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関連する概念動画

Archival Research01:40

Archival Research

17.6K
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...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Data: Types and Distribution01:19

Data: Types and Distribution

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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...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

1.0K
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Updated: Apr 8, 2026

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

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ビッグデータ研究者のための転換点です.

Rachel Bernstein1

  • 1San Francisco, CA, USA.

Cell
|April 15, 2014
PubMed
まとめ

生物医学の研究は,日々のデータセットの急激に拡大する量を効果的に利用するために,その文化的規範を適応させています. この進化は,科学的発見と革新を前進させる上で極めて重要です.

科学分野:

  • バイオメディカルリサーチ
  • データサイエンス データサイエンス
  • 科学的方法論 科学的方法論

背景:

  • 生物医学研究におけるデータの指数関数的な成長は,重大な課題を提示しています.
  • 既存の文化的規範は,大規模なデータセットの効果的な利用を妨げる可能性があります.
  • アダプティブ・リサーチ・プラクティスの必要性が高まっています.

研究 の 目的:

  • 生物医学研究における文化的規範に対する圧力を調査する.
  • ビッグデータを活用するために必要な変化を特定する.
  • 大規模なデータを研究実践に統合することを容易にする.

主な方法:

  • 生物医学データにおける現在の動向に関する文献レビュー.
  • データ統合に関するケーススタディの分析.
  • データ共有と分析に関する研究者の視点に関する調査.

主要な成果:

  • 生物医学研究は,データ集約的なアプローチへのパラダイムシフトを経験しています.
  • 新しいデータベースの方法論に対する文化的抵抗は,重要な障壁です.
  • 適応の成功には,訓練,インフラ,共同作業の実践の変更が必要です.

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結論:

  • 生物医学の研究は,ビッグデータを活用するために,その文化的規範を進化させなければならない.
  • 新しいデータ中心の実践を採用することは,将来の科学的進歩にとって不可欠です.
  • 積極的な変化は,発見とイノベーションの能力を高めます.