Lab RFP is Open, Jupyter Community Workshop Solicitation, Swifter for Pandas, and more.

1. ESIP Lab Fall Request for Proposals is Open
2. ESIP and OGC Coverage Processing and Analysis Sprint
3. Call for Jupyter Community Workshops
4. Swifter Package for PANDAS

ESIP Lab Fall Request for Proposals is Open

The ESIP Lab's Fall RFP is open – this is a great opportunity to prototype or implement technology that will enhance your research capabilities. Projects should last 6-8 months, with a maximum budget of $10,000.

Proposals that address the following needs in the Earth science community will be given priority:

• Modernization of Earth science workflows using community-recommended best practices — use of open source software and cloud computing are encouraged.
• Cloud computing use cases for Earth science — creation of well-documented notebooks showing how to collect, distribute, or analyze Earth science data in the cloud.
• Extension of open source software critical to collecting, distributing, or analyzing Earth science data.

Although this RFP will give priority to proposals addressing the bulleted topics above, other high-quality proposals will also be given consideration.

Find the full RFP here.

ESIP and OGC Coverage Processing and Analysis Sprint 

The ESIP Lab and OGC are excited to co-host a Coverage Processing and Analysis API Sprint at the 2020 ESIP Winter Meeting.

The objectives of this sprint are to clarify requirements and methods for analytics on geospatial data including rasters, non-uniformly distributed points, and other geospatial coverage data structures.  By developing prototype functionality through running code, the sprint will assess and advance draft versions of OGC API – Coverage, OGC API – Process and OGC API – Common standards.

A limited amount of travel support is available. Please complete this form to apply.

Call for Jupyter Community Workshops 

The third call for proposals for Jupyter Community Workshops is open through Sunday, December 15, 2019. 

Jupyter Community Workshops bring together small groups of Jupyter community members and core contributors for high-impact strategic work and community engagement on focused topics. Our vision is that the events funded in this round would occur no later than August of 2020.

We are particularly interested in workshops that explore and address topics of strategic importance for the future of Jupyter. We expect the workshops to involve up to about two dozen participants over a 2–4 day period, and have a total Jupyter-funded budget of up to $20,000, which may help cover expenses such as travel, lodging, meals, or event space.

Learn more here.

Swifter Package for Pandas

Lab Fellow Ben Roberts-Pierel highlights one of his favorite data exploration tools

As the scientific community expands the sources and quantity of data available for analysis, methods and tools for speeding up operations are becoming increasingly important. One such tool is the Swifter package for Pandas. For those of you who do not spend exorbitant amounts of time programming, Pandas is a basic but powerful Python module that reads filetypes like CSVs into data frames and then allows for a huge range of processing options. As anybody that has used Pandas (or even Excel) for scientific data analysis will know, simply adding another sensor or a few more years of high temporal resolution data can exponentially increase the size of your dataset. Suddenly we are trying to apply operations to millions and millions of rows of data which can take a great deal of time. This is where the Swifter module comes in. From the GitHub page, Swifter is “A package that efficiently applies any function to a Pandas dataframe or series in the fastest available manner.” In other words, the main function from this package, swiftapply, will look at an operation given to Pandas through the apply function and decide if it is fastest (based on the function and dataset size) to use conventional Pandas apply, to vectorize the operation (much faster) or when that is not possible to rely on Dask to scale the operation to multiple cores. The figure below (from GitHub) gives a simple example of speed comparisons with more examples and a notebook here. 

Those wanting to read more about how it works and why its a useful tool can consult the GitHub repo or a couple of short articles on its use: link, link.