Duke Translational Medicine Institute, Duke University, Durham, North Carolina, USA.
J Am Med Inform Assoc. 2014 Mar-Apr;21(2):200-3. doi: 10.1136/amiajnl-2013-002066. Epub 2013 Sep 27.
In the era of Big Data, omic-scale technologies, and increasing calls for data sharing, it is generally agreed that the use of community-developed, open data standards is critical. Far less agreed upon is exactly which data standards should be used, the criteria by which one should choose a standard, or even what constitutes a data standard. It is impossible simply to choose a domain and have it naturally follow which data standards should be used in all cases. The 'right' standards to use is often dependent on the use case scenarios for a given project. Potential downstream applications for the data, however, may not always be apparent at the time the data are generated. Similarly, technology evolves, adding further complexity. Would-be standards adopters must strike a balance between planning for the future and minimizing the burden of compliance. Better tools and resources are required to help guide this balancing act.
在大数据时代、组学规模技术以及对数据共享的呼声日益高涨的背景下，人们普遍认为，使用社区开发的、开放的数据标准至关重要。但人们远未就应使用哪些数据标准、选择标准的标准，甚至什么构成数据标准达成一致意见。简单地选择一个领域，并认为在所有情况下都应该使用哪些数据标准是不可能的。在给定项目中使用“正确”的标准通常取决于用例场景。然而，数据的潜在下游应用在生成数据时并不总是显而易见的。同样，技术也在不断发展，增加了更多的复杂性。潜在的标准采用者必须在规划未来和最小化合规负担之间取得平衡。需要更好的工具和资源来帮助指导这种平衡行为。
