Dataset_Levels/datacite.yml

authors:
-
    firstname: "Lukas"
    lastname: "Muttenthaler"
    affiliation: "Google DeepMind, Machine Learning Group, Technische Universität Berlin, BIFOLD, Berlin Institute for the Foundations of
    Learning and Data, Berlin, Germany"
    id: "ORCID:0000-0002-0804-4687"
-
    firstname: "Klaus"
    lastname: "Greff"
    affiliation: "Google DeepMind"
    id: "ORCID:0000-0001-6982-0937"
-
    firstname: "Frieda"
    lastname: "Born"
    affiliation: "Technische Universität Berlin, BIFOLD, Berlin Institute for the Foundations of
    Learning and Data, Berlin, Germany, Adaptive Memory and Decision Making (AMD), Max Planck Institute for Human Development,Berlin, Germany"
    id: "ORCID:0009-0002-1214-4864"
-
    firstname: "Bernhard"
    lastname: "Spitzer"
    affiliation: "Adaptive Memory and Decision Making (AMD), Max Planck Institute for Human Development, Berlin, Germany"
    id: "ORCID:0000-0001-9752-932X"
-
    firstname: "Simon"
    lastname: "Kornblith"
    affiliation: "Anthropic"
    id: "ORCID:0000-0002-9088-2443"
-
    firstname: "Michael C."
    lastname: "Mozer"
    affiliation: "Google DeepMind"
    id: "ORCID:0000-0002-9654-0575"
-
    firstname: "Klaus-Robert"
    lastname: "Müller"
    affiliation: "Google DeepMind, Machine Learning Group, Technische Universität Berlin, BIFOLD, Berlin Institute for the Foundations of
    Learning and Data, Berlin, Germany,Department of Artificial Intelligence, Korea University, Seoul, Max Planck Institute for Informatics, Saarbrücken, Germany"
    id: "ORCID:0000-0002-3861-7685"
-
    firstname: "Thomas"
    lastname: "Unterthiner"
    affiliation: "Google DeepMind"
    id: "ORCID:0000-0001-5361-3087"
-
    firstname: "Andrew K."
    lastname: "Lampinen"
    affiliation: "Google DeepMind"
    id: "ORCID:0000-0002-6988-8437"

title: "The Levels Dataset"

description:
  To validate that AligNet can indeed help to increase the alignment between models and humans,
  we used crowd-sourcing to collect a novel evaluation dataset of human semantic judgments across
  multiple levels of abstraction that we call Levels.

keywords:
  - AI alignment
  - human cognition
  - representation learning
  - computer vision


references:
-
  id: doi:10.48550/arXiv.2409.06509
  reftype: "IsSupplementTo"
  citation: "Muttenthaler, L., Greff, K., Born, F., Spitzer, B., Kornblith, S., Mozer, M.C., Müller, K.R., Unterthiner, T., Lampinen, A.K. : Aligning Machine and Human Visual Representations across Abstraction Levels"
-
  id: doi:10.5281/zenodo.13749102
  reftype: "IsReferencedBy"
  citation: "Born Frieda. (2024). Levels Collection Experiment Code (v1.0.0)"

license:
  name: "Open Data Commons Public Domain Dedication and License (PDDL) v1.0"
  url: "https://opendatacommons.org/licenses/pddl/1-0/"

resourcetype: Dataset

templateversion: 1.1