+1 (binding) -Jake
On Sun, Apr 19, 2015 at 1:00 AM, Mattmann, Chris A (3980) < chris.a.mattm...@jpl.nasa.gov> wrote: > OK all, discussion has died down, we have 3 mentors, I think it’s > time to proceed to a VOTE. > > I am calling a VOTE now to accept the Climate Model Diagnostic > Analyzer (CMDA) into the Apache Incubator. The VOTE is open for > at least the next 72 hours: > > [ ] +1 Accept Apache Climate Model Diagnostic Analyzer into the Apache > Incubator. > [ ] +0 Abstain. > [ ] -1 Don’t accept Apache Climate Model Diagnostic Analyzer into the > Apache Incubator > because… > > I’ll try and close the VOTE out on Friday. > > Of course I am +1! > > P.S. the text of the latest wiki proposal is pasted below: > > Cheers, > Chris > > > = Apache ClimateModelDiagnosticAnalyzer Proposal = > > == Abstract == > > The Climate Model Diagnostic Analyzer (CMDA) provides web services for > multi-aspect physics-based and phenomenon-oriented climate model > performance evaluation and diagnosis through the comprehensive and > synergistic use of multiple observational data, reanalysis data, and model > outputs. > > == Proposal == > > The proposed web-based tools let users display, analyze, and download > earth science data interactively. These tools help scientists quickly > examine data to identify specific features, e.g., trends, geographical > distributions, etc., and determine whether a further study is needed. All > of the tools are designed and implemented to be general so that data from > models, observation, and reanalysis are processed and displayed in a > unified way to facilitate fair comparisons. The services prepare and > display data as a colored map or an X-Y plot and allow users to download > the analyzed data. Basic visual capabilities include 1) displaying > two-dimensional variable as a map, zonal mean, and time series 2) > displaying three-dimensional variable’s zonal mean, a two-dimensional > slice at a specific altitude, and a vertical profile. General analysis can > be done using the difference, scatter plot, and conditional sampling > services. All the tools support display options for using linear or > logarithmic scales and allow users to specify a temporal range and months > in a year. The source/input datasets for these tools are CMIP5 model > outputs, Obs4MIP observational datasets, and ECMWF reanalysis datasets. > They are stored on the server and are selectable by a user through the web > services. > > === Service descriptions === > > 1. '''Two dimensional variable services''' > > * Map of two-dimensional variable: This services displays a two > dimensional variable as a colored longitude and latitude map with values > represented by a color scheme. Longitude and latitude ranges can be > specified to magnify a specific region. > > * Two dimensional variable zonal mean: This service plots the zonal mean > value of a two-dimensional variable as a function of the latitude in terms > of an X-Y plot. > > * Two dimensional variable time series: This service displays the average > of a two-dimensional variable over the specific region as function of time > as an X-Y plot. > > 2. '''Three dimensional variable services''' > > * Map of a two dimensional slice of a three-dimensional variable: This > service displays a two-dimensional slice of a three-dimensional variable > at a specific altitude as a colored longitude and latitude map with values > represented by a color scheme. > > * Three dimensional zonal mean: Zonal mean of the specified > three-dimensional variable is computed and displayed as a colored > altitude-latitude map. > > * Vertical profile of a three-dimensional variable: Compute the area > weighted average of a three-dimensional variable over the specified region > and display the average as function of pressure level (altitude) as an X-Y > plot. > > 3. '''General services''' > > * Difference of two variables: This service displays the differences > between the two variables, which can be either a two dimensional variable > or a slice of a three-dimensional variable at a specified altitude as > colored longitude and latitude maps > > * Scatter and histogram plots of two variables: This service displays the > scatter plot (X-Y plot) between two specified variables and the histograms > of the two variables. The number of samples can be specified and the > correlation is computed. The two variables can be either a two-dimensional > variable or a slice of a three-dimensional variable at a specific altitude. > > * Conditional sampling: This service lets user to sort a physical > quantity of two or dimensions according to the values of another variable > (environmental condition, e.g. SST) which may be a two-dimensional > variable or a slice of a three-dimensional variable at a specific > altitude. For a two dimensional quantity, the plot is displayed an X-Y > plot, and for a two-dimensional quantity, plot is displayed as a > colored-map. > > > == Background and Rationale == > > The latest Intergovernmental Panel on Climate Change (IPCC) Fourth > Assessment Report stressed the need for the comprehensive and innovative > evaluation of climate models with newly available global observations. The > traditional approach to climate model evaluation, which is the comparison > of a single parameter at a time, identifies symptomatic model biases and > errors but fails to diagnose the model problems. The model diagnosis > process requires physics-based multi-variable comparisons, which typically > involve large-volume and heterogeneous datasets, and computationally > demanding and data-intensive operations. We propose to develop a > computationally efficient information system to enable the physics-based > multi-variable model performance evaluations and diagnoses through the > comprehensive and synergistic use of multiple observational data, > reanalysis data, and model outputs. > > Satellite observations have been widely used in model-data > inter-comparisons and model evaluation studies. These studies normally > involve the comparison of a single parameter at a time using a time and > space average. For example, modeling cloud-related processes in global > climate models requires cloud parameterizations that provide quantitative > rules for expressing the location, frequency of occurrence, and intensity > of the clouds in terms of multiple large-scale model-resolved parameters > such as temperature, pressure, humidity, and wind. One can evaluate the > performance of the cloud parameterization by comparing the cloud water > content with satellite data and can identify symptomatic model biases or > errors. However, in order to understand the cause of the biases and > errors, one has to simultaneously investigate several parameters that are > integrated in the cloud parameterization. > > Such studies, aimed at a multi-parameter model diagnosis, require > locating, understanding, and manipulating multi-source observation > datasets, model outputs, and (re)analysis outputs that are physically > distributed, massive in volume, heterogeneous in format, and provide > little information on data quality and production legacy. Additionally, > these studies involve various data preparation and processing steps that > can easily become computationally demanding since many datasets have to be > combined and processed simultaneously. It is notorious that scientists > spend more than 60% of their research time on just preparing the dataset > before it can be analyzed for their research. > > To address these challenges, we propose to build Climate Model Diagnostic > Analyzer (CMDA) that will enable a streamlined and structured preparation > of multiple large-volume and heterogeneous datasets, and provide a > computationally efficient approach to processing the datasets for model > diagnosis. We will leverage the existing information technologies and > scientific tools that we developed in our current NASA ROSES COUND, MAP, > and AIST projects. We will utilize the open-source Web-service technology. > We will make CMDA complementary to other climate model analysis tools > currently available to the research community (e.g., PCMDI’s CDAT and > NCAR’s CCMVal) by focusing on the missing capabilities such as conditional > sampling, and probability distribution function and cluster analysis of > multiple-instrument datasets. The users will be able to use a web browser > to interface with CMDA. > > == Current Status == > > The current version of ClimateModelDiagnosticAnalyzer was developed by a > team at The Jet Propulsion Laboratory (JPL). The project was initiated as > a NASA-sponsored project (ROSES-CMAC) in 2011. > > == Meritocracy == > > The current developers are not familiar with meritocratic open source > development at Apache, but would like to encourage this style of > development for the project. > > == Community == > > While ClimateModelDiagnosticAnalyzer started as a JPL research project, it > has been used in The 2014 Caltech Summer School sponsored by the JPL > Center for Climate Sciences. Some 23 students from different institutions > over the world participated. We deployed the tool to the Amazon Cloud and > let every student each has his or her own virtual machine. Students gave > positive feedback mostly on the usability and speed of our web services. > We also collected a number of enhancement requests. We seek to further > grow the developer and user communities using the Apache open source > venue. During incubation we will explicitly seek increased academic > collaborations (e.g., with The Carnegie Mellon University) as well as > industrial participation. > > One instance of our web services can be found at: > http://cmacws4.jpl.nasa.gov:8080/cmac/ > > == Core Developers == > > The core developers of the project are JPL scientists and software > developers. > > == Alignment == > > Apache is the most natural home for taking the > ClimateModelDiagnosticAnalyzer project forward. It is well-aligned with > some Apache projects such as Apache Open Climate Workbench. > ClimateModelDiagnosticAnalyzer also seeks to achieve an Apache-style > development model; it is seeking a broader community of contributors and > users in order to achieve its full potential and value to the Climate > Science and Big Data community. > > There are also a number of dependencies that will be mentioned below in > the Relationships with Other Apache products section. > > > == Known Risks == > > === Orphaned products === > > Given the current level of intellectual investment in > ClimateModelDiagnosticAnalyzer, the risk of the project being abandoned is > very small. The Carnegie Mellon University and JPL are collaborating > (2014-2015) to build a service for climate analytics workflow > recommendation using fund from NASA. A two-year NASA AIST project > (2015-2016) will soon start to add diagnostic analysis methodologies such > as conditional sampling method, conditional probability density function, > data co-location, and random forest. We will also infuse the provenance > technology into CMDA so that the history of the data products and > workflows will be automatically collected and saved. This information will > also be indexed so that the products and workflows can be searchable by > the community of climate scientists and students. > > === Inexperience with Open Source === > > The current developers of ClimateModelDiagnosticAnalyzer are inexperienced > with Open Source. However, our Champion Chris Mattmann is experienced > (Champions of ApacheOpenClimateWorkbench and AsterixDB) and will be > working closely with us, also as the Chief Architect of our JPL section. > > === Relationships with Other Apache Products === > > Clearly there is a direct relationship between this project and the Apache > Open Climate Workbench already a top level Apache project and also brought > to the ASF by its Champion (and ours) Chris Mattmann. We plan on directly > collaborating with the Open Climate Workbench community via our Champion > and we also welcome ASF mentors familiar with the OCW project to help > mentor our project. In addition our team is extremely welcoming of ASF > projects and if there are synergies with them we invite participation in > the proposal and in the discussion. > > === Homogeneous Developers === > > The current community is within JPL but we would like to increase the > heterogeneity. > > === Reliance on Salaried Developers === > > The initial committers are full-time JPL staff from 2013 to 2014. The > other committers from 2014 to 2015 are a mix of CMU faculty, students and > JPL staff. > > === An Excessive Fascination with the Apache Brand === > > We believe in the processes, systems, and framework Apache has put in > place. Apache is also known to foster a great community around their > projects and provide exposure. While brand is important, our fascination > with it is not excessive. We believe that the ASF is the right home for > ClimateModelDiagnosticAnalyzer and that having > ClimateModelDiagnosticAnalyzer inside of the ASF will lead to a better > long-term outcome for the Climate Science and Big Data community. > > === Documentation === > > The ClimateModelDiagnosticAnalyzer services and documentation can be found > at: http://cmacws4.jpl.nasa.gov:8080/cmac/. > > === Initial Source === > > Current source resides in ... > > === External Dependencies === > > ClimateModelDiagnosticAnalyzer depends on a number of open source projects: > > * Flask > * Gunicorn > * Tornado Web Server > * GNU octave > * epd python > * NOAA ferret > * GNU plot > > == Required Resources == > > === Developer and user mailing lists === > > * priv...@cmda.incubator.apache.org (with moderated subscriptions) > * comm...@cmda.incubator.apache.org > * d...@cmda.incubator.apache.org > * us...@cmda.incubator.apache.org > > A git repository > > https://git-wip-us.apache.org/repos/asf/incubator-cmda.git > > A JIRA issue tracker > > https://issues.apache.org/jira/browse/CMDA > > === Initial Committers === > > The following is a list of the planned initial Apache committers (the > active subset of the committers for the current repository at Google code). > > * Seungwon Lee (seungwon....@jpl.nasa.gov) > * Lei Pan (lei....@jpl.nasa.gov) > * Chengxing Zhai (chengxing.z...@jpl.nasa.gov) > * Benyang Tang (benyang.t...@jpl.nasa.gov) > * Jia Zhang (jia.zh...@sv.cmu.edu) > * Wei Wang (wei.w...@sv.cmu.edu) > * Chris Lee (chris....@sv.cmu.edu) > * Xing Wei (xing....@sv.cmu.edu) > > > === Affiliations === > > JPL > > * Seungwon Lee > * Lei Pan > * Chengxing Zhai > * Benyang Tang > > CMU > > * Jia Zhang > * Wei Wang > * Chris Lee > * Xing Wei > > == Sponsors == > > NASA > > === Champion === > > Chris Mattmann (NASA/JPL) > > === Nominated Mentors === > > Greg Reddin<<BR>> > Chris Mattmann<<BR>> > Michael Joyce<<BR>> > James Carman > > === Sponsoring Entity === > > The Apache Incubator > > > > > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > Chris Mattmann, Ph.D. > Chief Architect > Instrument Software and Science Data Systems Section (398) > NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA > Office: 168-519, Mailstop: 168-527 > Email: chris.a.mattm...@nasa.gov > WWW: http://sunset.usc.edu/~mattmann/ > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > Adjunct Associate Professor, Computer Science Department > University of Southern California, Los Angeles, CA 90089 USA > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > > > > > > > -----Original Message----- > From: <Mattmann>, Chris Mattmann <chris.a.mattm...@jpl.nasa.gov> > Reply-To: "general@incubator.apache.org" <general@incubator.apache.org> > Date: Monday, March 23, 2015 at 1:55 AM > To: "general@incubator.apache.org" <general@incubator.apache.org> > Cc: "Pan, Lei (398K)" <lei....@jpl.nasa.gov>, "Lee, Seungwon (398K)" > <seungwon....@jpl.nasa.gov>, "Zhai, Chengxing (398K)" > <chengxing.z...@jpl.nasa.gov>, "Tang, Benyang (398J)" > <benyang.t...@jpl.nasa.gov>, "jia.zh...@west.cmu.edu" > <jia.zh...@west.cmu.edu> > Subject: [PROPOSAL] Climate Model Diagnostic Analyzer > > >Hi Everyone, > > > >I am pleased to submit for consideration to the Apache Incubator > >the Climate Model Diagnostic Analyzer proposal. We are actively > >soliciting interested mentors in this project related to climate > >science and analytics and big data. > > > >Please find the wiki text of the proposal below and the link up > >on the wiki here: > > > >https://wiki.apache.org/incubator/ClimateModelDiagnosticAnalyzerProposal > > > >Thank you for your consideration! > > > >Cheers, > >Chris > >(on behalf of the Climate Model Diagnostic Analyzer community) > > > >= Apache ClimateModelDiagnosticAnalyzer Proposal = > > > >== Abstract == > > > >The Climate Model Diagnostic Analyzer (CMDA) provides web services for > >multi-aspect physics-based and phenomenon-oriented climate model > >performance evaluation and diagnosis through the comprehensive and > >synergistic use of multiple observational data, reanalysis data, and model > >outputs. > > > >== Proposal == > > > >The proposed web-based tools let users display, analyze, and download > >earth science data interactively. These tools help scientists quickly > >examine data to identify specific features, e.g., trends, geographical > >distributions, etc., and determine whether a further study is needed. All > >of the tools are designed and implemented to be general so that data from > >models, observation, and reanalysis are processed and displayed in a > >unified way to facilitate fair comparisons. The services prepare and > >display data as a colored map or an X-Y plot and allow users to download > >the analyzed data. Basic visual capabilities include 1) displaying > >two-dimensional variable as a map, zonal mean, and time series 2) > >displaying three-dimensional variable’s zonal mean, a two-dimensional > >slice at a specific altitude, and a vertical profile. General analysis can > >be done using the difference, scatter plot, and conditional sampling > >services. All the tools support display options for using linear or > >logarithmic scales and allow users to specify a temporal range and months > >in a year. The source/input datasets for these tools are CMIP5 model > >outputs, Obs4MIP observational datasets, and ECMWF reanalysis datasets. > >They are stored on the server and are selectable by a user through the web > >services. > > > >=== Service descriptions === > > > >1. '''Two dimensional variable services''' > > > >* Map of two-dimensional variable: This services displays a two > >dimensional variable as a colored longitude and latitude map with values > >represented by a color scheme. Longitude and latitude ranges can be > >specified to magnify a specific region. > > > >* Two dimensional variable zonal mean: This service plots the zonal mean > >value of a two-dimensional variable as a function of the latitude in terms > >of an X-Y plot. > > > >* Two dimensional variable time series: This service displays the average > >of a two-dimensional variable over the specific region as function of time > >as an X-Y plot. > > > >2. '''Three dimensional variable services''' > > > >* Map of a two dimensional slice of a three-dimensional variable: This > >service displays a two-dimensional slice of a three-dimensional variable > >at a specific altitude as a colored longitude and latitude map with values > >represented by a color scheme. > > > >* Three dimensional zonal mean: Zonal mean of the specified > >three-dimensional variable is computed and displayed as a colored > >altitude-latitude map. > > > >* Vertical profile of a three-dimensional variable: Compute the area > >weighted average of a three-dimensional variable over the specified region > >and display the average as function of pressure level (altitude) as an X-Y > >plot. > > > >3. '''General services''' > > > >* Difference of two variables: This service displays the differences > >between the two variables, which can be either a two dimensional variable > >or a slice of a three-dimensional variable at a specified altitude as > >colored longitude and latitude maps > > > >* Scatter and histogram plots of two variables: This service displays the > >scatter plot (X-Y plot) between two specified variables and the histograms > >of the two variables. The number of samples can be specified and the > >correlation is computed. The two variables can be either a two-dimensional > >variable or a slice of a three-dimensional variable at a specific > >altitude. > > > >* Conditional sampling: This service lets user to sort a physical > >quantity of two or dimensions according to the values of another variable > >(environmental condition, e.g. SST) which may be a two-dimensional > >variable or a slice of a three-dimensional variable at a specific > >altitude. For a two dimensional quantity, the plot is displayed an X-Y > >plot, and for a two-dimensional quantity, plot is displayed as a > >colored-map. > > > > > >== Background and Rationale == > > > >The latest Intergovernmental Panel on Climate Change (IPCC) Fourth > >Assessment Report stressed the need for the comprehensive and innovative > >evaluation of climate models with newly available global observations. The > >traditional approach to climate model evaluation, which is the comparison > >of a single parameter at a time, identifies symptomatic model biases and > >errors but fails to diagnose the model problems. The model diagnosis > >process requires physics-based multi-variable comparisons, which typically > >involve large-volume and heterogeneous datasets, and computationally > >demanding and data-intensive operations. We propose to develop a > >computationally efficient information system to enable the physics-based > >multi-variable model performance evaluations and diagnoses through the > >comprehensive and synergistic use of multiple observational data, > >reanalysis data, and model outputs. > > > >Satellite observations have been widely used in model-data > >inter-comparisons and model evaluation studies. These studies normally > >involve the comparison of a single parameter at a time using a time and > >space average. For example, modeling cloud-related processes in global > >climate models requires cloud parameterizations that provide quantitative > >rules for expressing the location, frequency of occurrence, and intensity > >of the clouds in terms of multiple large-scale model-resolved parameters > >such as temperature, pressure, humidity, and wind. One can evaluate the > >performance of the cloud parameterization by comparing the cloud water > >content with satellite data and can identify symptomatic model biases or > >errors. However, in order to understand the cause of the biases and > >errors, one has to simultaneously investigate several parameters that are > >integrated in the cloud parameterization. > > > >Such studies, aimed at a multi-parameter model diagnosis, require > >locating, understanding, and manipulating multi-source observation > >datasets, model outputs, and (re)analysis outputs that are physically > >distributed, massive in volume, heterogeneous in format, and provide > >little information on data quality and production legacy. Additionally, > >these studies involve various data preparation and processing steps that > >can easily become computationally demanding since many datasets have to be > >combined and processed simultaneously. It is notorious that scientists > >spend more than 60% of their research time on just preparing the dataset > >before it can be analyzed for their research. > > > >To address these challenges, we propose to build Climate Model Diagnostic > >Analyzer (CMDA) that will enable a streamlined and structured preparation > >of multiple large-volume and heterogeneous datasets, and provide a > >computationally efficient approach to processing the datasets for model > >diagnosis. We will leverage the existing information technologies and > >scientific tools that we developed in our current NASA ROSES COUND, MAP, > >and AIST projects. We will utilize the open-source Web-service technology. > >We will make CMDA complementary to other climate model analysis tools > >currently available to the research community (e.g., PCMDI’s CDAT and > >NCAR’s CCMVal) by focusing on the missing capabilities such as conditional > >sampling, and probability distribution function and cluster analysis of > >multiple-instrument datasets. The users will be able to use a web browser > >to interface with CMDA. > > > >== Current Status == > > > >The current version of ClimateModelDiagnosticAnalyzer was developed by a > >team at The Jet Propulsion Laboratory (JPL). The project was initiated as > >a NASA-sponsored project (ROSES-CMAC) in 2011. > > > >== Meritocracy == > > > >The current developers are not familiar with meritocratic open source > >development at Apache, but would like to encourage this style of > >development for the project. > > > >== Community == > > > >While ClimateModelDiagnosticAnalyzer started as a JPL research project, it > >has been used in The 2014 Caltech Summer School sponsored by the JPL > >Center for Climate Sciences. Some 23 students from different institutions > >over the world participated. We deployed the tool to the Amazon Cloud and > >let every student each has his or her own virtual machine. Students gave > >positive feedback mostly on the usability and speed of our web services. > >We also collected a number of enhancement requests. We seek to further > >grow the developer and user communities using the Apache open source > >venue. During incubation we will explicitly seek increased academic > >collaborations (e.g., with The Carnegie Mellon University) as well as > >industrial participation. > > > >One instance of our web services can be found at: > >http://cmacws.jpl.nasa.gov:8080/cmac/ > > > >== Core Developers == > > > >The core developers of the project are JPL scientists and software > >developers. > > > >== Alignment == > > > >Apache is the most natural home for taking the > >ClimateModelDiagnosticAnalyzer project forward. It is well-aligned with > >some Apache projects such as Apache Open Climate Workbench. > >ClimateModelDiagnosticAnalyzer also seeks to achieve an Apache-style > >development model; it is seeking a broader community of contributors and > >users in order to achieve its full potential and value to the Climate > >Science and Big Data community. > > > >There are also a number of dependencies that will be mentioned below in > >the Relationships with Other Apache products section. > > > > > >== Known Risks == > > > >=== Orphaned products === > > > >Given the current level of intellectual investment in > >ClimateModelDiagnosticAnalyzer, the risk of the project being abandoned is > >very small. The Carnegie Mellon University and JPL are collaborating > >(2014-2015) to build a service for climate analytics workflow > >recommendation using fund from NASA. A two-year NASA AIST project > >(2015-2016) will soon start to add diagnostic analysis methodologies such > >as conditional sampling method, conditional probability density function, > >data co-location, and random forest. We will also infuse the provenance > >technology into CMDA so that the history of the data products and > >workflows will be automatically collected and saved. This information will > >also be indexed so that the products and workflows can be searchable by > >the community of climate scientists and students. > > > >=== Inexperience with Open Source === > > > >The current developers of ClimateModelDiagnosticAnalyzer are inexperienced > >with Open Source. However, our Champion Chris Mattmann is experienced > >(Champions of ApacheOpenClimateWorkbench and AsterixDB) and will be > >working closely with us, also as the Chief Architect of our JPL section. > > > >=== Relationships with Other Apache Products === > > > >Clearly there is a direct relationship between this project and the Apache > >Open Climate Workbench already a top level Apache project and also brought > >to the ASF by its Champion (and ours) Chris Mattmann. We plan on directly > >collaborating with the Open Climate Workbench community via our Champion > >and we also welcome ASF mentors familiar with the OCW project to help > >mentor our project. In addition our team is extremely welcoming of ASF > >projects and if there are synergies with them we invite participation in > >the proposal and in the discussion. > > > >=== Homogeneous Developers === > > > >The current community is within JPL but we would like to increase the > >heterogeneity. > > > >=== Reliance on Salaried Developers === > > > >The initial committers are full-time JPL staff from 2013 to 2014. The > >other committers from 2014 to 2015 are a mix of CMU faculty, students and > >JPL staff. > > > >=== An Excessive Fascination with the Apache Brand === > > > >We believe in the processes, systems, and framework Apache has put in > >place. Apache is also known to foster a great community around their > >projects and provide exposure. While brand is important, our fascination > >with it is not excessive. We believe that the ASF is the right home for > >ClimateModelDiagnosticAnalyzer and that having > >ClimateModelDiagnosticAnalyzer inside of the ASF will lead to a better > >long-term outcome for the Climate Science and Big Data community. > > > >=== Documentation === > > > >The ClimateModelDiagnosticAnalyzer services and documentation can be found > >at: http://cmacws.jpl.nasa.gov:8080/cmac/. > > > >=== Initial Source === > > > >Current source resides in ... > > > >=== External Dependencies === > > > >ClimateModelDiagnosticAnalyzer depends on a number of open source > >projects: > > > > * Flask > > * Gunicorn > > * Tornado Web Server > > * GNU octave > > * epd python > > * NOAA ferret > > * GNU plot > > > >== Required Resources == > > > >=== Developer and user mailing lists === > > > > * priv...@cmda.incubator.apache.org (with moderated subscriptions) > > * comm...@cmda.incubator.apache.org > > * d...@cmda.incubator.apache.org > > * us...@cmda.incubator.apache.org > > > >A git repository > > > >https://git-wip-us.apache.org/repos/asf/incubator-cmda.git > > > >A JIRA issue tracker > > > >https://issues.apache.org/jira/browse/CMDA > > > >=== Initial Committers === > > > >The following is a list of the planned initial Apache committers (the > >active subset of the committers for the current repository at Google > >code). > > > > * Seungwon Lee (seungwon....@jpl.nasa.gov) > > * Lei Pan (lei....@jpl.nasa.gov) > > * Chengxing Zhai (chengxing.z...@jpl.nasa.gov) > > * Benyang Tang (benyang.t...@jpl.nasa.gov) > > > > > >=== Affiliations === > > > >JPL > > > > * Seungwon Lee > > * Lei Pan > > * Chengxing Zhai > > * Benyang Tang > > > >CMU > > > > * Jia Zhang > > * Wei Wang > > * Chris Lee > > * Xing Wei > > > >== Sponsors == > > > >NASA > > > >=== Champion === > > > >Chris Mattmann (NASA/JPL) > > > >=== Nominated Mentors === > > > >TBD > > > >=== Sponsoring Entity === > > > >The Apache Incubator > > > > > > > > > >++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > >Chris Mattmann, Ph.D. > >Chief Architect > >Instrument Software and Science Data Systems Section (398) > >NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA > >Office: 168-519, Mailstop: 168-527 > >Email: chris.a.mattm...@nasa.gov > >WWW: http://sunset.usc.edu/~mattmann/ > >++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > >Adjunct Associate Professor, Computer Science Department > >University of Southern California, Los Angeles, CA 90089 USA > >++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > > > > > > > > > > > >--------------------------------------------------------------------- > >To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org > >For additional commands, e-mail: general-h...@incubator.apache.org > >
