cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4641860
title: Hygroscopicity of Water-soluble PM2.5 in rural northwest China: Contrasting Contributors between Summer and Winter

description: In this study, we investigated the chemical composition and hygroscopicity of water-soluble fraction in PM<sub>2.5</sub> collected from a rural site of Guanzhong Basin, a highly polluted region in northwest China. We found that inorganic components are the main substances promoting the hygroscopic properties of particulate matter in summer, while in winter, an increasing contribution from water-soluble organic matters (WSOM) was observed. The sulfate-nitrate-ammonium (SNA) concentration is higher on heavily polluted days, and a high concentration of water-soluble organic matter was observed in non-polluting days. Hygroscopic growth factors, g(RH), with elevated relative humidity (RH) of water-soluble matter (WSM) were measured for 50nm, 75nm, 100nm, 125nm, 150nm by hygroscopic tandem differential mobility analyzer (H-TDMA). An initial dry particle diameter of 100 nm was chosen as the research object due to the particle size has little effect on the hygroscopic growth factor at RH=90%. The g(90)<sub>WSM</sub> and κ<sub>WSM</sub> was in the range of 1.20~1.49 and 0.06~0.22 in summer, 1.24~1.49 and 0.08~0.22 in winter, respectively, which is similar to the hygroscopicity parameters of particulate matter produced by biomass burning. The g(90)<sub>WSOM</sub> and κ<sub>WSOM</sub>, obtained using E-AIM model and Zdanovskii-Stokes-Robinson (ZSR) approach, were in the range of 1.06~1.69 and 0.02~0.36 in summer, 1.06~1.58 and 0.02~0.28 in winter, respectively. The hygroscopicity parameters of water-soluble organic matter is close to the value of levoglucosan, an indicator of biomass burning, implying this region is highly affected by biomass burning. Overall, our results revealed that PM<sub>2.5</sub> in rural regions of Guanzhong Basin is mainly influenced by biomass burning.
DOI: 10.5281/zenodo.11123179
title: Asteroseismic measurement of core and envelope rotation rates for 2006 red giant branch stars

description: We provide mode identifications of all the ~2500 RGB stars and a table that includes all the fitting results.

In the file "mode_ID_for_2500_RGB.zip", you can find a lot of folders with KIC number in their folder names. In each folder, the files "KIC*l*n*fitting_detail.dat" show the Lorentzian profile fitting for l=0 or 2 modes (see l value in the file name). The first row is the fitting result (the frequency value is in the second column) and the second row is uncertainty.

for l=1 mixed modes, you can find them in the files "KIC*bin_points*for_l_1_m*.dat". This is just an approximation of the mode frequencies. You should run a Lorentzian profile fitting using these frequencies as initial guess to obtain a precise mode frequency.

 

For the table "all_red_giants_table.csv"

Column 1: KIC

Column 2: nu_max in microHz

Column 3 and 4: large separation Delta_nu and uncertainty, in microHz

Column 5 and 6: p-mode phase and uncertainty.

Column 7 and 8: second order coefficient of p-mode asymptotic relation.

Column 9 and 10: the term D related to the small separation, in microHz.

Column 11 and 12: period spacing Delta Pi 1 and the uncertainty, in seconds.

Column 13 and 14: coupling factor q and its uncertainty.

Column 15 and 16: g-mode phase and uncertainty.

Column 17 and 18: frequency shift and uncertainty, in microHz.

Coumn 19 and 20: angular frequency of core rotation, in microHz. Note to divided by 2pi.

Coumn 21 and 22: angular frequency of envelope rotation, in microHz. Note to divided by 2pi.

Column 23 and 24: effective temperature and uncertainty, in Kelvin.

Column 25 and 26: luminosity and uncertainty, in solar luminosity

Column 27 and 28: Fe/H and uncertainty.

Column 29 and 30: stellar mass and uncertainty, in solar mass.

Column 31 and 32: stellar radius and uncertainty, in solar radius.

Column 33 and 34: inclination of rotational axis and uncertainty, in degree.

Column 35 and 36: linewidth_0 and uncertainty, in microHz.

Column 37 and 38: age and uncertainty, in Gyr.

Column 39: Gaia ruwe.
DOI: 10.5281/zenodo.11123180
title: Asteroseismic measurement of core and envelope rotation rates for 2006 red giant branch stars

description: Column 1: KIC

Column 2: nu_max in microHz

Column 3 and 4: large separation Delta_nu and uncertainty, in microHz

Column 5 and 6: p-mode phase and uncertainty.

Column 7 and 8: second order coefficient of p-mode asymptotic relation.

Column 9 and 10: the term D related to the small separation, in microHz.

Column 11 and 12: period spacing Delta Pi 1 and the uncertainty, in seconds.

Column 13 and 14: coupling factor q and its uncertainty.

Column 15 and 16: g-mode phase and uncertainty.

Column 17 and 18: frequency shift and uncertainty, in microHz.

Coumn 19 and 20: angular frequency of core rotation, in microHz. Note to divided by 2pi.

Coumn 21 and 22: angular frequency of envelope rotation, in microHz. Note to divided by 2pi.

Column 23 and 24: effective temperature and uncertainty, in Kelvin.

Column 25 and 26: luminosity and uncertainty, in solar luminosity

Column 27 and 28: Fe/H and uncertainty.

Column 29 and 30: stellar mass and uncertainty, in solar mass.

Column 31 and 32: stellar radius and uncertainty, in solar radius.

Column 33 and 34: inclination of rotational axis and uncertainty, in degree.

Column 35 and 36: linewidth_0 and uncertainty, in microHz.

Column 37 and 38: age and uncertainty, in Gyr.

Column 39: Gaia ruwe.
DOI: 10.5281/zenodo.13207485
title: Asteroseismic measurement of core and envelope rotation rates for 2006 red giant branch stars

description: We provide mode identifications of all the ~2500 RGB stars and a table that includes all the fitting results.

In the file "mode_ID_for_2500_RGB.zip", you can find a lot of folders with KIC number in their folder names. In each folder, the files "KIC*l*n*fitting_detail.dat" show the Lorentzian profile fitting for l=0 or 2 modes (see l value in the file name). The first row is the fitting result (the frequency value is in the second column) and the second row is uncertainty.

for l=1 mixed modes, you can find them in the files "KIC*bin_points*for_l_1_m*.dat". This is just an approximation of the mode frequencies. You should run a Lorentzian profile fitting using these frequencies as initial guess to obtain a precise mode frequency.

 

For the table "all_red_giants_table.csv"

Column 1: KIC

Column 2: nu_max in microHz

Column 3 and 4: large separation Delta_nu and uncertainty, in microHz

Column 5 and 6: p-mode phase and uncertainty.

Column 7 and 8: second order coefficient of p-mode asymptotic relation.

Column 9 and 10: the term D related to the small separation, in microHz.

Column 11 and 12: period spacing Delta Pi 1 and the uncertainty, in seconds.

Column 13 and 14: coupling factor q and its uncertainty.

Column 15 and 16: g-mode phase and uncertainty.

Column 17 and 18: frequency shift and uncertainty, in microHz.

Coumn 19 and 20: angular frequency of core rotation, in microHz. Note to divided by 2pi.

Coumn 21 and 22: angular frequency of envelope rotation, in microHz. Note to divided by 2pi.

Column 23 and 24: effective temperature and uncertainty, in Kelvin.

Column 25 and 26: luminosity and uncertainty, in solar luminosity

Column 27 and 28: Fe/H and uncertainty.

Column 29 and 30: stellar mass and uncertainty, in solar mass.

Column 31 and 32: stellar radius and uncertainty, in solar radius.

Column 33 and 34: inclination of rotational axis and uncertainty, in degree.

Column 35 and 36: linewidth_0 and uncertainty, in microHz.

Column 37 and 38: age and uncertainty, in Gyr.

Column 39: Gaia ruwe.
DOI: 10.5281/zenodo.3572509
title: Pearl River Delta FVCOM model sea level rise scenarios

description: Model data presented in: De Dominicis, M., Wolf, J., Jevrejeva, S., Zheng, P., Hu. Z. "Future interactions between sea level rise, tides and storm surges in the world's largest urban area", Geophysical Research Letters (2020). An FVCOM (Finite Volume Community Ocean Model) implementation for the South China Sea and the Pearl River Delta is used to understand how future sea level rise, tides and typhoon storm surges can interact and affect coastal inundation. Four future sea level rise scenarios have been analyzed: 0.3 m, 0.5 m, 0.9 m and 2.1 m. Two typhoons that impacted the PRD have been modelled: Hato (2017) and Mangkhut (2018). The dataset consists of water elevation data for 40 model experiments: EXP01-10 allow to study tide and sea level rise interactions. The Pearl River Delta model has been run for 1 month forced only by tides and sea level rise at the model boundary. EXP11-15 (for Hato) and EXP26-30 (for Mangkhut) allow the study of tide, surge and sea level rise interactions; in these experiments the model has been fully forced by atmospheric forcing, tides and sea level rise. EXP16-20 (for Hato) and EXP31-35 (for Mangkhut) allow the study of tide and sea level rise interactions and are needed for the calculation of the surge, the model has been forced by tides and sea level rise only at the model boundary. The surge can be calculated as the difference between the fully forced run (EXP11-15 and EXP26-30) and the corresponding tide-only forced run (EXP16-20 and EXP31-35) for a given sea level rise. EXP21-25 (for Hato) and EXP35-40 (for Mangkhut) allow the study of the surge and sea level rise interactions only (no tides); for these experiments the model has been forced by atmospheric forcing and sea level rise at the model boundary.
DOI: 10.5281/zenodo.2648836
title: Data for BLH_NI_CI Analysis

description: This is the Data for the paper "An analytical framework for reservoir operation with combined natural inflow and controlled inflowAn analytical framework for reservoir operation with combined natural inflow and controlled inflow".
DOI: 10.5281/zenodo.4009367
title: Vesuvius deformation modeling, Comsol numerical model code

description: The java file represenst a set of coherent procedures that allow a multiparametric analysis in the computing environment of the commercial package Comsol. The files .txt and .dat are files needed by the code to run the model. They represents the data related to our model.
DOI: 10.5281/zenodo.4399187
title: Deep UWB

description: A dataset for UWB ranging error mitigation in indoor environments, built using Decawave EVB1000 devices and the firmware contiki-uwb. Additional information can be found in the attached file "readme.txt" or in the paper Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge. [ readme.txt ] * Every sample has the following structure: || CIR (157 float values) ||<br> || Error (in meters) ||<br> || Room (int) ||<br> || Obstacle (10 bool values) || * Room encoding: 0 -&gt; cross-room measurements<br> 1 -&gt; big room<br> 2 -&gt; medium room<br> 3 -&gt; small room<br> 4 -&gt; outdoor * Obstacle encoding: (1-hot encoding) 1000000000 -&gt; wall<br> 0100000000 -&gt; polystyrene plate<br> 0010000000 -&gt; plastic (trash bin and chair)<br> 0001000000 -&gt; plywood plate<br> 0000100000 -&gt; cardboard box<br> 0000010000 -&gt; LCD TV<br> 0000001000 -&gt; metal plate<br> 0000000100 -&gt; wood door<br> 0000000010 -&gt; glass plate<br> 0000000001 -&gt; metal window * Reading Code: # Import libraries<br> import pandas as pd<br> import numpy as np # Extract dataset<br> dataset = pd.read_pickle('dataset.pkl') # Select specific obstacle configurations<br> ds = np.asarray(dataset.loc[dataset['Objects']=='011111111'][['CIR','Error']]) # Select specific rooms<br> ds = np.asarray(dataset.loc[dataset['Room']==1][['CIR','Error']]) # Select all samples<br> ds = np.asarray(dataset[['CIR','Error']]) # Get X,y for training <br> X = np.vstack(ds[:,0])<br> y = np.array(ds[:,1])
DOI: 10.5281/zenodo.5602182
title: Sichuan GNSS data

description: The GNSS data is provided by GNSS data product service platform of China Earthquake Administration.
DOI: 10.1594/pangaea.890556
title: Age determination of Zoige_core1 peat core
DOI: 10.1594/pangaea.929367
title: Calibrated ages of Zoige (ZB10-C9) peat core from China

description: Calibrated ages of a high-resolution peat core from the Past Global Changes - Carbon in Peat on EArth through Time (PAGES_C-PEAT) Project.
DOI: 10.5281/zenodo.6195937
title: Dasineura oxycoccana, the blueberry gall midge distribution, and cultivation areas of blueberry in South Korea

description: Distribution data of Dasineura oxycoccana for species distribution modeling and cultivation areas of blueberry in South Korea
DOI: 10.5281/zenodo.6195938
title: Dasineura oxycoccana, the blueberry gall midge distribution, and cultivation areas of blueberry in South Korea

description: Distribution data of Dasineura oxycoccana for species distribution modeling and cultivation areas of blueberry in South Korea
DOI: 10.5281/zenodo.1136215
title: Dataset And Supplementary Files_Ict

description: Dataset and supplementary files of the study: Development of a scale to know the basic skills in the use of ICT in educative settings, that could be applied to students and teachers alike in the secondary and high school levels.
DOI: 10.5281/zenodo.3939459
title: IODP Expedition 309, Hole 1256D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 309: Superfast Spreading Crust 1
DOI: 10.17632/54hdhb722f.1
title: Effectiveness of the 8% Capsaicin Patch in Localized Neuropathic Pain: A Quasi-Experimental Study.

description: Background: Neuropathic pain, characterized by injury or disease of the somatosensory nervous system, presents symptoms such as burning sensation, numbness, allodynia, and hyperalgesia, significantly impacting patients' quality of life. Common causes include diabetes, postherpetic neuralgia, post-surgical processes, amputation, and HIV. Pharmacological treatments, such as gabapentin, pregabalin, and tricyclic antidepressants, are first-line therapies. However, non-pharmacological therapies like the 8% capsaicin patch are gaining recognition for their efficacy and fewer side effects. 

Objective: This study aims to analyse the management of the 8% capsaicin patch in patients with neuropathic pain by the nursing staff of the Chronic Pain Unit. 

Method: A longitudinal quasi-experimental pre-post test study was conducted at the Chronic Pain Unit of the Virgen del Rocío University Hospital from June 2016 to December 2021. The study included patients over 18 years with localized neuropathic pain, indicated for an 8% capsaicin patch. Patients were evaluated using the Numerical Verbal Scale (NVS), EuroQol 5, and the MOS questionnaire at baseline, one month, and three months post-application. Statistical analyses were performed using SPSS® version 27.0. 

Results: A total of 117 patients were enrolled, and 110 received treatment. The average age was 57.19 years, with 58.2% being female. Pain was primarily localized in the lower limb (63.6%), and post-surgical neuropathy was the most prevalent etiology (75.5%). Significant improvement in pain and health status was observed at one and three months post-application. Improvement in rest and sleep conditions was also noted, particularly at three months. The MCID was determined to be a reduction of 1.5 points in early assessments and 3 points in the final assessment. 

Discussion: The study showed a clear trend toward pain improvement with the use of the 8% capsaicin patch. Results are consistent with previous studies, indicating significant pain reduction and improved quality of life. The findings suggest that repeated application of the patch may lead to sustained pain relief and further quality of life improvements. Adverse events were minimal and manageable. 

Conclusion: The 8% capsaicin patch is effective in managing localized neuropathic pain, significantly improving pain, mobility, and quality of life. Nursing staff play a crucial role in the application and follow-up of this treatment, highlighting the importance of advanced training for optimal patient outcomes. Further research should focus on assessing the patch's synergistic capacity with pharmacological treatments and strategies to improve treatment adherence.
DOI: 10.5281/zenodo.4548869
title: UWB Localization Dataset

description: The dataset contains information concerning metrics provided by a pair of Decawave DW1000 tranceiver ICs. The dataset is recorded in indoor (hallway) and outdoor spaces. Each record of the dataset has the following fields: - <strong>sequence_number:</strong> A sequence number indicating the order the measurements were taken - <strong>environment</strong>: The type of environment used for the measurements (indoor/outdoor) - <strong>channel:</strong> The UWB channel used by the transceivers - <strong>pac_size</strong>: The preamble sequence is detected by cross-correlating in chunks which are a number of preamble symbols long. The size of chunk used is the PAC size. - <strong>prf:</strong> The pulse repetition frequency used in MHz - <strong>preamble_length</strong>: The number of symbols used in the preamble - <strong>datarate</strong>: The datarate in Mbps - <strong>first_path_power</strong>: The received power in dBm of the pulse corresponding to the first path. - <strong>rssi</strong>: The Received Signal Stength in dBm - <strong>distance</strong>: The distance between the receiver and the transmitter in meters
DOI: 10.5281/zenodo.4879638
title: Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor

description: Observation of meteorological factors was conducted at two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records. All three meteorological stations contain ground observations.
DOI: 10.5281/zenodo.4010285
title: New understanding of the spatiotemporal lightning activity over the Tibetan Plateau based on WWLLN data

description: The submitted data are associated with the figures in the paper titled "New understanding of the spatiotemporal lightning activity over the Tibetan Plateau based on WWLLN data". The following is the abstract of this paper. The spatiotemporal distribution of the lightning activity over the Tibetan Plateau (TP) has been previously studied based on data from the Lightning Imaging Sensor (LIS) aboard the polar-orbit Tropical Rainfall Measuring Mission satellite. In this study, using lightning data from the World Wide Lightning Location Network (WWLLN), we reinvestigated the lightning activity over the TP and found that the geographic and seasonal lightning distributions suggested by the WWLLN are somewhat different from those obtained from the LIS. In particular, 1) while the LIS indicates strong lightning activity over the northeastern TP, the WWLLN indicates relatively weak lightning activity over the region; 2) the WWLLN indicates the existence of a center with relatively frequent lightning activity over the south-by-west TP, which is not indicated by the LIS; furthermore, 3) the WWLLN indicates the most frequent lightning in August and September, while the LIS suggests July and June. The black body temperature data from the Fengyun-2E geostationary satellite (as a proxy of deep convection), thunderstorm day data, and local cloud-to-ground lightning data strongly support the lightning spatiotemporal distribution patterns suggested by the WWLLN. The difference between the WWLLN and LIS data is attributed to their very different sample numbers and observation methods, and the situation that lightning tends to occur in the lower part of TP thunderstorms. Owing to the time-continuous nature, the WWLLN data were also used to obtain the ten-day distributions of lightning in space and time and the diurnal variation of lightning. This study may further elucidate our knowledge of the lightning activity over the TP.
DOI: 10.5281/zenodo.5242952
title: Microscopy images of the DNA and RNA polymerase II distribution inside nuclei of cell cultures obtained from pluripotent zebrafish embryos

description: <strong>Image data description</strong> Color channels in the image data: First channel: DNA (Hoechst 33342) Second channel: Pol II Ser2P (Elongating RNA polymerase II, indirect immunofluorescence, STAR RED) Third channel: Pol II Ser5P (Recruited RNA polymerase II, indirect immunofluorescence, Alexa 594) <strong>Sample description</strong> Praimary cell cultures were prepared from pluripotent zebrafish embryos were collected at the sphere stage of development, treated with chemical inhibitors of transcription (Control, 500 mM Flavopiridol, 500 mM Triptolide, all for 30 min) and fixed overnight (2% formaldehyde in culutre media, 30 min, room temperature; followed by 8% formaldehyde for 15 min at room temperature). RNA polymerase in the recruited state (Pol II Ser5P) and the elongating state (Pol II Ser2P) were labeled by indirect immunofluorescence (permeabilization 0.5% Triton X-100 in PBS 15 min room temperature, 30 min blocking 4% BSA in PBST, rat IgG anti-Pol II Ser5P & rabbit IgG anti-Pol II Ser2P in 4% BSA in PBST overnight 4°C, anti-rat Alexa 594 & anti-rabbit STAR RED in 4% BSA in PBST overnight 4°C). Mounted in VectaShield H-1000 with 2 µM Hoechst 33342 added for fluorescent DNA labeling. Scan of lab book page is included in the repository. The data set contains images obtained from two samples for each condition. <strong>Imaging</strong> Microscopy images acquired using VisiTech iSIM with dual camera setup. Objective Nikon CFI SR HP Apo<br> TIRF 100XAC Oil, color channels acquired in a sequence to reduce overlap (DNA + Ser2P acquired simultaneously on two camerase, Ser5P acquired after on a single camera), z-stack settings optimized to ensure reliable xyz alignment of channels. Imags were cropped to the region with best signal and resolution in the DNA channel, same region used throughout the entire dataset. all imaging settings were kept unchanged over the course of acquisition, all images acquired in a single session of 4 hours. <strong>Advice for image processing</strong> Images can be loaded for processing with the OME bioformats importer. An import script for MatLab is available through the Hilbert lab: https://github.com/lhilbert/NuclearObjects_ImageAnalysis <strong>Author contributions</strong> AN & MS provided embryos, carried out cell culture, and prepared samples, LH performed microscopy