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| Created: | Mar 20, 2019 at 11:55 p.m. | |
| Last updated: | Apr 01, 2019 at 6:39 a.m. (Metadata update) | |
| Published date: | Apr 01, 2019 at 6:39 a.m. | |
| DOI: | 10.4211/hs.9985b3cb38c94cee872b28f6dcdef739 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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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.
Cyberseminar recording is available on Youtube at https://youtu.be/uQXuS1AB2M0
Subject Keywords
Content
README.md
Data access and time-series statistics cyberseminar
Repo for "Data access and time-series statistics" WaterHackWeek cyberseminar. This seminar took place on February 7, 2019. The seminar series, including abstracts and links to seminar recordings, is available at https://www.cuahsi.org/education/cyberseminars/waterhackweek-cyberseminar-series/
Conda environment and Jupyter
First make sure the miniconda or anaconda conda version is installed. See instructions below for miniconda. See below for installation instructions.
To install the conda environment used for running the Jupyter notebooks in this seminar, change to the directory where the environment.yml file is found, downloaded from this GitHub repository (or based on a git clone). Then, at the terminal, run:
bash
conda env create -f environment.yml
An environment called whwtimeseries will be created. Note that this environment doesn't include Jupyter. It assumes you are running Jupyterlab (or jupyter notebook) using a different conda environment where Jupyterlab is installed.
Install miniconda and setup jupyter lab
Steps taken from https://geohackweek.github.io/preliminary/01-conda-tutorial/ Instructions for MacOSX and Windows are also available there.
On linux: ```bash
Install miniconda
url=https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh wget $url -O miniconda.sh bash miniconda.sh -b -p $HOME/miniconda export PATH="$HOME/miniconda/bin:$PATH" conda update conda --yes
Create a conda environment with jupyterlab
conda create -n jupyterlab -c conda-forge python=3.6 jupyterlab nb_conda_kernels
Starting jupyter lab
source activate jupyterlab
Then run jupyter lab
jupyter lab ```
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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