Hdf Inside

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The HDF format is the foundation for sharing data in many communities that have created domain-specific conventions on top of HDF. This presentation was given at the Winter meeting of the Earth Science Information Partnership (ESIP).

Technology Education

The Earth Science Platform
Conventions

Tools
Formats

Services

1

Python and HDF
From the Introduction:
As a graduate student I didn’t care
that much about the details of how
my data were stored… As a
scientist, I eventually came to
recognize that the choices we make
for organizing and storing our data
are also choices about
communication. Not only do
standard, well-designed formats
make life easier for individuals, but
they make it possible to share data
with a global audience.

2

Python and HDF and Science have #HDFInside

4

Community

Using HDF to share data?
Tweet #HDFInside

16

Resource Description Framework (RDF) has entered the metadata scene for libraries in a major way over the last few years. While the promise of its Linked Data capabilities is exciting, the realities of changing data models, encoding practices, and even ontologies can put a check on that excitement. This session will explore these issues and discuss when this is worth doing and how to go about doing it.

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ESDIS and International Standards

Ted Habermann

The NASA Earth Science Data and Information System (ESDIS) is migrating documentation for their data and products towards International Standards developed by ISO Technical Committee 211 (ISO/TC211). In order to do this effectively, NASA must understand and participate in the ISO process. This presentation was given at a NASA ISO Seminar during November 2012. It outlines the ISO standards process and describes some extensions to the ISO standards that are being proposed to address ESDIS requirements not addressed in the original standard.

Metadata For Humans and Machines

Ted Habermann

New data access paradigms support a variety of human and machine access paths with data servers (THREDDS, https://www.unidata.ucar.edu/software/thredds/current/tds/ and Hyrax, http://opendap.org) that support multiple services for a given dataset. We need metadata that can describe those services and unambiguously differentiate between access paths for humans and for machines. The ISO 19115 metadata standard includes service metadata and allows data and services for that data to be described in the same record. I propose that we use the service metadata for machine access and the more traditional distribution information for human access. This talk was presented at the ESIP (espied.org) meeting during January 2014.

Wikis, Rubrics and Views: An Integrated Approach to Improving Documentation

Ted Habermann

For many years scientists and data managers have focused on creating metadata that supports the discovery of available data. This is important, but once data sets are discovered, users need metadata that supports use and understanding of those data. This talk describes a system developed to support the required metadata improvements using wikis, rubrics, and metadata views. The wikis provide a mechanism for the community to record experiences and lessons learned and provide high-quality examples. Rubrics provide a mechanism for consistent and clear quantitative evaluation of the completeness of metadata records. The results displays include integrated links to the wiki. Views provide views with connections to the wiki and on-going interactive learning. These tools can be used with metadata from any standard and can facilitate translation of the metadata between multiple standards.

For many years metadata development activities have focused on developing and sharing metadata for discovering data. This is important. Once data are discovered, metadata supporting use and understanding become important. Efforts to encourage scientists and data providers to create those metadata have had limited success. This talk describes some approaches and tools for supporting the organizational change efforts required to integrate use and understanding metadata into organizational cultures. These approaches are described in terms of the ideas presented in Switch: How to Change Things When Change is Hard.

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ESDIS Metadata Archive

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NASA's Earth Observing System (EOS) archive includes data collected over many years by many satellite instruments. These data are stored in the HDF format that includes data and metadata. The content of the metadata was examined for compliance with a set of conventions developed by the NASA science community at the beginning of the EOS Project (the HDF-EOS conventions). The initial results show that ~50% of the data files and 76% of the datasets have metadata that allows them to be used easily in standard tools. This talk was presented at the ESIP (espied.org) meeting during January 2014.

The HDF Product Designer – Interoperability in the First Mile

Ted Habermann

Interoperable data have been a long-time goal in many scientific communities. The recent growth in analysis, visualization and mash-up applications that expect data stored in a standardized manner has brought the interoperability issue to the fore. On the other hand, producing interoperable data is often regarded as a sideline task in a typical research team for which resources are not readily available. The HDF Group is developing a software tool aimed at lessening the burden of creating data in standards-compliant, interoperable HDF5 files. The tool, named HDF Product Designer, lowers the threshold needed to design such files by providing a user interface that combines the rich HDF5 feature set with applicable metadata conventions. Users can quickly devise new HDF5 files while at the same time seamlessly incorporating the latest best practices and conventions from their community. That is what the term interoperability in the first mile means: enabling generation of interoperable data in HDF5 files from the onset of their production. The tool also incorporates collaborative features, allowing team approach in the file design, as well as easy transfer of best practices as they are being developed. The current state of the tool and the plans for future development will be presented. Constructive input from interested parties is always welcome.

Earth Science Platform

Ted Habermann

Science platforms are made up of (at least) four planks: data formats, services, tools and conventions. I focus here on formats and conventions, specifically the HDF5 format, already used in many disciplines, and the Climate-Forecast and HDF-EOS Conventions. Many science disciplines have already agreed on HDF as the preferred format for storing and sharing data. It is well established in high performance computing and supports arbitrary grouping and annotation. Community conventions are critical for useful data on top of the format. The Climate-Forecast (CF) conventions were created for relatively simple gridded data types while the HDF-EOS conventions originally considered more complex data (swaths). Making simple conventions more complex makes adoption more difficult. Community input and the need for stable data processing systems must be balanced in governance of conventions.

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Hdf Augmentation: Interoperability in the Last Mile

Ted Habermann

Science data files are generally written to serve well-defined purposes for a small science teams. In many cases, the organization of the data and the metadata are designed for custom tools developed and maintained by and for the team. Using these data outside of this context many times involves restructuring, re-documenting, or reformatting the data. This expensive and time-consuming process usually prevents data reuse and thus decreases the total life-cycle value of the data considerably. If the data are unique or critically important to solving a particular problem, they can be modified into a more generally usable form or metadata can be added in order to enable reuse. This augmentation process can be done to enhance data for the intended purpose or for a new purpose, to make the data available to new tools and applications, to make the data more conventional or standard, or to simplify preservation of the data. The HDF Group has addressed augmentation needs in many ways: by adding extra information, by renaming objects or moving them around in the file, by reducing complexity of the organization, and sometimes by hiding data objects that are not understood by specific applications. In some cases these approaches require re-writing the data into new files and in some cases it can be done externally, without affecting the original file. We will describe and compare several examples of each approach.

ISO Metadata Improvements - Questions and Answers

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Granules and ISO Metadata

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Can ISO 19157 support current NASA data quality metadata?

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ISO 19157 provides a powerful framework for describing quality of Earth science datasets. As NASA migrates towards using that standard, it is important to understand whether and how existing data quality content fits into the ISO 19157 model. This talk demonstrates that fit and concludes that ISO 19157 can include all existing content and also includes new capabilities that can be very useful for all kinds of NASA data users.

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This presentation is intended to familiarize current HDF users and those considering adopting HDF with the range of technical documentation that we provide. We will discuss documents that are shipped with the product and those that are available from the HDF websites. We will touch on user documentation, supporting technical documents, RFCs and design documents, other papers and presentations, etc.

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This tutorial is designed for new HDF5 users. We will cover HDF5 abstractions such as datasets, groups, attributes, and datatypes. Simple C examples will cover the programming model and basic features of the API, and will give new users the knowledge they need to navigate through the rich collection of HDF5 interfaces. Participants will be guided through an interactive demonstration of the fundamentals of HDF5. This tutorial is for new HDF5 users.

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Hdf Inside

1. The Earth Science Platform Conventions Tools Formats Services 1

2. Python and HDF From the Introduction: As a graduate student I didn’t care that much about the details of how my data were stored… As a scientist, I eventually came to recognize that the choices we make for organizing and storing our data are also choices about communication. Not only do standard, well-designed formats make life easier for individuals, but they make it possible to share data with a global audience. 2

3. Python and HDF and Science 3

4. Python and HDF and Science have #HDFInside 4

5. Formats with HDF Inside HDF5 5

6. Formats with HDF Inside HDF5

7. Formats with HDF Inside HDF5

8. Formats with HDF Inside HDF5

9. Formats with HDF Inside HDF5

10. Formats with HDF Inside HDF5

11. Formats with HDF Inside HDF5

12. Formats with HDF Inside HDF5

13. Formats with HDF Inside HDF5

14. Formats with HDF Inside HDF5 HDF5 14

15. Community 15

16. Community Using HDF to share data? Tweet #HDFInside 16

Editor's Notes

New book on Pythin and HDF by Andrew Collette. The quote from the Introduction indicates that Andrew understands the important role of standard formats in data sharing.
Andrew works at the Colorado Center for Lunar Dust and Atmospheric Studies (http://lasp.colorado.edu/home/ccldas/). This large NASA facility is used by scientists in NASA and around the world to study very high speed dust impacts on instrument windows and other objects. Scientists take their observations home in HDF5 files.
The Colorado Center for Lunar Dust and Atmospheric Studies (http://lasp.colorado.edu/home/ccldas/) has HDFIndise.
The NASA Earth Observing System Data and Information System (https://earthdata.nasa.gov) has #HDFInside.
netCDF4 (http://www.unidata.ucar.edu/software/netcdf/) has #HDFInside
The Bathymetry Attributed Grid (BAG) developed by the Open Navigational Surface (http://www.opennavsurf.org) and used by the National Ocean Service (http://www.ngdc.noaa.gov/mgg/bathymetry/hydro.html) has #HDFInside
The NeXus Scientific Format has #HDFInside http://www.nexusformat.org
HDF5-FastQuery developed by the Visualization Groupat Lawrence Berkely National Lab (http://www-vis.lbl.gov/Events/SC05/HDF5FastQuery/) has #HDFInside
h5py (http://www.h5py.org) developed at the Colorado Center for Lunar Dust and Atmospheric Studies (http://lasp.colorado.edu/home/ccldas/) and PyTables (http://www.pytables.org/moin) have #HDFInside.
GLOBE Claritas (http://www.globeclaritas.com) seismic data processing software and h5vc software for scalable nucleotide tallies (http://www.bioconductor.org/packages/2.14/bioc/html/h5vc.html) have #HDFInside.
The KAIRA (Kilpisjärvi Atmospheric Imaging Receiver Array) dual array of omni-directional VHF radio antennas,a project of the Sodankylä Geophysical Observatory and the Low Frequency Array (LOFAR http://www.lofar.org) have #HDFInside.
MathWorks (MATLAB, http://www.mathworks.com ) and Exelis (IDL, ENVI, http://www.exelisinc.com/Pages/default.aspx) have #HDFInside.
The NASA Earth Observing System Data and Information System (https://earthdata.nasa.gov) has #HDFInside.netCDF4 (http://www.unidata.ucar.edu/software/netcdf/) has #HDFInsideThe Bathymetry Attributed Grid (BAG) developed by the Open Navigational Surface (http://www.opennavsurf.org) and used by the National Ocean Service (http://www.ngdc.noaa.gov/mgg/bathymetry/hydro.html) has #HDFInsideThe NeXus Scientific Format has #HDFInside http://www.nexusformat.orgHDF5-FastQuery developed by the Visualization Groupat Lawrence Berkely National Lab (http://www-vis.lbl.gov/Events/SC05/HDF5FastQuery/) has #HDFInsideh5py (http://www.h5py.org) developed at the Colorado Center for Lunar Dust and Atmospheric Studies (http://lasp.colorado.edu/home/ccldas/) and PyTables (http://www.pytables.org/moin) have #HDFInside.GLOBE Claritas (http://www.globeclaritas.com) seismic data processing software and h5vc software for scalable nucleotide tallies (http://www.bioconductor.org/packages/2.14/bioc/html/h5vc.html) have #HDFInside.The KAIRA (Kilpisjärvi Atmospheric Imaging Receiver Array) dual array of omni-directional VHF radio antennas,a project of the Sodankylä Geophysical Observatory and the Low Frequency Array (LOFAR http://www.lofar.org) have #HDFInside.MathWorks (MATLAB, http://www.mathworks.com ) and Exelis (IDL, ENVI, http://www.exelisinc.com/Pages/default.aspx) have #HDFInside.

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