name:opening **NeuroData Tools**
Joshua T. Vogelstein | {[BME](https://www.bme.jhu.edu/),[ICM](https://icm.jhu.edu/),[CIS](http://cis.jhu.edu/),[KNDI](http://kavlijhu.org/)}@[JHU](https://www.jhu.edu/)
.foot[[jovo@jhu.edu](mailto:jovo@jhu.edu) |
| [@neuro_data](https://twitter.com/neuro_data)] --- ### Tools 1. Data Management, Visualization, and Wrangling 1. [Cloud](https://neurodata.io/ndcloud/) 2. [ndmg](https://neurodata.io/ndmg) 3. [ndreg](https://neurodata.io/ndreg) 2. Statistical Machine Learning 4. [RerF](https://neurodata.io/rerf/) 5. [MGC](https://neurodata.io/mgc/) 6. [Graspy](https://neurodata.io/graspy) 3. Scalable Algorithms 8. [LOL](https://neurodata.io/lol) 7. [clusterNOR](https://github.com/flashxio/knor) 9. [FlashX](http://flashx.io/) --- ### [Cloud](https://neurodata.io/ndcloud/)
.footnote[[Vogelstein et al. (Nature Methods) 2018](https://www.nature.com/articles/s41592-018-0181-1)] --- ### [ndmg](https://neurodata.io/ndmg)
- A Low-Resource Reliable Pipeline to Democratize Multi-Modal Connectome Estimation and Analysis .footnote[[Kiar et al. (bioRxiv) 2018](https://www.biorxiv.org/content/10.1101/188706v6)] --- #### [ndreg](https://neurodata.io/ndreg)
- Large deformation diffeomorphic metric mapping (LDDMM) - Fully automatic (no landmarks) - Modalities: iDisco, CLARITY, MRI, histology, etc., - Species: human, rat, mouse, zebrafish... .footnote[[Kutten et al. (MICCAI) 2016](https://doi.org/10.1007/978-3-319-66182-7_32)] --- ### [RerF](https://neurodata.io/rerf)
- generalization of random forests - significantly improve over best machine learning algs on >100 benchmark problems .footnote[[Tomita et al. (arXiv) 2015](https://arxiv.org/abs/1506.03410)] --- ### [MGC (pronounced "magic")](https://neurodata.io/mgc)
.footnote[[Vogelstein et al. (eLife) 2019](https://elifesciences.org/articles/41690)] --- ### [Graspy](https://neurodata.io/graspy)
.footnote[[Athreya et al. (JMLR) 2018](http://jmlr.org/papers/v18/17-448.html)] --- ### [LOL](https://neurodata.io/lol)
.footnote[[Vogelstein et al. (arXiv) 2015](https://arxiv.org/abs/1709.01233)] --- ### [clusterNOR](https://github.com/flashxio/knor)
.footnote[[Mhembere et al. (arXiv) 2019](https://arxiv.org/abs/1902.09527)] --- ### [FlashX](flashx.io)
.footnote[[Zheng et al. (PPoPP) 2018](https://doi.org/10.1145/3178487.3178501)] --- ### References 1. Data Management & Viz: [Vogelstein et al. (Nature Methods) 2018](https://www.nature.com/articles/s41592-018-0181-1) 1. functional & diffusion-MRI: [Kiar et al. (bioRxiv) 2018](https://www.biorxiv.org/content/10.1101/188706v6) 1. Registration: [Kutten et al. (MICCAI) 2016](https://doi.org/10.1007/978-3-319-66182-7_32) 1. Classify/Regress: [Tomita et al. (arXiv) 2015](https://arxiv.org/abs/1506.03410) 1. Hypothesis Testing: [Vogelstein et al. (eLife) 2019](https://elifesciences.org/articles/41690) 1. Graph Stats: [Athreya et al. (JMLR) 2018](http://jmlr.org/papers/v18/17-448.html) 1. Dimension Reduction: [Vogelstein et al. (arXiv) 2015](https://arxiv.org/abs/1709.01233) 1. Clustering: [Mhembere et al. (arXiv) 2019](https://arxiv.org/abs/1902.09527) 1. Scalable Graph/ML Primitives: [Zheng et al. (PPoPP) 2018](https://doi.org/10.1145/3178487.3178501) --- ### Acknowledgements
Carey Priebe
Randal Burns
Michael Miller
Daniel Tward
Eric Bridgeford
Vikram Chandrashekhar
Drishti Mannan
Jesse Patsolic
Benjamin Falk
Kwame Kutten
Eric Perlman
Alex Loftus
Brian Caffo
Minh Tang
Avanti Athreya
Vince Lyzinski
Daniel Sussman
Youngser Park
Cencheng Shen
Shangsi Wang
Tyler Tomita
James Brown
Disa Mhembere
Ben Pedigo
Jaewon Chung
Greg Kiar
Jeremias Sulam
♥, 🦁, 👪, 🌎, 🌌
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