Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

598
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...
598
Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

833
The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
833
Archival Research01:40

Archival Research

16.0K
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...
16.0K
Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

1.1K
Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
1.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Model-based analysis of stop-signal data reveals robust neural and clinical correlates of evidence accumulation but not inhibition.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood.

Translational psychiatry·2026
Same author

A longitudinal DNA methylation atlas and its link to brain structure and mental health.

Molecular psychiatry·2026
Same author

Characterising a stress-sensitive default mode network (DMN) deficit in major psychiatric disorders.

Communications biology·2026
Same author

Neurocognitive characterization of behaviour and mental illness through time-varying brain network analysis.

Nature communications·2026
Same author

Functional Electrical Stimulation (FES) in Adults with Neurological Disorders and Foot Drop: Orthotic and Therapeutic Effects in Short- and Long-Term Users.

Bioengineering (Basel, Switzerland)·2026
Same journal

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores For Single-Molecule Sensing.

GigaScience·2026
Same journal

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

GigaScience·2026
Same journal

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

GigaScience·2026
Same journal

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026
Same journal

ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models.

GigaScience·2026
Same journal

ClusterGraph: a new tool for visualisation and compression of multidimensional data.

GigaScience·2026
查看所有相关文章

相关实验视频

Updated: Jul 24, 2025

gP2S, an Information Management System for CryoEM Experiments
13:01

gP2S, an Information Management System for CryoEM Experiments

Published on: June 10, 2021

5.4K

协作研究中心的数据管理策略.

Deepti Mittal1, Rebecca Mease2, Thomas Kuner3

  • 1Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany.

GigaScience
|July 4, 2023
PubMed
概括
此摘要是机器生成的。

有效的研究数据管理 (RDM) 对FAIR神经科学数据至关重要. 本研究介绍了大型财团的可持续RDM战略,解决异质数据生成的挑战,并促进增量采用.

更多相关视频

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

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

Published on: November 22, 2019

6.3K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.3K

相关实验视频

Last Updated: Jul 24, 2025

gP2S, an Information Management System for CryoEM Experiments
13:01

gP2S, an Information Management System for CryoEM Experiments

Published on: June 10, 2021

5.4K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

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

Published on: November 22, 2019

6.3K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.3K

科学领域:

  • 神经科学是一个神经科学.
  • 数据科学数据科学数据科学
  • 生物医学研究生物医学研究

背景情况:

  • 有效的研究数据管理 (RDM) 对于生成可查找,可访问,可互操作和可重复使用 (FAIR) 神经科学数据至关重要.
  • 由于数据类型和研究方法的多样性,多学科神经科学联盟面临着重大的RDM挑战.
  • 在其他研究需求中优先考虑RDM,并实施复杂联盟的连贯计划仍然很困难.

研究的目的:

  • 为大规模的多学科神经科学研究联盟提出一个实用的RDM战略.
  • 解决来自异质和多模式来源 (动物,人类,临床) 生成FAIR数据的挑战.
  • 提出可持续的解决方案,鼓励逐步采用RDM,同时满足特定的研究需求.

主要方法:

  • 为海德堡协作研究联盟制定和实施定制的RDM战略.
  • 专注于早期的RDM和FAIR数据生成原则.
  • 整合解决方案来管理各种数据类型,包括神经生理学,神经成像,遗传学和行为.

主要成果:

  • 在一个大型协作联盟中,成功实施了具体的RDM战略.
  • 该战略有助于从异质来源生成FAIR神经科学数据.
  • 开发了可持续的解决方案,以激励正在进行的RDM实践.

结论:

  • 实施积极和可持续的RDM战略对于大型神经科学联盟来说是可行的,也是有益的.
  • 这些策略对于最大限度地发挥多学科研究的影响和确保数据可用性至关重要.
  • 早期应对RDM挑战有助于实现开放科学和神经科学数据共享的长期目标.