Jove
Visualize
Contact Us

Related Concept Videos

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.2K
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...
37.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.3K
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...
43.3K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
Data Collection I01:30

Data Collection I

8.0K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
8.0K
Data Validation01:03

Data Validation

6.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.4K
Data Validation01:15

Data Validation

750
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
750

You might also read

Related Articles

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

Sort by
Same author

Large cities lose their growth advantage as countries urbanize.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

AMULED: Addressing Moral Uncertainty using Large language models for Ethical Decision-making.

Frontiers in artificial intelligence·2026
Same author

Not your mean green: beyond averages in mapping socio-spatial inequities in urban greenery for smart cities.

EPJ data science·2026
Same author

Bilateral choroidal tuberculoma in a patient of miliary tuberculosis.

Journal of ophthalmic inflammation and infection·2025
Same author

Dynamic calibration of low-cost PM<sub>2.5</sub> sensors using trust-based consensus mechanisms.

NPJ climate and atmospheric science·2025
Same author

Unilateral acute retinal necrosis in a young Indian male patient.

BMJ case reports·2025
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 Experiment Video

Updated: Jan 27, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Revisiting big data optimism: risks of data-driven black box algorithms for society.

Sachit Mahajan1, Dirk Helbing1,2

  • 1Computational Social Science, ETH Zurich, Zurich, Switzerland.

Ethics and Information Technology
|January 26, 2026
PubMed
Summary

Big data algorithms and artificial intelligence (AI) in science and policy can perpetuate bias and unfairness. Responsible innovation requires focusing on systemic resilience and participatory oversight, not just efficiency.

Keywords:
Algorithmic biasAlgorithmic governanceBig dataEthicsResponsible innovationSocietal impact

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

487
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K

Related Experiment Videos

Last Updated: Jan 27, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

487
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K

Area of Science:

  • Computer Science
  • Sociology
  • Public Policy

Background:

  • Big data algorithms and AI are increasingly used across science, society, and public policy.
  • These technologies aim to improve efficiency but often fall short of ensuring fairness or empowerment.
  • Issues like bias, measurement error, and over-reliance on prediction can lead to unfair and opaque outcomes.

Purpose of the Study:

  • To critically examine the ethical risks and societal side effects of big data and AI.
  • To advocate for a shift from short-term optimization to systemic resilience and participatory oversight.
  • To propose pathways for responsible innovation in data-driven technologies.

Main Methods:

  • Critical analysis of the application of big data algorithms and AI.
  • Examination of ethical considerations, including bias, fairness, and transparency.
  • Exploration of socio-economic impacts and power dynamics.

Main Results:

  • Big data and AI implementation can exacerbate existing inequalities and introduce new biases.
  • Automated decision-making may replace human judgment, leading to reduced fairness and transparency.
  • The pursuit of pure optimization overlooks crucial ethical risks and societal consequences.

Conclusions:

  • Responsible innovation in big data and AI necessitates a focus on ethical risks and societal side effects.
  • A reorientation towards "systemic resilience" and "participatory oversight" is crucial.
  • Integrating complexity science with constitutional and cultural values can foster symbiotic human-technology relationships.