Meet Giulia Migliore — a brainy gym rat with a background in biopsychology and a mission to make data science actually useful. Whether she’s lifting weights or launching LLMs, this now 23-year-old Italian is all about strength, structure, and smart thinking. With her first job in the pocket and a…
Meet Laurens: Fueled by protein powder and pull requests, Laurens de Bruin is the kind of guy who codes just as hard as he trains. With a mind for software and a barbell in hand, this 25-year-old AI graduate is bulking up more than just his GitHub profile. Whether he’s…
Meet Rutger Neeleman, a student who proves that being a dedicated academic doesn’t mean you have to be all work and no play. With just a year left before he wraps up his studies, this 26-year-old is determined to shake up the world of research—and maybe even change a few…
Starting a systematic review can feel like navigating through a maze, with countless articles and endless decisions. Enter ASReview – your trusty AI tool. ASReview isn’t just a one-trick pony; it’s more like a Swiss Army knife. Equipped with a variety of models, designed to tackle every different type of…
The ASReview team developed a procedure to overcome replication issues in creating a dataset for simulation studies to evaluate the performance of active learning models! In this blog post, we explain this procedure, called the “Noisy Label Filter (NLF) Procedure”. Background: Labelled Data needed for a Simulation Study In an…
Seven ways to integrate ASReview in your systematic review workflow Systematic reviewing using software implementing Active Learning (AL) is relatively new. Many users (and reviewers) have to get familiar with the many different ways how AL can be used in practice. In this blog post, we discuss seven ways meant…
Active Learning Explained The rapidly evolving field of artificial intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for such search tasks[1]. A well-established approach to increasing the efficiency of screening large amounts of textual data is screening prioritization[2, 3] with active learning[4]. Screening…
The Zen of Elas Elas is the Mascotte of ASReview and your Electronic Learning Assistant who will guide you through the interactive process of making decisions using Artificial Intelligence in ASReview. Elas comes with some essential principles: Humans are the Oracle The interaction between humans and machines will take us…
Five ways to get involved in ASReview ASReview LAB is open and free (Libre) software, maintained and developed by the ASReview team and its community, improving the software with input from its users. Below we introduce you to the world of open source and, in particular, the combination of open-source…
Meet Front-End Developer and ASReview Maintainer Yongchao Ma As a user of ASReview, you are probably most familiar with the user interface: the web browser page you use when actually screening your titles and abstracts. The magic behind this user interface is to a large extent created by Yongchao (Terry)…
This event has passed The winners of the hackathon were: Data pre-processing: Teamwork by: Raymon van Dinter, Lukas Schubotz and Stef van Buuren Visualization: Bianca Kramer Smart-screening: Teamwork by: Evi Hendrikx and Matthew van der Meer Curious to see all that was created during the hackathon? UPDATE: ASReview Hackathon for…
ODISSEI and ASReview collaboration: start of ASReview Academy Through our partnership with ODISSEI, we’re very excited to announce the start of ASReview Academy. The place where you can learn all you need to know about using ASReview to speed up your systematic review! ODISSEI ODISSEI (Open Data Infrastructure for Social…
Interview in Fortune In a time where a lot of research needs to be done as fast as possible, ASReview enables researchers to do exactly that. With the launch of the the covid19-extension, ASReview grasped international attention. Its impact was also discussed in an interview in Fortune magazine. See below…
The importance of abstracts ASReview LAB offers effective, reliable and transparent title-abstract screening. It combines machine (deep) learning models with active learning. The tool can reduce the number of titles you will have to screen by up to 90%. We know that the performance of ASReview LAB increases massively with…
The story behind ELAS Hey there! My name is Joukje Willemsen and I’m the creator of ELAS (the icon of ASReview) and the illustrations on the website. Who is ELAS? ASReview uses active learning and artificial intelligence, which can sound intimidating. Therefore, the challenge of designing the logo, was to…
ASReview team wins award The ASReview team wins the KNVI-Victorine van Schaickfonds Initiative Award! We are very happy with this recognition. The jury report: Victorine van Schaickfonds Initiative Award: ASR team onder leiding van Rens van de Schoot “De jury heeft besloten de initiatiefprijs 2019 toe te kennen aan het…
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