在全球卫生研究中协调数据可操作性和问责制:SARS-CoV-2病例
Nathanael Sheehan1, Sabina Leonelli1
1Egenis, Centre for the Study of Life Sciences, Exeter University, Exeter, UK.
Global public health
|December 11, 2024
概括
科学发现的可操作和可问责的数据基础设施是兼容的. 确保数据的代表性和社区参与,可以促进基因组数据共享的知识开发.
科学领域:
- 基因组数据科学 基因组数据科学
- 生物信息学是一种生物信息学.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 数据基础设施中的可操作性和问责制往往被视为相互矛盾的目标.
- 这种看似的权衡可能会阻碍有效的数据共享和知识开发.
研究的目的:
- 挑战在数据基础设施中可操作性和问责制是不相容的概念.
- 提出一个模型,通过代表性和问责制增强知识开发的数据可操作性.
主要方法:
- 对两个基因组数据基础设施进行SARS-CoV-2数据共享的比较分析.
- 检查数据的代表性,贡献者纳入和治理机制.
主要成果:
- 基因组数据共享的可操作性要求数据具有代表性,并对受试者和用户负责.
- 关键因素包括多样化的数据来源,治理反机制和透明的基础设施运营.
结论:
- 可操作性是通过响应和问责的数据基础设施实现的,而不仅仅是可访问性.
- 开放的沟通和社区参与对于有效的基因组数据共享和知识生成至关重要.
相关概念视频
Bias in Epidemiological Studies
155
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
155
Principles of Disease Surveillance
69
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
69
Strategies for Assessing and Addressing Confounding
83
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
83
Steps in Outbreak Investigation
106
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
106
Single Nucleotide Polymorphisms-SNPs
14.0K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
14.0K
Confounding in Epidemiological Studies
144
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
144


