Waterhackweek2019 Data Access and Time-series Statistics Cyberseminar

Data about water are found in many types of formats distributed by many different sources and depicting different spatial representations such as points, polygons and grids. How do we find and explore the data we need for our specific research or application? This seminar will present common challenges and strategies for finding and accessing relevant datasets, focusing on time series data from sites commonly represented as fixed geographical points. This type of data may come from automated monitoring stations such as river gauges and weather stations, from repeated in-person field observations and samples, or from model output and processed data products. We will present and explore useful data catalogs, including the CUAHSI HIS catalog accessible via HydroClient, CUAHSI HydroShare, the EarthCube Data Discovery Studio, Google Dataset search, and agency-specific catalogs. We will also discuss programmatic data access approaches and tools in Python, particularly the ulmo data access package, touching on the role of community standards for data formats and data access protocols. Once we have accessed datasets we are interested in, the next steps are typically exploratory, focusing on visualization and statistical summaries. This seminar will illustrate useful approaches and Python libraries used for processing and exploring time series data, with an emphasis on the distinctive needs posed by temporal data. Core Python packages used include Pandas, GeoPandas, Matplotlib and the geospatial visualization tools introduced at the last seminar. Approaches presented can be applied to other data types that can be summarized as single time series, such as averages over a watershed or data extracts from a single cell in a gridded dataset – the topic for the next seminar.

Cyberseminar recording is available on Youtube at https://youtu.be/uQXuS1AB2M0

水文数据存在多种格式，由众多不同来源发布，并涵盖点、多边形、网格等多种空间表达形式。我们应如何查找并探索满足特定研究或应用需求的数据？
本次网络研讨会将介绍查找与获取相关数据集的常见挑战及策略，重点聚焦于以固定地理点位形式呈现的站点时序数据（time series data）。此类数据可来源于河流水位站、气象站等自动化监测站点，也可来自实地重复观测与采样，或是模型输出及经处理的数据产品。
我们将介绍并探讨实用的数据目录资源，包括可通过HydroClient访问的CUAHSI HIS目录、CUAHSI HydroShare、EarthCube数据发现工作室（EarthCube Data Discovery Studio）、谷歌数据集搜索（Google Dataset Search）以及各机构专属目录。我们还将探讨Python语言下的程序化数据获取方法与工具，重点介绍ulmo数据获取包，并谈及数据格式与数据访问协议的社区标准所发挥的作用。
在获取到目标数据集后，后续通常会开展探索性分析，重点围绕数据可视化与统计汇总展开。本次研讨会将演示用于处理与探索时序数据的实用方法及Python库，重点关注时序数据所特有的分析需求。本次研讨会涉及的核心Python库包括Pandas、GeoPandas、Matplotlib，以及上一次研讨会介绍的地理空间可视化工具。
本次介绍的方法可推广至可汇总为单一时序数据的其他数据类型，例如流域平均值数据或网格数据集中单网格单元提取的数据——这也是下一次研讨会的主题。
本次网络研讨会的录像可在YouTube查看，链接为：https://youtu.be/uQXuS1AB2M0