Jove
Visualize
お問い合わせ

関連する概念動画

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

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
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
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する実験動画

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

ビッグデータ楽観論の再訪:社会に対するデータ駆動型ブラックボックスアルゴリズムのリスク

Sachit Mahajan1, Dirk Helbing1,2

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

Ethics and information technology
|January 26, 2026
PubMed
まとめ

科学と政策におけるビッグデータアルゴリズムと人工知能(AI)は、バイアスと不公平を永続させる可能性がある。責任あるイノベーションには、効率性だけでなく、システム的レジリエンスと参加型監視に焦点を当てる必要がある。

科学分野:

  • コンピューターサイエンス
  • 社会学
  • 公共政策

背景:

  • ビッグデータアルゴリズムとAIは、科学、社会、公共政策全体でますます利用されています。
  • これらの技術は効率の向上を目指していますが、公平性やエンパワーメントを確保するにはしばしば不十分です。
  • バイアス、測定誤差、予測への過度の依存などの問題は、不公平で不透明な結果につながる可能性があります。

研究 の 目的:

  • ビッグデータとAIの倫理的リスクと社会的副作用を批判的に検討すること。
  • 短期的な最適化からシステム的レジリエンスと参加型監視への移行を提唱すること。
  • データ駆動型技術における責任あるイノベーションの道筋を提案すること。

主な方法:

  • ビッグデータアルゴリズムとAIの応用の批判的分析。
  • バイアス、公平性、透明性を含む倫理的考慮事項の検討。
  • 社会経済的影響と権力力学の探求。

主要な成果:

  • ビッグデータとAIの実装は、既存の不平等を悪化させ、新たなバイアスをもたらす可能性があります。
  • 自動化された意思決定は人間の判断に取って代わり、公平性と透明性の低下につながる可能性があります。
キーワード:
アルゴリズムバイアスアルゴリズムガバナンスビッグデータ倫理責任あるイノベーション社会的影響

さらに関連する動画

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

関連する実験動画

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
  • 純粋な最適化の追求は、重要な倫理的リスクと社会的影響を見落としています。
  • 結論:

    • ビッグデータとAIにおける責任あるイノベーションには、倫理的リスクと社会的副作用への焦点が必要です。
    • 「システム的レジリエンス」と「参加型監視」への再方向付けが重要です。
    • 複雑系科学と憲法上および文化的な価値観を統合することで、共生的な人間と技術の関係を育むことができます。