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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
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The Paired Omics Data Platform is a community-based initiative standardizing links between genomic and metabolomics data in a computer readable format to further the field of natural products discovery. The goals are to link molecules to their producers, find large scale genome-metabolome associations, use genomic data to assist in structural elucidation of molecules, and provide a centralized database for paired datasets. This dataset contains the projects in http://pairedomicsdata.bioinformatics.nl/. The JSON documents adhere to the http://pairedomicsdata.bioinformatics.nl/schema.json JSON schema.
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King cobra predation events on Varanus nebulosus. Included: photographic evidence of each event, king cobra biometric data from most recent measurements and location information. .csv file column headings: folderid: The Zenodo folder ID containing the photographic evidence of event. obvdate: Date of observation (yyyy-mm-dd) obvtime: Time of observation (24hr) snakeid: Unique ID given to individual king cobras captured svlmm: King cobra snout-to-vent length (mm) tlmm: King cobra tail length (mm) totalmm: King cobra total length (mm) massg: King cobra mass (g) easting: UTM easting (UTM Zone 47N; Datum WGS84) northing: UTM northing (UTM Zone 47N; Datum WGS84) gpsaccm: Accuracy of GPS location (m) notes: Comments on predation event
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Python for heliospheric and planetary physics
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  • Software/Code
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This is the first stable release.
Data Types:
  • Software/Code
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This is the published dataset including analysis code for: Eckert RJ, Reaume A, Sturm AB, Studivan MS, and Voss JD (2020) Depth influences Symbiodiniaceae associations among Montastraea cavernosa corals on the Belize Barrier Reef. Frontiers in Microbiology. 11:518. doi: 10.3389/fmicb.2020.00518. Here, we describe the Symbiodiniaceae taxa found within previously-sampled and genotyped Montastraea cavernosa populations along a depth gradient on the Belize Barrier Reef by implementing high-throughput sequencing of the ITS2 region of Symbiodiniaceae ribosomal DNA and the SymPortal analysis framework.
Data Types:
  • Software/Code
  • File Set