cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10564913
title: Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset

description: In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the Balanced k-means algortihm, but in a fast manner.

General Dataset Structure

A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the Balanced k-means algortihm. This implementation was carried out in the MATLAB software WLANs-optimal-designs-methodology.

Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.

We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.

The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.

The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom.

Obtained Models

To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.

To manipulate the codes better, click here in the repository.
DOI: 10.57760/sciencedb.13813
title: Dataset for the Dark Triad traits and psychopathology project
title: Dataset for the Dark Triad traits and psychopathology project

description: This dataset includes data files, analysis code files, and questionnaires reported in the article “Will the Dark Triad engender poor mental health or vice versa? A three-wave random intercept cross-lagged panel analysis”. For methodological details, please see our article.
description: This dataset includes data files, analysis code files, and questionnaires reported in the article “Will the Dark Triad engender poor mental health or vice versa? A three-wave random intercept cross-lagged panel analysis”. For methodological details, please see our article.
DOI: 10.57760/sciencedb.10389
title: Integrative taxonomy reveals three new species and one new record of the genus Psychropotes (Echinodermata, Holothuroidea, Elasipodida) from the Kermadec Trench region and the Wallaby-Zenith Fracture Zone
title: Integrative taxonomy reveals three new species and one new record of the genus Psychropotes (Echinodermata, Holothuroidea, Elasipodida) from the Kermadec Trench region and the Wallaby-Zenith Fracture Zone

description: Three new species were discovered during the first China-New Zealand joint deep-diving scientific expedition from October 2022 to March 2023. Psychropotes diutiuscauda sp. nov., Psychropotes nigrimargaria  sp. nov., and Psychropotes asperatus sp. nov. were collected from the Kermadec Trench in the South Pacific Ocean and the Wallaby-Zenith Fracture Zone in the East Indian Ocean, with water depths of 6018–6605 m.
description: Three new species were discovered during the first China-New Zealand joint deep-diving scientific expedition from October 2022 to March 2023. Psychropotes diutiuscauda sp. nov., Psychropotes nigrimargaria  sp. nov., and Psychropotes asperatus sp. nov. were collected from the Kermadec Trench in the South Pacific Ocean and the Wallaby-Zenith Fracture Zone in the East Indian Ocean, with water depths of 6018–6605 m.
DOI: 10.57760/sciencedb.16228
title: China’s Mainland Shorelines Over 30 Years Using Landsat Time Series Data (1990-2019)
title: China’s Mainland Shorelines Over 30 Years Using Landsat Time Series Data (1990-2019)

description: Continuous monitoring of shoreline dynamics is essential to understanding the drivers of shoreline changes and evolution. A long-term shoreline dataset can describe the dynamic changes in the spatio-temporal dimension and provide information on the influence of anthropogenic activities and natural factors on coastal areas. This study, conducted on the Google Earth Engine platform, generated a dataset of China’s shorelines, including those of Hainan and Taiwan, from 1990 to 2019 using long time series of Landsat TM/ETM+/OLI images. First, we constructed a time series of the Modified Normalized Difference Water Index (MNDWI) with high-quality reconstruction by the harmonic analysis of time series (HANTS) algorithm. Second, the Otsu algorithm was used to separate land and water of coastal areas based on MNDWI value at high tide levels. Finally, a 30-year shoreline dataset was generated. This study presents an innovative and up-to-date dataset and comprehensive information on the status of China’s shoreline from 1990 to 2019, contributing to related research and policy implementation, especially in support of sustainable development.
description: Continuous monitoring of shoreline dynamics is essential to understanding the drivers of shoreline changes and evolution. A long-term shoreline dataset can describe the dynamic changes in the spatio-temporal dimension and provide information on the influence of anthropogenic activities and natural factors on coastal areas. This study, conducted on the Google Earth Engine platform, generated a dataset of China’s shorelines, including those of Hainan and Taiwan, from 1990 to 2019 using long time series of Landsat TM/ETM+/OLI images. First, we constructed a time series of the Modified Normalized Difference Water Index (MNDWI) with high-quality reconstruction by the harmonic analysis of time series (HANTS) algorithm. Second, the Otsu algorithm was used to separate land and water of coastal areas based on MNDWI value at high tide levels. Finally, a 30-year shoreline dataset was generated. This study presents an innovative and up-to-date dataset and comprehensive information on the status of China’s shoreline from 1990 to 2019, contributing to related research and policy implementation, especially in support of sustainable development.
DOI: 10.5281/zenodo.8380960
title: Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset

description: In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the Balanced k-means algortihm, but in a fast manner.


General Dataset Structure


A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the Balanced k-means algortihm. This implementation was carried out in the MATLAB software WLANs-optimal-designs-methodology.


Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.


We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.


The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.


The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom.


Obtained Models


To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.


To manipulate the codes better, click here in the repository.
DOI: 10.15468/dl.dd8pp7
title: Occurrence Download

description: A dataset listing the 8257 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Royal Ontario Museum Green Plant Herbarium (TRT)"
 ]
}

 The dataset's 8257 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001417-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.07505
title: NMR simulation of unsaturated porous media
title: NMR simulation of unsaturated porous media

description: The dataset includes unsaturated NMR simulation of porous media
description: The dataset includes unsaturated NMR simulation of porous media
DOI: 10.15468/dl.y4x84v
title: Occurrence Download

description: A dataset containing 1101484 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus (Linnaeus, 1766), Myocastor coypus (Molina, 1782))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1101484 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001409-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.01800
title: Climate change indices time series in Greece during 1960-2010
title: Climate change indices time series in Greece during 1960-2010

description: Based on the homogenized daily temperature and precipitation time series in Greece during 1960-2010, 26 climate change indices time series at 56 stations defined by ETCCDI were obtained. All datasets are completed by cooperation between Greece (Laboratory of Atmospheric Physics, Department of Physics, University of Patras) and China (Key Laboratory of Regional Climate-Environment in Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences).The information are as following:Climate change indices time series in Greece during 1960-2010.rar (1.2MB) There is a .xlsx file named ‘Information at 56 stations in Greece’, including four columns (No. Station No. Longitude, and Latitude) There are 1456 climate indexes .csv files(56 stations×26 climate indexes), in each .csv file, there are 2 columns (date and index records). 
description: Based on the homogenized daily temperature and precipitation time series in Greece during 1960-2010, 26 climate change indices time series at 56 stations defined by ETCCDI were obtained. All datasets are completed by cooperation between Greece (Laboratory of Atmospheric Physics, Department of Physics, University of Patras) and China (Key Laboratory of Regional Climate-Environment in Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences).The information are as following:Climate change indices time series in Greece during 1960-2010.rar (1.2MB) There is a .xlsx file named ‘Information at 56 stations in Greece’, including four columns (No. Station No. Longitude, and Latitude) There are 1456 climate indexes .csv files(56 stations×26 climate indexes), in each .csv file, there are 2 columns (date and index records).
DOI: 10.57760/sciencedb.11605
title: AUG and AUV data used in research “Direct observations on mesoscale eddies’ lifecycles and influences based on Automatic Underwater Vehicles in South China Sea”
title: AUG and AUV data used in research “Direct observations on mesoscale eddies’ lifecycles and influences based on Automatic Underwater Vehicles in South China Sea”

description: Data used in research “Direct observations on mesoscale eddies’ lifecycles and influences based on Automatic Underwater Vehicles in South China Sea”, including data from Two products of Chinese AUGs named “Sea Wing” and “Petrel”. Since 2014, we have conducted 11 experiments, totally collecting 24498 temperature and salinity profiles. 83 AUGs and 2 AUVs were deployed in northern SCS. In the experiments of 2017, 2019 and 2020, more than 10 AUGs were deployed. The largest AUG network was conducted in 2021, including 50 AUGs.
description: Data used in research “Direct observations on mesoscale eddies’ lifecycles and influences based on Automatic Underwater Vehicles in South China Sea”, including data from Two products of Chinese AUGs named “Sea Wing” and “Petrel”. Since 2014, we have conducted 11 experiments, totally collecting 24498 temperature and salinity profiles. 83 AUGs and 2 AUVs were deployed in northern SCS. In the experiments of 2017, 2019 and 2020, more than 10 AUGs were deployed. The largest AUG network was conducted in 2021, including 50 AUGs.
DOI: 10.57760/sciencedb.07839
title: MMFW-UAV Dataset
title: MMFW-UAV Dataset

description: Project OverviewThis project is a system research project on air-ground cooperative unmanned system, guided by Professor Fang Deng, Professor Chen Chen, Professor Maobin Lu, and Professor Hailing Fu from Beijing Institute of Technology. These research include air-to-air target identification, detection, collaborative (or non-collaborative) unmanned aerial vehicle systems, and unmanned aerial vehicle datasets, and more. Associate Professor fellow Lele Zhang and Research associate Wei Dong from Beijing Institute of Technology and Research associate Lele Xi from Hebei University of Science and Technology led Ph.D. candidates Yang Liu and Zhihao Sun to implement these projects. For more information, please visit the webpage.IntroductionThe MMFW-UAV contains a total of 147,441 fixed-wing UAVs images captured by multiple types of sensors (zoom, wide-range, and thermal imaging sensors), i.e., 49,147 pairs of "1-1-1" images, displaying the flight status of fixed-wing UAVs with different sizes, appearances, structures, and stabilized flight velocities from multiple aerial perspectives (top-down, horizontal, and bottom-up views), aiming to cover the full range of perspectives with multi-modal image data.
description: Project OverviewThis project is a system research project on air-ground cooperative unmanned system, guided by Professor Fang Deng, Professor Chen Chen, Professor Maobin Lu, and Professor Hailing Fu from Beijing Institute of Technology. These research include air-to-air target identification, detection, collaborative (or non-collaborative) unmanned aerial vehicle systems, and unmanned aerial vehicle datasets, and more. Associate Professor fellow Lele Zhang and Research associate Wei Dong from Beijing Institute of Technology and Research associate Lele Xi from Hebei University of Science and Technology led Ph.D. candidates Yang Liu and Zhihao Sun to implement these projects. For more information, please visit the webpage.IntroductionThe MMFW-UAV contains a total of 147,441 fixed-wing UAVs images captured by multiple types of sensors (zoom, wide-range, and thermal imaging sensors), i.e., 49,147 pairs of "1-1-1" images, displaying the flight status of fixed-wing UAVs with different sizes, appearances, structures, and stabilized flight velocities from multiple aerial perspectives (top-down, horizontal, and bottom-up views), aiming to cover the full range of perspectives with multi-modal image data.
DOI: 10.57760/sciencedb.15199
title: Identification of a novel Klebsiella phage containing a new combination of K2 and K30 depolymerases and therapeutic application
title: Identification of a novel Klebsiella phage containing a new combination of K2 and K30 depolymerases and therapeutic application

description: Statistical data
description: Statistical data
DOI: 10.57760/sciencedb.15181
title: Formation of Long-Circulating Microspheres Loaded with SIV and CAV for the Treatment of Hyperlipidemia
title: Formation of Long-Circulating Microspheres Loaded with SIV and CAV for the Treatment of Hyperlipidemia

description: Simvastatin (SIV) and carvacrol (CAV) were successfully co loaded into PEG-PLGA microspheres (SIV-CAV-PP-MS) through emulsion solvent evaporation technology, resulting in microspheres with high encapsulation efficiency and reduced particle size, which are beneficial for sustained release and prolonged circulation. 
description: Simvastatin (SIV) and carvacrol (CAV) were successfully co loaded into PEG-PLGA microspheres (SIV-CAV-PP-MS) through emulsion solvent evaporation technology, resulting in microspheres with high encapsulation efficiency and reduced particle size, which are beneficial for sustained release and prolonged circulation.
DOI: 10.57760/sciencedb.16328
title: Sustainable method to prepare nanocellulose by using deep eutectic solvents with enhanced stability and strcuctural uniformity
title: Sustainable method to prepare nanocellulose by using deep eutectic solvents with enhanced stability and strcuctural uniformity

description: In this study, an attempt was made to prepare nanocellulose between three feedstocks with different particle sizes and four different acidities of deep eutectic solvent(DES) and to find the pairing that would ensure high yield, good particle size distribution, stability, structural uniformity and low energy consumption for the preparation of nanocellulose. The effect of DES acidity and feedstock size on the prepared nanocellulose was also derived. The results showed that the greater the acidity of carboxylic acid DES, the smaller the effect of this scale change on the yield, and the higher the yield of nanocellulose, the smaller the particle size, the more uniform the particle size distribution, and the higher the crystallinity. The nanocellulose after this treatment also had carboxylation effects and different esterification due to the acidity differences of deep eutectic solvent and the structural differences of raw materials. The nanocellulose obtained after these treatments has higher thermal decomposition response.
description: In this study, an attempt was made to prepare nanocellulose between three feedstocks with different particle sizes and four different acidities of deep eutectic solvent(DES) and to find the pairing that would ensure high yield, good particle size distribution, stability, structural uniformity and low energy consumption for the preparation of nanocellulose. The effect of DES acidity and feedstock size on the prepared nanocellulose was also derived. The results showed that the greater the acidity of carboxylic acid DES, the smaller the effect of this scale change on the yield, and the higher the yield of nanocellulose, the smaller the particle size, the more uniform the particle size distribution, and the higher the crystallinity. The nanocellulose after this treatment also had carboxylation effects and different esterification due to the acidity differences of deep eutectic solvent and the structural differences of raw materials. The nanocellulose obtained after these treatments has higher thermal decomposition response.
DOI: 10.57760/sciencedb.19543
title: 197Au(γ,n) cross section RAW data
title: 197Au(γ,n) cross section RAW data

description: neutron                gamma run_No.   attenuation20240119_75D_Au_part001.zip 116 16020240119_77D_Au.zip 98(前5400s)16020240119_77D_Au_2.zip 99 16020240119_79D_Au_2_part001.zip 95(前7200s)16020240119_79D_Au_part001.zip 97 16020240119_81D_Au.zip 79 16020240119_83D_Au.zip 71 16020240119_85D_Au.zip 66 16020240119_87D_Au.zip 58 16020240119_89D_Au.zip 55 16520240119_91D_Au.zip 51 16020240119_93D_Au_2.zip 46 17020240119_95D_Au.zip 38 17020240119_97D_Au.zip 35 17020240119_99D_Au.zip 32 17520240120_101D_Au.zip 23 17520240119_103D_Au.zip 15 17520240119_105D_Au.zip 13 17520240119_106D_Au.zip 7 17520240119_108D_Au.zip 3 175
description: neutron                gamma run_No.   attenuation20240119_75D_Au_part001.zip 116 16020240119_77D_Au.zip 98(前5400s)16020240119_77D_Au_2.zip 99 16020240119_79D_Au_2_part001.zip 95(前7200s)16020240119_79D_Au_part001.zip 97 16020240119_81D_Au.zip 79 16020240119_83D_Au.zip 71 16020240119_85D_Au.zip 66 16020240119_87D_Au.zip 58 16020240119_89D_Au.zip 55 16520240119_91D_Au.zip 51 16020240119_93D_Au_2.zip 46 17020240119_95D_Au.zip 38 17020240119_97D_Au.zip 35 17020240119_99D_Au.zip 32 17520240120_101D_Au.zip 23 17520240119_103D_Au.zip 15 17520240119_105D_Au.zip 13 17520240119_106D_Au.zip 7 17520240119_108D_Au.zip 3 175
DOI: 10.5281/zenodo.7978300
title: Fast Indoor Radio Propagation Prediction Using Deep-Learning

description: We show a dataset composed by Radio Maps Estimation (RME) and Cells Maps Estimation (CME) for the 5GHz band WIFI in indoor scenarios: it has 60 indoor constructions plans and 1000 distributions initially for a training process and 20 indoor constructions plans and 50 distributions aditionals for a test process of access points to even construction. These distributions are random and several WLAN's structures: 1 to 5 access points.


The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.


Every coverage map have like maximum power delivered is Pr = Pt = 26 dBm (according to data from current commercial equipment) and like minimum power a value noise established in Pr = KTB, where K is the Boltzmann's constant, T the enviroment temperatura equal to 290°K and B the band width equal to 80MHz.


Dataset DeepFIRP is the result of a lot of simulations by a own software developed in MATLAB that work with the IEEE 802.11ax channel model.


The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom. We present file CSV with the data of coverage to use, too.


A application to this dataset and the codes used for generate it is found here, where we implement a U-Net model for theRME and CME in indoor enviroments. This investigation contribute in novels methods for estimate by fast way coverage and cells maps using deep-learning in comparation with the conventional phisics methods like dominath-path model or ray-tracing. Whats allows save a lot of amount time in the WLANs's designs.