This paper reports on a project funded through the Engineering Education and Centers (EEC) Division of the National Science Foundation. Since 2010, EEC has funded more than 500 proposals totaling over $150 million through engineering education research (EER) programs such as Research in Engineering Education (REE) and Research in the Formation of Engineers (RFE), to enhance understanding and improve practice. The resulting archive of robust qualitative and quantitative data represents a vast untapped potential to exponentially increase the impact of EEC funding and transform engineering education. But tapping this potential has thus far been an intractable problem, despite ongoing calls for data sharing by public funders of research. Changing the paradigm of single-use data collection requires actionable, proven practices for effective, ethical data sharing, coupled with sufficient incentives to both share and use existing data. To that end, this project draws together a team of experts to overcome substantial obstacles in qualitative data sharing by building a framework to guide secondary analysis in engineering education research (EER), and to test this framework using pioneering data sets. Herein, we report on accomplishments within the first year of the project during which time we gathered a group of 13 expert qualitative researchers to engage in the first of a series of working meetings intended to meet our project goals. We came into this first workshop with a potentially limiting definition of secondary data analysis and the idea that people would want to share existing datasets if we could find ways around anticipated hurdles. However, the workshop yielded a broader definition of secondary data analysis and revealed a stronger interest in creating new datasets designed for sharing rather than sharing existing datasets. Thus, we have reconceived our second phase as one that is a cohesive effort based on an inclusive “open cohort model” to pilot projects related to secondary data analysis.
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The OpenNeuro resource for sharing of neuroscience data
The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
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- Award ID(s):
- 1912266
- NSF-PAR ID:
- 10385260
- Date Published:
- Journal Name:
- eLife
- Volume:
- 10
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Purpose A new method for enhancing the sensitivity of diffusion MRI (dMRI) by combining the data from single (sPFG) and double (dPFG) pulsed field gradient experiments is presented.
Methods This method uses our JESTER framework to combine microscopic anisotropy information from dFPG experiments using a new method called diffusion tensor subspace imaging (DiTSI) to augment the macroscopic anisotropy information from sPFG data analyzed using our guided by entropy spectrum pathways method. This new method, called joint estimation diffusion imaging (JEDI), combines the sensitivity to macroscopic diffusion anisotropy of sPFG with the sensitivity to microscopic diffusion anisotropy of dPFG methods.
Results Its ability to produce significantly more detailed anisotropy maps and more complete fiber tracts than existing methods within both brain white matter (WM) and gray matter (GM) is demonstrated on normal human subjects on data collected using a novel fast, robust, and clinically feasible sPFG/dPFG acquisition.
Conclusions The potential utility of this method is suggested by an initial demonstration of its ability to mitigate the problem of gyral bias. The capability of more completely characterizing the tissue structure and connectivity throughout the entire brain has broad implications for the utility and scope of dMRI in a wide range of research and clinical applications.
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ALMA-IMF is an Atacama Large Millimeter/submillimeter Array (ALMA) Large Program designed to measure the core mass function (CMF) of 15 protoclusters chosen to span their early evolutionary stages. It further aims to understand their kinematics, chemistry, and the impact of gas inflow, accretion, and dynamics on the CMF. We present here the first release of the ALMA-IMF line data cubes (DR1), produced from the combination of two ALMA 12 m-array configurations. The data include 12 spectral windows, with eight at 1.3 mm and four at 3 mm. The broad spectral coverage of ALMA-IMF (∼6.7 GHz bandwidth coverage per field) hosts a wealth of simple atomic, molecular, ionised, and complex organic molecular lines. We describe the line cube calibration done by ALMA and the subsequent calibration and imaging we performed. We discuss our choice of calibration parameters and optimisation of the cleaning parameters, and we demonstrate the utility and necessity of additional processing compared to the ALMA archive pipeline. As a demonstration of the scientific potential of these data, we present a first analysis of the DCN (3–2) line. We find that DCN (3–2) traces a diversity of morphologies and complex velocity structures, which tend to be more filamentary and widespread in evolved regions and are more compact in the young and intermediate-stage protoclusters. Furthermore, we used the DCN (3–2) emission as a tracer of the gas associated with 595 continuum cores across the 15 protoclusters, providing the first estimates of the core systemic velocities and linewidths within the sample. We find that DCN (3–2) is detected towards a higher percentage of cores in evolved regions than the young and intermediate-stage protoclusters and is likely a more complete tracer of the core population in more evolved protoclusters. The full ALMA 12m-array cubes for the ALMA-IMF Large Program are provided with this DR1 release.
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David L. Brody (Ed.)The brain injury modeling community has recommended improving model subject specificity and simulation efficiency. Here, we extend an instantaneous (<1 sec) convolutional neural network (CNN) brain model based on the anisotropic Worcester Head Injury Model (WHIM) V1.0 to account for strain differences due to individual morphological variations. Linear scaling factors relative to the generic WHIM along the three anatomical axes are used as additional CNN inputs. To generate training samples, the WHIM is randomly scaled to pair with augmented head impacts randomly generated from real-world data for simulation. An estimation of voxelized peak maximum principal strain of the whole brain is said to be successful when the linear regression slope and Pearson’s correlation coefficient relative to directly simulated do not deviate from 1.0 (when identical) by more than 0.1. Despite a modest training dataset (N=1363 vs. ~5.7 k previously), the individualized CNN achieves a success rate of 86.2% in cross-validation for scaled model responses, and 92.1% for independent generic model testing for impacts considered as complete capture of kinematic events. Using 11 scaled subject-specific models (with scaling factors determined from pre-established regression models based on head dimensions and sex and age information, and notably, without neuroimages), the morphologically individualized CNN remains accurate for impacts that also yield successful estimations for the generic WHIM. The individualized CNN instantly estimates subject-specific and spatially detailed peak strains of the entire brain and thus, supersedes others that report a scalar peak strain value incapable of informing the location of occurrence. This tool could be especially useful for youths and females due to their anticipated greater morphological differences relative to the generic model, even without the need for individual neuroimages. It has potential for a wide range of applications for injury mitigation purposes and the design of head protective gears. The voxelized strains also allow for convenient data sharing and promote collaboration among research groups.more » « less
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Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. Recently, collaborative consortia have been successful in this goal, identifying and replicating common genetic variants influencing gross human brain structure as measured through magnetic resonance imaging. In this review, we contextualize imaging genetic associations as one important link in understanding the causal chain from genetic variant to increased risk for neuropsychiatric disorders. We provide examples in other fields of how identifying genetic variant associations to disease and multiple phenotypes along the causal chain has revealed a mechanistic understanding of disease risk, with implications for how imaging genetics can be similarly applied. We discuss current findings in the imaging genetics research domain, including that common genetic variants can have a slightly larger effect on brain structure than on risk for disorders like schizophrenia, indicating a somewhat simpler genetic architecture. Also, gross brain structure measurements share a genetic basis with some, but not all, neuropsychiatric disorders, invalidating the previously held belief that they are broad endophenotypes, yet pinpointing brain regions likely involved in the pathology of specific disorders. Finally, we suggest that in order to build a more detailed mechanistic understanding of the effects of genetic variants on the brain, future directions in imaging genetics research will require observations of cellular and synaptic structure in specific brain regions beyond the resolution of magnetic resonance imaging. We expect that integrating genetic associations at biological levels from synapse to sulcus will reveal specific causal pathways impacting risk for neuropsychiatric disorders.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.71774
