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快。 一个引人注目的例子是基本粒子物理学（EPP）实验，数千名科学家的合作在大型强子对撞机（LHC）上收集了大量数据，但其他学科已经并将继续观察到类似的增长。这些实验的复杂性和时间框架是这样的，只有当数据在很长一段时间内保持可访问和可分析时，才能实现充分的科学潜力。要成功实现这一目标，实现必要的长期数据保存，需要一种新的方法，使数据和必要的软件管理更加可靠和可生存。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。美国社区分析大量LHC数据的初步努力正在通过开放科学网格项目得到满足，该项目旨在促进此类大型和分布式实验。DASPOS提供了一个长期继续研究今天的分析的机会。DASPOS项目结合了EPP中数据处理的最新知识。该项目旨在提供一个通用的技术框架，确定并解决基本困难，以便同时推进几个学科的这些目标，这应有助于形成共同点和标准，里程碑和工作计划结构合理，并根据现有知识进行调整。计划通过研讨会进行激烈的交流，这是实现纳入各学科目标的自然途径。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项目不仅是合理的，而且是及时的。最近在数据保存和大数据管理方面的动态正在影响到几个国家和供资机构。事实上，现在已经在国家一级实施了几个项目，例如，2012年法国国家科学研究中心资助的PREDON项目，该项目旨在制定一个多学科的大数据管理新方法。德国和意大利正在研究其他类似的倡议。毫无疑问，协同作用将在国际范围内出现，而且DASPOS将在这方面发挥先锋和领导作用。总而言之，DASPOS提案的影响和优点是创新性的，可能具有变革性。这可能是在“大数据”挑战的背景下在科学数据管理方面取得重大进展的历史机遇。

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.