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Data Sharing:

Why share your data?  Data is shared for many reasons; it may be required by certain publishers, it may be required by certain federal funding agencies (e.g. NSF), it opens up research, it re-purposes data to answer new questions and facilitates new discoveries, and increases your research impact by making it citable by other researchers.

Data Sharing Preparations:

Once you decide to share your data, there are some data considerations that you need to prepare for like which file formats to use to ensure long term access, data documentation and metadata, and ownership and privacy issues like the data sharing implications with regards to copyright, intellectual property, and research participant confidentiality.

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Data can be shared in many ways.  The following list are some ways that data can be shared:

• Shared upon request through email.  The obvious weakness in this method of sharing is that it doesn’t permit long-term and easy discovery and preservation; e.g. if you leave the organization, the email is unfindable and it is also difficult to cite if someone want to use it.
• Posted on a personal or professional website.  The obvious weakness in this method of sharing is that there is no permanence or long term preservation; e.g. if you leave the organization, you might lose access to the website or the website might disappear with you.
• Posted as “supplemental material” on a publisher’s website.  The weakness in this method of sharing is that they publisher at some later date may decide to discontinue hosting your dataset.
• Submitted and deposited in an open repository or archive.  See 3.1 Data Preservation for information on repositories.  For commercial and discipline-specific repositories, there’s no guarantee that the company will stay in business for a long time and it may be expensive to host your data (i.e. there might be a cost).

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The following types of information are not required to be shared or archived by federal funding agencies:

• Preliminary analyses
• Communications with colleagues
• Physical objects (e.g. lab samples)
• Trade secrets
• Information that is regarded to be confidential until a researcher publishes the results for a journal paper
• Commercial information

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A common problem for researchers is researcher identification consistency throughout a researcher’s career due to name ambiguities.  Often times for example, a researcher may share the same name as someone else or have their name changed for various reasons; as a result of this, maintaining the link between you and your scholarly work is difficult.  Having a ORCID identifier solves this problem.

• ORCID is 16-digit number assigned to you by ORCID Inc.  Please note that ORCID registration is free to users and ORCID Inc. is a global, open, non-for-profit organization.  ORCID “provides a persistent digital identifier that distinguishes you from every other researcher and, through integration in key research workflows such as manuscript and grant submission, supports automated linkages between you and your professional activities ensuring that your work is recognized.”
• You can attach your ORCID identifier to all your work; e.g. CV, grant proposals, reports, dataset metadata, and your publication records.

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Data Citing:

When someone decides to use a dataset, it is important to cite that data.  Citing data is important for several reasons like it gives the dataset owner credit,  it gives users the ability to validate the dataset, and it allows for the data to be re-used.

Data Citation Elements:

There are some data features that are commonly used in a data citation and they are:

• Creator(s)/Author(s)
• Title
• Publication/Release date – the date of the dataset’s release
• Publisher – the archive, repository, or data center
• Identifier – the “unique public identifier” (e.g. DOI, Persistent Identifier etc.)

Additional Data Citation Elements:

As a rule of thumb, always try to provide as much information as possible.  There are additional citation elements that are used to describe a dynamic or larger dataset and they are:

• Version – the version of the dataset that is used in the paper analysis
• Access date – the date when the data was accessed for use in the citing paper
• Subset – e.g. list of variables or range of dates
• Verifier
• Location – the location of the dataset on the internet

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Data Citation Example:

There is no one standard for citing datasets; each respective data repository and publisher has their own guidelines.  The following is an example of a data citation taken from the DMPTool’s website:

Kumar, Sujai (2012): 20 Nematode Proteomes. figshare. https://doi.org/10.6084/m9.figshare.96035.v2 (Accessed 2016-09-06).

Source: DMPTool’s “Data Management General Guidance: DMP Tool”

Data Citation Guidelines:

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