cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3905429
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: <strong>Abstract</strong> 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. <strong>Documentation of dataset development</strong> 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. <strong>Dataset version history</strong> 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] 0.2.3, March 31, 2020: corrected a bug in the wind profile generation process which was pulling the wrong locations for wind farms outside the Western Interconnection. 0.2.4, April 15, 2020: added ramp rate limits to fossil fuel generators. 0.3.0, June 23, 2020: Scaling cost curves to more produce LMPs which are more reflective of historical market prices, updating the set of back-to-back HVDC converter stations to be more reflective of current capacities/locations, miscellaneous data fixes to a few erroneous cost curve and heat rate curve parameters.
DOI: 10.5258/soton/d1683
title: The Volcano Within: A study of womens lived experience of the journey through natural menopause

description: This dataset supports the thesis titled: The Volcano Within: A study of womens lived experience of the journey through natural menopause The dataset can be requested via https://library.soton.ac.uk/datarequest by bona fide researchers with ethical approval.
DOI: 10.5066/p9hub01s
title: Electrical Resistivity Tomography Data at Elizabeth Lake, Los Angeles County, California, 2019

description: The Santa Clara River Lakes, located along the San Andreas fault 19 miles northwest of Palmdale, California, were placed on the state?s ?303(d) List? or ?Impaired Water List? in 1996 for eutrophic conditions, high pH, and low dissolved oxygen. In 2016, the state adopted a Total Maximum Daily Load (TMDL) for nutrients (nitrogen and phosphorus) in the Santa Clara River Lakes. This study focuses on the largest of the three lakes, Lake Elizabeth, which is surrounded by the unincorporated town of Elizabeth Lake, CA. The local community uses on-site wastewater treatment systems instead of a centralized sewer system, resulting in potential contamination of groundwater. In response to concerns over the quality of water in the area and fluctuating water levels in Elizabeth Lake, the U.S. Geological Survey (USGS) cooperated with the Los Angeles Regional Water Quality Control Board to assess hydrologic conditions and water quality near Elizabeth Lake. As part of this work, the USGS did electrical resistivity tomography (ERT) survey lines at two locations across the southern edge of the lake near the community in March 2019.
DOI: 10.5281/zenodo.3753177
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: <strong>Abstract</strong> 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. <strong>Documentation of dataset development</strong> 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. <strong>Dataset version history</strong> 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] 0.2.3, March 31, 2020: corrected a bug in the wind profile generation process which was pulling the wrong locations for wind farms outside the Western Interconnection. 0.2.4, April 15, 2020: added ramp rate limits to fossil fuel generators.
DOI: 10.15468/dvzu5t
title: Redescription of Brachirus aspilos (Bleeker 1852), a senior synonym of four nominal species, with a note on the distribution of Dagetichthys marginatus (Boulenger 1900) (Pleuronectiformes: Soleidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Okamoto, Jo, Motomura, Hiroyuki (2021): Redescription of Brachirus aspilos (Bleeker 1852), a senior synonym of four nominal species, with a note on the distribution of Dagetichthys marginatus (Boulenger 1900) (Pleuronectiformes: Soleidae). Zootaxa 4908 (3): 354-368, DOI: https://doi.org/10.11646/zootaxa.4908.3.2
DOI: 10.25824/redu/a2ddd7
title: LODMF: Linked Open Data Maintenance Framework

description: LODMF is a framework responsible for keeping links and annotations of RDF datasets in an integrity state. The source code, datasets used as input, and generated data are available at the university public repository: https://gitlab.ic.unicamp.br/jreis/evoLOD-analysis.
DOI: 10.5281/zenodo.3530899
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: 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.
DOI: 10.5281/zenodo.3703327
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: 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.
DOI: 10.5281/zenodo.3727352
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: <strong>Abstract</strong> 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. <strong>Documentation of dataset development</strong> 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. <strong>Dataset version history</strong> 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.
DOI: 10.5281/zenodo.3728233
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: <strong>Abstract</strong> 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. <strong>Documentation of dataset development</strong> 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. <strong>Dataset version history</strong> 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]
DOI: 10.5281/zenodo.3735409
title: U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies

description: <strong>Abstract</strong> 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. <strong>Documentation of dataset development</strong> 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. <strong>Dataset version history</strong> 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] 0.2.3, March 31, 2020: corrected a bug in the wind profile generation process which was pulling the wrong locations for wind farms outside the Western Interconnection.
DOI: 10.5517/ccdc.csd.cc25l600
title: CCDC 2013481: Experimental Crystal Structure Determination

description: Related Article: Manuel Boniolo, Petko Chernev, Mun Hon Cheah, Philipp A. Heizmann, Ping Huang, Sergii I. Shylin, Nessima Salhi, Md Kamal Hossain, Arvind K. Gupta, Johannes Messinger, Anders Thapper, Marcus Lundberg|2021|Dalton Trans.|50|660|doi:10.1039/D0DT03695A
DOI: 10.5517/ccdc.csd.cc25l611
title: CCDC 2013482: Experimental Crystal Structure Determination

description: Related Article: Manuel Boniolo, Petko Chernev, Mun Hon Cheah, Philipp A. Heizmann, Ping Huang, Sergii I. Shylin, Nessima Salhi, Md Kamal Hossain, Arvind K. Gupta, Johannes Messinger, Anders Thapper, Marcus Lundberg|2021|Dalton Trans.|50|660|doi:10.1039/D0DT03695A
DOI: 10.5517/ccdc.csd.cc25l622
title: CCDC 2013483: Experimental Crystal Structure Determination

description: Related Article: Manuel Boniolo, Petko Chernev, Mun Hon Cheah, Philipp A. Heizmann, Ping Huang, Sergii I. Shylin, Nessima Salhi, Md Kamal Hossain, Arvind K. Gupta, Johannes Messinger, Anders Thapper, Marcus Lundberg|2021|Dalton Trans.|50|660|doi:10.1039/D0DT03695A
DOI: 10.5517/ccdc.csd.cc25v428
title: CCDC 2021109: Experimental Crystal Structure Determination

description: Related Article: Youliang He, Xiaoming Wang|2020|Org.Lett.|23|225|doi:10.1021/acs.orglett.0c03953
DOI: 10.5517/ccdc.csd.cc26kbhm
title: CCDC 2042450: Experimental Crystal Structure Determination

description: Related Article: Quan H. Tran, Xiqu Wang, Maurice Brookhart, Olafs Daugulis|2020|Organometallics|39|4704|doi:10.1021/acs.organomet.0c00696
DOI: 10.5517/ccdc.csd.cc26kbjn
title: CCDC 2042451: Experimental Crystal Structure Determination

description: Related Article: Quan H. Tran, Xiqu Wang, Maurice Brookhart, Olafs Daugulis|2020|Organometallics|39|4704|doi:10.1021/acs.organomet.0c00696
DOI: 10.5517/ccdc.csd.cc26kbkp
title: CCDC 2042452: Experimental Crystal Structure Determination

description: Related Article: Quan H. Tran, Xiqu Wang, Maurice Brookhart, Olafs Daugulis|2020|Organometallics|39|4704|doi:10.1021/acs.organomet.0c00696
DOI: 10.5517/ccdc.csd.cc26ls9x
title: CCDC 2043839: Experimental Crystal Structure Determination

description: Related Article: Yu-Hua Liu, Jun-Chao Liu, Hang Peng, Xue-Qin Huang, Wei-Qiang Liao, Zhong-Xia Wang|2021|CrystEngComm|23|264|doi:10.1039/D0CE01654C
DOI: 10.5517/ccdc.csd.cc25hc68
title: CCDC 2010759: Experimental Crystal Structure Determination

description: Related Article: Zhoumi Hu, Liang Fu, Pinhong Chen, Weiguo Cao, Guosheng Liu|2020|Org.Lett.|23|129|doi:10.1021/acs.orglett.0c03826
DOI: 10.5517/ccdc.csd.cc269nfm
title: CCDC 2035070: Experimental Crystal Structure Determination

description: Related Article: Tingting Xu, Minghui He, Lihui Fan, Ping Zhou, Zhenzhen Jiang, Yabing He|2021|Dalton Trans.|50|638|doi:10.1039/D0DT03410J
DOI: 10.25824/redu/bbzqxe
title: paper_cancer_medicine_2019

description: Publicação
DOI: 10.25824/redu/opcmdt
title: paper_epigenetics_2020.pdf

description: Paper submetido para epigenetics
DOI: 10.25824/redu/mol0wu
title: paper_Critical Reviews_2019.pdf
DOI: 10.25824/redu/kvf0x4
title: Molecular and Cellular Biochemistry_2018.pdf
DOI: 10.25824/redu/a7ldwl
title: clinical_miRNA_data.xlsx

description: Dados clínicos
DOI: 10.25824/redu/3giocf
title: secondary_analysis_deseq.tab

description: Analises Estatisticas do sequenciamento
DOI: 10.25824/redu/sbvjtj
title: primary_quantification.tab

description: Quantificação primaria
DOI: 10.25824/redu/mishls
title: ipa_plasma_down.xls

description: Avaliação_miRNA_IPA_down
DOI: 10.25824/redu/zl5cxi
title: Matriz_plasma_up_2.5.tab

description: matriz_miRNA_up
DOI: 10.25824/redu/tevipo
title: Matriz_plasma_down_2.5.tab

description: matriz_miRNA_down
DOI: 10.25824/redu/nw88ly
title: Matriz_plasma_up_2.5_kegg.tab
DOI: 10.25824/redu/dqyka5
title: Matriz_plasma_down_2.5_kegg.xlsx
DOI: 10.25824/redu/purthl
title: ipa_plasma_up.xls
DOI: 10.25824/redu/sv4shi
title: secondary_analysis_deseq_plasma.tab

description: Gene Globe Secondary analysis results (deseq normalization)
DOI: 10.25824/redu/t0ibn5
title: primary_quantification_plasma.tab

description: Gene Globe primary quantification results
DOI: 10.25824/redu/mo8hgq
title: Urina_validação_exc_miR39_2DP_MMO_JBF.tab
DOI: 10.25824/redu/qju9b0
title: Plasma_validação_exc_miR39_2DP.tab
DOI: 10.25824/redu/tgw65q
title: Plasma_validação.tab
DOI: 10.25824/redu/qlypq5
title: Urina_validação_exc_miR39_2DP.tab
DOI: 10.25824/redu/jena0t
title: Urina_validação.tab
DOI: 10.25824/redu/cqrzll
title: miRNAs_cts_urina.tab
DOI: 10.25824/redu/ht5x5q
title: miR203.xls
DOI: 10.25824/redu/4ajxxb
title: miR425_2.xls
DOI: 10.25824/redu/mkyjpv
title: julia1505.xls
DOI: 10.25824/redu/t1p7ag
title: miR425_3.xls
DOI: 10.25824/redu/ajedzg
title: miR425.xls
DOI: 10.25824/redu/awfznx
title: miR16.xls
DOI: 10.25824/redu/t1nsl6
title: julia_2002_1.xls
DOI: 10.25824/redu/00fssf
title: curva_miR615.xls
DOI: 10.25824/redu/dr3lcs
title: miR203_urina.eds
DOI: 10.25824/redu/2i61qn
title: julia_2002_2.xls
DOI: 10.25824/redu/ho1mjs
title: miR615_2.xls
DOI: 10.25824/redu/3bkupo
title: curva_miR39.xls
DOI: 10.25824/redu/dud8n6
title: miR615.xls
DOI: 10.25824/redu/okevwj
title: miR203_curva.eds
DOI: 10.25824/redu/j144dt
title: urinamiR39.xls
DOI: 10.25824/redu/7e4gub
title: miR425_urina3.eds
DOI: 10.25824/redu/dzbnkn
title: curva6125.eds
DOI: 10.25824/redu/odq9de
title: curva39.eds
DOI: 10.25824/redu/vzvzmb
title: miR3168_miR6125_plasma.eds
DOI: 10.25824/redu/tz5arv
title: miR425_curva.eds
DOI: 10.25824/redu/lgaot7
title: miR16_urina.eds
DOI: 10.25824/redu/4uhmac
title: miR615_curva.eds
DOI: 10.25824/redu/76brmy
title: miR425_urina2.eds
DOI: 10.25824/redu/m8srmf
title: miR16_plasma.eds
DOI: 10.25824/redu/xnjgdq
title: miR425_urina.eds
DOI: 10.25824/redu/dc7u7o
title: curva_miR39.eds
DOI: 10.25824/redu/ojdv2x
title: miR615_urina.eds
DOI: 10.25824/redu/xeane8
title: miR615_2.eds
DOI: 10.25824/redu/mnfzlf
title: miR4127_plasma.eds
DOI: 10.25824/redu/rfkmja
title: plasma_mir39.eds
DOI: 10.25824/redu/laozxq
title: miR39_urina.eds
DOI: 10.25824/redu/bc7lt8
title: Resultados_brutos_seq-20201206T143307Z-001.zip
DOI: 10.25824/redu/cwd1ft
title: clinical_miRNA_data.xlsx
DOI: 10.25824/redu/rzo8ar
title: clinical_miRNA_data.tab

description: Clinical data and miRNA (normalized results) data
DOI: 10.25824/redu/onl4dh
title: fastq_files.7z

description: fasq files of miRNA sequencing