A Feeling for the Algorithm

Working Knowledge and Big Data in Biology

Datos Bibliográficos

ID3924934
AutoresHallam Stevens (0000-0002-9083-3131, Ministry of Education, autor de correspondencia)
Año2017
Volumen32
Número1
Páginas151-174
Fecha de publicación2017-09-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaOsiris (JOURNAL)
Identificadores de la revistaISSN: 0369-7827 • E-ISSN: 1933-8287
EditorialUniversity of Chicago Press (PUBLISHER • US)
DOI10.1086/693516
OpenAlexW2766991892
IdiomaEN
Citas recibidas17
Referencias citadas24

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