Third Data Release
We would like to announce the third release of data as part of the Developing Human Connectome Project (dHCP) – an ERC-funded collaboration between King’s College London, Imperial College London and University of Oxford.
This is the project’s third open access data release which consists of images of 783 neonatal subjects (886 datasets). The imaging data includes structural imaging, structural connectivity data (diffusion MRI) and functional connectivity data (resting-state fMRI). This data release comes with minimal accompanying metadata: sex, age at birth, age at scan, birthweight, head circumference and radiology score. More specific information about the available data can be found in the data organisation notes. To access the data you will be required to agree to a simple data sharing agreement and will then be provided with access routes to two different modes of data download.
We invite colleagues in the field to explore and feedback on the value and characteristics of the image dataset. The image data have been processed using analysis pipelines that are subject to further development. If you use this data or the pipelines please cite the appropriate publications as detailed in the How to cite notes.
The project to date has successfully completed over 800 neonatal scans and 300 fetal scans. Further data releases are planned - these will be announced on the dHCP website when they are ready.
The research leading to these data has received funding from the European Research Council under the European Union Seventh Framework Programme (FP/20072013)/ERC Grant Agreement no. 319456. The work was also supported by the NIHR Biomedical Research Centres at Guys and St Thomas NHS Trust. We are grateful to the families who generously supported this trial. We would like to acknowledge Core support for data acquisition was provided by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. We are also thankful to the WU-Minn-Oxford Human Connectome Project consortium (1U54MH091657-01) for access to their computing resources.
Study data were collected and managed using REDCap electronic data capture tools hosted at King’s College, London.
REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.
Harris, PA., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., Conde, JG., Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81. DOI: 10.1016/j.jbi.2008.08.010
Harris, PA., Taylor, R., Minor, BL., Elliott, V., Fernandez, M., O’Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., Duda, SN., REDCap Consortium, The REDCap consortium: Building an international community of software partners, J Biomed Inform. 2019 May 9 DOI: 10.1016/j.jbi.2019.103208
Burns, SS., Browne, A., Davis, GN., Rimrodt, SL., Cutting, LE. PyCap (Version 1.0) Nashville, TN: Vanderbilt University and Philadelphia, PA: Childrens Hospital of Philadelphia. https://github.com/sburns/PyCap DOI: 10.5281/zenodo.9917