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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 generalization. Users can select specific column(s) to generalize (A). The result of generalizing the “Height” column is shown in (B).
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Figure 3: Data generalization. Users can select specific column(s) to generalize (A). The result of generalizing the “Height” column is shown in (B).

Mentions: After the images have been selected, users are prompted to select a corresponding data file that may contain demographic and/or behavioral data. Even after explicitly removing identifying information such as name and date of birth, it is still possible to link released records back to their identities by extreme values (e.g., a 96 year old), as well as matching some combination of non-identifying attributes such as sex or zip code. Data randomization (anonymization) and generalization approaches have been proposed to mitigate risk (Wang et al., 2004; Bayardo and Agrawal, 2005; Fung et al., 2007; El Emam et al., 2009). DeID provides a data generalization function to smooth values by rounding quantitative data and reduce the possibility of re-linking the corresponding data to the subject. As shown in Figure 3, data generalization of the “HEIGHT” column smoothed values while preserving the data structure. In addition, the data selection interface allows users to identify missing data and edit the cells if the data is available. A “Revert changes” button is provided to prevent inadvertent operations.


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 generalization. Users can select specific column(s) to generalize (A). The result of generalizing the “Height” column is shown in (B).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Data generalization. Users can select specific column(s) to generalize (A). The result of generalizing the “Height” column is shown in (B).
Mentions: After the images have been selected, users are prompted to select a corresponding data file that may contain demographic and/or behavioral data. Even after explicitly removing identifying information such as name and date of birth, it is still possible to link released records back to their identities by extreme values (e.g., a 96 year old), as well as matching some combination of non-identifying attributes such as sex or zip code. Data randomization (anonymization) and generalization approaches have been proposed to mitigate risk (Wang et al., 2004; Bayardo and Agrawal, 2005; Fung et al., 2007; El Emam et al., 2009). DeID provides a data generalization function to smooth values by rounding quantitative data and reduce the possibility of re-linking the corresponding data to the subject. As shown in Figure 3, data generalization of the “HEIGHT” column smoothed values while preserving the data structure. In addition, the data selection interface allows users to identify missing data and edit the cells if the data is available. A “Revert changes” button is provided to prevent inadvertent operations.

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.