DOC: Add long description including background/significance

long_description.md

@@ -0,0 +1,44 @@
+Magnetic resonance imaging (MRI) requires a set of preprocessing steps before
+any statistical analysis. In an effort to standardize preprocessing,
+we developed [fMRIPrep](https://fmriprep.org/en/stable/) (a preprocessing tool
+for functional MRI, fMRI), and generalized its standardization approach to
+other neuroimaging modalities ([NiPreps](https://www.nipreps.org/)). NiPreps
+brings standardization and ease of use to the researcher, and effectively
+limits the methodological variability within preprocessing. fMRIPrep is designed
+to be used across wide ranges of populations; however it is designed for (and
+evaluated with) human adult datasets. Infant MRI (i.e., 0-2 years) presents
+unique challenges due to head size (e.g., reduced SNR and increased partial
+voluming and rapid shifting in tissue contrast due to myelination. These and
+other challenges require a more specialized workflow. *NiBabies*, an open-source
+pipeline extending from fMRIPrep for infant structural and functional MRI
+preprocessing, aims to address this need.
+
+The workflow is built atop [Nipype](https://nipype.readthedocs.io) and encompases a large
+set of tools from well-known neuroimaging packages, including
+[FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/),
+[ANTs](https://stnava.github.io/ANTs/),
+[FreeSurfer](https://surfer.nmr.mgh.harvard.edu/),
+[AFNI](https://afni.nimh.nih.gov/),
+[Connectome Workbench](https://humanconnectome.org/software/connectome-workbench),
+and [Nilearn](https://nilearn.github.io/).
+This pipeline was designed to provide the best software implementation for each state of
+preprocessing, and will be updated as newer and better neuroimaging software becomes
+available.
+
+*NiBabies* performs basic preprocessing steps (coregistration, normalization, unwarping,
+segmentation, skullstripping etc.) providing outputs that can be
+easily submitted to a variety of group level analyses, including task-based or resting-state
+fMRI, graph theory measures, surface or volume-based statistics, etc.
+*NiBabies* allows you to easily do the following:
+
+  * Take fMRI data from *unprocessed* (only reconstructed) to ready for analysis.
+  * Implement tools from different software packages.
+  * Achieve optimal data processing quality by using the best tools available.
+  * Generate preprocessing-assessment reports, with which the user can easily identify problems.
+  * Receive verbose output concerning the stage of preprocessing for each subject, including
+    meaningful errors.
+  * Automate and parallelize processing steps, which provides a significant speed-up from
+    typical linear, manual processing.
+
+[Repository](https://github.com/nipreps/nibabies)
+[Documentation](https://nibabies.readthedocs.io/en/stable/)

setup.cfg

@@ -11,7 +11,7 @@ classifiers =
     Programming Language :: Python :: 3.9
 description = NeuroImaging Babies provides processing tools for magnetic resonance images of the brain in infants.
 license = Apache License, 2.0
-long_description = file:README.md
+long_description = file:long_description.md
 long_description_content_type = text/markdown; charset=UTF-8
 project_urls =
     Documentation=https://nibabies.readthedocs.io/en/latest/