cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/834vsxnb99
title: Urdu News Dataset 1M

description: The dataset – A Large-Scale News Dataset for Urdu Text Processing, to the best our knowledge, is the only available large-scale dataset on Urdu lan- guage for many NLP, Machine/Deep Learning tasks: text processing, classification, summarization, Named Entity Recognition, Topic Modeling, and Text Generation.

This dataset offers above 1 Million Urdu news stories text corpus for four distinct categories: Business & Economics, Science & Technology, Entertain- ment, and Sports. These four distinct categories are appropriately chosen to elim- inate any ambiguity therefore this dataset is suitable for many Urdu NLP tasks.
DOI: 10.17632/fr7zvy8j5s
title: Pulmonary (Lungs)  Sound

description: Data is collected and ground truth classification are performed by Fortis Hospital, Vasant Kunj, New Delhi, India. The pulmonary sound dataset is arranged in two sets: a normal set (11.28%) representing recordings obtained from healthy subjects and an abnormal set (88.72%) containing recordings from pulmonary patients. The lung sound was recorded with an e-stethoscope connected to a laptop via an amplifier. This setup contains e-stethoscope having a chest piece which is contacted to subject having a microphone to record signal (sampling rate 44,100 Hz) connected to a signal amplifier. The signal amplifier kit can specifically amplifies the sound range (70–2000 Hz) for - lung sounds (having amplifier controller, frequency con- troller) connected to earpiece (for listening live recordings) and laptop. A laptop is used to record the signal where the E-stethoscope is connected using an audio input. The dataset contains audio samples which are in a.wav audio format having a single channel with 16 bit per sample, 44,100 Hz sampling rate and 705kbps Bitrate. Each file is 10 s long. 
The data can be used for research purpose only. It is not for commercial purpose. 
Anyone using this data for any Publication need to cite the article: 
Baghel, N., Nangia, V. & Dutta, M.K. ALSD-Net: Automatic lung sounds diagnosis network from pulmonary signals. Neural Comput & Applic 33, 17103–17118 (2021). https://doi.org/10.1007/s00521-021-06302-1
DOI: 10.17632/gmrzt4f99w
title: Selection of drought tolerant barley genotypes using stress indices

description: The dataset introduces a new selection method, "MGIDI," for ranking drought-tolerant barley genotypes based on grain yield stress indices.
DOI: 10.6084/m9.figshare.21630038.v1
title: Supplementary Material for: Loss of Pten in renal tubular cells leads to water retention by upregulating AQP2

description: Introduction: Phosphatase and tensin (PTEN) is a multifunctional gene associated with the normal development and physiological function of various tissues including the kidney. However, its role in renal tubular reabsorption function has not been well elucidated. Methods: We generated a renal tubule-specific Pten knockout mouse model by crossing Ptenfl/fl mice with Ksp-Cre transgenic mice, evaluated the effect of Pten loss on renal tubular function, and investigated the underlying mechanisms. Results: Pten loss resulted in abnormal renal structure and function and water retention in multiple organs. Our results also demonstrated that aquaporin-2 AQP2, an important water channel protein, was upregulated and concentrated on the apical plasma membrane of collecting duct cells, which could be responsible for the impaired water balance in Pten loss mice. The regulation of Pten loss on AQP2 was mediated by Protein kinase B (AKT) activation. Discussion/Conclusions: Our results reveal a connection between PTEN gene inactivation and water retention, suggesting the importance of PTEN in normal kidney development and function.
DOI: 10.6084/m9.figshare.27992834.v1
title: The simulation results of system 1

description: The simulation results of system 1
DOI: 10.17632/ffn745r57z
title: Data for "Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics"

description: Prediction of Phakic Intraocular Lens Vault Using Machine Learning of Anterior Segment Optical Coherence Tomography Metrics. Authors: Kazutaka Kamiya, MD, PhD, Ik Hee Ryu, MD, MS, Tae Keun Yoo, MD, Jung Sub Kim MD, In Sik Lee, MD, PhD, Jin Kook Kim MD, Wakako Ando CO, Nobuyuki Shoji, MD, PhD, Tomofusa, Yamauchi, MD, PhD, Hitoshi Tabuchi, MD, PhD. We hypothesize that machine learning of preoperative biometric data obtained by the As-OCT may be clinically beneficial for predicting the actual ICL vault. Therefore, we built the machine learning model using Random Forest to predict ICL vault after surgery. This multicenter study comprised one thousand seven hundred forty-five eyes of 1745 consecutive patients (656 men and 1089 women), who underwent EVO ICL implantation (V4c and V5 Visian ICL with KS-AquaPORT) for the correction of moderate to high myopia and myopic astigmatism, and who completed at least a 1-month follow-up, at Kitasato University Hospital (Kanagawa, Japan), or at B&VIIT Eye Center (Seoul, Korea). This data file (RFR_model(feature=12).mat) is the final trained random forest model for MATLAB 2020a. Python version: *************************************************************** from sklearn.model_selection import train_test_split import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor # connect data in your google drive from google.colab import auth auth.authenticate_user() from google.colab import drive drive.mount('/content/gdrive') # Change the path for the custom data # In this case, we used ICL vault prediction using preop measurement dataset = pd.read_csv('gdrive/My Drive/ICL/data_icl.csv') dataset.head() #optimal features (sorted by importance) : # 1. ICL size 2. ICL power 3. LV 4. CLR 5. ACD 6. ATA # 7. MSE 8.Age 9. Pupil size 10. WTW 11. CCT 12. ACW y = dataset['Vault_1M'] X = dataset.drop(['Vault_1M'], axis = 1) # Split the dataset to train and test data, if necessary. # For example, we can split data to 8:2 as a simple validation test train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0) # In our study, we already defined the training (B&VIIT Eye Center, n=1455) and test (Kitasato University, n=290) dataset, this code was not necessary to perform our analysis. # Optimal parameter search could be performed in this section parameters = {'bootstrap': True, 'min_samples_leaf': 3, 'n_estimators': 500, 'criterion': 'mae' 'min_samples_split': 10, 'max_features': 'sqrt', 'max_depth': 6, 'max_leaf_nodes': None} RF_model = RandomForestRegressor(**parameters) RF_model.fit(train_X, train_y) RF_predictions = RF_model.predict(test_X) importance = RF_model.feature_importances_
DOI: 10.17632/jt5p98m2yf
title: Data for: Dynamic Defense Strategy Against Advanced Persistent Threat Under Heterogeneous Networks

description: data.mat represents the operations of different kinds of nodes acquired from the real information system. count.mat represents that fusing data observed from monitoring nodes, sink nodes and the Internet together. count1.mat, count2.mat, and count3.mat denote the performance of only using nodes in the monitor area, sink area, and the Internet respectively. count12.mat means fusing monitoring nodes and sink nodes. count13.mat means fusing monitoring nodes and the Internet nodes. count23.mat means fusing sink nodes and the Internet nodes. ROCF.mat, ROCN.mat, ROCI.mat, and ROCS.mat denote the ROC curves of NetF and other single features respectively.
DOI: 10.17632/mf4gf36r9n
title: data of acp-2022-250

description: This data is from the study which is submitted the Atmospheric Chemistry and Physics. This data were the VOC concentrations which were collected at three supersites (Jinshan: JS; Pudong: PD; Qingpu: QP) in Shanghai, China from January 1 to March 31 2019. At the JS site, the VOCs was collected and analyzed by an online gas chromatography (GC866, Chromato, France) equipped with flame ionization detector (FID). In brief, after the removal of water, the samples were separated for low-carbon (C2-C6) and high-carbon (C6-C12) compounds at the temperature of -5℃ and 25℃, respectively. Then the gas was analyzed by FID after high temperature desorption (380℃) and column chromatographic separation. At the PD site, VOCs was measured by gas chromatography (GC580-FID, PE, USA) and TD300. The samples were separated at -30℃, after water was removed. Then the gas was determined by FID, after high temperature desorption (325℃) and column chromatographic separation. At the QP site, VOCs were determined by gas chromatography (GC5000 BTX/VOC, AMA, German) and a flame ionization detector (FID). The samples were condensed low-carbon (C2-C6) compounds and high-carbon (C6-C12) compounds under the temperatures of 15℃ and 30℃, respectively. Then the gas was analyzed by FID after high temperature desorption (230℃) and column chromatographic separation. The R2 of all of the VOCs were ≥ 0.995. The accuracy of 95 % of compounds was ≤ ± 20 %. The minimum detection limit (MDL) of most VOC components was ≤ 0.15 ppb. This data provide new insights into the accurate air-quality control at a city level in China due to a wide variety of land-use types, and highlight that the simultaneous multiple-site measurements in the megacity or city cluster could be more appropriate to fully understand the VOC characteristics relative to a single-site measurement performed normally.
DOI: 10.6084/m9.figshare.13322975.v1
title: Time-resolved X-ray scattering and spectroscopy data and MD simulations reveal solvent motions coupled to intramolecular elecron transfer

description: This dataset contains:- time-resolved X-ray scattering data and Fe kbeta emission data measured at LCLS upon metal-to-metal charge-transfer transition in a bimetallic Fe-Ru complex dissolved in water- results of non-equilibrium MD simulations used to interpret the data, specifically the radial distribution functions (RDFs) and the scattering signals calculated as Fourier transform of the RDFs-scripts that can be used to plot the data and the simulations<br>This dataset is link to publication: https://www.nature.com/articles/s41557-020-00629-3
DOI: 10.6084/m9.figshare.155653.v1
title: Cooperative Teaching Styles among Literature Teachers in Misamis Oriental General Comprehensive High School

description: This study investigates the extent of utilization of cooperative teaching styles among literature teachers of Misamis Oriental General Comprehensive High School. The study’s sample contained 336 fourth year level students of MOGCHS.
DOI: 10.6084/m9.figshare.20134481.v13
title: The effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas

description: We aim to study the effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas. Horizontal and vertical distributions of DMSP, DMSP lyase activity, DMS, and the abundances of bacteria, DMSP-consuming bacteria (DCB), DMSO-consuming bacteria and picophytoplankton in the Bohai Sea (BS) and Yellow Sea (YS) were investigated in autumn 2020. Bacterial community composition was conducted in February of 2021.
DOI: 10.6084/m9.figshare.26347213.v1
title: MAGs from Vigneron et al. <b>Genomic insights into novel extremophile lineages of the PVC superphylum from the thalassohaline lake Dziani Dzaha</b>

description: Applying genome-centric metagenomics on the hypersaline and hyperalkaline waters of Lake Dziani Dzaha (Mayotte), we identified novel extremophilic candidate lineages sibling to Elusimicrobia and Omnitrophota and defined novel halophilic clades within the Planctomycetota–Verrucomicrobiota–Chlamydiota (PVC) superphylum.
DOI: 10.17632/89j2vt22rj
title: Research Data

description: Research Data
DOI: 10.6084/m9.figshare.14069504.v1
title: Development and Initial Validation of the Hardiness Based Parenting Behaviors Questionnaire (HBPBQ)

description: A 5-point scale ranging from “never=1” to “always=4” was offered for the answers. Items ph5, ph8, ph18, ph26,ph 28, ph35, and ph40 scored reversed. In the current data, the above items are reversed.
DOI: 10.17632/ny5x3bkd56
title: A dataset to measure China biodiversity risk

description: Extinctions of biological populations are becoming more frequent and have important implications for related sectors. As a result, the risks associated with biodiversity have received increasing attention and are considered to be entirely new risk factors. To understand the drivers of biodiversity risk, it is crucial to measure biodiversity risk at multiple levels, especially in developing countries. From perspectives of macro-government, meso-industry, and micro-companies, we use machine learning and text mining methods to measure the biodiversity risk of the Chinese market from 2000 to 2023, by using official media news texts, related fund holding data, and listed companies’ annual report texts. Specifically, our data features a measure of biodiversity risk in each of the three dimensions. Unlike previous biodiversity risk measurements, our data can reflect China's biodiversity risk from multiple perspectives, including macro-government, meso-industry, and micro-firms. Also our biodiversity risk data can be clustered on categorical domains such as time, city, and industry. As a result, our data can be matched with most relevant studies. Our biodiversity risk macro-data comes from the news data of Chinese mainstream media between 2013 and 2023, and we adopt a machine learning approach to text mining to obtain the biodiversity risk of 5,394 trading days. Our biodiversity risk meso-data comes from more than 40 funds related to conceptual themes such as ‘bioprotection’ listed between 2015 and 2023. Our micro-biodiversity risk indicators are extracted from the annual reports of 5,606 listed firms listed on the Shanghai Stock Exchange, Shenzhen Stock Exchange and Beijing Stock Exchange from 2000 to 2023.
DOI: 10.17632/t4fs2xycjn
title: NWA 12774-Data

description: The data deposited here include the EPMA data for olivine, augite, plagioclase, spinel, chromite, and ulvöspinel in the quenched angrite NWA 12774 and the LA-ICP-MS data for augite in NWA 12774.
DOI: 10.25440/smu.21895896.v1
title: Data and code for "Global value chains and the CPTPP"

description: This record contains the data and code for the published journal article "Global value chains and the CPTPP". <br> TABLE 2: PARTICIPATION OF CPTPP MEMBERS IN GVC (2005 - 2015) Run "GVCparticipation.do" For CPTPP countries: the code directly extracts the Foreign Content for VS index (Hummels, Ishii and Yi, 2001), and the GVC index (Borin and Mancini, 2017). For World: the code extracts gross export (GEXP), traditional trade component (DAVAX), and the foreign contents (FC) between each country and their importers. Then sum up the components and calculate the GVC share using equation (2). TABLE 3: GVC INTENSITY OF CPTPP COMPARED TO OTHER TRADE BLOCS (2015) Use the decomposition for individual country from the GVC_participation.do to calculate the member's GVC share using equation (2). Sum the GVC share across all the members of the trade bloc. TABLE 4: KEY DOWNSTREAM TRADE PARTNERS OF CPTPP MEMBERS (2015) Run "Table4_Downstream_partners.do" The decomposition for Domestic contents (DC) and Traditional trade (TT) of each country with respect to each trade block is saved in the corresponding sheet of "downstream_partners.xlsm" Calculate the percentage trade of each country with regards to its downstream partners using equation (3). TABLE 5: KEY UPSTREAM TRADE PARTNERS OF CPTPP MEMBERS (2015) Run "Table5_Upstream_partners.do" The decomposition for Foreign contents (FC) of each country with respect to each trade block is saved in the corresponding sheet of "upstream_partners.xlsm" Calculate the percentage trade of each country with regards to its upstream partners using equation (4). TABLE 7: PARTICIPATION IN GVC AT SECTORAL LEVEL Run "Table7_GVCsectors.do" TABLE 8: KEY DOWNSTREAM PARTNER IN SELECTED SECTORS Run "Table 8_Downstream_partners_sectors.do" The decomposition for Domestic contents (DC) and Traditional trade (TT) of each country at the sectoral level is saved in "DC_by_sect_manuf" (for manufacturing sectors) and "DC_by_sect_svc" (for service sectors) Calculate the percentage trade of each country to its downstream partners at sectoral level using equation (3). TABLE 9: KEY UPSTREAM PARTNER IN SELECTED SECTORS Run "Table 9_Upstream_partners_sectors.do" The decomposition for Foreign contents (FC) of each country at the sectoral level is saved in the corresponding sheet Calculate the percentage trade of each country to its downstream partners using equation (4). <br> TABLE A.4: Key Downstream Partners by Alternative Formula To obtain the decomposition for Foreign contents (FC), importer content, and the difference between domestic contents and the traditional trade (DC - TT), run "Table A4_Downstream_partners_apd.do" Calculate the percentage trade of each country with regards to its downstream partners using equation (8)
DOI: 10.6084/m9.figshare.22938797.v2
title: A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species

description:  
Authors:
 Diogo Castilho1,2*, Danilo Tedesco3, Carlos Hernandez3, Beata E. Madari1,2, Ignacio Ciampitti3*
<br>
1 Graduate Program in Agronomy, Federal University of Goiás, Goiânia, GO, Brazil
2 Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, GO, Brazil.
3 Department of Agronomy, Kansas State University, 1712 Claflin Rd., Manhattan, KS, 66506, USA
 
A new dataset was described and summarized the state of the art in drone-based plant traits assessment, resulting from a systematic search of Scopus and Web of Science databases. This dataset contains the result of extracting data of 14 plant traits (yield [t ha-1], plant N density [g m-2], plant N concentration [%], plant N accumulation [g m-2], NUE - plant N content [kg ha-1], NNI, N uptake [kg ha-1], N rate [kg ha-1], leaf N density [g m-2], leaf N content [g m-2], leaf N concentration [%], leaf N accumulation [g m-2], canopy N content [g m-2], and biomass [t ha-1]) from 41 papers with a total of 11189 observations for 11 major crop species from 13 countries around the globe.
<br>
Many vegetation indices derived from unmanned aerial vehicle could track the relative yield, N uptake, and leaf N concentration in wheat, cotton and rice species. This method may not only be used to evaluate the N management practices but also may have application in making in-season N management decisions. Furthermore, this dataset is relevant to develop precise and broad diagnostic of N nutritional status of wide crop species in different sites and conditions.
<br>
For more information related to this dataset or codes, please contact the corresponding author at: diogocastilho6@hotmail.com
<br>
For cite the dataset, please use:
Castilho, D., Tedesco, D.,  Hernandez, C., Madari, E. B., Ciampitti, I.   
A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species. figshare https://figshare.com/s/177cdcd70963a415925b (2023).
DOI: 10.6084/m9.figshare.23509536.v1
title: Tectofugal Visual System Fiber Tracts Only

description: The file presented here is a 3D model file containing data from both diceCT and histological datasets. This file is specific to the fiber tracts utilized by the dominant tectofugal visual processing pathway. There are a number of primary tracts used by this system such as the tectothalamic tract, posterior commissure, lateral forebrain bundle, intra-isthmic tract, optic tract and many more. Additionally, we have created representations of projections between several of the tectofugal nuclei that don't necessarily use a designated fiber tract. In total we present the fiber connections between 18 unique nuclear structures occupying several distinct brain regions. This 3D model of the tectofugal fiber system is the most exhaustive to date in any avian species to our knowledge. This model is particular to the chicken but can technically represent the bulk of avian species (i.e. laterally-eyed birds) though you will likely see species specific differentiation.
DOI: 10.6084/m9.figshare.28553693.v2
title: Experimental Data on Unconfined Compressive Strength of Cement-Treated Clayey Silt from Northwestern Iași, Romania

description: OverviewThis dataset contains experimental measurements of Unconfined Compressive Strength (UCS) for cement-treated clayey silt (clSi) collected from northwestern Iași, Romania. The data represents the results of a comprehensive laboratory testing program investigating the influence of cement content, curing period, and compaction rate on the mechanical properties of stabilized soil.Material CharacteristicsSoil PropertiesThe soil used in this study is identified as clayey SILT (clSi) according to EN ISO 14688 standards, with the following composition and properties:<b>Grain size distribution</b>: 64% silt, 27% clay, 9% sand<b>Atterberg limits</b>: Liquid Limit (LL) = 36%, Plastic Limit (PL) = 24%, Plasticity Index (PI) = 12%<b>Physical properties</b>: Bulk unit weight (γ<sub>bulk</sub>) = 16.84 kN/m³, moisture content (w) of 20.24%<b>Void characteristics</b>: Void ratio (e) = 0.87, Porosity (n) = 38.9%, Degree of saturation (Sr) = 0.62<b>Compaction characteristics</b>: Optimum Moisture Content (OMC) = 14.9%, maximum dry unit weight (γ<sub>d,max</sub>) = 15.37 kN/m<sup>3</sup>Cement PropertiesThe stabilization agent used was Portland Cement (PC) type CEM II/B-M(S-LL) 42.5 R conforming to the European standard EN 197-1. This composite cement is characterized by:<b>Composition</b>: 65–79% clinker, 6–20% granulated blast furnace slag (S), and 6–20% limestone (LL) by mass<b>Chemical composition</b>: 56.2% CaO, 19.8% SiO₂, 7.1% Al₂O₃, among other oxides<b>Strength class</b>: 42.5 MPa minimum compressive strength at 28 days<b>Early strength</b>: Rapid initial strength developmentExperimental MethodologyCylindrical specimens (3.8 cm diameter, 7.6 cm height) were prepared with varying cement contents (0%, 2.5%, 5%, 7.5%, and 10% by dry weight) and compacted at different rates (0.75, 1.0, and 1.25 mm/min) using static compaction. The specimens were prepared at the optimum moisture content of 14.9% and target dry unit weight of 15.37 kN/m³. The cement-treated samples were cured under controlled conditions (23°C) for periods of 1, 7, 14, and 28 days before UCS testing.Dataset ContentsThe dataset includes 185 UCS measurements with the following variables:Cement content (% by dry weight of soil)Curing period (days)Compaction rate (mm/min)Unconfined Compressive Strength (kPa)Research ApplicationsThis dataset supports research on:Soil stabilization techniques for infrastructure developmentPredictive modeling of cement-treated soil strength parametersOptimization of cement dosage for ground improvement projectsMachine learning applications in geotechnical engineeringTime-dependent behavior of stabilized soilsData FormatThe primary dataset is provided in CSV format.
DOI: 10.6084/m9.figshare.6279005.v1
title: Starch granules: a data collection of 40 food species

description: A collection of starch granules synthesized in 40 food plants was assembled. After extraction and purification, samples were subjected to optic and scanning electron microscopy investigations. Detailed images and descriptions of the starch granules are provided, evidencing all their morphological and structural features. In particular, shape, size, position and visibility of the <i>hilum</i>, organization of the <i>lamellae</i>, presence of fissures and surface elements (i.e. pores, pressure facets), density of granules per mg of sample and percentage of amylose content were reported. Generally, the results showed that starch synthesis in organisms belonging to the same species or genus follows conserved patterns, although some differences can be always detected. In conclusion, the present work represents a starch Botanical Atlas fundamental for botanic, archaeological and food quality researches.