A Feeling for the Algorithm

Working Knowledge and Big Data in Biology

Bibliographic Data

ID3924934
AuthorsHallam Stevens (0000-0002-9083-3131, Ministry of Education, corresponding author)
Year2017
Volume32
Issue1
Pages151-174
Publication date2017-09-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueOsiris (JOURNAL)
Journal identifiersISSN: 0369-7827 • E-ISSN: 1933-8287
PublisherUniversity of Chicago Press (PUBLISHER • US)
DOI10.1086/693516
OpenAlexW2766991892
LanguageEN
Citations received17
References cited24

The term “Big Data” may serve as a useful marker for particular kinds of questions, practices, and relationships for collecting and using data. Some of the ways of talking about Big Data suggest that there might be something, if not entirely new, then at least importantly different at work in Big Data practices and problem-solving approaches. By using three examples taken from the biomedical sciences—artificial neural networks, the construction of reference genomes, and the usage of the Ensembl database—this essay shows how Big Data practices cannot be understood as mere scaling up of pen-and-paper methods but constitute qualitatively different kinds of knowledge-making practices. These practices are characterized particularly by types of human-computer interaction that are labeled “a feeling for the algorithm.”

Artificial neural network · Big data · Biology · Cognitive science · Data mining · Data science · Ensembl · Feeling · Genome · Genomics · Bioinformatics and Genomic Networks · Biomedical Text Mining and Ontologies · Computer Science · Genetics, Bioinformatics, and Biomedical Research · Psychology · Social Psychology · Artificial Intelligence