nyuuzyou/wikis huggingface dataset:
DOI system for models

=DOI system for models=
CSDMS encourages and supports the effort of developers to provide their numerical models as truly open source. Open source code reduces redundancy; it guarantees reuse and it makes research more replicable. At the same time CSDMS strives to ensure that model developers receive recognition for their work, even when code is submitted which is not (yet) described in a scientific journal. Therefore CSDMS has adopted the DOI system.
DOI or Digital Object Identifier system is used to identify intellectual property in the digital environment. It is used principally by publishers, and is an implementation of the Handle System for persistent identifiers. The International DOI Federation (IDF) appoints Registration Agencies who allocate DOI prefixes, register DOI Names, and provide the necessary infrastructure to allow registrants to declare and maintain metadata.
CSDMS collaborates with Zenodo, to assign unique identifiers for all models that are physically part of the CSDMS repository. Zenodo strongly supports and participates in efforts that promote research code. Given their experience in assigning DOIs, they are a logical partner for collaboration for CSDMS’ effort. 
By adopting the DOI system CSDMS guarantees the long-term archiving and availability of model source codes. An assigned DOI name will be guaranteed to redirect to the appropriate source code and associated metadata location.
==How it works==
Only those models of which 1) the source code is submitted to the CSDMS repository, and 2) which are formally described in associated model metadata,
will get a unique DOI. The DOI will be provided for each new version of the model, given that this is a major upgrade of the source code. This means that several DOIs could be point to the same model, but each to a different version of associated codes.
==How to apply for a DOI==
* Newly & upgraded source codeA DOI will be assigned to the source code when requested by emailing CSDMSsupport@colorado.edu.
==Why cite a model?==
CSDMS is convinced that models should be cited just like articles and books. Citing models makes it possible to:
* Easily reuse and replicate research as other researchers can directly locate the referenced model
* Provide credit to the model developer as citations are harvested by e.g. Web of Science
* Funding agencies to track usage, so to measure impact
==How do I cite a model?==
Models can be seen as ‘data’ and therefore we endorse citations defined by ‘DataCite guidelines’. Unfortunately these are only recommendations for data citations. Following these guidelines closely, CSDMS strongly recommends the following structure for citing a model:
 
::ModelDeveloper (PublicationYear). ModelName, ModelVersion. Identifier
So e.g.:
::Tucker, G., (2010). CHILD version 2010.07.06. doi: 10.1594/IEDA/100102
==Resolve a DOI name==
Copy the DOI that you like to resolve.
Than, for example, go to: http://dx.doi.org/ and past the DOI in that link.
Dakota

= Dakota =
__NOTOC__
 Dakota | right | link=https://dakota.sandia.gov 
Dakota, developed at Sandia National Laboratories,
is a software toolkit
that provides an interface between computational models and a library of iterative systems analysis methods.
Dakota helps answer questions such as:
* What are the most influential parameters in my model? → sensitivity analysis
* How robust and reliable is my model? → uncertainty quantification
* What is the best performing design? → optimization
* What model parameter values best match experimental data? → calibration
Dakota has an extensive library of analysis techniques, including:
* parameter studies
* design of experiments 
* design of computer experiments (DACE)
* sampling (MC or LHS)
* local and global reliability
* adaptive sampling
* stochastic expansion
* epistemic methods
* gradient-based local and global optimization
* derivative-free local and global optimization
* nonlinear least squares
* surrogate models
== Links ==
* The Dakota home page is https://dakota.sandia.gov
** Download Dakota
** Install Dakota
* The Dakota 6.5 (released November 2016) documentation is available at https://dakota.sandia.gov//sites/default/files/docs/6.5/html-ref/index.html
* Adam Stephens and Laura Swiler, members of the Dakota development team at Sandia, gave a clinic at the 2014 CSDMS Annual Meeting which included a detailed discussion of sensitivity analysis, uncertainty quantification, and calibration in Dakota. Their presentation (PDF) is here.
* Building Dakota on Ubuntu 12.04 LTS
* The CSDMS Dakota Interface, Dakotathon
== Todo ==
A list of topics that should be addressed in this page.
Or linked to.
* Why Dakota? Talk about about the need for UQ in modeling, and how Dakota provides a framework to help.
* An overview of how Dakota works. Write this up in a more digestible manner than the way it's presented in the Dakota documentation.
* A simple example of using Dakota; e.g., Greg's. (Make this another transcluded page.)
* Installing and running Dakota on a personal computer
* Setting up Dakota on beach
* Using Dakota on beach
Dakotathon

=The CSDMS Dakota Interface=
__NOTOC__
 350px | right 
Dakota is a software toolkit developed at Sandia National Laboratories
that provides an interface between models and a library of analysis methods,
including support for sensitivity analysis, uncertainty quantification, optimization, and calibration techniques.
Dakota is a powerful tool, but its learning curve is steep:
the user not only must understand the structure and syntax of the Dakota input file,
but also must develop intermediate code
that allows Dakota to set up and run a model,
read outputs from the model,
and calculate a response statistic from the outputs.
The CSDMS Dakota Interface, or Dakotathon,
is a Python package that wraps and extends Dakota’s file-based user interface.
It simplifies the process of configuring and running a Dakota experiment,
and it allows a Dakota experiment to be scripted.
Dakotathon creates the Dakota input file and provides a generic analysis driver. 
Any model componentized in the CSDMS modeling framework
automatically works with Dakotathon.
Dakotathon has a plugin architecture, so models not wrapped into the CSDMS modeling framework
can be accessed by Dakotathon by programmatically extending a template;
an example is provided in the Dakotathon distribution.
<noinclude>
Currently, six Dakota analysis methods have been implemented from the much larger Dakota library:
* vector parameter study,
* centered parameter study,
* multidim parameter study,
* sampling,
* polynomial chaos, and
* stochastic collocation.
For instructions on installing and using Dakotathon,
please see Dakotathon's CSDMS Model page.
Dakotathon has been installed in the wmt-testing instance on beach.colorado.edu.
To use it, login to beach, and from a shell prompt execute:
 source /home/csdms/wmt/_testing/conda/bin/activate
This configures the conda environment for this WMT executor.
Now, when you run Python or IPython,
the Dakotathon package will be available to import.
== Links ==
* The CSDMS Model page for Dakotathon
* The source code for Dakotathon can be found at https://github.com/csdms/dakota
** It includes examples
* The latest developer documentation for Dakotathon is available at http://csdms-dakota.readthedocs.io
* The Dakota home page is https://dakota.sandia.gov
** It includes instructions for downloading and installing Dakota
* The Dakota 6.5 (released November 2016) documentation is available at https://dakota.sandia.gov//sites/default/files/docs/6.5/html-ref/index.html
* Mpiper gave a talk on Dakotathon at the 2016 AGU Fall Meeting; a repository of experiments is here
Mpiper (talk) 14:09, 27 December 2016 (MST)
<noinclude>
Data

===Data===
__TOC__
==== Data available per category ====
* Climate
* Cryosphere
* Human dimensions
* Hydrography
* Land cover
* Oceanography
* River discharge
* Surface properties
* Sea level
* Substrates
* Topography
* All data
==== Submit your data ====
To submit your data to the list, please complete this Data form. Feel free to contact us or use the wiki if you want to update the information about a model that is already listed.
Data TWG-subgroup

=Data Terrestrial Subgroup=
== Group description ==
The Data Terrestrial Subgroup was formed to address the long-term goal identified at the CSDMS 2.0 meeting of comparing models and data. 
Tasks include development of CSDMS Datasets for model testing, validation, etc., organized by research area, and listed under two categories: 
# Existing data, properly formatted and documented; and 
# Desired data critical for CSDMS objectives, flagging up data products that should be obtained (either newly measured or distilled from existing field/lab datasets). 
Includes populating CSDMS web site with data from natural or anthropogenic experiments in a common, consistent format.
==How to Contribute==
# Suggest dataset/model pairs for further integration
== Links ==
# Data Rescue Challenge: an award created to improve preservation and access of research data, particularly of dark data, and share the varied ways that these data are being processed, stored, and used. Deadline: October 13, 2013
== Tasks ==
* Identify interested members (add your name to the list!)
*# Leslie Hsu
*# Albert Kettner	
=Data Terrestrial Subgroup=
== Group description ==
The Data Terrestrial Subgroup was formed to address the long-term goal identified at the CSDMS 2.0 meeting of comparing models and data. 
Tasks include development of CSDMS Datasets for model testing, validation, etc., organized by research area, and listed under two categories: 
# Existing data, properly formatted and documented; and 
# Desired data critical for CSDMS objectives, flagging up data products that should be obtained (either newly measured or distilled from existing field/lab datasets). 
Includes populating CSDMS web site with data from natural or anthropogenic experiments in a common, consistent format.
==How to Contribute==
# Suggest dataset/model pairs for further integration
== Links ==
# Data Rescue Challenge: an award created to improve preservation and access of research data, particularly of dark data, and share the varied ways that these data are being processed, stored, and used. Deadline: October 13, 2013
== Tasks ==
* Identify interested members (add your name to the list!)
*# Leslie Hsu
*# Albert Kettner
Data components peryear

==Data components per calendar year== 
Data components are datasets that, by utilizing the BMI, can seamlessly interact with numerical models. More information about data components, and how you can contribute data components can be found at here.
Year	Data components	
2025	() 	
2024	() 	
2023	() 	
2022	() 	
2021	() 	
The CSDMS Integration Facility started developing Data Components early 2021.
Data download

= Download data =
Data 	Data
 	Climate data
Nr. of datasets: 
Type: Tropical cyclones, Tropical rainfall, Global station data.
 	Cryosphere data
Nr. of datasets: 
Type: Paleo ice sheet thickness, sea ice concentrations, world glaciers.
left|x150px|120px|link=Human dimensions data 	Human dimensions data
Nr. of datasets: 
Type: Population of the world.
 	Hydrography data
Nr. of datasets: 
Type: Global drainage direction map, Hydro1k, HydroSHEDs, SWBD.
 	Land cover data
Nr. of datasets: 
Type: Land cover dataset of America.
 	Ecosystem dynamics data
Nr. of datasets: 
Type: Datasets that describe ecosystems
 	Oceanography
Nr. of datasets: 
Type: Ocean layers, surface currents, coral reefs, sediment thickness, tides, waves.
 	River discharge data
Nr. of datasets: 
Type: Global runoff, historical water levels, streamflow, sediment data,
 	Surface properties data
Nr. of datasets: 
Type: World soil database.
 	Sea level data
Nr. of datasets: 
Type: Mean sea level over time.
left|120px|link=Substrates data|alt=Substrates data	Substrates data
Nr. of datasets: 
Type: Material and features of the global seafloor, geological map.
left|120px|link=Topography data|alt=Topography data	Topography data
Nr. of datasets: 
Type: Land and ocean relief data at various scales, and world vector shoreline
left|120px|link=All data|alt=All data 	All data
Nr. of datasets: 
Type: All of the data described above.
Data needs

= Data needs =
The CSDMS Data Repository describes important data fields useful in CSDMS models and provides links to data centers for download. CSDMS distinguishes between at least 3 data types relevant for modeling: 
# boundary or initialization data, 
# model algorithm test or benchmark data, and 
# integrated datasets for model validation of coupled systems.
Model test files and validation files are explicitly solicited within the model submission process.
Deltas 2007

===Dynamics and Vulnerability of River Delta Systems, Boulder, CO===
Date: September 26 to 28, 2007 	
Location: Boulder, Colorado, USA	
Agenda:	Agenda as 	
 	
 	
 	
 	
Talks:
</table>
Workshop reports
|-
| class="presentations_col1"| WG Group green
| class="presentations_col2"| WG report: Scientific Challenges & Questions and vulnerability questions
| class="presentations_col3"| 
|-
| class="presentations_col1"| WG Group blue
| class="presentations_col2"| WG report: Scientific Challenges & Questions and vulnerability questions
| class="presentations_col3"| 
|-
| class="presentations_col1"| WG Group red
| class="presentations_col2"| Scientific Challenges & Questions and vulnerability questions 
| class="presentations_col3"| 
|-
| class="presentations_col1"| WG1
| class="presentations_col2"| WG report: Areas for new disciplinary and interdisciplinary research
| class="presentations_col3"| 
|-
| class="presentations_col1"| WG2
| class="presentations_col2"| WG report: Database development
| class="presentations_col3"| 
|-
| class="presentations_col1"| WG3
| class="presentations_col2"| WG report: Modeling and scenarios modeling
| class="presentations_col3"| 
|-
|}
Workshop Output:Output of the workshop is published a LOICZ report: 35, Dynamics and Vulnerability Of Delta Systems, Irina Overeem and James P. M. Syvitski, Contributing Authors Robert G. Brakenridge, John W. Day Jr., Liviu Giosan, Mark T. Hannon, Philip R. Hill, W. Gregory Hood, Ilan Kelman, Albert J. Kettner, Hartwig H. Kremer, Robert Nicholls, Chris Paola, Juan D. Restrepo, Yoshiki Saito, Alex de Sherbinin, Charles Simenstad, Charles J. Vörösmarty, Juergen Weichselgartner
download at LOICZ site.
Deltas short course

===Deltas: Dynamics, Morphology and Observing of delta processes and human impacts ===
contributed by J. Syvitski 
This first lecture reviews the difficulty of defining a delta, reviews concepts of distributary channels and avulsion that are so intrinsically important in deltas, reviews recent progress on modeling an quantifying aggradation in the delta plain, reviews processes in the marine domain and the concept and effect of relative sea level rise 
Delta Dynamics and Theory PPT
The second lecture discusses morphology of deltas. It reviews the classical ternary division of deltas into fluvial-dominated, wave-dominated and tidal-dominated deltas. But more complex deltas, influenced by a mix of controlling facotrs, are shown and discussed as well. 
There is special emphasis on deltas during sea level fall and periglacial deltas.: 
Delta Morphology PPT‎
Deltas have attracted humans from early civilizations onwards. Humans are now increasingly changing the environment and deltas are heavily impacted by humans. This lecture shares examples of human actions affecting deltas: engineering of distributaries and the river channels, groundwater and hydrocarbon extraction causing subsidence, upstream trapping of sediments behind dams, dampening of river discharge peaks and a variety of agricultural practices.
 Deltas in the Anthropocene PPT
Satellite-based sensors are increasingly used to image delta processes and changes in delta morphology. This fourth lecture highlights the use of NASA's SRTM (Shuttle Radar Topography Mission) data for mapping areas near or below sea level in deltaic regions, the use of MODIS visble bandwidth reflectance data to observe sediment concentrations and flooding, the use of microwave AMSR-E data for water discharge estimates and more. 
Satellite Observations of Deltas PPT
===References===
* Overeem, I., Syvitski, J.P.M., 2009. Dynamics and Vulnerability of Delta Systems. LOICZ Reports and Studies No. 35. Germany.
* Syvitski, J.P.M., Kettner, A.J., Hannon, M. T., Hutton, E.W.H., Overeem, I., Brakenridge, G.R., Day, J., Vörösmarty, C., Saito, Y., Giosan, L., Nicholls, R.J., 2009. Sinking Deltas due to Human Activities. Nature Geoscience 2, 681 - 686.
* Higgins, S. A., Overeem I., Tanaka, A., Syvitski, J.P.M., (2013). Land Subsidence at Aquaculture Facilities in the Yellow River Delta, China. Geophysical Research Letters. 40,15, 898–3902, 16 August 2013.
* Brakenridge, G.R., Syvitski, J.P.M., Overeem, I., Higgins, S. A., Kettner, A.J., Stewart-Moore, J.A., Westerhoff, R., 2013. Global Mapping of Storm Surges and the Assessment of Delta Vulnerability, Natural Hazards. 66:1295-1312.
DeprecatedPageMessage

Display the following info on a deprecated page:
This page has out-of-date information.
Please see
HPC
for the latest information on this topic.
This page has out-of-date information.
Please see the
HPC
page for the latest information on this topic.
 center | 50px | Out-of-date page 
This page is out of date.
Please see the
PyMT
page for information on the Python Modeling Toolkit,
the successor to CMT.
 center | 50px | Out-of-date page 
This page is out of date.
Please see the
HPC
page for information on accessing and using Blanca, the CSDMS HPCC.
Diversity Inclusion

325 px | right Following its host institute, the Community Surface Dynamics Modeling System - Integration Facility (CSDMS-IF) is committed to building an inclusive and diverse community where all people from groups underrepresented in science and academia are welcome, safe, and supported. We believe that the perspectives that come from different backgrounds, expertise, and experiences enrich science by broadening the range of approaches and contributions. Therefore, we are committed to create a diverse and inclusive culture that appreciates all individuals across all identities, including race, gender, gender identity/expression, age, ethnicity, ability, sexual orientation, socioeconomic status, religious affiliation, or national origin and culture.
More specifically, CSDMS supports underrepresented students to participates in the CSDMS annual meetings. During those meetings the community has the opportunity to learn more about practices and resources for contributing to the Justice Equity Diversity Inclusion (JEDI) progress, either through clinics or keynote presentations that often are provided by volunteers of the community. Furthermore, CSDMS is committed to have a diverse governance body that consists of the Executive Committee and a Steering Committee, to embrace the perspectives that come from different backgrounds, expertise, and experiences.
EKT 2008

=The Education and Knowledge Transfer (EKT) Working Group Meeting=
Sponsors:
Community Surface Dynamics Modeling System (CSDMS) 
Focus:
Fully integrated EKT components focused on three end-user groups: researchers, planners and educators
*Researchers with model and visualization tools for the testing of hypotheses in support of data interpretation, and development of field programs. The archiving of benchmark data sets, documented source code, and the ability to download models with user-friendly graphical interfaces are key objectives.
*Planners with decision-making tools to run scenarios, and relate GIS output to environmental factors and land use while quantifying uncertainties.
*Educators with pre-packaged models to help illustrate surface processes, teaching tools to build intuition with “what-if”-type model runs, case studies that integrate field data and model simulations, and exploratory exercises for students. Our principal audiences are university students, professionals, teachers at the secondary school and college levels, and the general public. 
Agenda:
The Second Circular contains the final agenda as 
Meeting Notes:
These are the meeting notes worddoc (October 22, 2008)
Talks:
</table>
EKT contribute

Contribute:
* Movies
* Labs (coming soon)
* Lectures (coming soon)
==Submit Teaching Material==
CSDMS welcomes submissions of teaching material, whether it be your coolest model animations, or material that you developed for undergraduate and graduate courses in earth surface processes. If you have images or animations they can be easily added to the respective wiki pages. For more comprehensive material, or material that needs some modifications to be served here, please contact CSDMS: csdms@colorado.edu.
----
Information on this website is provided as a public service and is intended for educational and research purposes. Materials developed by CSDMS, its members, participants and partners, are the intellectual property of those respective individuals, who reserve all applicable rights to their respective intellectual property.
As an organization devoted to the creation, discovery, dissemination and sharing of knowledge and research, CSDMS holds a strong commitment to comply with all applicable laws regarding intellectual property.
Please note: It is not the intent of CSDMS to violate or infringe upon any copyrights. Our use is presumed to be appropriate and in accordance with "Fair Use" provisions. If any copyrighted material has been inappropriately included on these pages, please inform us. Immediate action will be taken to rectify the situation, either by removing the materials or obtaining appropriate permission.
The University of Colorado at Boulder encourages the fair use of copyrighted materials in support of its academic and research mission, and strives to provide clear guidance to faculty, students, and staff who wish to use any materials that may be copyrighted in their teaching and research.
At the same time, the university is legally required to establish policy stating that faculty, staff, students, and community members must obey all state and federal laws respecting the copyrights and trademarks of others. Learn more at https://www.colorado.edu/libraries/copyright-information.
ESPIn

ESPIn | center | link=https://csdms.colorado.edu
 
__NOTOC__
The Earth Surface Processes Institute (ESPIn) is a six-day immersive training experience for 25 early career scientists, including graduate students, postdoctoral fellows, researchers, and faculty.
ESPIn is designed to help participants advance their research in earth and planetary surface processes
by teaching skills in numerical modeling; in modern, collaborative, scientific software development;
and in the use of open source community cyberinfrastructure.
A mix of experienced scientists, visiting faculty, and research software engineers provide instruction.
ESPIn is a combination of learning and doing.
ESPIn uses the CSDMS Ivy course material, with topics including:
* Project Jupyter
* Introduction to the Shell
* Text Editors and Development Environments
* The Anaconda Distribution
* Introduction to Version Control with Git and GitHub
* Python for Modeling
* Landlab
* The Permamodel Toolkit
* The Basic Model Interface
* The Python Modeling Toolkit
* High-Performance Computing
* Best Practices in Scientific Software Development
ESPIn kicks off with a big ideas, mini poster session:
participants create a poster from two sheets of paper, one with research interests, the other with personal interests.
This session helps participants introduce themselves and find shared interests. 
ESPIn isn't just about instruction.
Daily time blocks are dedicated to working in small, self-organized teams on a research, data analysis, and coding project.
Participants can also work on developing and advancing their own research.
Get a taste of past ESPIn team projects by checking out their webinars and labs.
ESPIn includes dedicated time for a local field trip, peer-to-peer networking, and professional development,
including a career panel.
ESPIn concludes with team project presentations, which set up participants to continue to develop ideas.
For questions regarding ESPIn, please contact us at csdms@colorado.edu.