Workshop on Databases and Data-sharing in Paleoanthropology

古人类学家对原始人类以及相关的动物、植物和地质背景的大量数据集进行了积累和分析，获得了许多令人兴奋的新见解和新发现。现代计算能力和网络能力极大地促进了这些数据集的收集、分析和传播。结果，古人类学和相关学科的数据库的规模和数量都增加了。然而，数据集是由收集它们的研究人员控制的，即使在它们被分析和发表之后，它们也可能无法广泛使用。使古人类学资料更容易获得和使用，是研究人员多年来所关心的问题。值得赞赏的是，在这个过程中有不同的利益相关者，包括但不一定限于：在现场收集材料的人，分析其他人收集的数据的人，以及负责管理和照顾各个博物馆的古人类学收藏品的人。另一个问题是研究人员（尤其是学生）在不知道这些数据已经收集并可用于研究的情况下重复收集类似的数据集。重复收集同一标本的数据不仅是对有限的研究时间和资金的不合理利用，而且可能不利于这些标本的保护。考虑到这些问题，pi将在纽约举行为期两天的研讨会，将这些不同利益相关者的代表以及来自姐妹学科（如脊椎动物古生物学和系统博物馆学）的同事聚集在一起，就这一主题交换意见并规划新的方法。讨论主题将包括：在数据库和数据访问方面进行合作的必要性；纳入的数据类型（目录“元数据”、测量数据、数字图像）；获取此类数据的级别；可以采用的数据库系统的类型，以及它们是否应该是本地的、集中的或连接的。这个项目的智力价值在于，它将把古人类学内外不同群体的代表聚集在一起，共同考虑促进数据交换的价值和实现方法。本次研讨会更广泛的影响是，数据库开发和开放获取数据的问题以及分布方式是许多科学学科普遍感兴趣的问题；关于本次会议审议的报告将对不同领域的同事有价值，并可能降低研究人员考虑将其数据选择为数据库的障碍；来自世界各地的同事在相互教育的同时，有助于建立基础设施和全球合作。改善对数据的获取，允许对更广泛的数据集进行抽样，将有利于培养高级学生，并减少通过重复测量而产生的重复工作。人类起源的研究是一个公众感兴趣的话题，更广泛的数据获取将扩大公众对古人类学和相关领域的理解和兴趣。最后，随着越来越多的数据类型被量化（因此适合建立数据库），这些问题在未来只会越来越多。

The accumulation and analysis of large datasets, on hominids, as well as associated faunal, floral, and geological contexts, has allowed paleoanthropologists many exciting new insights and findings. Modern computing power and networking capabilities have greatly facilitated the collection, analysis, and dissemination of these datasets. As a consequence, the size and number of databases have increased in paleoanthropology and allied disciplines. However, datasets are controlled by the researcher(s) who collected them, and they may not be broadly available, even after they have been analyzed and published. Making paleoanthropological data more widely accessible and readily useable has been a concern among researchers for many years. It is appreciated that there are diverse stake holders in this process, including, but not necessarily limited to: those who collect material in the field, those who analyze data collected by others, and those charged with the management and care of paleoanthropological collections housed in various museums.A further issue is repeated collection of similar datasets by researchers (and especially students) unaware that such data have already been collected and may be available for study. Repeated collection of data on the same specimens is not only a poor use of limited research time and funds, but also potentially detrimental to the conservation of those specimens. Given these concerns, the PIs will hold a two-day workshop in New York to bring together representatives of these different stake holders, as well as colleagues from sister disciplines such as vertebrate paleontology and systematic museology, to exchange ideas and plan new approaches to this topic. Discussion topics will include: the need for collaboration on databasing and data access; the types of data to be included (catalogue "metadata", metrical data, digital imagery); levels of access to such data; types of database systems which could be employed and whether they should be local, centralized or linked.The intellectual merit of this project is that it will bring together representatives of diverse communities within and outside paleoanthropology to consider the value of facilitating data exchange and implementing ways to do so. The broader impacts of this workshop are that database development and the questions of open access to data and means of distribution are of general interest across a great range of science disciplines; a report on the deliberations at this meeting will be of value to colleagues in diverse fields, and may lower the barriers to database implementation for researchers considering this option for their data; the inclusion of colleagues from around the world helps to build infrastructure and global collaboration as they educate each other. Improved access to data, allowing sampling of broader datasets, will benefit the training of advanced students and also reduce duplication of effort through repetition of measurements. The study of human origins is a topic of great public interest, and wider access to data will broaden public understanding of and interest in paleoanthropology and related fields. Finally, as more and more types of data are quantified (and therefore lend themselves to databasing) these concerns will only grow in the future.