Research Data Management: Data Sharing

As seen in the USGS data lifecycle model, sharing is an essential step in completing your research. Open data sharing is where scientific practice around digital data is heading, a fact embraced by evolving federal laws and policies. With the U.S. DOT's continued support of open science, open access data sharing has become the standard for all U.S. DOT funded research. 

Moving towards sharing is a culture change. This culture change affects every decision about how we collect, analyze, and share our data. While many would consider data sharing a process that takes place at the end of the research process, steps should be taken throughout the research process to prepare your data for sharing. This includes but is not limited to:

1. Creating and updating a Data Management Plan throughout the research process
2. Having the Principal Investigator (PI) or a data manager keep data, its versions, file naming, and hierarchy organized
3. Keep an organized research notebook, or even use an electronic lab notebook
4. Create backups of your research materials to ensure no loss of work
5. Keep track of any and all changes you make to your data
6. Define and list your variables before and during experimentation
7. If collecting Personally Identifiable Information (PII), ensure that all of those variables are removed before publication and note which variables were removed

Some Key Benefits of Data Sharing

• It encourages new discovery
• It increases the credibility of your work, as it can be verified and used by experts in your field
• It boosts awareness to your work as a researcher
• It amplifies your resume, CV, ORCID profile, or any other public profile of your accomplishments as a researcher
• It furthers the use of your research, as researchers across disciplines and practices can use your data for their projects
• It contributes to the global effort of open science
• It reduces research waste and redundancy by sharing your scientific results. Sharing all research, including negative or null results, benefits your research community

Learn more about the benefits of Open Science with Open Research Europe's Introduction to Open Research

The goal of data citation is to provide scientific transparency and attribution. Data citations benefit the researcher, funding organization, data repositories, scientific community, and general public. Data citations serve many purposes:  

• To aid scientific trustworthiness and reproducibility  

• To provide fair credit for data creators or authors, data stewards 

• To ensure scientific transparency and reasonable accountability for data authors and stewards;  

• To aid in tracking the impact of a dataset and the associated repository 

• To help data authors verify how their data are being used

• To help future users identify how others have used the data.

Sample Data Citations

• U.S. Department of Transportation, Bureau of Transportation Statistics. (2020). Commodity Flow Survey (CFS) 2017 [datasets]. https://doi.org/10.21949/1522565 

• U.S. Department of Transportation, Federal Aviation Administration. Civil Aerospace Medical Institute. (2024). Gene Expression and Biomarker Utility in Postmortem Samples [supporting dataset]. https://doi.org/10.21949/1530703

Adding to Your ORCID Profile

Ensure that your ORCID profile is up to date with all of your research outputs. For more information on how to add to your ORCID profile, please visit ORCID's site.

Add works to your ORCID record – ORCID 

The FAIR Principles were created by FORCE11 in 2014 and were designed to provide guidelines for making data more F-indable, A-ccessible, I-nteroperable, and R-eusable.

• To be Findable:
  • F1. (meta)data are assigned a globally unique and eternally persistent identifier.
  • F2. data are described with rich metadata.
  • F3. (meta)data are registered or indexed in a searchable resource.
  • F4. metadata specify the data identifier.
• To be Accessible:
  • A1  (meta)data are retrievable by their identifier using a standardized communications protocol.
  • A1.1 the protocol is open, free, and universally implementable.
  • A1.2 the protocol allows for an authentication and authorization procedure, where necessary.
  • A2 metadata are accessible, even when the data are no longer available.
• To be Interoperable:
  • I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
  • I2. (meta)data use vocabularies that follow FAIR principles.
  • I3. (meta)data include qualified references to other (meta)data.
• To be Reusable:
  • R1. meta(data) have a plurality of accurate and relevant attributes.
  • R1.1. (meta)data are released with a clear and accessible data usage license.
  • R1.2. (meta)data are associated with their provenance.
  • R1.3. (meta)data meet domain-relevant community standards.

Additionally, it is important to note that the FAIR Principles need to be part of the planning process and cannot be added on at the end. 

The FAIR Principles can seem like a challenging task at first glance, since there are many points that have to be met to achieve FAIR data. However, it is best to look at FAIR as a spectrum that you are working towards. It isn't something you are going able to achieve immediately. 

For more on the FAIR Data Principles go to https://www.force11.org/group/fairgroup/fairprinciples 

Comparing Persistent Identifiers

There are countless persistent identifiers that can be used to link to your research, project, or even yourself. Understanding each of the persistent identifier types and their purpose is fundamental to choosing the right ones for your project. At the National Transportation Library, we use 3 kinds of persistent identifiers: Digital Object Identifiers (DOIs), ORCID IDs, and Research Organization Registry IDs (RORs). Why use three different persistent identifiers? Each of these identifiers serves a unique purpose that cannot be fulfilled by another identifier type. To better understand the identifiers, please consult the table below. 

Organization	Use	Information They Store
	Digital Object Identifiers are used to identify academic, professional, and government information. They can be used on a wide variety of digital materials, including reports, publications, datasets, websites, collections, and even groups of objects. DOIs function as a persistent link for an object. Even if the URL for an object is changed, the DOI can be updated to link to that object again. They also are great for citations, as they can link to specific works easily.	DOIs can contain a wide variety of information about an object. To function, they only need a URL. However, DOI metadata contains information about a resource. The metadata can contain elements such as title, authorship, publication, keywords for search, associated publications, and more.
	ORCID IDs are identifiers that link to a researcher or individual. They link works and authorship across disciplines and organizations a person would create information under. Additionally, they ensure that authorship is correctly attributed to the researcher, regardless of similar names. Attributing proper authorship verifies that you get credit for your work.	ORCID IDs contain information on the researcher, including their biographical information, employment history, education and qualifications, current memberships and organizations, funding, works created, and peer review activities.
	Research Organization Registry IDs are identifiers that link to a specific research organization or entity. They are commonly used in combination with other identifiers to create more complete citation information. This ID ensures that information that would be attributed to an organization, such as funding or publishing, is correctly findable and traceable to the organization.	ROR IDs contain information about the name of the organization, any acronyms or aliases, the location of the organization, details such as the organization type, status, Wikipedia URL, any related organizations, and any other related identifiers.