1.1. Create a dataset

We are about to start the educational course DataLad-101. In order to follow along and organize course content, let us create a directory on our computer to collate the materials, assignments, and notes in.

Since this is DataLad-101, let’s do it as a DataLad dataset. You might associate the term “dataset” with a large spreadsheet containing variables and data. But for DataLad, a dataset is the core data type: As noted in A brief overview of DataLad, a dataset is a collection of files in folders, and a file is the smallest unit any dataset can contain. Although this is a very simple concept, datasets come with many useful features. Because experiencing is more insightful than just reading, we will explore the concepts of DataLad datasets together by creating one.

Find a nice place on your computer’s file system to put a dataset for DataLad-101, and create a fresh, empty dataset with the datalad create command (datalad-create manual).

Note the command structure of datalad create (optional bits are enclosed in [ ]):

datalad create [--description "..."] [-c <config options>] PATH

What is the description option?

The optional --description flag allows you to provide a short description of the location of your dataset, for example with

datalad create --description "course on DataLad-101 on my private Laptop" -c text2git DataLad-101

If you want, use the above command instead of the create command below to provide a description. Its use will not be immediately clear, the chapter Collaboration) will show you where this description ends up and how it may be useful.

Let’s start:

$ datalad create -c text2git DataLad-101
[INFO] Creating a new annex repo at /home/me/dl-101/DataLad-101 
[INFO] Running procedure cfg_text2git 
[INFO] == Command start (output follows) ===== 
[INFO] == Command exit (modification check follows) ===== 
create(ok): /home/me/dl-101/DataLad-101 (dataset)

This will create a dataset called DataLad-101 in the directory you are currently in. For now, disregard -c text2git. It applies a configuration template, but there will be other parts of this book to explain this in detail.

Once created, a DataLad dataset looks like any other directory on your file system. Currently, it seems empty.

$ cd DataLad-101
$ ls    # ls does not show any output, because the dataset is empty.

However, all files and directories you store within the DataLad dataset can be tracked (should you want them to be tracked). Tracking in this context means that edits done to a file are automatically associated with information about the change, the author of the edit, and the time of this change. This is already informative important on its own – the provenance captured with this can for example be used to learn about a file’s lineage, and can establish trust in it. But what is especially helpful is that previous states of files or directories can be restored. Remember the last time you accidentally deleted content in a file, but only realized after you saved it? With DataLad, no mistakes are forever. We will see many examples of this later in the book, and such information is stored in what we will refer to as the history of a dataset.

This history is almost as small as it can be at the current state, but let’s take a look at it. For looking at the history, the code examples will use git log, a built-in Git command1 that works right in your terminal. Your log might be opened in a terminal pager that lets you scroll up and down with your arrow keys, but not enter any more commands. If this happens, you can get out of git log by pressing q.

$ git log
commit c74e2b7951db566d5ccce7faeb2f86584d4d6321
Author: Elena Piscopia <[email protected]>
Date:   Mon Feb 17 08:48:47 2020 +0100

    Instruct annex to add text files to Git

commit dbaf9fae9f69b9bfea1a6b7e2f374335f41702c1
Author: Elena Piscopia <[email protected]>
Date:   Mon Feb 17 08:48:46 2020 +0100

    [DATALAD] new dataset

We can see two commits in the history of the repository. Each of them is identified by a unique 40 character sequence, called a shasum. Highlighted in this output is information about the author and about the time, as well as a commit message that summarizes the performed action concisely. In this case, both commit messages were written by DataLad itself. The most recent change is on the top. The first commit written to the history therefore states that a new dataset was created, and the second commit is related to the -c text2git option (which uses a configuration template to instruct DataLad to store text files in Git, but more on this later). Even though these commits were produced by DataLad, in most other cases, you will have to create the commit and an informative commit message yourself.

Note for Git users

datalad create uses git init and git-annex init. Therefore, the DataLad dataset is a Git repository. Large file content in the dataset is tracked with git-annex. An ls -a reveals that Git has secretly done its work:

$ ls -a # show also hidden files
.
..
.datalad
.git
.gitattributes

For non-Git-Users: these hidden dot-directories are necessary for all git magic to work. Please do not tamper with them, and, importantly, do not delete them.

Congratulations, you just created your first DataLad dataset! Let us now put some content inside.

Footnotes

1

A tool we can recommend as an alternative to git log is tig. Once installed, exchange any git log command you see here with the single word tig.