DOI: 10.5061/dryad.qbzkh18fr
title: Our Dreams, Our Selves: Automatic Interpretation of Dream Reports

description: 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 dreambank.net. This dataset
 contains those algorithmic annotations.
description: Original data was gathered from dreambank.net. 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: https://dreams.ucsc.edu/Coding/ For details on how the annotation algorithm works see research paper associated with this dataset: http://dx.doi.org/10.1098/rsos.192080
description: 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