Comments (5)
as the demand of Scientific Data
this paper should be composed of:
Title (110 characters maximum, including whitespaces)
Abstract (170 words maximum, no references)
Background & Summary (700 words maximum)
Methods (unlimited length)
Data Records (unlimited length)
Technical Validation (unlimited length)
Usage Notes (unlimited length)
Code Availability
References
Figures (generally no more than three per manuscript)
Tables (generally no more than ten per manuscript)
from m2g.
Abstract:
Connectomes in human brain is very complex but it is the fundamental of doing advance research in cognitive science, finding treatments of brain diseases and brain-computer interface. Right now, most of the open source datasets are original dMRI files which need researchers to do a complicated further processing by themselves to get the connectomes. In this way, we search xxxx different datasets and use the stat-of-the-art pipeline called m2g to generate the worldโs biggest human brain connectomes datasets, which contains xxxx samples and have been checked by clinical biomarkers. Researchers from every fields can directly use our high-level datasets to do your advance analysis about the human brain.
from m2g.
Sprint 1 Goals: run m2g in all open source dMRI datasets and get the connectomes dataset. Write the draft of our paper.
from m2g.
- need more details about what datasets will be run
- need to define what it means for the draft of the paper to be done
- I am guessing the paper will take longer than this to write
from m2g.
Sprint 1 Goals:
write the draft of the Title, Abstract, Background & Summary, Methods, Data Records, Usage Notes, Code Availability
run all of the dataset that we have (will have a list after searching) and gererate the connectomes.
from m2g.
Related Issues (20)
- Get QA intermediate registration work again HOT 1
- QA on Tensor Estimation HOT 1
- pure python for fast HOT 2
- @wilttang is ready
- Remove ActStoppingCriteron
- Issue with CSD Model
- Write a data descriptor paper, correct neuroparc atlas labels, create analysis code for analyzing m2g results. Datasets used: https://docs.google.com/spreadsheets/d/1Vr4uL6LZ2qtYdMztYYvf7dRJCvf14NG9x7rERJGPGaI/edit?usp=drive_web&ouid=109524036410778138686 HOT 5
- Writing a paper HOT 3
- Write a paper HOT 8
- Write a Data Descriptors Paper HOT 3
- Write a paper of data description HOT 4
- Make m2g_bids and dwi_pipeline arguments agree with each other?
- Flesh-out parcellation finding
- Harvard Oxford Label Correspondence HOT 1
- Enhancements
- Possible steps to add to DTI pipeline HOT 2
- Migrate travis test to new domain HOT 1
- TODO for version v1.0.0
- how can find the list of parcellation we can use as input
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from m2g.