# Scientific data

Objectives

• Get an overview of different formats for scientific data

• Understand performance pitfalls when working with big data

• Learn how to work with the NetCDF format through Xarray

• Discuss the pros and cons of open science

• Learn how to mint a DOI for your project or dataset

Instructor note

• 30 min teaching/type-along

• 20 min exercises

## Types of scientific data

### Bit and Byte

The smallest building block of storage in the computer is a bit, which stores either a 0 or 1. Normally a number of 8 bits are combined in a group to make a byte. One byte (8 bits) can represent/hold at most $$2^8$$ distint values. Organising bytes in different ways can represent different types of information, i.e. data.

### Numerical Data

Different numerical data types (e.g. integer and floating-point numbers) can be represented by bytes. The more bytes we use for each value, the larger is the range or precision we get, but more bytes require more memory.

For example, integers stored with 1 byte (8 bits) have a range from [-128, 127], while with 2 bytes (16 bits) the range becomes [-32768, 32767]. Integers are whole numbers and can be represented exactly given enough bytes. However, for floating-point numbers the decimal fractions can not be represented exactly as binary (base 2) fractions in most cases which is known as the representation error. Arithmetic operations will further propagate this error. That is why in scienctific computing, numerical algorithms have to be carefully designed to not accumulate errors, and floating-point numbers are usally allocated with 8 bytes to make sure the inaccuracy is under control and does not lead to unsteady solutions.

Single vs double precision

In many computational modeling domains, it is common practice to use single precision in some parts of the modeling to achieve better performance at an affordable cost to the accuracy. For example in climate simulations, molecular dynamics and machine learning.

Have you used single precision in your modeling? Did you observe higher performance?

### Text Data

When it comes to text data, the simplest character encoding is ASCII (American Standard Code for Information Interchange) and was the most common character encodings until 2008 when UTF-8 took over. The orignal ASCII uses only 7 bits for representing each character and therefore encodes only 128 specified characters. Later it became common to use an 8-bit byte to store each character in memory, providing an extended ASCII.

As computers became more powerful and the need for including more characters from other languages like Chinese, Greek and Arabic became more pressing, UTF-8 became the most common encoding. UTF-8 uses a minimum of one byte and up to four bytes per character.

### Data and storage format

In real scientific applications, data is complex and structured and usually contains both numerical and text data. Here we list a few of the data and file storage formats commonly used.

#### Tabular Data

A very common type of data is “tabular data”. Tabular data is structured into rows and columns. Each column usually has a name and a specific data type while each row is a distinct sample which provides data according to each column (including missing values). The simplest and most common way to save tabular data is via the so-called CSV (comma-separated values) file.

#### Gridded Data

Gridded data is another very common data type in which numerical data is normally saved in a multi-dimentional rectangular grid. Most probably it is saved in one of the following formats:

• Hierarchical Data Format (HDF5) - Container for many arrays

• Network Common Data Form (NetCDF) - Container for many arrays which conform to the NetCDF data model

• Zarr - New cloud-optimized format for array storage

Metadata consists of various information about the data. Different types of data may have different metadata conventions.

In Earth and Environmental science, there are widespread robust practices around metadata. For NetCDF files, metadata can be embedded directly into the data files. The most common metadata convention is Climate and Forecast (CF) Conventions, commonly used with NetCDF data.

When it comes to data storage, there are many types of storage formats used in scietific computing and data analysis. There isn’t one data storage format that works in all cases, so choose a file format that best suits your data.

#### CSV (comma-separated values)

Key features

• Type: Text format

• Packages needed: NumPy, Pandas

• Good for sharing/archival: Yes

• Tidy data:

• Ease of use: Great

• Array data:

• Ease of use: Ok for one or two dimensional data. Bad for anything higher.

• Best use cases: Sharing data. Small data. Data that needs to be human-readable.

CSV is by far the most popular file format, as it is human-readable and easily shareable. However, it is not the best format to use when you’re working with big data.

Important

When working with floating point numbers, you should be careful to save the data with enough decimal places so that you won’t lose precision.

1. You may lose data precision simply because you do not save the data with enough decimals

2. CSV writing routines in Pandas and NumPy try to avoid such problems by writing floating point numbers with enough precision, but they are not perfect.

3. Storage of high-precision CSV files is usually very inefficient storage-wise.

4. Binary files, where floating point numbers are represented in their native binary format, do not suffer from these problems.

#### HDF5 (Hierarchical Data Format version 5)

Key features

• Type: Binary format

• Packages needed: Pandas, PyTables, h5py

• Space efficiency: Good for numeric data.

• Good for sharing/archival: Yes, if datasets are named well.

• Tidy data:
• Speed: Ok

• Ease of use: Good

• Array data:
• Speed: Great

• Ease of use: Good

• Best use cases: Working with big datasets in array data format.

HDF5 is a high performance storage format for storing large amounts of data in multiple datasets in a single file. It is especially popular in fields where you need to store big multidimensional arrays such as physical sciences.

#### NetCDF4 (Network Common Data Form version 4)

Key features

• Type: Binary format

• Packages needed: Pandas, netCDF4/h5netcdf, xarray

• Space efficiency: Good for numeric data.

• Good for sharing/archival: Yes.

• Tidy data:
• Speed: Ok

• Ease of use: Good

• Array data:
• Speed: Good

• Ease of use: Great

• Best use cases: Working with big datasets in array data format. Especially useful if the dataset contains spatial or temporal dimensions. Archiving or sharing those datasets.

NetCDF4 is a data format that uses HDF5 as its file format, but it has standardized structure of datasets and metadata related to these datasets. This makes it possible to be read from various different programs.

NetCDF4 is by far the most common format for storing large data from big simulations in physical sciences.

The advantage of NetCDF4 compared to HDF5 is that one can easily add additional metadata, e.g. spatial dimensions (x, y, z) or timestamps (t) that tell where the grid-points are situated. As the format is standardized, many programs can use this metadata for visualization and further analysis.

#### There’s more

• Feather: a portable file format for storing Arrow tables or data frames (from languages like Python or R)

• Parquet: a standardized open-source columnar storage format for use in data analysis systems

#### Xarray

Xarray is a Python package that builds on NumPy but adds labels to multi-dimensional arrays. It also borrows heavily from the Pandas package for labelled tabular data and integrates tightly with dask for parallel computing. NumPy, Pandas and Dask will be covered in later episodes.

Xarray is particularly tailored to working with NetCDF files. It reads and writes to NetCDF files using the open_dataset() / open_dataarray() functions and the to_netcdf() method. Explore these in the exercise below!

## Sharing data

The Open Science movement encourages researchers to share research output beyond the contents of a published academic article (and possibly supplementary information).

### Pros and cons of sharing data (from Wikipedia)

In favor:

• Open access publication of research reports and data allows for rigorous peer-review

• Science is publicly funded so all results of the research should be publicly available

• Open Science will make science more reproducible and transparent

• Open Science has more impact

• Open Science will help answer uniquely complex questions

Against:

• Too much unsorted information overwhelms scientists

• Potential misuse

• The public will misunderstand science data

• Increasing the scale of science will make verification of any discovery more difficult

• Low-quality science

### FAIR principles

(This image was created by Scriberia for The Turing Way community and is used under a CC-BY licence. The image was obtained from https://zenodo.org/record/3332808)

“FAIR” is the current buzzword for data management. You may be asked about it in, for example, making data management plans for grants:

• Findable

• Will anyone else know that your data exists?

• Solutions: put it in a standard repository, or at least a description of the data. Get a digital object identifier (DOI).

• Accessible

• Once someone knows that the data exists, can they get it?

• Usually solved by being in a repository, but for non-open data, may require more procedures.

• Interoperable

• Is your data in a format that can be used by others, like csv instead of PDF?

• Or better than csv. Example: 5-star open data

• Reusable

• Is there a license allowing others to re-use?

Even though this is usually referred to as “open data”, it means considering and making good decisions, even if non-open.

FAIR principles are usually discussed in the context of data, but they apply also for research software.

Note that FAIR principles do not require data/software to be open.

Discuss open science

• Do you share any other research outputs besides published articles and possibly source code?

• Discuss pros and cons of sharing research data.

### Services for sharing and collaborating on research data

To find a research data repository for your data, you can search on the Registry of Research Data Repositories re3data platform and filter by country, content type, discipline, etc.

International:

• Zenodo: A general-purpose open access repository created by OpenAIRE and CERN. Integration with GitHub, allows researchers to upload files up to 50 GB.

• Figshare: Online digital repository where researchers can preserve and share their research outputs (figures, datasets, images and videos). Users can make all of their research outputs available in a citable, shareable and discoverable manner.

• EUDAT: European platform for researchers and practitioners from any research discipline to preserve, find, access, and process data in a trusted environment.

• Dryad: A general-purpose home for a wide diversity of datatypes, governed by a nonprofit membership organization. A curated resource that makes the data underlying scientific publications discoverable, freely reusable, and citable.

• The Open Science Framework: Gives free accounts for collaboration around files and other research artifacts. Each account can have up to 5 GB of files without any problem, and it remains private until you make it public.

Sweden:

## Exercises

Use Xarray to work with NetCDF files

First create an Xarray dataset:

import numpy as np
import xarray as xr

ds1 = xr.Dataset(
data_vars={
"a": (("x", "y"), np.random.randn(4, 2)),
"b": (("z", "x"), np.random.randn(6, 4)),
},
coords={
"x": np.arange(4),
"y": np.arange(-2, 0),
"z": np.arange(-3, 3),
},
)
ds2 = xr.Dataset(
data_vars={
"a": (("x", "y"), np.random.randn(7, 3)),
"b": (("z", "x"), np.random.randn(2, 7)),
},
coords={
"x": np.arange(6, 13),
"y": np.arange(3),
"z": np.arange(3, 5),
},
)


Then write the datasets to disk using to_netcdf() method:

ds1.to_netcdf("ds1.nc")
ds2.to_netcdf("ds2.nc")


You can read an individual file from disk by:

ds1 = xr.open_dataset("ds1.nc")


But you can also read both at once into an aggregated dataset object using the open_mfdataset() method:

ds = xr.open_mfdataset('ds*.nc')


• Explore the hierarchical structure of the ds1 and ds2 datasets in a Jupyter notebook by typing the variable names in a code cell and execute. Click the disk-looking objects on the right to expand the fields.

• Explore the ds dataset and compare its dimensions to the ds1 and ds2 datasets. Have the two datasets been merged?

Get a DOI by connecting your repository to Zenodo

Digital object identifiers (DOI) are the backbone of the academic reference and metrics system. In this exercise you will see how to make a GitHub repository citable by archiving it on the Zenodo archiving service. Zenodo is a general-purpose open access repository created by OpenAIRE and CERN.

For this exercise you need to have a GitHub account and at least one public repository that you can use for testing. If you need a new repository, you can fork for example this one (click the “fork” button in the top right corner and fork it to your username).

1. Sign in to Zenodo using your GitHub account. For this exercise, use the sandbox service: https://sandbox.zenodo.org/login/. This is a test version of the real Zenodo platform.

3. Find the repository you wish to publish, and flip the switch to ON.

4. Go to GitHub and create a release by clicking the Create a new release on the right-hand side (a release is based on a Git tag, but is a higher-level GitHub feature).

5. Creating a new release will trigger Zenodo into archiving your repository, and a DOI badge will be displayed next to your repository after a minute or two.

6. You can include the DOI badge in your repository’s README file by clicking the DOI badge and copy the relevant format (Markdown, RST, HTML).