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DeID - a data sharing tool for neuroimaging studies.

Song X, Wang J, Wang A, Meng Q, Prescott C, Tsu L, Eckert MA - Front Neurosci (2015)

Bottom Line: We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies.This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP.DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, Clemson University Clemson, SC, USA.

ABSTRACT
Funding institutions and researchers increasingly expect that data will be shared to increase scientific integrity and provide other scientists with the opportunity to use the data with novel methods that may advance understanding in a particular field of study. In practice, sharing human subject data can be complicated because data must be de-identified prior to sharing. Moreover, integrating varied data types collected in a study can be challenging and time consuming. For example, sharing data from structural imaging studies of a complex disorder requires the integration of imaging, demographic and/or behavioral data in a way that no subject identifiers are included in the de-identified dataset and with new subject labels or identification values that cannot be tracked back to the original ones. We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies. This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP. DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing. DeID can be obtained at http://www.nitrc.org/projects/deid.

No MeSH data available.


Data selection. (A) Image files are selected. (B) An xls or txt data file is selected. Note that missing values are highlighted in red to inform users about missingness that might be correctable. The highlighted columns such as ID and DOB will be pre-removed in a subsequent step shown in Figure 5.
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Figure 2: Data selection. (A) Image files are selected. (B) An xls or txt data file is selected. Note that missing values are highlighted in red to inform users about missingness that might be correctable. The highlighted columns such as ID and DOB will be pre-removed in a subsequent step shown in Figure 5.

Mentions: Users are provided with an interface to choose the source data (image and data files) on a local disk, as shown in Figure 2. Analyze 7.5 (hdr/img) and NIfTI (nii) format image files are supported in DeID. These images can include voxels representing the face as these voxels will be removed during the skull-stripping step described below. The option to share neuroimaging data that has already been skull-stripped or that do not require stripping (e.g., 4D diffusion datasets) is also supported to utilize other auditing functions of DeID to meet users' various demands. It is not uncommon that multiple copies of an image are collected for each subject and for this reason DeID was designed to accept multiple images for each subject. All of the images can be selected from a single directory or stored within subject-specific directories.


DeID - a data sharing tool for neuroimaging studies.

Song X, Wang J, Wang A, Meng Q, Prescott C, Tsu L, Eckert MA - Front Neurosci (2015)

Data selection. (A) Image files are selected. (B) An xls or txt data file is selected. Note that missing values are highlighted in red to inform users about missingness that might be correctable. The highlighted columns such as ID and DOB will be pre-removed in a subsequent step shown in Figure 5.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585207&req=5

Figure 2: Data selection. (A) Image files are selected. (B) An xls or txt data file is selected. Note that missing values are highlighted in red to inform users about missingness that might be correctable. The highlighted columns such as ID and DOB will be pre-removed in a subsequent step shown in Figure 5.
Mentions: Users are provided with an interface to choose the source data (image and data files) on a local disk, as shown in Figure 2. Analyze 7.5 (hdr/img) and NIfTI (nii) format image files are supported in DeID. These images can include voxels representing the face as these voxels will be removed during the skull-stripping step described below. The option to share neuroimaging data that has already been skull-stripped or that do not require stripping (e.g., 4D diffusion datasets) is also supported to utilize other auditing functions of DeID to meet users' various demands. It is not uncommon that multiple copies of an image are collected for each subject and for this reason DeID was designed to accept multiple images for each subject. All of the images can be selected from a single directory or stored within subject-specific directories.

Bottom Line: We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies.This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP.DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, Clemson University Clemson, SC, USA.

ABSTRACT
Funding institutions and researchers increasingly expect that data will be shared to increase scientific integrity and provide other scientists with the opportunity to use the data with novel methods that may advance understanding in a particular field of study. In practice, sharing human subject data can be complicated because data must be de-identified prior to sharing. Moreover, integrating varied data types collected in a study can be challenging and time consuming. For example, sharing data from structural imaging studies of a complex disorder requires the integration of imaging, demographic and/or behavioral data in a way that no subject identifiers are included in the de-identified dataset and with new subject labels or identification values that cannot be tracked back to the original ones. We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies. This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP. DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing. DeID can be obtained at http://www.nitrc.org/projects/deid.

No MeSH data available.