cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/ec4q-yb42
title: This is the Matlab code for the Switch Opening and Exchange (SOE) method used in paper "Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration" published in IEEE Transactions on Smart Grid

description: The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.
DOI: 10.21227/hgpw-2s80
title: This is the Matlab code for the Switch Opening and Exchange (SOE) method used in paper "Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration" published in IEEE Transactions on Smart Grid

description: The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.
DOI: 10.18112/openneuro.ds002563.v1.0.0
title: TODO: name of the dataset
DOI: 10.34619/fjpw-md86
title: Absolute Metabolite Concentrations and Implied Enzyme Active Site Occupancy in Escherichia coli [Datasets]

description: Data from the manuscript: Bennett BD, Kimball EH, Gao M, Osterhout R, Van Dien SJ, Rabinowitz JD. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol. 2009;5(8):593-599. doi:10.1038/nchembio.186
DOI: 10.5281/zenodo.3668974
title: Automated Isolation for White-box Test Generation

description: We present an approach for automated isolation in white-box test generation. This publication contains the data and the corresponding analysis script written in R.
DOI: 10.5519/qd.htgvr5wg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (47 records)
DOI: 10.5281/zenodo.3668968
title: UAV-based DEMs

description: <strong>LC1_all_DEMs.tif </strong>is a multiband raster file that contains 5 DEMs for the study area Lancang 1: Band 1 : Non-filtered DEM Band 2 : filtered DEM (method 1) Band 3 : filtered DEM (method 2) Band 4 : filtered DEM (method 3) Band 5 : filtered DEM (method 4) <strong>LC2_all_DEMs.tif</strong> is a multiband raster file that contains 5 DEMs for the study area Lancang 2: Band 1 : Non-filtered DEM Band 2 : filtered DEM (method 1) Band 3 : filtered DEM (method 2) Band 4 : filtered DEM (method 3) Band 5 : filtered DEM (method 4)
DOI: 10.5281/zenodo.3668969
title: UAV-based DEMs

description: <strong>LC1_all_DEMs.tif </strong>is a multiband raster file that contains 5 DEMs for the study area Lancang 1: Band 1 : Non-filtered DEM Band 2 : filtered DEM (method 1) Band 3 : filtered DEM (method 2) Band 4 : filtered DEM (method 3) Band 5 : filtered DEM (method 4) <strong>LC2_all_DEMs.tif</strong> is a multiband raster file that contains 5 DEMs for the study area Lancang 2: Band 1 : Non-filtered DEM Band 2 : filtered DEM (method 1) Band 3 : filtered DEM (method 2) Band 4 : filtered DEM (method 3) Band 5 : filtered DEM (method 4)
DOI: 10.18112/openneuro.ds002564.v1.0.0
title: TODO: name of the dataset
DOI: 10.5517/ccdc.csd.cc22gggh
title: CCDC 1920526: Experimental Crystal Structure Determination

description: Related Article: William A. Howard, Kraig A. Wheeler, Daniela Buccella|2020|J.Mol.Struct.|1220|128750|doi:10.1016/j.molstruc.2020.128750
DOI: 10.5517/ccdc.csd.cc24f1bz
title: CCDC 1978740: Experimental Crystal Structure Determination

description: Related Article: Xin Chang, Pei-Long Ma, Hong-Chao Chen, Chuan-Ying Li, Peng Wang|2020|Angew.Chem.,Int.Ed.|59|8937|doi:10.1002/anie.202002289
DOI: 10.5517/ccdc.csd.cc24jrm2
title: CCDC 1982314: Experimental Crystal Structure Determination

description: Related Article: Jia-Pan Niu, Jing Nie, Shen Li, Jun-An Ma|2020|Chem.Commun.|56|8687|doi:10.1039/D0CC02213F
description: Related Article: Shen Li|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24jrn3
title: CCDC 1982315: Experimental Crystal Structure Determination

description: Related Article: Jia-Pan Niu, Jing Nie, Shen Li, Jun-An Ma|2020|Chem.Commun.|56|8687|doi:10.1039/D0CC02213F
description: Related Article: Shen Li|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24k888
title: CCDC 1982799: Experimental Crystal Structure Determination

description: Related Article: Alessandro Bismuto, Gary S. Nichol, Fernanda Duarte, Michael J. Cowley, Stephen P. Thomas|2020|Angew.Chem.,Int.Ed.|59|12731|doi:10.1002/anie.202003468
DOI: 10.5517/ccdc.csd.cc24k899
title: CCDC 1982800: Experimental Crystal Structure Determination

description: Related Article: Alessandro Bismuto, Gary S. Nichol, Fernanda Duarte, Michael J. Cowley, Stephen P. Thomas|2020|Angew.Chem.,Int.Ed.|59|12731|doi:10.1002/anie.202003468
DOI: 10.5517/ccdc.csd.cc24m13y
title: CCDC 1984499: Experimental Crystal Structure Determination

description: Related Article: Gioele Colombo, Antonio Romeo, G. Attilio Ardizzoia, Julien Furrer, Bruno Therrien, Stefano Brenna|2020|Dyes Pigm.|182|108636|doi:10.1016/j.dyepig.2020.108636
DOI: 10.3886/e117762v1
title: Data and code for: Automatic stabilizers in a low-rate environment

description: In a world where monetary policy cannot assume responsibility for stabilization policy, there is a strong need for fiscal policy to address stabilization issues. In this context, we argue for “semi-automatic stabilizers” , aimed at reducing unemployment slumps rather than output recessions. We show that the hole left by the limits on monetary policy implies a large role for fiscal policy in general, and for semi-automatic stabilizers in particular. Finally, we argue that the design of stabilizers, whether they focus on mechanisms that rely primarily on income or on intertemporal substitution effects, depends crucially on the general design of discretionary policy. <br>
DOI: 10.6084/m9.figshare.11859750.v4
title: Simulated spectrum peak wave period (3-hourly, 150a) from FIO-ESM v2.0 abrupt-4xCO2 experiment r1i1p1f1

description: Simulated spectrum peak wave period (3-hourly, 150a) from FIO-ESM v2.0 abrupt-4xCO2 experiment (CO2 abruptly quadrupled and then held constant) r1i1p1f1, which is branch from Jan 1, 301 of piControl experiment. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.5281/zenodo.3669025
title: The Yeast Atlas of Appalachia: Species and phenotypic diversity of herbicide resistance in wild yeast

description: Supplemental Tables
DOI: 10.5281/zenodo.3669024
title: The Yeast Atlas of Appalachia: Species and phenotypic diversity of herbicide resistance in wild yeast

description: Supplemental Tables
DOI: 10.34619/1cke-3f59
title: Differential glucose repression in common yeast strains in response to HXK2 deletion [Datasets]

description: Data from the manuscript: Kümmel A, Ewald JC, Fendt SM, et al. Differential glucose repression in common yeast strains in response to HXK2 deletion. FEMS Yeast Res. 2010;10(3):322-332. doi:10.1111/j.1567-1364.2010.00609.x
DOI: 10.7910/dvn/tdoapg/2lt2ly
title: URLs-v2.pdf

description: :unav
DOI: 10.34619/y0mr-bg52
title: Metabolic Fluxes during Strong Carbon Catabolite Repression by Malate in Bacillus subtilis [Datasets]

description: Data from the manuscript: Kleijn RJ, Buescher JM, Le Chat L, Jules M, Aymerich S, Sauer U. Metabolic fluxes during strong carbon catabolite repression by malate in Bacillus subtilis. J Biol Chem. 2010;285(3):1587-1596. doi:10.1074/jbc.M109.061747
DOI: 10.5281/zenodo.3668997
title: The Archaeology of Vidarbha, Maharashtra: 2016 Regional Survey Data

description: This dataset comprises the results of regional archaeological surveys carried out in Vidarbha in 2016, and contains the following data groups: photographs, site plans, and written records of all archaeological sites and remains encountered during survey. This dataset is released under a Creative Commons Attribution 4.0 International licence (CC-BY). For legal terms of the licence see: https://creativecommons.org/licenses/by/4.0/legalcode If reused, data should be cited as: Hawkes, J., Abbas, R. and Toraskar, S. (2020). The Archaeology of Vidarbha, Maharashtra: 2016 Regional Survey Data. Dataset. http://doi.org/10.5281/zenodo.3668997. For further information about this dataset, including details of the context, methods used to collected data, and ways in which they can be reused, see: Hawkes, J., Abbas, R. and Toraskar, S. (2020 Under Review). Multi-period regional survey data from Vidarbha, Maharashtra, India. <em>Journal of Open Archaeological Data</em>.
DOI: 10.34619/dcvx-q138
title: Metabolic Fluxes during Strong Carbon Catabolite Repression by Malate in Bacillus subtilis [Datasets]

description: Data from the manuscript: Kleijn RJ, Buescher JM, Le Chat L, Jules M, Aymerich S, Sauer U. Metabolic fluxes during strong carbon catabolite repression by malate in Bacillus subtilis. J Biol Chem. 2010;285(3):1587-1596. doi:10.1074/jbc.M109.061747
DOI: 10.5281/zenodo.3618208
title: Data from Open Research Data: SNSF monitoring report 2017-2018

description: This file collection is part of Open Research Data: SNSF monitoring report 2017-2018 (doi 10.5281/zenodo.3618123). This report gives a first overview on the research data management practices adopted by researchers since the introduction of the SNSF Open Research Data Policy in 2017. Please cite this data collection as:<br> Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Data from Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618209) Further information is given in the corresponding monitoring report<br> Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618123) <strong>Contact</strong> Swiss National Science Foundation (SNSF) Open Research Data group E-mail: ord@snf.ch
DOI: 10.5281/zenodo.3618209
title: Data from Open Research Data: SNSF monitoring report 2017-2018

description: This file collection is part of Open Research Data: SNSF monitoring report 2017-2018 (doi 10.5281/zenodo.3618123). This report gives a first overview on the research data management practices adopted by researchers since the introduction of the SNSF Open Research Data Policy in 2017. Please cite this data collection as:<br> Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Data from Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618209) Further information is given in the corresponding monitoring report<br> Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618123) <strong>Contact</strong> Swiss National Science Foundation (SNSF) Open Research Data group E-mail: ord@snf.ch
DOI: 10.18112/openneuro.ds002568.v1.0.0
title: TODO: name of the dataset
DOI: 10.5281/zenodo.3669126
title: Shoreline position, wave height and wave period datasets at Narrabeen (Australia), Grand Popo (Benin) and Nha Trang (Vietnam)

description: Shoreline position, wave height and wave period datasets at Narrabeen (Australia), Grand Popo (Benin) and Nha Trang (Vietnam) (.mat files)
DOI: 10.18112/openneuro.ds002567.v1.0.0
title: TODO: name of the dataset
DOI: 10.5281/zenodo.3669128
title: Appendix B for Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer

description: Appendix B for Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer
DOI: 10.5281/zenodo.3669127
title: Appendix B for Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer

description: Appendix B for Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer
DOI: 10.18112/openneuro.ds002565.v1.0.0
title: TODO: name of the dataset
DOI: 10.5281/zenodo.1064886
title: IN01022 Halsi Grant of Harivarman, Year 5. Sanskrit XML file

description: IN01022 Halsi Grant of Harivarman, Year 5. Sanskrit XML file (without metadata).
DOI: 10.5281/zenodo.1064885
title: IN01022 Halsi Grant of Harivarman, Year 5. Sanskrit XML file

description: IN01022 Halsi Grant of Harivarman, Year 5. Sanskrit XML file (without metadata).
DOI: 10.34619/78qa-a695
title: 13C‐metabolic flux analysis for batch culture of Escherichia coli and its pyk and pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites [Datasets]

description: Data from the manuscript: Toya Y, Ishii N, Nakahigashi K, et al. 13C-metabolic flux analysis for batch culture of Escherichia coli and its Pyk and Pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites. Biotechnol Prog. 2010;26(4):975-992. doi:10.1002/btpr.420
DOI: 10.5281/zenodo.3669335
title: Seismic and Pressure data for Wang and Tanimoto

description: Seismic and pressure data output files used for calculating half-space rigidity values at TA stations.
DOI: 10.34740/kaggle/dsv/1385578
title: US counties COVID 19 dataset
DOI: 10.5281/zenodo.3669344
title: Downsampled Disentanglement Datasets - Falcor3D and Isaac3D

description: <strong>New disentanglement datasets</strong> This data repository contains the <em>Falcor3D</em> and <em>Isaac3D</em> datasets for disentanglement learning, where the image resolution is 128x128. <strong><em>Falcor3D</em></strong> The Falcor3D dataset consists of <code>233,280</code> images based on the 3D scene of a living room. The meta code corresponds to all possible combinations of <code>7</code> factors of variation: lighting_intensity (5) lighting_x-dir (6) lighting_y-dir (6) lighting_z-dir (6) camera_x-pos (6) camera_y-pos (6) camera_z-pos (6) Note that the number <code>m</code> behind each factor represents that the factor has <code>m</code> possible values, uniformly sampled in the normalized range of variations [0, 1]. Each image has as filename <em><code>padded_index.png</code></em> where <pre><code>index = lighting_intensity * 46656 + lighting_x-dir * 7776 + lighting_y-dir * 1296 + lighting_z-dir * 216 + camera_x-pos * 36 + camera_y-pos * 6 + camera_z-pos </code></pre> padded_index = index padded with zeros such that it has 6 digits. <em><strong>Isaac3D</strong></em> The Isaac3D dataset consists of <code>737,280</code> images, based on the 3D scene of a kitchen. The meta code corresponds to all possible combinations of <code>9</code> factors of variation: object_shape (3) object_scale (4) camera_height (4) robot_x-movement (8) robot_y-movement (5) lighting_intensity (4) lighting_y-dir (6) object_color (4) wall_color (4) Similarly, the number <code>m</code> behind each factor represents that the factor has <code>m</code> possible values, uniformly sampled in the normalized range of variations [0, 1]. Each image has as filename <em><code>padded_index.png</code></em> where <pre><code>index = object_shape * 245760 + object_scale * 30720 + camera_height * 6144 + robot_x-movement * 1536 + robot_y-movement * 384 + lighting_intensity * 96 + lighting_y-dir * 16 + object_color * 4 + wall color </code></pre> padded_index = index padded with zeros such that it has 6 digits.
DOI: 10.5281/zenodo.3669343
title: Downsampled Disentanglement Datasets - Falcor3D and Isaac3D

description: <strong>New disentanglement datasets</strong> This data repository contains the <em>Falcor3D</em> and <em>Isaac3D</em> datasets for disentanglement learning, where the image resolution is 128x128. <strong><em>Falcor3D</em></strong> The Falcor3D dataset consists of <code>233,280</code> images based on the 3D scene of a living room. The meta code corresponds to all possible combinations of <code>7</code> factors of variation: lighting_intensity (5) lighting_x-dir (6) lighting_y-dir (6) lighting_z-dir (6) camera_x-pos (6) camera_y-pos (6) camera_z-pos (6) Note that the number <code>m</code> behind each factor represents that the factor has <code>m</code> possible values, uniformly sampled in the normalized range of variations [0, 1]. Each image has as filename <em><code>padded_index.png</code></em> where <pre><code>index = lighting_intensity * 46656 + lighting_x-dir * 7776 + lighting_y-dir * 1296 + lighting_z-dir * 216 + camera_x-pos * 36 + camera_y-pos * 6 + camera_z-pos </code></pre> padded_index = index padded with zeros such that it has 6 digits. <em><strong>Isaac3D</strong></em> The Isaac3D dataset consists of <code>737,280</code> images, based on the 3D scene of a kitchen. The meta code corresponds to all possible combinations of <code>9</code> factors of variation: object_shape (3) object_scale (4) camera_height (4) robot_x-movement (8) robot_y-movement (5) lighting_intensity (4) lighting_y-dir (6) object_color (4) wall_color (4) Similarly, the number <code>m</code> behind each factor represents that the factor has <code>m</code> possible values, uniformly sampled in the normalized range of variations [0, 1]. Each image has as filename <em><code>padded_index.png</code></em> where <pre><code>index = object_shape * 245760 + object_scale * 30720 + camera_height * 6144 + robot_x-movement * 1536 + robot_y-movement * 384 + lighting_intensity * 96 + lighting_y-dir * 16 + object_color * 4 + wall color </code></pre> padded_index = index padded with zeros such that it has 6 digits.
DOI: 10.6084/m9.figshare.11859750.v5
title: Simulated spectrum peak wave period (3-hourly, 150a) from FIO-ESM v2.0 abrupt-4xCO2 experiment r1i1p1f1

description: Simulated spectrum peak wave period (3-hourly, 150a) from FIO-ESM v2.0 abrupt-4xCO2 experiment (CO2 abruptly quadrupled and then held constant) r1i1p1f1, which is branch from Jan 1, 301 of piControl experiment. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.6073/pasta/ecb7dbb9f1729cdd26a3a5da5779710c
title: GIS05 GIS Coverages Defining Konza Prairie Burn History (1977-present)

description: This dataset contains a comprehensive record of burn histories for the Konza Prairie Biological Station (KPBS) dating from 1972. Burn history data contains date burned, area burned and type of treatment (prescribed burns, complete and partial burns, and wildfires). These data are available as zipped (.zip) shapefiles (.shp).
DOI: 10.5281/zenodo.3968422
title: Predicting plasmid contigs from assemblies using single copy marker genes, plasmid genes, kmers

description: Introduction: Antimicrobial resistant (AMR) genes in bacteria are often carried on plasmids. Since these plasmids can spread the AMR genes between bacteria, it is important to know if the genes are located on highly transferable plasmids or in the more stable chromosomes. Whole genome sequence (WGS) analysis makes it easy to determine if a strain contains a resistance gene, however, it is not easy to determine if the gene is located on the chromosome or on a plasmid as genome sequence assembly generally results in 50-300 DNA fragments (contigs). With our newly developed prediction tool, we analyze the composition of these contigs to predict their likely source, plasmid or chromosomal. This information can be used to determine if a resistant gene is chromosomally located or on a plasmid. The tool is optimized for 19 different bacterial species, including Campylobacter, E. coli, and Salmonella, and can also be used for metagenomic assemblies. Methods: The tool identifies the number of chromosomal marker genes, plasmid replication genes and plasmid typing genes using CheckM and DIAMOND Blast, and determines pentamer frequencies and contig sizes per contig. A prediction model was trained using Random Forest on an extensive set of plasmids and chromosomes from 19 different bacterial species and validated on separate test sets of known chromosomal and plasmid contigs of the different bacteria. Results: Prediction of plasmid contigs was nearly perfect when calculated based on number of correctly predicted bases, with up to 99% specificity and 99% sensitivity. Prediction of small contigs remains difficult, since these contigs consists primarily of repeated sequences present in both plasmid and chromosome, e.g. transposases. Conclusion: The newly developed tool is able to determine if contigs are chromosomal or plasmid with a very high specificity and sensitivity (up to 99%) and can be very useful to analyze WGS data of bacterial genomes and their antimicrobial resistance genes. Plasmid databases can be downloaded from: http://klif.uu.nl/download/plasmid_db/ Data used for training can be downloaded here: http://klif.uu.nl/download/plasmid_db/trainingsets2/