Principles of the ODC

The spirit of the ODC is to promote the open exchange of data, tools, and ideas to accelerate treatments and cures for spinal cord and traumatic brain injuries. As far as possible, we adhere to Open Science and FAIR Data Principles.

Open Science

Open Science is a movement to make scientific research, including its processes, methodology, and outputs, more transparent.

Open Science Principles*

• Research data, datasets, databases, and protocols should be in the public domain

• This status ensures the ability to freely distribute, copy, re-format, and integrate data from research into new research, ensuring that as new technologies are developed that researchers can apply those technologies without legal barriers

• Scientific citation, attribution, and acknowledgment traditions should be cultivated in norms

*These principles were drafted by Science Commons and presented at Policy and Technology for e-Science, a satellite workshop in conjunction with the Euroscience Open Forum (ESOF) 2008

Open Science is not simply making the final product of research available (i.e., Open Access publishing of scientific articles), it involves integrating various practices throughout different stages of the research lifecycle. Open Science practices include (but are not limited to):

• Pre-registering studies

• Sharing methodology and research protocols

• Creating and using open-source software

• Sharing code for analysis pipelines and algorithms

• Publishing pre-prints

• Sharing raw (or minimally transformed) data

• Publishing scientific articles open access

FAIR Data

FAIR stands for Findable, Accessible, Interoperable, and Reusable (Wilkinson et al., 2016). FAIR establishes a framework for data sharing and defines a set of recommendations developed by FORCE11 (The Future of Research Communications and e-Scholarship) for successful data dissemination. More details can be found at the GO-FAIR initiative. FAIR data encompasses four principles:

• Findable: data should be able to be found with enough explicit metadata to be searchable;

• Accessible: data should be accessible to others in some form;

• Interoperable: data should be able to integrate with other datasets of the same nature using structured formats and standard definitions such as common data elements;

• Reusable: data should include sufficient documentation and meet community standards in order to enable subsequent reuse of the data by others.

The ODC has been developed to follow the FAIR principles. To ensure the success of data sharing with the ODC-SCI and ODC-TBI, tools and functionalities have been designed to meet the needs of the SCI and TBI research communities.

Learn more about FAIR here and here

Incorporating Open Science & FAIR Data into the ODC

The ODC uses a few key strategies to ensure published datasets follow Open Science and FAIR Data principles:

Tidy Data Format

Datasets uploaded to the ODC need to be organized in Tidy Data Format (a set of principles for organizing and structuring data) — this helps make data machine readable &#xNAN;FAIR: interoperable, reusable

Descriptive Metadata

Datasets uploaded to the ODC are accompanied by two types of descriptive metadata:

• Narrative Metadata (project-level metadata): information that helps to make the dataset searchable &#xNAN;FAIR: findable, accessible

• Data Dictionary (dataset-level metadata): information that provides granular details about the data itself &#xNAN;FAIR: interoperable, reusable

Editorial Review

To publish data to the Public Space, a DOI must be requested — this triggers the editorial review process. The editorial board and data team review submitted datasets to ensure submissions:

• Are within the scope of the ODC-SCI or ODC-TBI

• Are organized in tidy format

• Have complete metadata (narrative and dataset-associated documents)

• Meet the minimum data requirements (i.e., include all required variables in the dataset file)

  • SCI: 17 variables (ODC-SCI Community Data Elements, CoDEs)

  • TBI: 9 variables (from the PRECISE-TBI Common Data Elements)

The editorial review process generally takes a few days, however it results in higher quality shared data and ensures that shared data is FAIR.