The current trend towards large-scale research projects with big quantities of data from multiple sources require robust and efficient data handling. This thesis explores techniques for automatizing research data pipelines. Specifically, two tasks related to automation within a long-term research project in cognitive neuroscience are addressed. The first task is to develop a tool for automatic transcribing of paper-based questionnaires using computer vision. Questionnaires containing continuous scales, so called visual analog scales (VASs), are used extensively in e.g. psychology. Despite this, there currently exists no tool for automatic decoding of these types of questionnaires. The resulting computer vision system for automatic questionnaire transcribing we present, called "VASReader", reliably detects VAS marks with an accuracy of 98%, and predicts their position with a mean absolute error of 0.3 mm when compared to manual measurements. The second task addressed in this thesis project is to investigate whether machine learning can be used to detect anomalies in Magnetic Resonance Imaging (MRI) data. An implementation of the unsupervised anomaly detection technique Isolation Forest shows promising results for the detection of anomalous data points. The model is trained on image quality metric (IQM) data extracted from MRI. However, it is concluded that the site of scanning and MRI machine model used affect the IQMs, and that the model is more prone to classify data points originating from machines and institutions that have less support in the database as anomalous. An important conclusion from both tasks is that automation is possible and can be a great asset to researchers, if an appropriate level and type of automation is selected.