Results for “GitHub” — Page 3

DOE OSTI2020

Learning Spectral Templates for Photometric Redshift Estimation from Broadband Photometry

Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived from spectral synthesis models. Both of these approaches have their limitations: either the sample sizes are small and often not representative of the diversity of galaxy colors, or the model colors can be biased (often as a function of wavelength), which introduces systematics in the derived redshifts. In this paper, we learn the underlying spectral energy distributions from an ensemble of ∼100 K galaxies with measured redshifts and colors. We show that we are able to reconstruct emission and absorption lines at a significantly higher resolution than the broadband filters used to measure the photometry for a sample of 20 spectral templates. We find that our training algorithm reduces the fraction of outliers in the derived photometric redshifts by up to 28%, bias up to 91%, and scatter up to 25%, when compared to estimates using a standard set of spectral templates. We discuss the current limitations of this approach and its applicability for recovering the underlying properties of galaxies. Our derived templates and the code used to produce these results are publicly available in a dedicated Github repository: https://github.com/dirac-institute/photoz-template-learning.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

cnor_pub: R code and data for nitrous oxide synthesis by purified bacterial cNOR

This zipped archive of a GitHub repository includes the experimental data (csv files) collected for the reduction of NO to N2O by purified Paracoccus denitrificans cytochrome c nitric oxide reductase (cNOR) and the R code (qmd files) used to analyze these data. A link to the corresponding GitHub repository is also provided.

Hegg, Eric L. [GLBRC - Michigan State University]↗

DOE OSTI2024

MSD CoP Webinar: Using Meta-Repositories To Facilitate Open Science in MSD Research

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: In this webinar, we will describe the use of a GitHub meta-repository, for documenting and disseminating the tools and data supporting MSD publications. The goal is to make it easier for others to understand the flow of data and code through your experiment and to be able to reproduce your results and figures with only the information you have provided for them. The webinar will cover the role of open science in the MSD community, the origins and purpose of meta-repositories, and step-by-step instructions and best practices for building a meta-repository starting from the GitHub template (https://github.com/IMMM-SFA/metarepo). We will also discuss how to leverage MSD-LIVE (https://msdlive.org/) in your meta-repository, provide links to numerous examples you can learn from, and discuss the role of meta-repositories in the IM3 project's open science mandates. Presenters : Chris R. Vernon, Casey D. Burleyson, Jennie Rice, and Mengqi Zhao Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: February 22nd, 2024 from 2-3 PM ET

Open Science↗

DOE OSTI2022

WaterTAP3 Model Input Data for NAWI's Eight Source Water Baseline Analyses

This folder contains the input data for the WaterTAP3 model that was used for the eight NAWI (National Alliance for Water Innovation) source water baselines studies published in the Environmental Science and Technology special issue: Technology Baselines and Innovation Priorities for Water Treatment and Supply. There are also eight other separate DAMS submissions, one per source water, that include the model results for the published studies. In this data submission, all model inputs across the eight baselines are included. The data structure and content are described in a README.txt file. For more details on how to use the data in WaterTAP3 please refer to the model documentation and GitHub site found at "WaterTAP3 Github" linked in the submission resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.

informatics↗

NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods.

knowledge↗

NASA NTRS2020

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗

NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗

NASA NTRS2023

Data Sharing in Radiation Biology: Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally "Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗

NASA NTRSDate not supplied

The Nasa SRA Process as It Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗

NASA NTRSDate not supplied

The NASA SRA Process as it Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗

DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

nmrrr : A Reproducible Workflow for Binning and Visualizing NMR Spectra From Environmental Samples

Nuclear magnetic resonance (NMR) spectroscopy is a useful tool for detection and identification of molecular structural information, with increasing applications in environmental sciences. NMR instrument outputs are however heterogeneous and require extensive post-processing, creating barriers to their use and application by non-specialists. Here, we report on a new open-source R package, nmrrr, that processes and visualizes spectral data obtained from one-dimensional solution-state and solid-state NMR experiments; the package also performs relevant calculations commonly applied in natural organic matter communities, such as computing the relative abundance of various functional groups. We document the package's installation, dependencies, and functions; and provide a standard workflow for processing NMR data. This package is currently available on CRAN and GitHub, and community contributions are welcome.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Pilgrim Hot Springs: GEOPHIRES Inputs and Outputs for Direct-Use Geothermal District Heating and Cooling

This dataset includes files for a techno-economic analysis conducted using the GEOPHIRES simulator to examine the feasibility of expanding a larger district heating site in a remote location: Pilgrim Hot Springs, Alaska. Files included here are GEOPHIRES inputs and outputs for five different scenarios with varying demand, cycle, and system design characteristics to analyze. Also included is the link to the GEOPHIRES GitHub, as well as a link to the dataset that contains the energy modelling used to determine the heating demand for the district. For a list of the differences between scenarios, see the included "Input Overview.txt" file. Fields included in the input files are: subsurface technical parameters, surface technical parameters, financial parameters, capital and O&M parameters, as well as simulation parameters. The output files are case reports that summarize all equipment, reservoir characteristics, costs, and heating profiles.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities. The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

TEAMER: Triton Systems Oscillating Water Column Modeling Data and Report

This dataset provides the output of six Wave Energy Converter Simulator (WEC-Sim) simulations and accompanying documentation for the modeling of Triton Systems' oscillating water column (OWC) system at tank scale (validated using available data for tuning the model, Tests 1-2) and deployment scale (for which no validation data is available, Tests 4-6). Included are the output data in a MATLAB file structure, a comprehensive report on the modeling and design of the Triton OWC system, and a link to the WEC-Sim GitHub page. This work was supported by funding from TEAMER RFTS 5 (Request for Technical Support).

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

UNIFI's Grid-Forming (GFM) Inverter Reference Design: A Tutorial on Modeling, Control, and Experimental Implementation of GFM Inverters

The UNIFI Consortium's tutorial on grid-forming (GFM) inverters provides a comprehensive guide to the modeling, control, and experimental implementation of GFM inverters. As the integration of renewable energy accelerates, the transition from traditional grid following (GFL) to GFM inverters is crucial to ensure stable and sustainable power systems. This document outlines a reference design for three-phase and single-phase GFM inverters developed at the University of Texas at Austin. The tutorial also provides step-by-step guidance for accessing and using UNIFI’s GitHub repository, enabling users to design, build, and test GFM inverters efficiently. By fostering collaboration and equipping users with accessible resources, this initiative aims to drive widespread adoption of GFM technology across academia, utilities, and industries.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Catalyst Design in Nitrate Removal

Based on the volcano plot developed by Dr. Goldsmith group (Report linked in submission), we utilized DFT (density functional theory) calculations to search for bimetallic materials in the application of catalysts in aqueous nitrate removal. The calculations are conducted via the high-throughput automated workflow package developed by our group (Github linked in submission) using VASP commercial first-principles calculation software.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗