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Related Concept Videos

Archival Research01:40

Archival Research

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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

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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

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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

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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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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Shifting ground for big data researchers.

Rachel Bernstein1

  • 1San Francisco, CA, USA.

Cell
|April 15, 2014
PubMed
Summary

Biomedical research is adapting its cultural norms to effectively utilize the rapidly expanding volume of daily data sets. This evolution is crucial for advancing scientific discovery and innovation.

Area of Science:

  • Biomedical Research
  • Data Science
  • Scientific Methodology

Background:

  • The exponential growth of data in biomedical research presents significant challenges.
  • Existing cultural norms may hinder the effective utilization of large datasets.
  • There is a growing need for adaptive research practices.

Purpose of the Study:

  • To explore the pressures on cultural norms in biomedical research.
  • To identify necessary changes for harnessing big data.
  • To facilitate the integration of large-scale data into research practices.

Main Methods:

  • Literature review of current trends in biomedical data.
  • Analysis of case studies on data integration.
  • Surveys on researcher perspectives regarding data sharing and analysis.

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Main Results:

  • Biomedical research is experiencing a paradigm shift towards data-intensive approaches.
  • Cultural resistance to new data-driven methodologies is a significant barrier.
  • Successful adaptation requires changes in training, infrastructure, and collaborative practices.

Conclusions:

  • Biomedical research must evolve its cultural norms to leverage big data.
  • Adopting new data-centric practices is essential for future scientific progress.
  • Proactive changes will enhance the capacity for discovery and innovation.