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Our Dreams, Our Selves: Automatic Interpretation of Dream Reports

Cite this dataset

Aiello, Luca Maria; Quercia, Daniele; Fogli, Alessandro (2020). Our Dreams, Our Selves: Automatic Interpretation of Dream Reports [Dataset]. Dryad.


Sleep scientists have shown that dreaming helps people improve their waking lives, and they have done so by developing sophisticated content analysis scales. Over the years, sleep scientists have developed hundreds of increasingly sophisticated ways of coding dreams. One of the the best validated and most widely used scale is the Hall and Van de Castle’s. The Hall–Van de Castle dream coding system consists of ten categories (and their sub-categories) of elements appearing in dreams, together with detailed rules to recognize and measure those elements from written reports. The system became a standard reference for quantitative dream analysis, thanks to its objective approach that facilitates reproducibility and high inter-coder reliability. In practice, the ten categories are not all of equal importance in capturing the psycho-pathological aspects of a dream’s content. Dream scientists determined that the three categories of Characters, Social Interactions and Emotions are the most valuable ones and are usually more informative than all the remaining ones combined. These are described as follows:

  • Characters. People, animals and imaginary figures who appear in the dream; 
  • Interactions. Interactions among characters of three types: friendly, sexual and aggressive;
  • Emotions. Markers of positive or negative emotions in the dream.

Each of these three categories is quantified by a set of metrics. For example, the Characters category includes the percentages of male, animal, and imaginary characters that appears in the dream.

As most dream content analysis scales, the Hall–Van de Castle dream coding system  is complex and, as such, require human intervention. As a result, annotations have been mostly done manually, which is time-consuming, does not scale. To tackle this challenge, we designed a Natural Language Processing (NLP) tool that automatically scores dream reports by operationalizing the Hall–Van de Castle coding system. We validated the tool’s effectiveness on hand-annotated dream reports and applied it to a set of 20k+ dream reports from This dataset contains those algorithmic annotations.


Original data was gathered from We used NLP tools to annotate the dreams according to the Hall-Van de Castle dream coding system.

For details about the coding system, see:

For details on how the annotation algorithm works see research paper associated with this dataset:

Usage notes

Format is as follows:

  • dream_id: Incremental identifier of the dream
  • dreamer: short string identifier of the dreamer (from dreambank)
  • description: short description of the dreamer (from dreambank)
  • dream_date: approximate date of when the dream was reported; expressed in free-text, format may vary (from dreambank)
  • dream_language: language of dream
  • text_dream: the actual dream report, written by the dreamer
  • characters_code: Hall-Van de Castle (HVC) code that encode the characters present in the dream
  • emotions_code: HVC code that encode the emotions present in the dream
  • aggression_code: HVC code that encode the aggression interactions present in the dream
  • friendliness_code: HVC code that encode the friendlyinteractions present in the dream
  • sexuality_code: HVC code that encode the sexual interactions present in the dream
  • Male: %of male characters
  • Animal: %of animal characters
  • Friends: %of characters that are friends to the dreamer
  • Family: %of characters that are members of the dreamer's family
  • Dead&Imaginary: %of dead or imaginary characters
  • Aggression/Friendliness: ratio between aggressive and friendly interactions
  • A/CIndex: aggressions per number of characters 
  • F/CIndex: friendly interactions per number of characters 
  • S/CIndex: sexual interactions per number of characters 
  • NegativeEmotions: %expressed emotions that are negative