SHADES: The SHAD Exploration System
SHADES, the SHAD Exploration System, is a platform for Exploratory Data Analytics. SHADES is designed as a client-server architecture: the client interfaces with the end user via high productivity languages (e.g. Python) and GUI/Web applications (e.g. Jupyter Notebooks); the server runs an interpreter, which executes commands issued by the client on one or multiple machines, and maintains data in memory. The server commands are implemented with the SHAD library, taking advantage of its portability, performance and scalability features. The server can indeed run on any machine/runtime system supported by the SHAD library. and, as regular SHAD apps, the commands implementations exploit massive multithreading for performance. Currently, the communication between client and server occurs via TCP-IP messages.
deps_arXiv2020
This paper presents a novel data-driven method for learning deep constrained continuous control policies and dynamical models of the controlled system. By leveraging partial knowledge of system dynamics and constraint enforcing multi-objective loss functions, the method can learn from small and static datasets, handle time-varying state and input constraints and enforce the stability properties of the controlled system.We use a continuous control design example to demonstrate the performance of the method on three distinct tasks: system identification, control policy learning, and simultaneous system identification and policy learning.
E3SMv0-HiLAT
The software, titled E3SMv0-HiLAT, is a novel, modified version of the Community Earth System Model version 1 (Hurrell et al., 2013, https://doi.org/10.1175/BAMS-D-12), intended for study of high-latitude processes. E3SMv0-HiLAT incorporates changes and new features in the atmospheric model; these changes affect aerosol transport to high northern latitudes and reduce shortwave cloud bias over the Southern Ocean. Additionally, new features are introduced to the ocean biogeochemistry to improve representation of high-latitude phytoplankton groups, and two-way coupling is implemented between the biogeochemistry in the sea ice and ocean models. The modifications also include a dynamic coupling of the ocean flux of aerosol precursors into the atmosphere model, which enables these marine emissions of aerosol precursor emissions to respond to changes in sea ice extent, ocean stratification and associated nutrient availability, and atmospheric state.
Soil carbon data: long tail recovery
In this project, we will produce 1) customized ingestion scripts for soil carbon related data sets that are in existing repositories, and 2) scripts for output databases that conform to common templates. We will do this via an open community project on GitHub where data providers (or data discoverers) will link to existing soils data, coders will script up ingestion code, and reviewers will QA/QC the code. We will also design an educational component to teach data management and basic scripting skills needed to participate in this project as a coder.
Pacifica
Pacifica is a data management system used to collect, analyze, search and track data collected on instruments.
Provenance Environment (ProvEn) Services SOFTWARE
The Provenance Environment (ProvEn) software is designed to monitor and analyze distributed scientific workflow application behavior in the context of its environment. At its core, ProvEn supports provenance capture, collection, storage and searches and uses an innovative hybrid approach to capturing and federating semantic provenance and time series metrics data. The ProvEn software consists of a client (Describe Anything Provenance Interface (DAPI) and HArvest Provenance Interface (HAPI)), a service cluster (i.e. ProvEn services), and a Web dashboard. The DAPI and HAPI client software for disclosing provenance and metrics streaming messages to a services where they can be persisted. The ProvEn services consists of a cluster that provide services to interface with compatible 3rd party databases for collection, storage, search, and retrieval using RESTful interfaces. The ProvEn dashboard interfaces with the ProvEn server to support message browsing and monitoring.