cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7931999
title: Example testing images for PFU detection neural network

description: This is the example testing images used for the paper of "Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning". The data has been compressed into eight zip files from .001 to .008. To access the data, please follow these steps: 1) Download all eight zipped files. 2) Once the download is complete, click on any one of the zipped files to start the extraction process. 3) The extracted files will automatically be organized into one folder. After extraction, there are three subfolders inside it: 1) Network input: it contained 4 holographic phase images (.mat files) at 12h, 13h, 14h and 15h of incubation for an example postive well and an example negative well. 2) Network output: It contains the network output image (PFU probability map) fot the example postive well and the example negative well. 3) Detection result: It contains the final binary detection result images after thresholding fot the example postive well and the example negative well.
DOI: 10.5281/zenodo.7931998
title: Example testing images for PFU detection neural network

description: This is the example testing images used for the paper of "Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning". The data has been compressed into eight zip files from .001 to .008. To access the data, please follow these steps: 1) Download all eight zipped files. 2) Once the download is complete, click on any one of the zipped files to start the extraction process. 3) The extracted files will automatically be organized into one folder. After extraction, there are three subfolders inside it: 1) Network input: it contained 4 holographic phase images (.mat files) at 12h, 13h, 14h and 15h of incubation for an example postive well and an example negative well. 2) Network output: It contains the network output image (PFU probability map) fot the example postive well and the example negative well. 3) Detection result: It contains the final binary detection result images after thresholding fot the example postive well and the example negative well.
DOI: 10.34740/kaggle/dsv/5675761
title: YouTube Daily
DOI: 10.6084/m9.figshare.22189198.v3
title: inSPIRE-External-Utilities

description: External Utilities used by inSPIRE. <br> Note this includes script from the open source fragpipe tool. Source of this script https://github.com/Nesvilab/FragPipe <br>
DOI: 10.5281/zenodo.7831815
title: or1kr3

description: or1kr3
DOI: 10.5281/zenodo.7831814
title: or1kr3

description: or1kr3
DOI: 10.5281/zenodo.7831685
title: or1kr2

description: or1kr2
DOI: 10.5281/zenodo.7831684
title: or1kr2

description: or1kr2
DOI: 10.5281/zenodo.7831506
title: or1krp1

description: or1krp1
DOI: 10.5281/zenodo.7831505
title: or1krp1

description: or1krp1
DOI: 10.5281/zenodo.7828709
title: o1ar

description: o1ar
DOI: 10.5281/zenodo.7828708
title: o1ar

description: o1ar
DOI: 10.5281/zenodo.7334439
title: glyc

description: glyc datasets
DOI: 10.5281/zenodo.7334438
title: glyc

description: glyc datasets
DOI: 10.5281/zenodo.6559627
title: Gad part 3

description: Gad
DOI: 10.5281/zenodo.6559626
title: Gad part 3

description: Gad
DOI: 10.5281/zenodo.6559274
title: Gad-part1

description: Gad data
DOI: 10.5281/zenodo.6559273
title: Gad-part1

description: Gad data
DOI: 10.5281/zenodo.6559470
title: Gad part 2

description: Gad
DOI: 10.5281/zenodo.6559469
title: Gad part 2

description: Gad
DOI: 10.5281/zenodo.6607607
title: C143

description: C143
DOI: 10.5281/zenodo.6607606
title: C143

description: C143
DOI: 10.5281/zenodo.7932602
title: The data from CropPol which are not included in my linear mixed models and the reasons.

description: The data from CropPol which are not included in my linear mixed models and the reasons. Note_for_not_include column refers to the reason why these data was excluded.
DOI: 10.5281/zenodo.7932601
title: The data from CropPol which are not included in my linear mixed models and the reasons.

description: The data from CropPol which are not included in my linear mixed models and the reasons. Note_for_not_include column refers to the reason why these data was excluded.
DOI: 10.34740/kaggle/dsv/5676044
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.21080470.v8
title: Effects of secondary carbonate precipitation and dissolution on Changjiang (Yangtze) river chemistry and estimates of silicate weathering rates

description: Supporting data of Li et al., Global Biogeochemical cycles
DOI: 10.6084/m9.figshare.21080470.v7
title: Effects of secondary carbonate precipitation and dissolution on Changjiang (Yangtze) river chemistry and estimates of silicate weathering rates

description: Supporting data of Li et al., Global Biogeochemical cycles
DOI: 10.6084/m9.figshare.22815806
title: Extracted tweets (refer descriptives for details).

description: The data set is a collection of tweets extracted from Twitter for two specific platforms. It is date bound.
DOI: 10.6084/m9.figshare.22815806.v1
title: Extracted tweets (refer descriptives for details).

description: The data set is a collection of tweets extracted from Twitter for two specific platforms. It is date bound.
DOI: 10.6084/m9.figshare.22815833
title: <strong>Качество жизни в стране как функция параметров Викиданных</strong> - таблица рейтинга качества жизни, построенного на основе Викиданных. CSV-файл.

description: <strong>Качество жизни в стране как функция параметров Викиданных</strong> - таблица рейтинга качества жизни, построенного на основе Викиданных. CSV-файл. Петрозаводский Государственный университет, Гулякова Мария, 2023г.
DOI: 10.6084/m9.figshare.22815833.v1
title: <strong>Качество жизни в стране как функция параметров Викиданных</strong> - таблица рейтинга качества жизни, построенного на основе Викиданных. CSV-файл.

description: <strong>Качество жизни в стране как функция параметров Викиданных</strong> - таблица рейтинга качества жизни, построенного на основе Викиданных. CSV-файл. Петрозаводский Государственный университет, Гулякова Мария, 2023г.
DOI: 10.5281/zenodo.7932429
title: Emission input data for WRF-CMAQ

description: Emission input data for WRF-CHIMERE in eastern China during 2017.
DOI: 10.34740/kaggle/dsv/5676225
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7932389
title: Chemical initial and boundary conditions for WRF-CMAQ

description: Chemical initial and boundary conditions for WRF-CMAQ in eastern China during 2017.
DOI: 10.25921/nfdj-wh45
title: EM710 Water Column Sonar Data Collected During NF2201
DOI: 10.5281/zenodo.7932748
title: Análisis de los Tweets con mayor Interacción de las cuentas del Gobierno de España de Educación y Cultura

description: Selección de los 10 Tweets con mayor número de "me-gustas" y de Retweet (eliminados los duplicados) de las cuentas vinculadas al Ministerio de Cultura y Deporte y Ministerio de Educación y Formación Profesional, durante el periodo: marzo 2020 a marzo 2021.
DOI: 10.5281/zenodo.7932747
title: Análisis de los Tweets con mayor Interacción de las cuentas del Gobierno de España de Educación y Cultura

description: Selección de los 10 Tweets con mayor número de "me-gustas" y de Retweet (eliminados los duplicados) de las cuentas vinculadas al Ministerio de Cultura y Deporte y Ministerio de Educación y Formación Profesional, durante el periodo: marzo 2020 a marzo 2021.
DOI: 10.6084/m9.figshare.22815908
title: Wildfire strains characterization

description: Characterization and screening of strains
DOI: 10.34740/kaggle/dsv/5676368
title: 2021 World Population (updated daily)
DOI: 10.25921/50sa-1z57
title: EK60 Water Column Sonar Data Collected During NF2202
DOI: 10.6084/m9.figshare.22815926
title: MatScholarNER

description: MatScholar dataset from https://pubs.acs.org/doi/10.1021/acs.jcim.9b00470
DOI: 10.34740/kaggle/dsv/5676430
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7931558
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2007

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2007. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2006</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2008</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2007 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7931557
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2007

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2007. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2006</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2008</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2007 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7932282
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2006

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2006. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2005</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2007</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2006 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7932281
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2006

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2006. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2005</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2007</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2006 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5517/ccdc.csd.cc2dg1lj
title: CCDC 2218037: Experimental Crystal Structure Determination

description: Related Article: Kazuya Nakashima, Rie Suizu, Shuhei Morishita, Noriaki Tsurumachi, Masahiro Funahashi, Hyuma Masu, Kunio Awaga|2023|ACS Mater. Au|3|201|doi:10.1021/acsmaterialsau.2c00081
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21780797.v1
title: Factors causing the increase in arsenic concentration in brown rice due to high temperatures during the ripening period and its reduction by applying converted furnace slag

description: It has been pointed out that the concentration of arsenic (As) in brown rice increases due to high temperatures during the ripening period, and fertilization that can reduce As in brown rice even under high-temperature is required. The effectiveness of using converted furnace slag (CFS) to reduce the As concentration in brown rice was investigated. Rice plants were cultivated in Wagner pots (1/5000a) filled with gray lowland soil added CFS under ambient conditions up to 1 week after heading. Those pots were moved to a temperature gradient chamber set at four temperature levels (ambient, mildly-high, moderate-high, and super-high) and kept until harvest. An analysis of covariance using the mean daytime temperature as a covariate showed significant linearity of regression for As concentrations, indicating that high temperature increased the concentration of As in brown rice. The application of 30 g of CFS significantly reduced 20% of the As concentration in the rice, suggesting that CFS can be an effective countermeasure to reduce the As concentration in brown rice under high-temperature conditions. Since inorganic As flows into brown rice through sieve tubes, the cause of the increase in As concentration in brown rice due to high temperature was investigated by comparing the concentrations with carbon dynamics. The significant positive correlation between the As/carbon ratio and the As concentration in brown rice was observed, suggesting that high temperature reduces the rate of carbon translocation which is one of the essential factors in the increase in As concentration in brown rice due to high temperature.
DOI: 10.6084/m9.figshare.21780797
title: Factors causing the increase in arsenic concentration in brown rice due to high temperatures during the ripening period and its reduction by applying converted furnace slag

description: It has been pointed out that the concentration of arsenic (As) in brown rice increases due to high temperatures during the ripening period, and fertilization that can reduce As in brown rice even under high-temperature is required. The effectiveness of using converted furnace slag (CFS) to reduce the As concentration in brown rice was investigated. Rice plants were cultivated in Wagner pots (1/5000a) filled with gray lowland soil added CFS under ambient conditions up to 1 week after heading. Those pots were moved to a temperature gradient chamber set at four temperature levels (ambient, mildly-high, moderate-high, and super-high) and kept until harvest. An analysis of covariance using the mean daytime temperature as a covariate showed significant linearity of regression for As concentrations, indicating that high temperature increased the concentration of As in brown rice. The application of 30 g of CFS significantly reduced 20% of the As concentration in the rice, suggesting that CFS can be an effective countermeasure to reduce the As concentration in brown rice under high-temperature conditions. Since inorganic As flows into brown rice through sieve tubes, the cause of the increase in As concentration in brown rice due to high temperature was investigated by comparing the concentrations with carbon dynamics. The significant positive correlation between the As/carbon ratio and the As concentration in brown rice was observed, suggesting that high temperature reduces the rate of carbon translocation which is one of the essential factors in the increase in As concentration in brown rice due to high temperature.
DOI: 10.6084/m9.figshare.21786532
title: Oxalic acid treated low grade rock phosphate can be a potent supplemental P source to grow wheat in inceptisol

description: High grade rock phosphates (HGRP) are only used to prepare phosphorus (P) fertilizers. But the finite reserve of HGRP is pushing the agricultural sectors to use low grade rock phosphates (LGRP) for growing crops. In this study an attempt was made to treat LGRP with oxalic acid and use it as a supplement to P fertilizer for growing wheat in Inceptisol. Incubation study revealed that oxalic acid treated rock phosphate (RP) can significantly increase soil available P status as compared to inert untreated RP treatment. Pot study established that oxalic acid treated RP (@60 mg P<sub>2</sub>O<sub>5</sub> kg<sup>−1</sup>) under DAP<sub>30</sub>+ RP<sub>60(OA)</sub> treatment has supplemented 50% P fertilizer without affecting wheat yields and P uptake. DAP<sub>30</sub>+ RP<sub>60(OA)</sub> treatment also had maintained significantly higher Saloid-P, organic-P and statistically at par available P, dehydrogenase, acid phosphatase and alkaline phosphatase activities as compared to chemical fertilizer treatment (DAP<sub>60</sub>). Thus it can be inferred that, application of oxalic acid treated LGRP (@ 60 mg P<sub>2</sub>O<sub>5</sub> kg<sup>−1</sup>) can supplement 50% of the P fertilizer demand to grow wheat in Inceptisol.
DOI: 10.6084/m9.figshare.21786532.v1
title: Oxalic acid treated low grade rock phosphate can be a potent supplemental P source to grow wheat in inceptisol

description: High grade rock phosphates (HGRP) are only used to prepare phosphorus (P) fertilizers. But the finite reserve of HGRP is pushing the agricultural sectors to use low grade rock phosphates (LGRP) for growing crops. In this study an attempt was made to treat LGRP with oxalic acid and use it as a supplement to P fertilizer for growing wheat in Inceptisol. Incubation study revealed that oxalic acid treated rock phosphate (RP) can significantly increase soil available P status as compared to inert untreated RP treatment. Pot study established that oxalic acid treated RP (@60 mg P<sub>2</sub>O<sub>5</sub> kg<sup>−1</sup>) under DAP<sub>30</sub>+ RP<sub>60(OA)</sub> treatment has supplemented 50% P fertilizer without affecting wheat yields and P uptake. DAP<sub>30</sub>+ RP<sub>60(OA)</sub> treatment also had maintained significantly higher Saloid-P, organic-P and statistically at par available P, dehydrogenase, acid phosphatase and alkaline phosphatase activities as compared to chemical fertilizer treatment (DAP<sub>60</sub>). Thus it can be inferred that, application of oxalic acid treated LGRP (@ 60 mg P<sub>2</sub>O<sub>5</sub> kg<sup>−1</sup>) can supplement 50% of the P fertilizer demand to grow wheat in Inceptisol.
DOI: 10.25921/hs4v-qv93
title: EM2040 Water Column Sonar Data Collected During NF2202
DOI: 10.5281/zenodo.7296341
title: Possible High COVID-19 Airborne Infection Risk in Deep and Poorly Ventilated 2D Street Canyons

description: Dataset for Figures 2-4 of the article named Possible High COVID-19 Airborne Infection Risk in Deep and Poorly Ventilated 2D Street Canyons
DOI: 10.5281/zenodo.7296340
title: Possible High COVID-19 Airborne Infection Risk in Deep and Poorly Ventilated 2D Street Canyons

description: Dataset for Figures 2-4 of the article named Possible High COVID-19 Airborne Infection Risk in Deep and Poorly Ventilated 2D Street Canyons
DOI: 10.25921/5x0r-qn43
title: EM710 Water Column Sonar Data Collected During NF2202
DOI: 10.34740/kaggle/dsv/5676685
title: COVID Vaccination in World (updated daily)
DOI: 10.57967/hf/0645
title: fact-ai