Credit for reproducible research

# Credit for reproducible research

Prerequisite Importance Notes
Reproducibility Useful but not essential
Open research Useful but not essential

## Summary

Reproducible research is great, but spending time on it will reduce the time you have available for activities by which researchers are traditionally measured, such as writing papers. But what if you could get credit for your reproducibility efforts as well?

## How this will help you/ why this is useful

Academic research is a reputation economy, and citations are the currency. Most research institutions' promotion and hiring criteria depend to a greater or lesser extent on your publishing record: how many articles you have published, how "important" the journals were, and how many times each article has been cited.

This is a well established practice, and while it has its problems at least all stakeholders understand what's involved. One of the consequences of this system is that labour which doesn't result in published articles tends to be ignored, discouraging researchers from making their data more open or specialising in software development.

Establishing good citation practice for non-article content is a step towards recognising this valuable work and encouraging more people to take it up. If you can demonstrate the impact of your reproducible research work in addition to more traditional research outputs, you can justify spending more time on doing things right.

## Making it easy to cite your stuff

There are many reasons why authors don't cite the data and software that they use, but one of the biggest ones is that it's not clear how. You can go a long way to reducing this barrier by following a few steps to make it as easy as possible.

### Open research

The first step is to ensure that you have something worth citing. Practising open research isn't essential to get credit for your data or software, but it makes it much easier for others to build on your work in a way that acknowledges your contribution. There is a growing body of evidence that shows open research tends to be cited more than non-open research of equivalent quality and significance.

Learn more about: * [Making your research open][open research] * [How to make your research FAIR][rdm] [rdm]: /rdm/rdm.html ### Show people how to do it Showing an example reference in the most common referencing format in your discipline serves two purposes: 1. It demonstrates that software & data are actually things that can be cited; 2. It gives authors a reference that they can copy and paste directly into their document. If you use GitHub, GitLab or similar, consider creating a CITATION file in each repository containing an example reference. ### Add machine-readable referencing information You can go one better by allowing people to import information about your research objects into their preferred referencing database. If BibTeX is popular in your field, post a .bib file of *all* your outputs, not just your papers; if it's Endnote, make an Endnote export available. If possible, provide several formats: you won't need to update these very often and it will pay off.

### Publish in software & data journals

It's perfectly possible to cite a dataset or software package directly, and most major publishers now permit this in their style guides. However, it can sometimes help to have a more conventional paper to cite, and this is where software and data journals come in. These journals are similar to methods journals, in that they tend not to include significant results but instead focus on describing data and software in sufficient detail to allow reuse. Some examples include:

## How to cite data?

A dataset citation includes all of the same components as any other citation:

• Author
• Title
• Year of publication
• Publisher (for data, this is often the data repository where it is housed),
• Version (if indicated)
• Access information (a URL or DOI)

APA citation style: Author/Rightsholder. (Year). Title of data set (Version number). [Retrieved from https://] OR [DOI]

See also this example provided by FORCE11.

### Where to cite data in papers?

You should cite your dataset directly in the paper in places where it is relevant, just like publications, as well as in a data availability statement at the end of the paper (similar to the acknowledgement section). You can find examples of these statements in the publishers' (research data) author policies, or below:

### Data availability statement examples:

Using the Digital Object Identifier (DOI): “The data that support the findings of this study are openly available in [repository name] at http://doi.org/[doi].”

If no DOI is issued:

• “The data that support the findings of this study are openly available in [repository name] at [URL], reference number [reference number].”

When there is an embargo period you can reserve your DOI and still include a reference to the dataset in your paper:

• “The data that support the findings will be available in [repository name] at [URL / DOI] following a [6 month] embargo from the date of publication to allow for commercialisation of research findings.”

When data cannot be made available:

• “Restrictions apply to the data that support the findings of this study. [Explain nature of restrictions, for example, if the data contains information that could compromise the privacy of research participants] Data are available upon reasonable request by contacting [name and contact details] and with permission of [third party name].”
• “The data that support the findings of this study are available upon request. Access conditions and procedures can be found at [URL to restricted access repository such as EASY.]”

## How to make software citeable?

A software citation has a lot of the same elements as a data citation, described above, and are described in more detail in the Software Citation Principles. When using others software, it is vital to cite and attribute it properly.

To make your code citeable, you can use the integration between Zenodo and GitHub.

• Create a file to tell people how to cite your software. Use this handy guide to format the file.
• Link your GitHub account with a Zenodo account. This guide explains how.
• You can tell Zenodo what information or metadata you want to include with your software by adding a zenodo.json file, described here.
• On Zenodo, flip the swith to the 'on' position for the GitHub repository you want to release
• On GitHub, click the Create a new release button. Zenodo should automatically be notified and should make a snapshot copy of the current state of your repository (just one branch, without any history), and should also assign a persistent identifier (DOI) to that snapshot.
• Use the DOI in any citations of your software and tell any collaborators and users to do the same!

## Making sure YOU get cited by using ORCID to increase your visibility

### What is ORCID?

• ORCID is short for ‘Open Researcher and Contributor iD'
• ORCID is a long lasting unique identifier for you as a researcher, comparable to a personal identification number that your government may issue to you.

You can watch this short video for more information https://vimeo.com/97150912

### Why should you get an ORCID?

• To distinguish yourself from others with the same or a similar name;
• To enable others to find all your outputs/related outputs so they can use and cite them
• All your outputs will remain linked to your unique identifier even if you change your name or your institute. This way, you only have to enter the information once.
• to access or sign up to services that utilise ORCID, for example publisher requirements, funding management portals (ResearchFish), the CRIS (current research information system) system at your institute, like the Zenodo repository.
• you can add your ORCID to your CV/resume so that anyone can have a look at all your research outputs.

### When do you use your ORCID?

You can use your ORCID iD whenever you’re prompted to do so, give your trusted organisations (funders, publishers, institutions) permission to automatically update your ORCID record.

## Checklist

### For authors

• [ ] Pick out key datasets and software your conclusions rely on
• [ ] Cite data and software just like you would cite a paper
• [ ] Publish your own data/software and cite that too
• [ ] Get an ORCID ID!

### For data creators

• [ ] Deposit your data in a stable repository
• [ ] Get a persistent identifier (DOI) for your data
• [ ] Include an example citation in your dataset's README file

### For software developers

• [ ] Deposit key version snapshots of your software in a stable repository
• [ ] Get a distinct persistent identifier for each key version
• [ ] Include an example citation in your software manual