updated datacite file

datacite.yml

@@ -240,22 +240,23 @@ authors:
 # A title to describe the published resource.
 title: "Automated quality control of small animal MR neuroimaging data"
 
+
 # Additional information about the resource, e.g., a brief abstract.
-description: "This dataset containes all datasets used to develop the AIDAqc pipeline and corresponds to the following Publication (DOI): tbd"
+description: "MRI is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large data sets for potential poor quality outliers can be a challenge. We present AIDAqc, a machine learning-assisted automated Python-based command-line tool for the quality assessment of small animal MRI data. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection is based on the combination of interquartile range and the machine learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. AIDAqc was challenged in a large heterogeneous dataset collected from 18 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater variability (mean Fleiss Kappa score 0.17) is high when identifying poor quality data. A direct comparison of AIDAqc results therefore showed only low to moderate concordance. In a manual post-hoc validation of AIDAqc output, agreement was high (>70%). The outlier data can have a significant impact on further post-processing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility."
 
 # List of keywords the resource should be associated with.
 # Give as many keywords as possible, to make the resource findable.
 keywords:
-  - Quality-control
+  - Quality control
   - MRI
-  - Preclinical-imaging
+  - Preclinical imaging
   - DataLad
   - Animal models
   - Imaging
   - fMRI analysis
   - Machine learning
-  - Motion compensation and analysis
-  - Functional imaging (e.g. fMRI)
+  - Motion analysis
+  - Functional imaging
   - Diffusion weighted imaging
   - Image quality assessment
 
@@ -278,9 +279,8 @@ funding:
   - "Queensland NMR Network (QNN)"
   - "European Union’s Horizon 2020 research and innovation program EOSC-Life—Providing an open collaborative space for digital biology in Europe; Grant agreement No 824087"
   - "Italian Ministry for Education and Research (FOE funding to the Euro-BioImaging Multi-Modal Molecular Imaging Italian Node)"
-  - "NIH S10 OD025016"
+  - "NIH S10 OD025016;R01AG057931"
   - "NIH/National Institute on Aging P01AG026572"
-  - "NIH R01AG057931"
   - "Center for Innovation in Brain Science"
   - "Spanish Research Agency; Grant PID2020-118546RB-I00"
   - "Horizon Europe programs CANDY under grant agreement nos. 847818"
@@ -291,8 +291,7 @@ funding:
   - "Marie Skłodowska-Curie-COFUND agreement; Grant No. 713673"
   - "SNSF Eccellenza PCEFP2_194260"
   - "CIBM Center for Biomedical Imaging of the UNIL, CHUV, EPFL, HUG, and UNIGE"
-  - "SNSF Eccellenza PCEFP2_194260"
-  - "DGF; project BO 4484/2-1 and EXC-2049-390688087 NeuroCure"
+  - "DFG project BO 4484/2-1 and EXC-2049-390688087 NeuroCure"
   - "German Federal Ministry of Education and Research (BMBF) under the ERA-NET NEURON scheme; 01EW1811 and 01EW2305"
   - "Charité 3R | Replace, Reduce, Refine"
 references:
@@ -307,20 +306,3 @@ resourcetype: Dataset
 # Do not edit or remove the following line
 templateversion: 1.2
 
-# Additional information about the resource, e.g., a brief abstract.
-description: "MRI is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large data sets for potential poor quality outliers can be a challenge. We present AIDAqc, a machine learning-assisted automated Python-based command-line tool for the quality assessment of small animal MRI data. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection is based on the combination of interquartile range and the machine learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. AIDAqc was challenged in a large heterogeneous dataset collected from 18 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater variability (mean Fleiss Kappa score 0.17) is high when identifying poor quality data. A direct comparison of AIDAqc results therefore showed only low to moderate concordance. In a manual post-hoc validation of AIDAqc output, agreement was high (>70%). The outlier data can have a significant impact on further post-processing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility."
-
-# List of keywords the resource should be associated with.
-# Give as many keywords as possible, to make the resource findable.
-keywords:
-  - Quality-control
-  - MRI
-  - Preclinical-imaging
-  - DataLad
-  - Animal models and imaging
-  - fMRI analysis
-  - Machine learning
-  - Motion compensation and analysis
-  - Functional imaging (e.g. fMRI)
-  - Diffusion weighted imaging
-  - Image quality assessment