@@ -547,6 +547,7 @@ <h2>Project Ideas</h2>
547
547
</ td >
548
548
< tr > < td colspan ="2 " class ="good "> Status: Ideas page in progress</ td > </ tr >
549
549
</ tr >
550
+
550
551
< tr >
551
552
<!-- Logo -->
552
553
< td rowspan ="4 " class ="logo ">
@@ -581,10 +582,70 @@ <h2>Project Ideas</h2>
581
582
< tr > < td > < a href ="http://kivy.org "> Website</ a > |
582
583
< a href ="https://kivy.org/docs/contact.html "> Contact</ a > |
583
584
< a href ="https://kivy.org/docs/gsoc.html "> Ideas Page</ a >
585
+ </ td >
586
+ < tr > < td colspan ="2 " class ="good "> Status: Ideas page in progress</ td > </ tr >
587
+ </ tr >
588
+
589
+ < tr >
590
+
591
+ <!-- Logo -->
592
+ < td rowspan ="4 " class ="logo ">
593
+ < img src ="http://www.hydra-cg.com/img/logo.png " width ="128px "> </ td >
594
+ <!-- Info -->
595
+ < td > < h4 > HYDRA W3C Group</ h4 > </ td >
596
+ < tr > < td > A Python server/middleware to automate Web APIs navigation using intelligent clients. This project aims to:
597
+ < ul >
598
+ < li > create a metadata-powered REST API leveraging HYDRA framework,</ li >
599
+ < li > define a design for future development of client/server interactions using smart clients,</ li >
600
+ < li > use graphs and machine-learning to solve complex queries using HYDRA framework,</ li >
601
+ < li > define a middleware (low-level client) to collect requests from external
602
+ clients and provide the requested data using reasoning and machine-learning algorithms</ li > .
603
+ </ ul >
604
+ </ td > </ tr >
605
+ < tr > < td > < a href ="http://www.hydra-cg.com/ "> Website</ a > |
606
+ < a href ="https://www.w3.org/community/hydra/ "> Contact</ a > |
607
+ < a href ="http://hydra-gsoc.appspot.com/s "> Ideas Page</ a >
608
+ </ td >
609
+ < tr > < td colspan ="2 " class ="good "> Status: Ideas page in progress</ td > </ tr >
610
+ </ tr >
611
+
584
612
585
613
</ td >
586
614
< tr > < td colspan ="2 " class ="good "> Status: Ideas page in progress</ td > </ tr >
587
615
</ tr >
616
+
617
+ <!-- Logo -->
618
+ < td rowspan ="4 " class ="logo ">
619
+ < img src ="http://www.statsmodels.org/devel/_static/statsmodels_hybi_banner.png "
620
+ width ="300px "> </ td >
621
+ <!-- Info -->
622
+ < td > < h4 > Statsmodels</ h4 > </ td >
623
+ < tr > < td > Statsmodels is a general purpose Python package for data analysis, statistics and econometrics </ td > </ tr >
624
+ < tr > < td > < a href ="http://www.statsmodels.org/devel/ "> Website</ a > |
625
+ < a href ="http://groups.google.com/group/pystatsmodels "> Contact</ a > |
626
+ < a href ="https://github.com/statsmodels/statsmodels/wiki/Google-Summer-of-Code-2017 "> Ideas Page</ a >
627
+ </ td >
628
+ < tr > < td colspan ="2 " class ="good "> Status: Ideas page in progress</ td > </ tr >
629
+ </ tr >
630
+
631
+ < tr >
632
+ <!-- Logo -->
633
+ < td rowspan ="4 " class ="logo ">
634
+ < img src ="http://91.68.209.10/bmi/martinos.org/mne/stable/_static/mne_logo.png "
635
+ width ="256px "> </ td >
636
+ <!-- Info -->
637
+ < td > < h4 > MNE-Python</ h4 > </ td >
638
+ < tr > < td > MNE is a free and open source software designed for processing electroencephalography (EEG) and magnetoencephalography (MEG) data. EEG and MEG data analysis requires advanced numerics, signal processing, statistics and dedicated visualization tools. MNE-Python is a pure Python package built on top of numpy, scipy, matplotlib and scikit-learn.
639
+ </ td > </ tr >
640
+ < tr >
641
+ < td >
642
+ < a href ="http://martinos.org/mne/ "> Website</ a > |
643
+ < a href ="http://github.com/mne-tools/mne-python "> Contact</ a > |
644
+ < a href ="https://github.com/mne-tools/mne-python/wiki/GSOC-Ideas "> Ideas Page</ a >
645
+ </ td >
646
+ < tr > < td colspan ="2 " class ="good "> Status: Ideas page in progress</ td > </ tr >
647
+ </ tr >
648
+
588
649
</ table >
589
650
590
651
< a name ="schedule " />
0 commit comments