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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

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

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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

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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

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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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Un terreno cambiante para los investigadores de big data.

Rachel Bernstein1

  • 1San Francisco, CA, USA.

Cell
|April 15, 2014
PubMed
Resumen

La investigación biomédica está adaptando sus normas culturales para utilizar de manera efectiva el volumen en rápida expansión de los conjuntos de datos diarios. Esta evolución es crucial para el avance del descubrimiento científico y la innovación.

Área de la Ciencia:

  • Investigación biomédica en la investigación biomédica.
  • Ciencia de datos Ciencia de datos.
  • Metodología científica Metodología científica.

Sus antecedentes:

  • El crecimiento exponencial de los datos en la investigación biomédica presenta desafíos significativos.
  • Las normas culturales existentes pueden obstaculizar la utilización efectiva de grandes conjuntos de datos.
  • Existe una creciente necesidad de prácticas de investigación adaptativas.

Objetivo del estudio:

  • Explorar las presiones sobre las normas culturales en la investigación biomédica.
  • Para identificar los cambios necesarios para el aprovechamiento de grandes volúmenes de datos.
  • Facilitar la integración de datos a gran escala en las prácticas de investigación.

Principales métodos:

  • Revisión de la literatura de las tendencias actuales en los datos biomédicos.

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  • Análisis de estudios de caso sobre la integración de datos.
  • Encuestas sobre las perspectivas de los investigadores con respecto al intercambio y análisis de datos.

    • Principales resultados:

    • La investigación biomédica está experimentando un cambio de paradigma hacia enfoques intensivos en datos.
    • La resistencia cultural a las nuevas metodologías basadas en datos es una barrera significativa.
    • La adaptación exitosa requiere cambios en la capacitación, la infraestructura y las prácticas de colaboración.

    Conclusiones:

    • La investigación biomédica debe evolucionar sus normas culturales para aprovechar los grandes datos.
    • La adopción de nuevas prácticas centradas en los datos es esencial para el futuro progreso científico.
    • Los cambios proactivos mejorarán la capacidad de descubrimiento e innovación.