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Related Concept Videos

Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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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 Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Data Collection I01:30

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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...
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Synthetic Biology02:55

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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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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Related Experiment Video

Updated: Mar 2, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons

Kathleen M Jagodnik1, Simon Koplev1, Sherry L Jenkins1

  • 1Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA.

Journal of Biomedical Informatics
|May 15, 2017
PubMed
Summary
This summary is machine-generated.

Biomedical research generates vast data, requiring infrastructure and tools for sharing and analysis. The NIH Big Data to Knowledge (BD2K) Commons initiative aims to create a virtual environment for accessing and integrating these valuable research datasets.

Keywords:
AccessibilityBig DataFAIR principlesFindabilityInteroperabilityReusability

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Area of Science:

  • Biomedical Informatics
  • Big Data Science
  • Computational Biology

Background:

  • Biomedical research is experiencing exponential growth in data volume and diversity.
  • Challenges include computational infrastructure, secure data sharing, data integration tools, and standardized analysis methods.
  • A robust ecosystem is needed to support this data explosion and accelerate discovery.

Purpose of the Study:

  • To review trends and considerations in advancing Big Data science for biomedical research.
  • To provide insights from a meeting of the NIH Big Data to Knowledge (BD2K) Commons Framework Pilots Working Group (CFPWG).
  • To clarify goals and pilot projects for realizing the BD2K Commons vision.

Main Methods:

  • Review of highlights from a two-day meeting of the BD2K CFPWG.
  • Discussion of existing gaps in supporting Big Data in biomedical research.
  • Analysis of trends and considerations for future development.

Main Results:

  • Identified key challenges in computational infrastructure, data sharing, and analysis standardization.
  • Highlighted the need for a virtual environment to facilitate the use, interoperability, and discoverability of research data.
  • Emphasized the role of the BD2K Commons initiative in addressing these challenges.

Conclusions:

  • Advancing Big Data science in biomedical research requires a coordinated effort to build a supportive ecosystem.
  • The BD2K Commons initiative is crucial for enabling digitally enabled research through shared digital objects.
  • Continued focus on pilot projects and addressing identified gaps is essential for realizing the BD2K Commons vision.