1. Population dynamics play a central role in the historical and current development of fundamental and applied ecological science. The nascent culture of open data promises to increase the value of population dynamics studies to the field of ecology. However, synthesis of population data is constrained by the difficulty in identifying relevant datasets, by the heterogeneity of available data and by access to raw (as opposed to aggregated or derived) observations.
2. To obviate these issues, we built a relational database, popler, and its R client, the library "popler". popler accommodates the vast majority of population data under a common structure, and without the need for aggregating raw observations. The "popler" R library is designed for users unfamiliar with the structure of the database and with the SQL language. This R library allows users to identify, download, explore and cite datasets salient to their needs.
3. We implemented popler as a PostgreSQL instance, where we stored population data originated by the United States Long Term Ecological Research (LTER) Network. Our focus on the US LTER data aims to leverage the potential of this vast open data resource. The database currently contains 305 datasets from 25 LTER sites. popler is designed to accommodate automatic updates of existing datasets, and to accommodate additional datasets from LTER as well as non‐LTER studies.
4. The combination of the online database and the R library "popler" is a resource for data synthesis efforts in population ecology. The common structure of popler simplifies comparative analyses, and the availability of raw data confers flexibility in data analysis. The "popler" R library maximizes these opportunities by providing a user‐friendly interface to the online database.

中文翻译：

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popler：从长期生态研究（LTER）网络中提取和合成种群时间序列的r包

1. 人口动态在基础和应用生态科学的历史和当前发展中起着核心作用。新生的开放数据文化有望提高人口动态研究在生态学领域的价值。但是，人口数据的合成受到难以识别相关数据集的限制，可用数据的异质性以及对原始（相对于汇总或派生）观测值的访问的限制。
2. 为了避免这些问题，我们建立了一个关系数据库popler及其R客户端库“ popler”。popler可以在一个通用结构下容纳绝大多数人口数据，而无需汇总原始观测值。“ popler” R库是为不熟悉数据库结构和SQL语言的用户设计的。该R库允许用户识别，下载，浏览和引用符合其需求的数据集。
3. 我们将popler实现为PostgreSQL实例，在其中存储了由美国长期生态研究（LTER）网络产生的人口数据。我们对美国LTER数据的关注旨在利用这一巨大的开放数据资源的潜力。该数据库当前包含来自25个LTER站点的305个数据集。popler旨在适应现有数据集的自动更新，并适应来自LTER和非LTER研究的其他数据集。
4. 在线数据库和R库“ popler”的组合是用于种群生态学中数据综合工作的资源。Popler的通用结构简化了比较分析，原始数据的可用性为数据分析提供了灵活性。“ popler” R库通过为在线数据库提供用户友好的界面来最大程度地利用这些机会。