RAPID: Investigating the Efficacy of Coordination of Damage Assessment Initiatives following the April 2015 Nepal Earthquake

2015年4月尼泊尔地震导致大量地方、国家和国际损失数据收集工作。许多参与其中的组织在应对自然灾害方面有很长的历史，并得到国家政府或其他公认组织的授权。尼泊尔会议还出现了其他一些协调不力的努力，导致各种终端用户对数据的准确性、技术严格性或公开性感到困惑。这一快速反应研究(RAPID)项目旨在审视自地震以来产生的损害数据集，并评估每个数据集在目标和最终用途方面的影响。这些信息将有助于制定数据质量基准标准，任何灾害事件后的数据收集工作都可以使用这些标准。最终的影响是减少未来数据集的不确定性，并支持最终用户更好地了解数据的目的、谱系和对其特定用例的适当性。该项目将从多个来源收集数据，并汇集尼泊尔的损害数据数据库。与数据供应商和终端用户进行的国内访谈将有助于了解每个数据集的影响。最后，将制定最佳做法标准，帮助用户了解和比较未来活动中的数据。随着数据收集的技术能力不断增强，数据处理、存储和在线共享的进步，显然对开放标准和互操作性的需求正在增加。因此，至关重要的是，数据质量衡量标准是明确和可访问的，以帮助公开比较数据并减少不确定性，以避免意外滥用损坏数据集。

The April 2015 Nepal earthquake has resulted in a large number of local, national and international damage data collection efforts. Many organizations involved have a long history of responding to natural disasters and do so with a mandate from the national government or other recognized organization. There are other efforts that have emerged for the Nepal event that are not as coordinated, resulting in confusion to a variety of end-users as to the veracity, technical rigor, or openness of the data. This Rapid Response Research (RAPID) project aims to take a view on the damage datasets generated since the earthquake and assess the impact of each dataset in terms of the objectives and eventual end-use. This information will feed the development of data quality benchmarking criteria that can be used by any data collection effort following disaster events. The eventual impact is to reduce the uncertainty in future datasets and support the end-user to better understand the purpose, pedigree and appropriateness of the data for their specific use case. The project will collect data from a number of sources and bring together a database of damage data for Nepal. In-country interviews with data suppliers and end-users will help the understanding of the impact of each dataset. Finally, criteria of best-practice will be generated to help inform users understand and compare data in future events. With evermore increasing technical capabilities for data collection, advancement of data processing, storage and online sharing, it is clear that the demand for open standards and interoperability is increasing. Therefore, it is crucial that data quality measures are clear and accessible to help openly compare data and reduce uncertainty to avoid unintended misuse of damage datasets.