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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.


Associative match showing matching image filename and ID values. A mismatch can be corrected manually by editing the cells or the user can select options to search the path for matching values, correct mismatches that are due to multiple images for a single subject, or provide a matching string pattern.
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Figure 4: Associative match showing matching image filename and ID values. A mismatch can be corrected manually by editing the cells or the user can select options to search the path for matching values, correct mismatches that are due to multiple images for a single subject, or provide a matching string pattern.

Mentions: A new and unique ID is assigned to each subject in the data file and the subject's corresponding image(s). This step unlinks the ID value to any personal health identifiers in the contributor's records that can typically be tracked using the original ID. Once the image and data files are selected, the system will link them according to the common unique ID that appears in both image file names and the first column in the spreadsheet. This step connects the images and associated variable values in the data file (Figure 4). This step also helps users to verify that the image files and variable values are correctly matched. The status column will display MISMATCH when an item is not matched (Figure 4). A mismatch can occur because an image is missing for a case in a data file, there are multiple images for each subject, or because the filename is not an exact match for the ID label in the data file. The latter two conditions are dealt with by selecting a box indicating that multiple files are present for each subject and by searching the path for the matching ID label or by specifying a wildcard pattern. Users can select a missing value option that will fill the data file with a missing data code for the former case in which an image is present for a case with missing data in the data file.


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)

Associative match showing matching image filename and ID values. A mismatch can be corrected manually by editing the cells or the user can select options to search the path for matching values, correct mismatches that are due to multiple images for a single subject, or provide a matching string pattern.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: Associative match showing matching image filename and ID values. A mismatch can be corrected manually by editing the cells or the user can select options to search the path for matching values, correct mismatches that are due to multiple images for a single subject, or provide a matching string pattern.
Mentions: A new and unique ID is assigned to each subject in the data file and the subject's corresponding image(s). This step unlinks the ID value to any personal health identifiers in the contributor's records that can typically be tracked using the original ID. Once the image and data files are selected, the system will link them according to the common unique ID that appears in both image file names and the first column in the spreadsheet. This step connects the images and associated variable values in the data file (Figure 4). This step also helps users to verify that the image files and variable values are correctly matched. The status column will display MISMATCH when an item is not matched (Figure 4). A mismatch can occur because an image is missing for a case in a data file, there are multiple images for each subject, or because the filename is not an exact match for the ID label in the data file. The latter two conditions are dealt with by selecting a box indicating that multiple files are present for each subject and by searching the path for the matching ID label or by specifying a wildcard pattern. Users can select a missing value option that will fill the data file with a missing data code for the former case in which an image is present for a case with missing data in the data file.

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.