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README.md

README.md

Introduction to Research Data Management

These materials were prepared for the 4th ArcTrain Annual Meeting. The course was first held on September 12 and 13, 2017 and is structured in three blocks of lectures and exercises. The schedule is as follows:

09:00 - 09:15 Welcome and Overview
09:15 - 09:45 Lecture: Research Data and Data Management Planning
09:45 - 10:15 Exercise: Write a Data Management Plan with DMPonline
10:15 - 10:30 Break
10:30 - 11:00 Lecture: Working with Research Data
11:00 - 11:30 Exercise: Versioning Data with GitHub
11:30 - 11:45 Break
11:45 - 12:15 Lecture: Sharing and Archiving Research Data
12:15 - 12:45 Exercise: Archive Data with figshare and PANGAEA
12:45 - 13:00 Closing, final questions and comments

Welcome and Overview

Provides an outline for the course.

Lecture: Research Data and Data Management Planning

The first lecture introduces the notions of data, from abstract definitions to more practical ones. We discuss the concept of research data lifecycle and various data types, approaches to format and model data, as well as standardization of formats and models. We discuss the distinction between data and metadata as well as why good data management is important.

Exercise: Creating a Data Management Plan with DMPonline

DMPonline is a tool by the UK Digital Curation Centre and the University of California Curation Center that supports you in creating, reviewing, and sharing data management plans that meet institutional and funder requirements.

First, create an account. If your organization is not listed, choose 'Other' and add the name of your organization, e.g. 'University of Bremen'. Once signed in, you can edit your profile. Do you have an ORCID iD? You can link it to your DMPonline profile.

The main page shows your plans, including plans that have been shared with you.

Let's create a plan. You'll need to answer a few questions so that DMPonline can setup the DMP template. Select the 'European Commission (Horizon 2020)' as funding organisation.

Projects that participate in the Horizon 2020 Open Research Data Pilot are required to develop a Data Management Plan. Projects starting from January 2017 are by default part of the Open Data Pilot. See also the Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020.

On the first page (Plan details) you find instructions on the Initial DMP, Detailed DMP and Final review DMP. The Initial DMP must be submitted (as a deliverable) within the first 6 months of the project. The Detailed DMP is meant to capture finer details and updates as the project progresses. The Final review DMP is meant for the final project review and may be suitable also for intermediate reviews. Scroll through the sections and see what kind of questions are asked.

Select the Initial DMP tab and provide some answers to the questions. As you answer questions, you will see the progress bar updating itself. DMPonline also records the time and the person that answered the question.

DMPonline supports the export of DMPs in various formats, including PDF. Try this functionality with your Initial DMP.

Explore the differences between the Initial DMP and the Detailed DMP (and Final review DMP).

DMPonline also supports sharing DMPs with your collaborators. This is a useful functionality. Try to share your DMP with a fellow student. You will need the email. Note the permission setting.

Lecture: Working with Research Data

The second lecture discusses aspects of working with research data, such finding and accessing data, data retrieval and reuse, relational databases and other technologies used to store and version data.

Exercise: Versioning Data with GitHub

Git supports collaborative work on documents, typically software source code but it can be any (text) file including articles, slides, posters---or data files.

To use Git, you need to install Git on your computer and choose an online Git repository service. GitHub is arguably the most popular. GitHub is free only for public projects, meaning that you need a paid plan if you want to manage your documents in private repositories. There are alternatives, including some that include private repositories in their free plan. Bitbucket is an example.

The course material is managed in a GitHub repository. The repository also contains an example data file. The goal in this exercise is to collaboratively make changes to this file.

Let's create an account on GitHub. If you already have an account, sign in. Next, fork the course material repository to your account.

Hint: Look for the Fork buttom on the top-right.

This will create a copy of the lecture course material into your GitHub account.

In order to make changes, you need to clone the forked repository to your computer with the following command (substitute [YOUR_ACCOUNT_NAME] accordingly):

git clone https://github.com/[YOUR_ACCOUT_NAME]/RDM101.git

Hint: On Windows you need to start a Git Bash.

Now you can navigate into the RDM101/data folder and edit the example.csv data file. Try to change a temperature value, and save the file.

With the following command

git status

you are told about any changes in your working directory. You will see that the file example.csv has been modified. You can now commit this change and push it to GitHub, using the following commands

git commit -a -m "modified temperature value"
git push

In order to push, you need to provide your GitHub username and password.

Now, check the latest commit on GitHub. Hint: Look for the latest commit id. GitHub provides a good overview of the changes you made.

As you have corrected the data, you now want to inform the source to pull the change so that it can be integrated. For this, go to the Pull requests tab. The change can be merged; you can simple create the pull request. The source is then notified and can merge the change.

Naturally, GitHub repositories can have collaborators, which can be managed under settings. This is a way to give your collaborators rights to push and pull changes directly from your repository, thus bypassing pull requests. With the following command you can updated your working directory to include changes others may have pushed

git pull

Lecture: Sharing and Archiving Research Data

The third lecture explores drivers behind the Open Data movement, discusses some of the benefits and challenges of sharing data, issues such as data identification and citation, and the role of data repositories.

Exercise: Archiving data with figshare and PANGAEA

Enabling reproducibility of your research is not easy, even for in silico studies. Enabling repurposing of research data you have collected is similarly a challenge. An important subtask is to archive research data, understood broadly to include everything needed to reproduce your research (from collected observational data to software, samples, etc.).

In order to archive research data you will need to create and publish a "release" of relevant materials. For digital data, such as datasets and software, this means creating a package that collects and documents the materials. Important here is that you make sure that, given the package, it is possible to replicate your research. Furthermore, it means publishing the package so that it can be accessed. There is a maturing infrastructure that supports you with publishing, archiving and preserving research data, often following international principles, guidelines, and best practices.

Here we look at two online services that support publishing research data: figshare and PANGAEA. There are important differences between these two services, so that it is actually hard to compare them. PANGAEA specializes for Earth & Environmental Sciences whereas figshare does not specialize on a domain. PANGAEA specializes for research data, in particular numeric data as well as images, video and sound whereas figshare accepts, in addition, slides, software, papers, posters. Both figshare and PANGAEA support the unambiguous identification of published materials by means of DOI. PANGAEA's specialization enables data and metadata standardization at the time of ingestion. Contrary to figshare, where data are published in original form, PANGAEA curates and ingests submitted data into a database. As a result, data on figshare are very heterogeneous whereas data on PANGAEA are more standardized. PANGAEA can thus provide an integrated data warehouse that supports powerful operations, such as data retrieval for selected parameters, time intervals and regions in space over the complete data holding.

Let's start with figshare. If you do not have an account yet, you'll need to sign up. Once signed in, explore fighare's functionality. Check out your profile and link your ORCID iD. You can complete your profile with links to social media and biographical information. Under "My data" you will see a list of data you published with figshare.

As an exercise, try to publish our example.csv data file (or any other file you like to try). Start by creating a new item. Upload the file and document it with metadata, e.g. title, authors, description, etc. You can save the newly created item at any time. Look at the functionalities for embargo, private link, and DOI. You can use the private link to give access to others, e.g. by sharing the link in personal communication. The displayed DOI is at first reserved. You can used it, e.g. to cite your data in an article but the DOI will only be resolvable (via doi.org) after you publish the data on figshare. Be careful here because published items can no longer be deleted! Figshare allows you to create collections, which is a useful feature to group items together, for instance because they are part of an article.

Next, let's look at PANGAEA. To submit data, you will need to register as a new user. Submitting data at PANGAEA will create a new issue in their issue tracker. The submission form is similar to the one you saw at figshare. The key difference is that you need to treat this form as you treat journal article submission forms: no tests here. The reason is that a data submission triggers a response with human curators that will handle the issue. Obviously, they don't like to be triggered for nothing.

As a further exercise, check your PANGAEA profile and connect your account to your ORCID iD. Doing so will enable three things. First, you will be able to sign into PANGAEA using your ORCID credentials, as an alternative to the PANGAEA credentials. Second, as a contributor to dataset publications, you will be unambiguouly identified: your (ambiguous) name will be linked with your (unambiguous) ORCID iD. Third, PANGAEA will communicate links between your ORCID iD and DOIs of datasets you have co-authored with ORCID, meaning that your contributions to dataset publications will be recorded on your ORCID record.

Note: To activate such updates on your ORCID record you will need to create a DataCite profile at profiles.datacite.org by sign in with ORCID.

Finally, let's take a look at the PANGAEA data warehouse. The traditional way to search PANGAEA is by keywords and filtering by facets. The shown results are data publications (including collections). If you search for temperature, PANGAEA will currently find about 137K datasets. You can further filter them, e.g. in space-time. For August 2017, there are currently 8 datasets (matching temperature). Technically, you can select them individually, download and integrate the data. However, a query over the entire set is easier done uing the data warehouse.

As an exercise, sign into PANGAEA, perform a keyword search, filter the results, and start the data warehouse. Hint: The button can be found on the right hand side. You can now select available parameters and geocodes, say date/time, latitude and longitude, etc. Select some. Note that on some parameters you can apply methods, e.g. average over the year. Start the query. The result is a file that contains the corresponding data, including the origin of values. Note that the data are compiled from multiple origins (identified by the corresponding DOI).

Closing

Closing remarks, final questions and comments.