MCRLab provide access to the following Data Sets:

  1. The SENS-IT ontology aims to describe people’s surrounding in much perceptible status by aggregating the corresponding sensory data into a textual format. Please download the ontology from here: SENS-IT
  2.   Human Affective States Ontology (HASO):    The Human Affective States Ontology (HASO) has been developed in the OWL language. It provides knowledge and a common vocabulary regarding human affective states (emotion, mood, sentiment), in a machine-accessible or machine-readable format. Nowadays, humans and computer applications often need to communicate and share knowledge. However, everyone expresses themselves in his or her own language, with different terms and meanings. Ontologies aim to unify the terms and meanings in order to enable effective communication between people and computers. Ontologies capture the domain knowledge and provide an approved understanding of the domain. The study of human emotion, mood, and sentiment is significant as these concepts have an impact on human behavior. Building an ontology for this domain allows us to then build a semantic application.
    1.  HASO (download Ontology from here: Proposed Ontology Human Affective States HASO covers a wide range of human affective states and therefore many topics. Through modularization, we create modules that handle parts of the ontology.
    2. Ontology modularization (download here: Proposed Ontology Human Affective States HASO Modularization) aids in scalability, reusability, and validation process.
  3. We are pleased to release the dataset related to the paper  “A Dataset for Psychological Human Needs Detection from Social Networks“ is available under the Early Access area on IEEEXplore  “Digital Object Identifier:10.1109/ACCESS.2017.2706084
    The dataset itself can be downloaded from:
  4. Sentiment Analysis on Multi-view Social Data: We are pleased to release our new MVSA dataset including more tweets and annotations. In the new dataset, each tweet is annotated by three annotators. We name this dataset as MVSA-multiple. Please go to the following page to download the data set:
  5. Mudva dataset: 1. MUDVA: A Multi-Sensory Dataset for the Vehicular CPS Applications: download the paper and access the full data set under   – the paper is: 
    1. Kazi Masudul Alam, Mohammad Hariz, Seyed Vahid Hosseinioun, Mukesh Saini, and Abdulmotaleb El Saddik, “MUDVA: A Multi-Sensory Dataset for the Vehicular CPS Applications”, in Proceedings of the 2016 IEEE Workshop on Multimedia Signal Processing (MMSP 2016), 21-23 September 2016, Montreal, Canada