cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3744784
title: Indexed hg38 human genome from NCBI

description: Raw data obtained from https://www.ncbi.nlm.nih.gov/assembly/88331 Processed using - bwa mem for indexing
DOI: 10.5281/zenodo.3744785
title: Indexed hg38 human genome from NCBI

description: Raw data obtained from https://www.ncbi.nlm.nih.gov/assembly/88331 Processed using - bwa mem for indexing
DOI: 10.5281/zenodo.3746198
title: Transmission efficiency drives host-microbe associations

description: MatLab code to accompany "Transmission efficiency drives host-microbe associations" Modelling is divided into three files - Asexual hosts, Male mate choice and Female mate choice.
DOI: 10.5281/zenodo.3746199
title: Transmission efficiency drives host-microbe associations

description: MatLab code to accompany "Transmission efficiency drives host-microbe associations" Modelling is divided into three files - Asexual hosts, Male mate choice and Female mate choice.
DOI: 10.15468/hg3x6b
title: A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth

description: This dataset contains the digitized treatments in Plazi based on the original journal article John R. Hutchinson, Karl T. Bates, Julia Molnar, Vivian Allen, Peter J. Makovicky (2011): A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth. PLoS ONE 6, No. 10: e 26037, DOI: 10.1371/journal.pone.0026037
DOI: 10.21227/7a6p-9b91
title: GSET Somi: A Game-Specific Eye Tracking Dataset for Somi

description: This is an eye tracking dataset of 84 computer game players who played the side-scrolling cloud game Somi. The game was streamed in the form of video from the cloud to the player. The dataset consists of 135 raw videos (YUV) at 720p and 30 fps with eye tracking data for both eyes (left and right). Male and female candidates were asked to play the game in front of a remote eye-tracking device. For each player, we recorded gaze points, video frames of the gameplay, and mouse and keyboard commands. For each video frame, a list of its game objects with their locations and sizes was also recorded. This data, synchronized with eye-tracking data, allows one to calculate the amount of attention that each object or group of objects draw from each player. This dataset can be used for designing and testing game-specific visual attention models.
DOI: 10.5281/zenodo.3746178
title: A 63-bp insertion in exon 2 of the porcine KIF21A gene is associated with arthrogryposis multiplex congenita

description: This dataset includes genotype and phenotype data of 11 affected (case) and 23 unaffected (control) pigs for AMC case-control haplotype association testing and a VCF file of 809 candidate variants compatible with recessive inheritance of AMC. Genotype data (AMC.bed, AMC.bim and AMC.fam): 34 pigs genotyped with Illumina PorcineSNP60 Genotyping Bead chips. Phenotype data (AMC.pheno): the first, second and third column is Family ID, Animal ID and phenotype (1=case;2=control), respectively. AMC candidate variants compatible with recessive inheritance (AMC_candidate_muations.vcf.gz and AMC_candidate_muations.vcf.gz.tbi). Sequencing data of five animals in AMC pedigree have also been deposited at the Sequence Read Archive of the NCBI at the BioProject PRJNA622908 under sample accessions SAMN14532191, SAMN14532478, SAMN14532769, SAMN14532790 and SAMN14532792.
DOI: 10.18112/openneuro.ds002694.v1.0.0
title: my study
DOI: 10.5281/zenodo.3743479
title: Experiment data used in DeepHelicon

description: The data is for the paper titled DeepHelicon: accurate prediction of inter-helical residue contacts in transmembrane protein by residual neural networks.<br> It contains four sub-folders as follows:<br> 1. Fasta: protein sequences of TRAIN, PREVIOUS, TEST.<br> 2. PDB: protein native structures of TRAIN, PREVIOUS, TEST.<br> 3. Predictions: contact predictions of PREVIOUS and TEST, which are predicted by contact prediction methods mentioned in DeepHelicon paper.<br> 4. 3D modelling: 3D models are generated by CONFOLD2 and guided by residue contacts predicted by DeepHelicon and DeepMetaPSICOV and guided by secondary structures predicted by SCRATCH1D.
DOI: 10.5281/zenodo.3743478
title: Experiment data used in DeepHelicon

description: The data is for the paper titled DeepHelicon: accurate prediction of inter-helical residue contacts in transmembrane protein by residual neural networks.<br> It contains four sub-folders as follows:<br> 1. Fasta: protein sequences of TRAIN, PREVIOUS, TEST.<br> 2. PDB: protein native structures of TRAIN, PREVIOUS, TEST.<br> 3. Predictions: contact predictions of PREVIOUS and TEST, which are predicted by contact prediction methods mentioned in DeepHelicon paper.<br> 4. 3D modelling: 3D models are generated by CONFOLD2 and guided by residue contacts predicted by DeepHelicon and DeepMetaPSICOV and guided by secondary structures predicted by SCRATCH1D.
DOI: 10.7910/dvn/k8e5qr/1rsjtw
title: COVID-19_Med_Chem.doc

description: Article.
DOI: 10.7910/dvn/k8e5qr/kbuovx
title: COVID-19_Med_Chem.pdf

description: Article.
DOI: 10.15468/5px4sp
title: Catalogue of species-group names assigned to Copris Geoffroy, Coptodactyla Burmeister, Litocopris Waterhouse, Microcopris Balthasar, Paracopris Balthasar, Pseudocopris Ferreira, Pseudopedaria Felsche, Sinocopris Ochi, Kon and Bai, and Thyregis Blackburn (Coleoptera: Scarabaeidae: Scarabaeinae: Coprini)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zidek, Jiri (2020): Catalogue of species-group names assigned to Copris Geoffroy, Coptodactyla Burmeister, Litocopris Waterhouse, Microcopris Balthasar, Paracopris Balthasar, Pseudocopris Ferreira, Pseudopedaria Felsche, Sinocopris Ochi, Kon and Bai, and Thyregis Blackburn (Coleoptera: Scarabaeidae: Scarabaeinae: Coprini). Insecta Mundi 756: 1-24, DOI: http://doi.org/10.5281/zenodo.3746274
DOI: 10.25592/uhhfdm.857
title: Dataset: a geodata derived neighborhood graph of german municipalities and their metadata

description: The graph presented in this dataset contains all German municipalities and their metadata keyed by NUTS Level 3, provided by the DE BKG as of 06/2019 - changed. All data renderings provided here were done in Cytoscape 3.7.1 (Season file for replication purposes is included in the archives). NUTS is a categorization system for European regions ranging from whole nations (NUTS 0), countries (NUTS 1 and 2), municipalities (NUTS 3) to even smaller regions in some cases (LAU). The AGS (amtlicher Gemeindeschlüssel), which is unique to germany, is also included in the Metadata. Thus the data can be linked with european and german institution sourced data at the same time. <br> The archive files contain the following items: • Edge-List (NutsA-NutsB) wth NUTS only • Node-List with all metadata provided by BKG files. • Example Edge-List with Population-Density-Metric connection values • Season-File for Cytoscape 3.7.1 to reproduce the renderings in this paper All table files are in CSV format with UTF-8 encoding and ; delimiter. Special character like ö, ä, ß, ü have been replaced (with oe, ae, ss, ue) to ensure compatibility.
DOI: 10.25592/uhhfdm.858
title: Dataset: a geodata derived neighborhood graph of german municipalities and their metadata

description: The graph presented in this dataset contains all German municipalities and their metadata keyed by NUTS Level 3, provided by the DE BKG as of 06/2019 - changed. All data renderings provided here were done in Cytoscape 3.7.1 (Season file for replication purposes is included in the archives). NUTS is a categorization system for European regions ranging from whole nations (NUTS 0), countries (NUTS 1 and 2), municipalities (NUTS 3) to even smaller regions in some cases (LAU). The AGS (amtlicher Gemeindeschlüssel), which is unique to germany, is also included in the Metadata. Thus the data can be linked with european and german institution sourced data at the same time. <br> The archive files contain the following items: • Edge-List (NutsA-NutsB) wth NUTS only • Node-List with all metadata provided by BKG files. • Example Edge-List with Population-Density-Metric connection values • Season-File for Cytoscape 3.7.1 to reproduce the renderings in this paper All table files are in CSV format with UTF-8 encoding and ; delimiter. Special character like ö, ä, ß, ü have been replaced (with oe, ae, ss, ue) to ensure compatibility.
DOI: 10.17632/gkv25b4r8g.1
title: Supplemental Materials: The role of PPARγ downregulated signaling in psoriasis

description: Tables and images with supplemental material discussed in "The role of PPARγ downregulated signaling in psoriasis".
DOI: 10.15468/dearn6
title: On the distribution of Phlegyas Stål (Hemiptera: Heteroptera: Pachygronthidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Leavengood, John M. Jr. (2020): On the distribution of Phlegyas Stål (Hemiptera: Heteroptera: Pachygronthidae). Insecta Mundi 758: 1-4, DOI: http://doi.org/10.5281/zenodo.3746276
DOI: 10.7281/t1/1ecvb0/k1deam
title: DustOff-monthly-average_wrfout_d01_2003-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/wz7vbg
title: DustOff-monthly-average_wrfout_d01_2003-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/7f7ywv
title: DustOff-monthly-average_wrfout_d01_2003-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/blcbdw
title: DustOff-monthly-average_wrfout_d01_2003-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/ctmmp3
title: DustOff-monthly-average_wrfout_d01_2003-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/eajebm
title: DustOff-monthly-average_wrfout_d01_2003-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/po3oqr
title: DustOff-monthly-average_wrfout_d01_2003-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/ypaqot
title: DustOff-monthly-average_wrfout_d01_2003-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/z7c1iv
title: DustOff-monthly-average_wrfout_d01_2003-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/aipdku
title: DustOff-monthly-average_wrfout_d01_2003-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/bivjea
title: DustOff-monthly-average_wrfout_d01_2003-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/dnqmph
title: DustOff-monthly-average_wrfout_d01_2003-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/jypyjm
title: DustOff-monthly-average_wrfout_d01_2003-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/keahwb
title: DustOff-monthly-average_wrfout_d01_2003-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/l5svgx
title: DustOff-monthly-average_wrfout_d01_2003-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/svrpqp
title: DustOff-monthly-average_wrfout_d01_2003-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/szxnnd
title: DustOff-monthly-average_wrfout_d01_2003-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/u9iet1
title: DustOff-monthly-average_wrfout_d01_2003-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/1tk4nl
title: DustOff-monthly-average_wrfout_d01_2003-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/chlvzb
title: DustOff-monthly-average_wrfout_d01_2004-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/djzpzr
title: DustOff-monthly-average_wrfout_d01_2003-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/dnc8ul
title: DustOff-monthly-average_wrfout_d01_2004-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/fi2tt7
title: DustOff-monthly-average_wrfout_d01_2004-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/phmsen
title: DustOff-monthly-average_wrfout_d01_2004-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/vfodtj
title: DustOff-monthly-average_wrfout_d01_2004-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/zjwh9l
title: DustOff-monthly-average_wrfout_d01_2003-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/zytiie
title: DustOff-monthly-average_wrfout_d01_2004-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/3oazbx
title: DustOff-monthly-average_wrfout_d01_2004-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/76ah1l
title: DustOff-monthly-average_wrfout_d01_2004-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/9zltf8
title: DustOff-monthly-average_wrfout_d01_2004-03_06_mean.nc
DOI: 10.7281/t1/1ecvb0/ounhjs
title: DustOff-monthly-average_wrfout_d01_2004-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/sgfybm
title: DustOff-monthly-average_wrfout_d01_2004-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/somubi
title: DustOff-monthly-average_wrfout_d01_2004-05_12_mean.nc
DOI: 10.7281/t1/1ecvb0/vyvzlg
title: DustOff-monthly-average_wrfout_d01_2004-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/wf00ws
title: DustOff-monthly-average_wrfout_d01_2004-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/zwqwok
title: DustOff-monthly-average_wrfout_d01_2004-07_18_mean.nc
DOI: 10.7281/t1/1ecvb0/1ob1ei
title: DustOff-monthly-average_wrfout_d01_2004-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/5ausug
title: DustOff-monthly-average_wrfout_d01_2004-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/dm9x4p
title: DustOff-monthly-average_wrfout_d01_2004-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/gcgrg6
title: DustOff-monthly-average_wrfout_d01_2004-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/kksjiu
title: DustOff-monthly-average_wrfout_d01_2004-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/lakyn0
title: DustOff-monthly-average_wrfout_d01_2004-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/njhdda
title: DustOff-monthly-average_wrfout_d01_2004-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/4dafxq
title: DustOff-monthly-average_wrfout_d01_2004-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/bzibff
title: DustOff-monthly-average_wrfout_d01_2004-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/dm2a8b
title: DustOff-monthly-average_wrfout_d01_2004-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/duymm6
title: DustOff-monthly-average_wrfout_d01_2004-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/gkpkfc
title: DustOff-monthly-average_wrfout_d01_2004-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/gldfju
title: DustOff-monthly-average_wrfout_d01_2004-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/mtmi0n
title: DustOff-monthly-average_wrfout_d01_2004-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/a1olbg
title: DustOff-monthly-average_wrfout_d01_2004-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/a6hwgz
title: DustOff-monthly-average_wrfout_d01_2005-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/dk1bsa
title: DustOff-monthly-average_wrfout_d01_2005-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/gmwrrl
title: DustOff-monthly-average_wrfout_d01_2005-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/jptxbx
title: DustOff-monthly-average_wrfout_d01_2005-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/kaoomn
title: DustOff-monthly-average_wrfout_d01_2004-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/libtbm
title: DustOff-monthly-average_wrfout_d01_2005-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/riwpew
title: DustOff-monthly-average_wrfout_d01_2005-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/ypy9ho
title: DustOff-monthly-average_wrfout_d01_2005-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/38ari8
title: DustOff-monthly-average_wrfout_d01_2005-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/akzhwk
title: DustOff-monthly-average_wrfout_d01_2005-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/aulfzu
title: DustOff-monthly-average_wrfout_d01_2005-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/cueink
title: DustOff-monthly-average_wrfout_d01_2005-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/o7orkn
title: DustOff-monthly-average_wrfout_d01_2005-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/uog6pp
title: DustOff-monthly-average_wrfout_d01_2005-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/ykafew
title: DustOff-monthly-average_wrfout_d01_2005-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/ymaxnt
title: DustOff-monthly-average_wrfout_d01_2005-09_00_mean.nc
DOI: 10.25592/uhhfdm.859
title: Dataset: a geodata derived neighborhood graph of german municipalities and their metadata

description: The graph presented in this dataset contains all German municipalities and their metadata keyed by NUTS Level 3, provided by the DE BKG as of 06/2019 - changed. All data renderings provided here were done in Cytoscape 3.7.1 (Season file for replication purposes is included in the archives). NUTS is a categorization system for European regions ranging from whole nations (NUTS 0), countries (NUTS 1 and 2), municipalities (NUTS 3) to even smaller regions in some cases (LAU). The AGS (amtlicher Gemeindeschlüssel), which is unique to germany, is also included in the Metadata. Thus the data can be linked with european and german institution sourced data at the same time. <br> The archive files contain the following items: • Edge-List (NutsA-NutsB) wth NUTS only • Node-List with all metadata provided by BKG files. • Example Edge-List with Population-Density-Metric connection values • Season-File for Cytoscape 3.7.1 to reproduce the renderings in this paper All table files are in CSV format with UTF-8 encoding and ; delimiter. Special character like ö, ä, ß, ü have been replaced (with oe, ae, ss, ue) to ensure compatibility.
DOI: 10.7281/t1/1ecvb0/b2x5at
title: DustOff-monthly-average_wrfout_d01_2005-03_06_mean.nc
DOI: 10.7281/t1/1ecvb0/cmmx9e
title: DustOff-monthly-average_wrfout_d01_2005-07_18_mean.nc
DOI: 10.7281/t1/1ecvb0/evhpiw
title: DustOff-monthly-average_wrfout_d01_2005-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/fyzili
title: DustOff-monthly-average_wrfout_d01_2005-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/huvipy
title: DustOff-monthly-average_wrfout_d01_2005-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/j1dqas
title: DustOff-monthly-average_wrfout_d01_2005-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/moposi
title: DustOff-monthly-average_wrfout_d01_2005-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/py85sa
title: DustOff-monthly-average_wrfout_d01_2005-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/urrndx
title: DustOff-monthly-average_wrfout_d01_2005-05_12_mean.nc
DOI: 10.7281/t1/1ecvb0/9qy00e
title: DustOff-monthly-average_wrfout_d01_2005-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/d8eokh
title: DustOff-monthly-average_wrfout_d01_2005-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/dy1amz
title: DustOff-monthly-average_wrfout_d01_2005-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/h1gx4p
title: DustOff-monthly-average_wrfout_d01_2005-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/qn4rnf
title: DustOff-monthly-average_wrfout_d01_2005-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/w1d0fn
title: DustOff-monthly-average_wrfout_d01_2005-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/z6rwip
title: DustOff-monthly-average_wrfout_d01_2005-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/7xutgs
title: DustOff-monthly-average_wrfout_d01_2006-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/fzdrfk
title: DustOff-monthly-average_wrfout_d01_2006-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/hfrehg
title: DustOff-monthly-average_wrfout_d01_2006-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/jmx2wm
title: DustOff-monthly-average_wrfout_d01_2006-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/lglnoc
title: DustOff-monthly-average_wrfout_d01_2006-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/nifgyp
title: DustOff-monthly-average_wrfout_d01_2006-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/rqx0tf
title: DustOff-monthly-average_wrfout_d01_2005-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/xmfahc
title: DustOff-monthly-average_wrfout_d01_2006-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/2xyywb
title: DustOff-monthly-average_wrfout_d01_2006-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/3uxzcc
title: DustOn-monthly-average_wrfout_d01_2014-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/9ieor6
title: DustOn-monthly-average_wrfout_d01_2014-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/b3emjx
title: DustOff-monthly-average_wrfout_d01_2006-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/dxarcj
title: DustOff-monthly-average_wrfout_d01_2006-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/f7jywd
title: DustOn-monthly-average_wrfout_d01_2014-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/fdryh0
title: DustOff-monthly-average_wrfout_d01_2006-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/i0dez1
title: DustOn-monthly-average_wrfout_d01_2014-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/maoeoq
title: DustOn-monthly-average_wrfout_d01_2014-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/purr67
title: DustOff-monthly-average_wrfout_d01_2006-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/q27g9f
title: DustOn-monthly-average_wrfout_d01_2014-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/wlhnm1
title: DustOff-monthly-average_wrfout_d01_2006-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/xh9wpm
title: DustOff-monthly-average_wrfout_d01_2006-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/1gjnxy
title: DustOn-monthly-average_wrfout_d01_2014-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/1swofb
title: DustOff-monthly-average_wrfout_d01_2006-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/3vwh1m
title: DustOff-monthly-average_wrfout_d01_2006-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/4h0ayp
title: DustOn-monthly-average_wrfout_d01_2014-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/bgtzkw
title: DustOn-monthly-average_wrfout_d01_2014-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/gsemad
title: DustOn-monthly-average_wrfout_d01_2014-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/hyi7a8
title: DustOff-monthly-average_wrfout_d01_2006-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/i9vgin
title: DustOn-monthly-average_wrfout_d01_2014-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/jociev
title: DustOff-monthly-average_wrfout_d01_2006-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/of3yfa
title: DustOff-monthly-average_wrfout_d01_2006-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/pawsah
title: DustOn-monthly-average_wrfout_d01_2014-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/wcl9fk
title: DustOn-monthly-average_wrfout_d01_2014-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/xdgnws
title: DustOn-monthly-average_wrfout_d01_2014-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/yvaloq
title: DustOff-monthly-average_wrfout_d01_2006-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/52k0nr
title: DustOff-monthly-average_wrfout_d01_2006-05_12_mean.nc
DOI: 10.7281/t1/1ecvb0/afha6o
title: DustOn-monthly-average_wrfout_d01_2015-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/cy9fp1
title: DustOn-monthly-average_wrfout_d01_2015-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/evx1c7
title: DustOn-monthly-average_wrfout_d01_2015-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/fjqkd5
title: DustOff-monthly-average_wrfout_d01_2006-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/fq7u4i
title: DustOn-monthly-average_wrfout_d01_2015-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/gtmxkx
title: DustOff-monthly-average_wrfout_d01_2006-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/gwpmnl
title: DustOff-monthly-average_wrfout_d01_2006-03_06_mean.nc
DOI: 10.7281/t1/1ecvb0/ivt9cl
title: DustOff-monthly-average_wrfout_d01_2006-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/kgsguu
title: DustOn-monthly-average_wrfout_d01_2015-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/lv0ss2
title: DustOff-monthly-average_wrfout_d01_2006-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/mhkd7q
title: DustOn-monthly-average_wrfout_d01_2014-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/ovhcno
title: DustOff-monthly-average_wrfout_d01_2006-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/pslhqd
title: DustOn-monthly-average_wrfout_d01_2015-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/q32lyh
title: DustOn-monthly-average_wrfout_d01_2014-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/rt1lto
title: DustOff-monthly-average_wrfout_d01_2006-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/xgh4m7
title: DustOn-monthly-average_wrfout_d01_2015-05_06_mean.nc
DOI: 10.21227/6rpt-5d39
title: Accompanying Data to Paper "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks"

description: This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks. Moreover, thereduced complexity of the proposed framework allows its practicalimplementation through the use of deep neural networks. Specifically, thanks toits reduced complexity, the proposed method can be used to train an artificialneural network to predict the optimal resource allocation. This is in contrastwith other power control methods based on deep learning, which train the neuralnetwork based on suboptimal power allocations due to the large complexity thatgenerating large training sets of optimal power allocations would have withavailable global optimization methods. As a benchmark, we also develop a novelfirst-order optimal power allocation algorithm. Numerical results show that aneural network can be trained to predict the optimal power allocation policy.