensuring full transparency of methodology and providing a template for updating syntheses so they can serve as dynamic resources. However, faced with the daunting task of updating a nearly 3 year-old synthesis with potentially just as many citations to go through, the team balked and thought – can computers make this task easier? And so grew the idea for Colandr – how can we apply machine learning and natural language processing algorithms to make this process of synthesis faster, more efficient, and more affordable? In a collaboration between the SNAPP Evidence-Based Conservation group, DataKind, and Conservation International – a team of data and computer science volunteers spent the better part of 18 months building Colandr, which was launched in July 2017 at the International Congress for Conservation Biology in Cartagena, Colombia.

SPEAKER:
Samantha Cheng, Ph.D., Director of Conservation Evidence at the World Wildlife Fund.
Samantha Cheng is the Director of Conservation Evidence at the World Wildlife Fund and works across the organization and with external organizations to advance evidence-informed practice in conservation programs. She is an interdisciplinary conservation scientist whose work draws on biological, social, and computer sciences, to understand connections between nature and human well-being and build tools and assessments for evidence synthesis and monitoring and evaluation and learning. She runs two online open access tools to facilitate assessing and accessing evidence—Colandr and the Evidence for Nature and People Data Portal.

ABOUT THE TI METHODS SPEAKER SERIES:
The TI Methods Speaker Series are offered free of charge and everyone is welcome. The event is held at noon on the last Wednesday of each month. During the COVID-19 pandemic, while physical distancing measures are in effect, the TI Methods Speaker Series are offered via videoconference. The presentations are recorded and the video recordings are posted online. Click here to view the scheduled topics for 2022 and click here to view a list of TI Methods Speaker Series talks offered in 2021.

LEARN MORE:
https://www.ti.ubc.ca/2022/05/20/colandr/