The AgeGuess database: An open online resource on chronological and perceived ages of people aged 3-100

Publikation: Bidrag til tidsskriftTidsskriftartikelForskning

Abstrakt

In many developed countries, human life expectancy has doubled over the last 180 years from ~40 to ~80 years. Underlying this great advance is a change in how we age, yet our understanding of this change remains limited. Here we present a unique database rich with possibilities to study the human ageing process: the AgeGuess.org database on people's perceived and chronological ages. Perceived age (i.e. how old one looks to others) correlates with biological age, a measure of a person's health condition in comparison to the average of same-aged peers. Determining biological age usually involves elaborate molecular and cellular biomarkers. Using instead perceived age as a biomarker of biological age enables us to collect large amounts of data on biological age through a citizen science project, where people upload pictures of themselves and guess the ages of other people at http://www.ageguess.org. It furthermore allows to collect data retrospectively, because people can upload photographs of themselves when they were younger or of their parents and grandparents. We can thus study the temporal variation in the gap between perceived age and chronological age to address questions such as whether we now age slower or delay ageing until older ages. The here presented perceived age data span birth cohorts from the years 1877 to 2014. Since 2012 the database has grown to now contain around 200,000 perceived age guesses. More than 4000 citizen scientists from over 120 countries of origin have uploaded ~5000 facial photographs. We detail how the data are collected, where the data can be downloaded free of charge, and the contained variables. Beyond ageing research, the data present a wealth of possibilities to study how humans guess ages and to use this knowledge for instance in advancing and testing emerging applications of artificial intelligence and deep learning algorithms.
OriginalsprogEngelsk
Tidsskriftarxiv.org
Antal sider12
StatusUdgivet - 16. mar. 2018

Bibliografisk note

12 pages, 3 figures

Emneord

  • q-bio.QM
  • physics.soc-ph

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