About Hierarchical Data Formats - HDF5

The Hierarchical Data Format version 5 (HDF5), is an open source file format that supports large, complex, heterogeneous data. HDF5 uses a "file directory" like structure that allows you to organize data within the file in many different structured ways, as you might do with files on your computer. The HDF5 format also allows for embedding of metadata making it self-describing.

Hierarchical Structure - A file directory within a file

The HDF5 format can be thought of as a file system contained and described within one single file. Think about the files and folders stored on your computer. You might have a data directory with some temperature data for multiple field sites. These temperature data are collected every minute and summarized on an hourly, daily and weekly basis. Within one HDF5 file, you can store a similar set of data organized in the same way that you might organize files and folders on your computer. However in a HDF5 file, what we call "directories" or "folders" on our computers, are called groups and what we call files on our computer are called datasets.

2 Important HDF5 Terms

• Group: A folder like element within an HDF5 file that might contain other groups OR datasets within it.
• Dataset: The actual data contained within the HDF5 file. Datasets are often (but don't have to be) stored within groups in the file.

An HDF5 file containing datasets, might be structured like this:

HDF5 is a Self Describing Format

HDF5 format is self describing. This means that each file, group and dataset can have associated metadata that describes exactly what the data are. Following the example above, we can embed information about each site to the file, such as:

• The full name and X,Y location of the site
• Description of the site.
• Any documentation of interest.

Similarly, we might add information about how the data in the dataset were collected, such as descriptions of the sensor used to collect the temperature data. We can also attach information, to each dataset within the site group, about how the averaging was performed and over what time period data are available.

One key benefit of having metadata that are attached to each file, group and dataset, is that this facilitates automation without the need for a separate (and additional) metadata document. Using a programming language, like R or Python, we can grab information from the metadata that are already associated with the dataset, and which we might need to process the dataset.

Compressed & Efficient subsetting

The HDF5 format is a compressed format. The size of all data contained within HDF5 is optimized which makes the overall file size smaller. Even when compressed, however, HDF5 files often contain big data and can thus still be quite large. A powerful attribute of HDF5 is data slicing, by which a particular subsets of a dataset can be extracted for processing. This means that the entire dataset doesn't have to be read into memory (RAM); very helpful in allowing us to more efficiently work with very large (gigabytes or more) datasets!

Heterogeneous Data Storage

HDF5 files can store many different types of data within in the same file. For example, one group may contain a set of datasets to contain integer (numeric) and text (string) data. Or, one dataset can contain heterogeneous data types (e.g., both text and numeric data in one dataset). This means that HDF5 can store any of the following (and more) in one file:

• Temperature, precipitation and PAR (photosynthetic active radiation) data for a site or for many sites
• A set of images that cover one or more areas (each image can have specific spatial information associated with it - all in the same file)
• A multi or hyperspectral spatial dataset that contains hundreds of bands.
• Field data for several sites characterizing insects, mammals, vegetation and climate.
• A set of images that cover one or more areas (each image can have unique spatial information associated with it)
• And much more!

Open Format

The HDF5 format is open and free to use. The supporting libraries (and a free viewer), can be downloaded from the HDF Group website. As such, HDF5 is widely supported in a host of programs, including open source programming languages like R and Python, and commercial programming tools like Matlab and IDL. Spatial data that are stored in HDF5 format can be used in GIS and imaging programs including QGIS, ArcGIS, and ENVI.

Summary Points - Benefits of HDF5

• Self-Describing The datasets with an HDF5 file are self describing. This allows us to efficiently extract metadata without needing an additional metadata document.
• Supporta Heterogeneous Data: Different types of datasets can be contained within one HDF5 file.
• Supports Large, Complex Data: HDF5 is a compressed format that is designed to support large, heterogeneous, and complex datasets.
• Supports Data Slicing: "Data slicing", or extracting portions of the dataset as needed for analysis, means large files don't need to be completely read into the computers memory or RAM.
• Open Format - wide support in the many tools: Because the HDF5 format is open, it is supported by a host of programming languages and tools, including open source languages like R and Python and open GIS tools like QGIS.

In this tutorial you will use the free HDFView tool to explore HDF5 files and the groups and datasets contained within. You will also see how HDF5 files can be structured and explore metadata using both spatial and temporal data stored in HDF5!

Installing HDFView

Select the HDFView download option that matches the operating system (Mac OS X, Windows, or Linux) and computer setup (32 bit vs 64 bit) that you have.

This tutorial was written with graphics from the VS 2012 version, but it is applicable to other versions as well.

Hierarchical Data Format 5 - HDF5

Hierarchical Data Format version 5 (HDF5), is an open file format that supports large, complex, heterogeneous data. Some key points about HDF5:

• HDF5 uses a "file directory" like structure.
• The HDF5 data models organizes information using Groups. Each group may contain one or more datasets.
• HDF5 is a self describing file format. This means that the metadata for the data contained within the HDF5 file, are built into the file itself.
• One HDF5 file may contain several heterogeneous data types (e.g. images, numeric data, data stored as strings).

For more introduction to the HDF5 format, see our About Hierarchical Data Formats - What is HDF5? tutorial.

In this tutorial, we will explore two different types of data saved in HDF5. This will allow us to better understand how one file can store multiple different types of data, in different ways.

Part 1: Exploring Temperature Data in HDF5 Format in HDFView

The first thing that we will do is open an HDF5 file in the viewer to get a better idea of how HDF5 files can be structured.

Open a HDF5/H5 file in HDFView

To begin, open the HDFView application. Within the HDFView application, select File --> Open and navigate to the folder where you saved the NEONDSTowerTemperatureData.hdf5 file on your computer. Open this file in HDFView.

If you click on the name of the HDF5 file in the left hand window of HDFView, you can view metadata for the file. This will be located in the bottom window of the application.

Explore File Structure in HDFView

Next, explore the structure of this file. Notice that there are two Groups (represented as folder icons in the viewer) called "Domain_03" and "Domain_10". Within each domain group, there are site groups (NEON sites that are located within those domains). Expand these folders by double clicking on the folder icons. Double clicking expands the groups content just as you might expand a folder in Windows explorer.

Notice that there is metadata associated with each group.

Double click on the OSBS group located within the Domain_03 group. Notice in the metadata window that OSBS contains data collected from the NEON Ordway-Swisher Biological Station field site.

Within the OSBS group there are two more groups - Min_1 and Min_30. What data are contained within these groups?

Expand the "min_1" group within the OSBS site in Domain_03. Notice that there are five more nested groups named "Boom_1, 2, etc". A boom refers to an arm on a tower, which sits at a particular height and to which are attached sensors for collecting data on such variables as temperature, wind speed, precipitation, etc. In this case, we are working with data collected using temperature sensors, mounted on the tower booms.

Speaking of temperature - what type of sensor is collected the data within the boom_1 folder at the Ordway Swisher site? HINT: check the metadata for that dataset.

Expand the "Boom_1" folder by double clicking it. Finally, we have arrived at a dataset! Have a look at the metadata associated with the temperature dataset within the boom_1 group. Notice that there is metadata describing each attribute in the temperature dataset. Double click on the group name to open up the table in a tabular format. Notice that these data are temporal.

So this is one example of how an HDF5 file could be structured. This particular file contains data from multiple sites, collected from different sensors (mounted on different booms on the tower) and collected over time. Take some time to explore this HDF5 dataset within the HDFViewer.

Part 2: Exploring Hyperspectral Imagery stored in HDF5

Next, we will explore a hyperspectral dataset, collected by the NEON Airborne Observation Platform (AOP) and saved in HDF5 format. Hyperspectral data are naturally hierarchical, as each pixel in the dataset contains reflectance values for hundreds of bands collected by the sensor. The NEON sensor (imaging spectrometer) collected data within 428 bands.

A few notes about hyperspectral imagery:

• An imaging spectrometer, which collects hyperspectral imagery, records light energy reflected off objects on the earth's surface.
• The data are inherently spatial. Each "pixel" in the image is located spatially and represents an area of ground on the earth.
• Similar to an Red, Green, Blue (RGB) camera, an imaging spectrometer records reflected light energy. Each pixel will contain several hundred bands worth of reflectance data.

Read more about hyperspectral remote sensing data:

• About Hyperspectral Remote Sensing Data tutorial on this site.
• White paper by Dr Peg Shippert

Let's open some hyperspectral imagery stored in HDF5 format to see what the file structure can like for a different type of data.

Open the file. Notice that it is structured differently. This file is composed of 3 datasets:

• Reflectance,
• fwhm, and
• wavelength.

It also contains some text information called "map info". Finally it contains a group called spatial info.

Let's first look at the metadata stored in the spatialinfo group. This group contains all of the spatial information that a GIS program would need to project the data spatially.

Next, double click on the wavelength dataset. Note that this dataset contains the central wavelength value for each band in the dataset.

Finally, click on the reflectance dataset. Note that in the metadata for the dataset that the structure of the dataset is 426 x 501 x 477 (wavelength, line, sample), as indicated in the metadata. Right click on the reflectance dataset and select Open As. Click Image in the "display as" settings on the left hand side of the popup.

In this case, the image data are in the second and third dimensions of this dataset. However, HDFView will default to selecting the first and second dimensions

Let’s tell the HDFViewer to use the second and third dimensions to view the image:

1. Under height, make sure dim 1 is selected.
2. Under width, make sure dim 2 is selected.

Notice an image preview appears on the left of the pop-up window. Click OK to open the image. You may have to play with the brightness and contrast settings in the viewer to see the data properly.

Explore the spectral dataset in the HDFViewer taking note of the metadata and data stored within the file.

About HDF5

The HDF5 file can store large, heterogeneous datasets that include metadata. It also supports efficient data slicing, or extraction of particular subsets of a dataset which means that you don't have to read large files read into the computers memory / RAM in their entirety in order work with them.

Read more about HDF5 here.

HDF5 in R

To access HDF5 files in R, we will use the rhdf5 library which is part of the Bioconductor suite of R libraries. It might also be useful to install the free HDF5 viewer which will allow you to explore the contents of an HDF5 file using a graphic interface.

More about working with HDFview and a hands-on activity here.

First, let's get R setup. We will use the rhdf5 library. To access HDF5 files in R, we will use the rhdf5 library which is part of the Bioconductor suite of R packages. As of May 2020 this package was not yet on CRAN.

# Install rhdf5 package (only need to run if not already installed)
#install.packages("BiocManager")
#BiocManager::install("rhdf5")

# Call the R HDF5 Library
library("rhdf5")

# set working directory to ensure R can find the file we wish to import and where
# we want to save our files
wd <- "~/Git/data/" #This will depend on your local environment 
setwd(wd) 

Read more about the rhdf5 package here.

Create an HDF5 File in R

Now, let's create a basic H5 file with one group and one dataset in it.

# Create hdf5 file
h5createFile("vegData.h5")

## [1] TRUE

# create a group called aNEONSite within the H5 file
h5createGroup("vegData.h5", "aNEONSite")

## [1] TRUE

# view the structure of the h5 we've created
h5ls("vegData.h5")

##   group      name     otype dclass dim
## 0     / aNEONSite H5I_GROUP

Next, let's create some dummy data to add to our H5 file.

# create some sample, numeric data 
a <- rnorm(n=40, m=1, sd=1) 
someData <- matrix(a,nrow=20,ncol=2)

Write the sample data to the H5 file.

# add some sample data to the H5 file located in the aNEONSite group
# we'll call the dataset "temperature"
h5write(someData, file = "vegData.h5", name="aNEONSite/temperature")

# let's check out the H5 structure again
h5ls("vegData.h5")

##        group        name       otype dclass    dim
## 0          /   aNEONSite   H5I_GROUP              
## 1 /aNEONSite temperature H5I_DATASET  FLOAT 20 x 2

View a "dump" of the entire HDF5 file. NOTE: use this command with CAUTION if you are working with larger datasets!

# we can look at everything too 
# but be cautious using this command!
h5dump("vegData.h5")

## $aNEONSite
## $aNEONSite$temperature
##              [,1]       [,2]
##  [1,]  0.33155432  2.4054446
##  [2,]  1.14305151  1.3329978
##  [3,] -0.57253964  0.5915846
##  [4,]  2.82950139  0.4669748
##  [5,]  0.47549005  1.5871517
##  [6,] -0.04144519  1.9470377
##  [7,]  0.63300177  1.9532294
##  [8,] -0.08666231  0.6942748
##  [9,] -0.90739256  3.7809783
## [10,]  1.84223101  1.3364965
## [11,]  2.04727590  1.8736805
## [12,]  0.33825921  3.4941913
## [13,]  1.80738042  0.5766373
## [14,]  1.26130759  2.2307994
## [15,]  0.52882731  1.6021497
## [16,]  1.59861449  0.8514808
## [17,]  1.42037674  1.0989390
## [18,] -0.65366487  2.5783750
## [19,]  1.74865593  1.6069304
## [20,] -0.38986048 -1.9471878

# Close the file. This is good practice.
H5close()

Add Metadata (attributes)

Let's add some metadata (called attributes in HDF5 land) to our dummy temperature data. First, open up the file.

# open the file, create a class
fid <- H5Fopen("vegData.h5")
# open up the dataset to add attributes to, as a class
did <- H5Dopen(fid, "aNEONSite/temperature")

# Provide the NAME and the ATTR (what the attribute says) for the attribute.
h5writeAttribute(did, attr="Here is a description of the data",
                 name="Description")
h5writeAttribute(did, attr="Meters",
                 name="Units")

Now we can add some attributes to the file.

# let's add some attributes to the group
did2 <- H5Gopen(fid, "aNEONSite/")

h5writeAttribute(did2, attr="San Joaquin Experimental Range",
                 name="SiteName")

h5writeAttribute(did2, attr="Southern California",
                 name="Location")

# close the files, groups and the dataset when you're done writing to them!
H5Dclose(did)

H5Gclose(did2)

H5Fclose(fid)

Working with an HDF5 File in R

Now that we've created our H5 file, let's use it! First, let's have a look at the attributes of the dataset and group in the file.

# look at the attributes of the precip_data dataset
h5readAttributes(file = "vegData.h5", 
                 name = "aNEONSite/temperature")

## $Description
## [1] "Here is a description of the data"
## 
## $Units
## [1] "Meters"

# look at the attributes of the aNEONsite group
h5readAttributes(file = "vegData.h5", 
                 name = "aNEONSite")

## $Location
## [1] "Southern California"
## 
## $SiteName
## [1] "San Joaquin Experimental Range"

# let's grab some data from the H5 file
testSubset <- h5read(file = "vegData.h5", 
                 name = "aNEONSite/temperature")

testSubset2 <- h5read(file = "vegData.h5", 
                 name = "aNEONSite/temperature",
                 index=list(NULL,1))
H5close()

Once we've extracted data from our H5 file, we can work with it in R.

# create a quick plot of the data
hist(testSubset2)