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Abstract Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. Documentation of dataset development A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Please cite as: Y. Xu, Nathan Myhrvold, Dhileep Sivam, Kaspar Mueller, Daniel J. Olsen, Bainan Xia, Daniel Livengood, Victoria Hunt, Benjamin Rouillé d'Orfeuil, Daniel Muldrew, Merrielle Ondreicka, Megan Bettilyon, "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE PES General Meeting, Montreal, Canada, 2020. Dataset version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data. 0.2.1, March 25, 2020: corrected a bug in the wind profile generation process which was pulling the wrong locations for wind farms outside the Western Interconnection.
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Abstract Planning for power systems with high penetrations of variable renewable energy requires higher spatial and temporal granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. Documentation of dataset development A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Please cite as: Y. Xu, Nathan Myhrvold, Dhileep Sivam, Kaspar Mueller, Daniel J. Olsen, Bainan Xia, Daniel Livengood, Victoria Hunt, Benjamin Rouillé d'Orfeuil, Daniel Muldrew, Merrielle Ondreicka, Megan Bettilyon, "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE PES General Meeting, Montreal, Canada, 2020. Dataset version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data. 0.2.1, March 25, 2020: [erroneous upload] 0.2.2, March 26, 2020: [erroneous upload]
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Planning for power systems with high penetrations of variable renewable energy requires higher spatial and tempo- ral granularity. However, most publicly available test systems are of insufficient fidelity for developing methods and tools for high- resolution planning. This paper presents methods to construct open-access test systems of high spatial granularity to more accurately represent current infrastructure and high temporal granularity to represent variability of demand and renewable resources. To demonstrate, a high-resolution test system representing the United States is created using only publicly available data. This test system is validated by running it in a production cost model, with results validated against historical generation to ensure that they are representative. The resulting open source test system can support power system transition planning and aid in development of tools to answer questions around how best to reach decarbonization goals, using the most effective combinations of transmission expansion, renewable generation, and energy storage. A paper describing the process of developing the dataset is available at https://arxiv.org/abs/2002.06155. Version history 0.1, January 31, 2020: initial data upload. 0.2, March 10, 2020: addition of Tabular Data Package metadata, modifications to cost curves and transmission capacities aimed at more closely matching optimization results to historical data.
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Abstract Motivation Antibodies are widely used experimental reagents to test expression of proteins. However, they might not always provide the intended tests because they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable and irreproducible research results. While many proposals have been developed to deal with the problem of antibody specificity, they may not scale well to deal with the millions of antibodies that have ever been designed and used in research. In this study, we investigate the feasibility of automatically extracting statements about antibody specificity reported in the literature by text mining, and generate reports to alert scientist users of problematic antibodies. Results We developed a deep neural network system called Antibody Watch and tested its performance on a corpus of more than two thousand articles that report uses of antibodies. We leveraged the Research Resource Identifiers (RRID) to precisely identify antibodies mentioned in an input article and the BERT language model to classify if the antibodies are reported as nonspecific, and thus problematic, as well as inferred the coreference to link statements of specificity to the antibodies that the statements referred to. Our evaluation shows that Antibody Watch can accurately perform both classification and linking with F-scores over 0.8, given only thousands of annotated training examples. The result suggests that with more training, Antibody Watch will provide useful reports about antibody specificity to scientists.
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Compressed fastqs for raw sequences of clinical isolates of Escherichia coli infection from Toronto, Canada in 2018 (Dataset 2). Sequencing details outlined in associated publication. Performed using Illumina NextSeq platform.
Data Types:
  • Document
  • File Set
Lateral movements of materials and energy in coastal wetlands, due mainly to tidal activities, have been recognized as key processes in understanding the biogeochemical cycles of ecosystems. However, our understanding of the roles of lateral movement in shaping ecosystem functions remains limited. Here, we quantified the effects of lateral sediment transport on total carbon (C, inorganic + organic) and nitrogen (N) pools in plants and soils in two dominant wetland types: invasive Spartina alterniflora (Spartina) marshes and native Phragmites australis (Phragmites) marshes in coastal Shanghai of the Yangtze Estuary. We found that the accreted sediments across the water-marsh gradients caused by lateral movement resulted in contrasting C and N contents between the two communities. The sediment load and C and N pools in the plants and soils of the Spartina marshes were significantly higher than those in the adjacent Phragmites marshes. The shifts in species composition and community structure not only altered the C and N balance but also enhanced the ecosystem net primary productivity (NPP). Our findings highlight the importance of lateral transport in altering ecosystem structure after Spartina invasion. The ecosystem C and N pools were significantly higher in the invaded ecosystems than in the native community. Our study also reveals that the plant density and structures can alter tidal hydrodynamics and the lateral transportations of sediments, which in turn influence ecosystem C and N cycle. The C accumulation processes of the native and invaded marshes were further complicated by the contrasting productivities of the ecosystems.
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Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with multi-objectives on the tumors and organs-at-risk. Unfortunately, clinically relevant dose–volume constraints are nonconvex, so convex formulations and algorithms cannot be directly applied to the problem. We propose a novel approach to handle dose–volume constraints while preserving their nonconvexity, as opposed to previous efforts which focused on convex approximations. The proposed method is amenable to efficient algorithms based on partial minimization and naturally adapts to handle maximum-dose constraints and cases of infeasibility. We demonstrate our approach using the CORT dataset, and show that it is easily adaptable to radiation treatment planning with dose–volume constraints for multiple tumors and organs-at-risk.
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  • Software/Code
  • File Set
Geosoup is a python package for geospatial data manipulation using GDAL and GDAL bindings in python. This package is a minimalistic software distribution for limited manipulation of common geospatial data types such as rasters, vectors and samples. All the heavy lifting is done by GDAL, numpy and, scipy.
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  • File Set
This repository contains genome annotation files for the genome assembly reported in Kapheim et al. 2020.
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  • Sequencing Data
  • Tabular Data
  • Dataset
various fixes
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