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Related Concept Videos

Archival Research01:40

Archival Research

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...
Ethics in Research01:56

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.
Ethical Standards I01:25

Ethical Standards I

The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
Ethical Standards II01:23

Ethical Standards II

Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy and...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
Legal Guidelines for Documentation01:06

Legal Guidelines for Documentation

The legal guidelines for nursing documentation are essential for ensuring accurate, professional, and ethical recording of patient care. The guidelines are discussed here:

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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

Published on: November 22, 2019

Data management practices for collaborative research.

Charles P Schmitt1, Margaret Burchinal

  • 1Renaissance Computing Institute, University of North Carolina at Chapel Hill Chapel Hill, NC, USA.

Frontiers in Psychiatry
|August 4, 2011
PubMed
Summary
This summary is machine-generated.

Effective data and knowledge management are crucial for research success. This paper reviews challenges and solutions for data quality, security, and integration in collaborative scientific endeavors.

Keywords:
collaborative researchdata entrydata integrationdata management

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Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
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Area of Science:

  • Maternal-infant health research
  • Biomedical informatics
  • Data science

Background:

  • Research success hinges on robust data and knowledge management practices.
  • Existing solutions address data entry quality, standards, and ontologies.
  • Adoption of quality assurance is inconsistent across scientific fields.

Purpose of the Study:

  • To review data and knowledge management challenges in collaborative research.
  • To discuss theoretical and practical approaches for addressing these challenges.
  • To highlight issues in data security, privacy, and integration.

Main Methods:

  • Literature review of data management best practices.
  • Analysis of challenges in collaborative research environments.
  • Discussion of solutions for data quality, security, and integration.

Main Results:

  • Collaborative research introduces complexities in data security, privacy, and federated access.
  • Merging evolving ontologies across diverse datasets is a significant challenge.
  • Automated data acquisition and integrating different assay types increase risks and require new analytical tools.

Conclusions:

  • Addressing data management challenges is essential to maintain data quality and research integrity.
  • Implementing effective strategies for data security, privacy, and integration is critical for collaborative science.
  • Proactive management of data complexities ensures efficient and reliable research outcomes.