Data and Software Preservation for Open Science (DASPOS)

科学数据的收集速度比以往任何时候都要快。一个显著的例子是在基本粒子物理学（EPP）实验中，数千名科学家合作在大型强子对撞机（LHC）上收集大量数据，但其他学科已经并将继续观察到类似的增长。由于这些实验的复杂性和时间范围，只有在数据在较长时间内保持可获取和可分析的情况下，才能充分发挥科学潜力。成功实现这一长期数据保存目标的可能性需要一种新颖的方法，这种方法将使数据和必要的软件管理更加稳固和可生存。随着目前实验工作的发展和大数据范式的发展，长期数据保存将成为一个更加关键的问题。美国社区为分析大量LHC数据所做的初步努力，正被开放科学网格项目所满足，该项目旨在促进如此大规模和分布式的实验。DASPOS提供了一个机会，可以继续长期研究今天的分析。DASPOS项目结合了目前在EPP中处理数据的最先进的知识。该项目旨在提供一个通用的技术框架，在其中确定和解决基本困难，目的是同时推进几个学科的这些目标，这将促进共性和标准的出现。里程碑和工作计划的结构良好，并适合于呈现知识。计划通过研讨会进行密集的交流，这是实现各种学科融合目标的自然途径。记录这些研讨会的意图是这个项目的一个有价值的组成部分，就像原型和软件挑战等具体目标一样。DASPOS项目不仅健全而且及时。数据保存和大数据管理方面的最新动态正在影响到一些国家和资助机构。事实上，一些国家级的项目已经开始实施，包括PREDON项目，该项目由法国国家科学研究中心于2012年资助，旨在为大数据管理准备一种多学科的新方法。德国和意大利也在研究其他类似的举措。毫无疑问，协同效应将在国际范围内出现，而且很明显，DASPOS将在这方面发挥先锋和主导作用。总之，DASPOS提案的影响和优点具有创新性和潜在的变革性。这是在“大数据”挑战背景下，科学数据管理取得重大进展的历史性机遇。

Scientific data is being collected at a higher rate than ever. A striking example is in elementary particle physics (EPP) experiments, where collaborations of thousands of scientists collect huge amounts of data at the Large Hadron Collider (LHC), but other disciplines have and are continuing to observe similar growth. The complexity and time frame of these experiments is such that the full scientific potential can only be realized when the data remains accessible and analyzable through extended periods. The possibility of successfully meeting this goal for the necessary long term data preservation requires a novel approach that will make the data and necessary software management more solid and survivable. The long term data preservation will become an even more critical issue as present experimental efforts evolve and the Big Data paradigm develops. The initial efforts of the US community to analyze the large volume of LHC data is being satisfied by the Open Science Grid project, designed to facilitate such large and distributed experiments. DASPOS provides an opportunity to continue to study today's analysis over the long term. The DASPOS project incorporates the present state-of-the-art knowledge in working with data in EPP. This project aims to provide a generic technological framework where the basic difficulties are identified and solved with the aim of advancing these goals simultaneously for several disciplines, which should facilitate the emergence of commonalities and standards.The milestones and the work plan are well structured and adapted to present knowledge. Intense communications via workshops is planned and is a natural path for the goal of inclusion of the various disciplines. The intention to document these workshops is a valuable component of this project, as are concrete goals such as prototypes and software challenges.The DASPOS project is not only sound but also timely. The recent dynamics in data preservation and large data management is now reaching several countries and funding agencies. In fact, several projects at national levels are now installed, including for instance, the PREDON project, financed by CNRS-France in 2012 to prepare a multidisciplinary novel approach to big data management. Other similar initiatives are under study in Germany and Italy. It is no question that synergies will emerge at an international scale, and is also clear that DASPOS will play a pioneering and leading role in this context. In conclusion, the impact and merit of the DASPOS proposal is innovative and potentially transformational. This can be an historical opportunity to make a significant advance in the scientific data management in the context of the "Big Data" challenge.