Waterhackweek 2019 Cyberseminar: Data access and time-series statistics


Authors:
Owners: Anthony M. CastronovaEmilio MayorgaYifan Cheng
Type: Resource
Storage: The size of this resource is 197.8 MB
Created: Aug 27, 2019 at 2:38 p.m.
Last updated: Aug 28, 2019 at 5:38 p.m.
Citation: See how to cite this resource
Sharing Status: Public
Views: 2372
Downloads: 44
+1 Votes: Be the first one to 
 this.
Comments: No comments (yet)

Abstract

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.

Coverage

Temporal

Start Date: 02/07/2019
End Date: 02/07/2019

Content

    No files to display.

Related Resources

This resource belongs to the following collections:
Title Owners Sharing Status My Permission
CUAHSI's 2019 Cyberseminar Series: Waterhackweek Anthony Castronova  Public &  Shareable Open Access

How to Cite

Mayorga, E., Y. Cheng (2019). Waterhackweek 2019 Cyberseminar: Data access and time-series statistics, HydroShare, http://www.hydroshare.org/resource/7fb35a9b23624a07b57ab0208039e311

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

Comments

There are currently no comments

New Comment

required