cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/mshf-4n40
title: Online Offline Learning for Sound-based Indoor Localization Using Low-cost Hardware Data

description: Matlab Implementation of the described Online Offline Learning Architecture (OOLA) and Visualizations/Plots of Simulations (https://ieeexplore.ieee.org/document/8869796).The files includeData Generation SourcecodesMicrocontroller sourcecode (path:DataGeneration/controller.ino)Node-RED flow (path:DataGeneration/node_RED_flow.txt)Python file manipulation (path:DataGeneration/Python/prepare_data_format.py)Sound file generator (path:DataGeneration/soundGenerator.m)Raw dataOffline data (path:Matlab/load and preprocess offline data/data)Online data (path:Matlab/online data load and preprocess/data)Preprocessed dataOffline data (path:Matlab/load and preprocess offline data/Data3600.mat)Online data (path:Matlab/online data load and preprocess/DataOnline3600.mat)The Online Offline Learning Architecture (OOLA)Configurable (path:Matlab/OOL/InitOOL.m)With basic 1NN & K-Means implementations (path:Matlab/OOL/)Plots and visualisations of simulationsBasic LVQ simulator (path:Plots/LVQ Simulator/LVQSimulatorV003.m)K-Means/LVQ comparison simulator (path:Plots/LVQ Simulator/kmeanslvqprototypes.m)Overall results bar charts (path:Plots/ool results plot 1/plotergebnisse.m)StatisticsK-Means prototypes classification rates (path:Matlab/lvq and kmeans test/kMeansPrototypes)LVQ prototypes classification rates (path:Matlab/lvq and kmeans test/LVQPrototypes)OOLA classification rates depending on configurations (path:Results/OOL/)OOLA processing data for classification (path:Results/OOL/)
DOI: 10.6073/pasta/f3527e702c8bc0f04837188b91b1cb91
title: Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona

description: This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset.
DOI: 10.17632/p46tx55872.1
title: Data for: Influence of oceanographic variability on catch rate of yellowfin tuna (Thunnus albacares) cohorts in the Indian Ocean

description: The upload research data are including the fishery data of area coordinate, fishing effort (number of hooks), fishing date, number of catches and CPUE of immature and mature cohorts, and marien environmental date of SST, Chl-a, SSHD and MIL for 5° spatial grid from 2003 to 2012.
DOI: 10.21227/g7qz-8758
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score socred by the model deployed at sentiment.live. The LSTM model is still in beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be added to this dataset collection every week.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/kdzr-0868
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score socred by the model deployed at sentiment.live. The LSTM model is still in beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be added to this dataset collection every week.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/wgxg-bv46
title: Twitter Sentiment Analysis

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score socred by the model deployed at sentiment.live. The LSTM model is still in beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores of our model will be made available publicly after the project leads to a targetted summit.New databases will be added to this dataset collection every week.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/zj9n-nj18
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/rdza-1n97
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score estimated by the model deployed at sentiment.live. The LSTM model is still in beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.New databases will be added to this dataset collection every week.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/y912-gc20
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score socred by the model deployed at sentiment.live. The LSTM model is still in beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.New databases will be added to this dataset collection every week.####current status###databases: 2,tweets count: 3 million-plus The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/0vhn-3v57
title: High Order Approximate Power Flow Solutions

description: This report outlines the derivation of the first-, second-, and third-order Taylor series expansions of the power flow solution; it is the Electronic Companion of the following paper:R. A. Jabr, “High-order approximate power flow solutions and circular arithmetic applications,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5053-5062, November 2019.The derivation is carried out in complex variables via the use of Wirtinger calculus.
DOI: 10.17632/zfw6347cjn.1
title: Data for: Increasing tsunami risk through intensive urban densification in metropolitan areas: a mixed-method, longitudinal analysis

description: ArcGIS raster layers of tsunami threat and vulnerability for the case study in five different speed scenarios of people: 0.89, 1.1, 1.52, 1.79 and 3.85 m/s, respectively.
DOI: 10.21227/85q9-ay51
title: High Order Approximate Power Flow Solutions

description: This report outlines the derivation of the first-, second-, and third-order Taylor series expansions of the power flow solution; it is the Electronic Companion of the following paper:R. A. Jabr, “High-order approximate power flow solutions and circular arithmetic applications,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5053-5062, November 2019.The derivation is carried out in complex variables via the use of Wirtinger calculus.
DOI: 10.17632/jxx5d7n96r.1
title: Data for: Drinking through rosé-coloured glasses: Influence of wine colour on the perception of aroma and flavour in wine experts and novices

description: In this study, we assess how wine colour – specifically the pink hue of rosé wines – can influence both the perceived aroma and flavour in a large sample of wine novices and experts. Participants (N=168) tasted three wines – a white wine (W), a rosé wine (R), and the same white wine dyed to match the rosé (R´) – and freely selected three aroma and three flavour descriptors from a list. They also rated wine liking, flavour intensity, and description difficulty for each wine.
DOI: 10.21227/dphm-wb72
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/vy5r-7183
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/2pgf-eg54
title: High Order Approximate Power Flow Solutions

description: This report outlines the derivation of the first-, second-, and third-order Taylor series expansions of the power flow solution; it is the Electronic Companion of the following paper:R. A. Jabr, “High-order approximate power flow solutions and circular arithmetic applications,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5053-5062, November 2019.The derivation is carried out in complex variables via the use of Wirtinger calculus.
DOI: 10.21227/4yaa-zs10
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/xwrn-x795
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/ygzv-c406
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.25919/5db40eac90abd
title: Parkes observations for project P1032 semester 2019OCTS_02

description: We propose timing observations of southern double neutron star (DNS) systems to obtain precise estimates of the component masses and thus increase the statistics of neutron star mass distribution. The improved sensitivity of the Ultra Wideband Low (UWL) receiver will provide a 3X increase in the precision of the time of arrivals (ToAs) of the pulses compared to the multibeam data. This has the potential to perform quick measurements of masses for a number of binary systems in the next 1-2 years. We will also use MeerKAT observations at crucial orbital phases to increase our chances of mass measurements. Increase in the number of measured NS masses has significant implications for understanding NS internal structure and for stellar and binary evolution physics.
DOI: 10.25345/c5qx0b
title: MassIVE MSV000084509 - Bombardier mediates delivery of short-form Bomanins in the Drosophila Toll response
title: Toll mediates a robust and effective innate immune response across vertebrates and invertebrates. In Drosophila melanogaster, activation of Toll by systemic infection drives the accumulation of a rich repertoire of immune effectors in hemolymph, including the recently characterized Bomanins as well as the classical antimicrobial peptides (AMPs). Here we report the functional characterization of a Toll-induced hemolymph protein encoded by the bombardier (CG18067) gene. Using the CRISPR-Cas9 system to generate a precise deletion, we found that Bombardier is required for Toll-mediated defense against fungi and Gram-positive bacteria. Assaying cell-free hemolymph from these flies, we found that the Bomanin-dependent candidacidal activity observed in hemolymph is also dependent on Bombardier, but not on the antifungal AMPs Drosomycin and Metchnikowin. Using mass spectrometry, we demonstrated that deletion of Bombardier results in the specific absence of short-form Bomanins from hemolymph. Flies lacking Bombardier also exhibited a defect in pathogen tolerance that we trace to an autoimmune disorder triggered by Toll activation. These results lead us to a model in which the presence of Bombardier in wild-type flies enables the proper folding, secretion, or intermolecular associations of short-form Bomanins, and the absence of Bombardier disrupts one or more of these steps, resulting in defects in both immune resistance and tolerance.
DOI: 10.25919/5db47eeda1d7e
title: Parkes observations for project P885 semester 2019OCTS_02

description: We propose to observe the magnetars 1E 1547.0-5408 and PSR J1622-4950 approximately weekly, to track their rotation, flux density, and polarisation, as we have been doing through this project. Also, as of late 2018, XTE J1810-197 is again a radio magnetar after 10 years dormant! Its chaotic rotation is better tracked in detail elsewhere, but weekly observations with the UWL receiver are already providing unique measurements of its radio spectrum and broad-band behaviour. Our observations provide an important window into remarkably dynamic magnetar magnetospheres, and characterise the conditions under which radio emission takes place therein, placing constraints on the emission mechanisms, which in at least some significant respects differ from those of ordinary pulsars.
DOI: 10.5281/zenodo.3519403
title: Photic generation of 11-cis-retinal in bovine retinal pigment epithelium

description: Raw datasets for MS-based RGR identification. For additional information please see: Zhang et al. JBC 2019
DOI: 10.5281/zenodo.3519404
title: Photic generation of 11-cis-retinal in bovine retinal pigment epithelium

description: Raw datasets for MS-based RGR identification. For additional information please see: Zhang et al. JBC 2019
DOI: 10.5281/zenodo.3519844
title: Sound Events for Surveillance Applications

description: The Sound Events for Surveillance Applications (SESA) dataset files were obtained from Freesound. The dataset was divided between train (480 files) and test (105 files) folders. All audio files are WAV, Mono-Channel, 16 kHz, and 8-bit with up to 33 seconds. # Classes: 0 - Casual (not a threat) 1 - Gunshot 2 - Explosion 3 - Siren (also contains alarms)
DOI: 10.7910/dvn/qufrj5/gsibnu
title: gen_monthly_data.do

description: :unav
DOI: 10.7910/dvn/qufrj5/ke8sfg
title: aggregate_raw_iqvia_data.do

description: :unav
DOI: 10.7910/dvn/qufrj5/ncbabc
title: figure_I.do

description: :unav
DOI: 10.7910/dvn/qufrj5/nuomgv
title: ad_transition_adults.do

description: :unav
DOI: 10.17632/5t8p3rwhxb.1
title: Data for: Numerical study of the effect of vascular bed on heat transfer during high intensity focused ultrasound (HIFU) ablation of the liver tumor

description: This is a sample file of simulations in Comsol Multiphysics 5.3 and is related to the case of a tumor embedded in the vascular bed with a density of 0.2.
DOI: 10.7910/dvn/qufrj5/4dpcfg
title: table_I.do

description: :unav
DOI: 10.7910/dvn/qufrj5/apdhoq
title: readme.docx

description: :unav
DOI: 10.7910/dvn/qufrj5/gjagha
title: violate-guideline.tab

description: :unav
DOI: 10.7910/dvn/qufrj5/nnxmpc
title: table_II.do

description: :unav
DOI: 10.7910/dvn/qufrj5/ovkzo0
title: table_VII.do

description: :unav
DOI: 10.7910/dvn/qufrj5/pwofr5
title: tables_III_IV.do

description: :unav
DOI: 10.7910/dvn/qufrj5/qyhspb
title: prepare_data_for_entropy.do

description: :unav
DOI: 10.7910/dvn/qufrj5/yf1kto
title: gen_npi_provider_id_crosswalk.do

description: :unav