Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Information Processing Approach01:30

Information Processing Approach

80
The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
80
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

538
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
538
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

49
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
49
Data: Types and Distribution01:19

Data: Types and Distribution

760
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
760
Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Making Microplastics Matter: Classification, Politics and the Lives of Microplastics as Environmental Objects.

Social studies of science·2026
Same journal

Reframing Trust in Citizen Science: Comparative Insights From Two German Initiatives.

Social studies of science·2026
Same journal

The Nine Lives of Trap-Neuter-Return in the Canary Islands.

Social studies of science·2026
Same journal

Money-Work: Science Funding as Relational Socioeconomics.

Social studies of science·2026
Same journal

Attending to 'Computational Universalism': Practices, Frictions, Events.

Social studies of science·2026
Same journal

Enacting Protected Areas Through Monitoring: Knowledge Practices as Multispecies Spatiotemporal Achievements in Neusiedler See-Seewinkel National Park.

Social studies of science·2026

Related Experiment Video

Updated: Jul 24, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.3K

Academic data science: Transdisciplinary and extradisciplinary visions.

Anissa Tanweer1, James Steinhoff2

  • 1University of Washington, Seattle, WA, USA.

Social Studies of Science
|July 7, 2023
PubMed
Summary
This summary is machine-generated.

Data science is viewed as either separate from or integrated within academic fields. This study proposes data science as an "extradiscipline," facilitating knowledge exchange while respecting disciplinary boundaries.

Keywords:
big datadata sciencedisciplinesethnographyinterdisciplinaritytransdisciplinarity

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
00:08

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.1K

Related Experiment Videos

Last Updated: Jul 24, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.3K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
00:08

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.1K

Area of Science:

  • Academic knowledge production
  • Data science studies
  • Science and Technology Studies (STS)

Background:

  • Data science is an emerging academic field with undefined characteristics.
  • Understanding how data science is perceived is crucial for its development.
  • Existing research lacks a clear framework for data science's academic positioning.

Purpose of the Study:

  • To explore how stakeholders define data science within a university setting.
  • To identify contrasting visions of data science's role and nature.
  • To propose a new conceptualization of data science as an extradiscipline.

Main Methods:

  • Qualitative study of participants involved in establishing data science at a research university.
  • Analysis of how participants defined data science and their relation to it.
  • Examination of contrasting perspectives on data science's disciplinary integration.

Main Results:

  • Two primary visions of data science emerged: transdisciplinary and extradisciplinary.
  • A transdisciplinary view sees data science as separate from and imposing on academic domains.
  • An extradisciplinary view, more prevalent, positions data science as integrated and facilitating cross-disciplinary exchange.

Conclusions:

  • The extradisciplinary concept offers a more accurate reflection of data science's current reality.
  • This framing has significant implications for the future maturation of the data science field.
  • The extradiscipline concept provides novel avenues for STS research on academic knowledge production and disciplinarity.