Data extraction is traditionally the most time-consuming and exhausting stage of evidence synthesis. Manually combing through hundreds of PDFs to find specific variables is not just slow but it is prone to human error.
The ACRES DataExtractor (https://extractor.acres.or.ug) changes the game. It is a purpose-built, AI-driven engine designed to pull critical data from reports and publications in a fraction of the time it takes a human reviewer.
By utilizing advanced Machine Learning and Natural Language Processing (NLP), the DataExtractor reads uploaded PDF documents and automatically identifies the variables of interest. This isn’t just about speed but also shifting the researcher’s role from data entry to data analysis.
- We’ve integrated the tool directly with Zotero, allowing you to import your entire research library with a single click. No more manual uploading or fragmented workflows.
- Users define exactly what they are looking for; whether it’s sample sizes, p-values, or geographic locations. The tool then scans the text, extracts the data, and organizes it into a clean, tabular format.
The DataExtractor was developed to eliminate the bottleneck in Evidence-Informed Decision-Making. By reducing the extraction phase from weeks to days, we enable:
- Get high-quality evidence to policymakers while the window for decision-making is still open.
- Automated extraction reduces the risk of oversight and fatigue errors that occur during manual reviews.
- Once the data is extracted, you can review it in the browser or export it instantly as a CSV for further statistical modelling.
Revolutionizing the Workflow
The ACRES DataExtractor is a vital resource for any researcher or reviewer who values precision and speed. We empower the global knowledge community to focus on what matters most through automating the grunt work: making informed decisions that save lives and strengthen systems.
