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

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

96.0K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
96.0K
Data: Types and Distribution01:19

Data: Types and Distribution

2.2K
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...
2.2K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

41.3K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
41.3K
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

8.7K
The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
8.7K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

48.4K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
48.4K
Forced Transdifferentiation01:28

Forced Transdifferentiation

2.5K
Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Translational uncoupling of renin synthesis and activity reveals a mechanism of kidney vascular remodeling.

Clinical science (London, England : 1979)·2026
Same author

Deciphering covalent kinase inhibitor binding landscape through structural kinome profiling.

European journal of medicinal chemistry·2026
Same author

Governing real-world health data as a public utility.

Science (New York, N.Y.)·2026
Same author

AI-powered programmable virtual humans toward human physiologically-based drug discovery.

Drug discovery today·2025
Same author

CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13.

Journal of chemical information and modeling·2025
Same author

Biological databases in the age of generative artificial intelligence.

Bioinformatics advances·2025

Related Experiment Video

Updated: Apr 6, 2026

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

2.4K

Let's Make Gender Diversity in Data Science a Priority Right from the Start.

Francine D Berman1, Philip E Bourne2

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America.

Plos Biology
|July 28, 2015
PubMed
Summary

Data science can advance gender diversity by increasing women

More Related Videos

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:24

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

1.7K
Skeletal Muscle Gender Dimorphism from Proteomics
09:29

Skeletal Muscle Gender Dimorphism from Proteomics

Published on: December 14, 2011

13.0K

Related Experiment Videos

Last Updated: Apr 6, 2026

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
09:55

Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology

Published on: September 28, 2022

2.4K
Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:24

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

1.7K
Skeletal Muscle Gender Dimorphism from Proteomics
09:29

Skeletal Muscle Gender Dimorphism from Proteomics

Published on: December 14, 2011

13.0K

Area of Science:

  • Data science as an interdisciplinary field.
  • Its role in STEM education and workforce development.

Background:

  • Data science is a rapidly growing field with significant innovation potential.
  • It presents opportunities to improve gender diversity and inclusion.

Purpose of the Study:

  • To explore strategies for enhancing gender diversity in data science.
  • To identify challenges and solutions for women's participation and advancement.

Main Methods:

  • Qualitative analysis of current data science landscape.
  • Review of existing diversity and inclusion initiatives.

Main Results:

  • Two key challenges identified: increasing women's entry and improving retention/advancement.
  • Organizational culture and skill acquisition are critical factors.

Conclusions:

  • Addressing gender diversity requires a dual approach: skill development and cultural evolution.
  • Proactive efforts are needed to foster an inclusive data science environment for women.