[scikit-learn] Note of appreciation to Scikit-learn team

[Raga Markely]

Will definitely acknowledge scikit-learn, scipy, etc community in papers,
posters, talks, etc.. i also saw suggested citations on scikit-learn
website.. i will include these as well..if there is anything else that will
be helpful, please let us know..

Sincerely hope that all of your contributions (not just the codes, but also
tutorial in scipy conference, books & blogs that you have published, etc)
will help you in your careers in many different ways..

Best,
Raga

On Mar 23, 2017 11:15 AM, "Andreas Mueller" <t3kcit at gmail.com> wrote:

> I want to join Gaël in thanking you for saying thanks.
> It's great to see appreciation of the work that the scientific python
> community does.
> I don't think I've seen anyone cite scipy in their research work, even
> though it is the backbone for so many papers.
> It's important for us that the academic environment recognizes software
> contributions, because many
> of us rely on academic funding to do this work.
>
> Best,
> Andy
>
> On 03/22/2017 01:10 AM, Brown J.B. wrote:
>
> To all organizers, developers, and maintainers involved in the
> Scikit-learn project,
>
> I would like to share a recent article that researchers from MIT, ETH, and
> Kyoto University (myself) have published about building efficient models
> for drug discovery and pharmaceutical data mining.
>
> In short, it demonstrates through replicate experiment that neither big
> data nor complex AI such as deep learning are necessary for efficient drug
> discovery, and that active learning can guide/assist decision making
> processes in the real world.
>
> The paper's success is underpinned by the use of Scikit-learn's
> RandomForestClassifier implementation combined with other techniques
> developed in the work.
> Therefore, it is a by-product of the volunteerism, hard work, and
> dedication by those involved in scikit-learn.
>
> As the senior author of this study, I wish to share my great appreciation
> for your efforts.
> While I am strongly limited in time and can barely contribute to this
> community, I cannot thank all of you enough for your work - it has made an
> impact.
>
> We are working on theoretical extensions of the work now, as well as
> pushing the technology forward in applied discovery sciences (in
> agricultural, pharmaceutical, and medical areas).  In the theory and
> real-world applications, scikit-learn is indispensible.
>
> We have made the paper open access, and hope that such will inspire this
> community as well as those in applied sciences.
> You will see that the open source software community has been listed in
> the Acknowledgments.
> Certainly, we would welcome even the most casual of comments about the
> paper.
>
> The paper can be retrieved from here:
> http://www.future-science.com/doi/abs/10.4155/fmc-2016-0197
>
> With kindest regards and sincere appreciation,
> J.B. Brown
> Kyoto University Graduate School of Medicine
> Junior Associate Professor and Principal Investigator
> http://statlsi.med.kyoto-u.ac.jp/~jbbrown
>
> PS - To those of you involved in the matplotlib, scipy, and numpy
> projects, your forwarding of this to those projects would be appreciated.
> They were also critical.
>
>
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