cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21814164
title: Growth and degradation ability of lignin degrading isolates

description: The sheet contains raw data of the bacterial growth from CFU count and abs 280 nms showing the degradtion of phenolic compounds.
DOI: 10.6084/m9.figshare.19672527.v2
title: All_PPIs_per_sample_per_gene

description: 4,363 samples' CSV files for all proteins showing the number index, gene name (Gene), plasmid status (Plasmid, where 0=plasmid-related), and total number of PPIs (Interactions) for each sample.
DOI: 10.6084/m9.figshare.13102561.v1
title: Last chance to see the ice: visitor motivation at Montenvers-Mer-de-Glace, French Alps

description: Anthropogenic climate change is causing significant changes to the cryosphere. Glaciers and glacier tourism are directly impacted, and glacier tourism operators must adapt to the disappearance of their main resource. Recent studies carried out in this context have demonstrated that Last Chance Tourism (LCT) has emerged as the motivation behind viewing or visiting a natural resource before it disappears. The most frequently visited French glacier is Montenvers-Mer-de-Glace (1920 m a.s.l.), which is becoming a LCT destination. Results from both qualitative and quantitative methods demonstrate that visitors come to Montenvers mainly for its environmental features and for LCT purposes. Exploratory Factor Analysis (EFA) shows that motivations can be classified into LCT and five other factors. Qualitative data identify ‘Fame’ as an unexpected motivational category, and different motivational dimensions of each category are explored. Concerning LCT motivation, four dimensions are revealed: observing, understanding, a sense of urgency, and witnessing environmental changes. Moreover, the more respondents are aware of climate change, the more they consider LCT motivations important. These results lead us to link LCT to cognitive dissonance. Finally, we propose that future research should explore LCT and its relationship with media coverage and pro-environmental behaviour.
DOI: 10.6084/m9.figshare.19674054
title: aaa

description: aaaa
DOI: 10.6084/m9.figshare.19674054.v2
title: aaa

description: aaaa
DOI: 10.6084/m9.figshare.19674054.v1
title: Plasmid_PPI.csv

description: Plasmid_PPI.csv
DOI: 10.6084/m9.figshare.22067492.v1
title: Tn358_3.1.bw

description: 11111
DOI: 10.6084/m9.figshare.22067492
title: Tn358_3.1.bw

description: 11111
DOI: 10.6084/m9.figshare.19672527.v3
title: aaa

description: aaaa
DOI: 10.6084/m9.figshare.22067480
title: Identifying gaps and priority areas to guide management planning of a protected area network to support snow leopard (Panthera uncia) in Asia

description: Final dataset of conefor results
DOI: 10.6084/m9.figshare.22067480.v1
title: Identifying gaps and priority areas to guide management planning of a protected area network to support snow leopard (Panthera uncia) in Asia

description: Final dataset of conefor results
DOI: 10.34740/kaggle/dsv/4975344
title: AutoXGB
DOI: 10.6084/m9.figshare.19672527
title: Data for all PPI analysis

description: 4363 samples' CSV files for all proteins with gene name, plasmid status and the total number of PPIs per sample
DOI: 10.6084/m9.figshare.19672527.v4
title: Data for all PPI analysis

description: 4363 samples' CSV files for all proteins with gene name, plasmid status and the total number of PPIs per sample
DOI: 10.15151/esrf-es-1034440861
title: Substrate binding and transport by the C. glabrata multidrug transporter Cdr1

description: Cdr1 is a membrane transporter, that confer resistance to pathogenic yeast by expelling drugs out of the cell. This mechanism of resistance is the main route of resistance to azoles, a family of compounds widely used in clinical settings. Infections of Candida species in immunocompromised patients is a major problem with compromised outcome for patients. We purify Cdr1 from C. glabrata, and got nice particle distribution on grids of this protein with different substrates and conditions of the transport mechanism. 600 movies data collection yielded a 5Ã reconstruction. We apply for time to get high resolution data
DOI: 10.6084/m9.figshare.17121335
title: Dataset of "A Numerical Investigation of the Effect of Wave-induced Mixing on Tropical Cyclones using a Coupled Ocean-Atmosphere-Wave Model"

description: <b>Model output data including TC intensity, size, sea surface temperature, mixed layer depth, latent heat fluxes, sensible heat fluxes and so on. Subsurface buoy data including water temperature and depth. Initial conditons for the idea</b><b>l</b><b>ized case.</b>
DOI: 10.6084/m9.figshare.17121335.v4
title: Dataset of "A Numerical Investigation of the Effect of Wave-induced Mixing on Tropical Cyclones using a Coupled Ocean-Atmosphere-Wave Model"

description: <b>Model output data including TC intensity, size, sea surface temperature, mixed layer depth, latent heat fluxes, sensible heat fluxes and so on. Subsurface buoy data including water temperature and depth. Initial conditons for the idea</b><b>l</b><b>ized case.</b>
DOI: 10.6084/m9.figshare.22067525
title: Questionnaire Items.docx

description: This is a supplement questionnaire for the article.
DOI: 10.6084/m9.figshare.22067525.v1
title: Questionnaire Items.docx

description: This is a supplement questionnaire for the article.
DOI: 10.6084/m9.figshare.22067582
title: Tn370_2.6.bw

description: 111111
DOI: 10.6084/m9.figshare.22067582.v1
title: Tn370_2.6.bw

description: 111111
DOI: 10.5517/ccdc.csd.cc29wnhc
title: CCDC 2141743: Experimental Crystal Structure Determination

description: Related Article: Guijuan Zheng, Keyu Lv, Hao Wang, Lang Huang, Yuanyuan Feng, Biao Gao, Yenan Sun, Yaofeng Li, Jiangeng Huang, Pengfei Jin, Xulin Xu, F. David Horgen, Chao Fang, Guangmin Yao|2023|J.Am.Chem.Soc.|145|3196|doi:10.1021/jacs.2c12963
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: 10.5281/zenodo.7620771
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2015

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 LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2015. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2014</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7620770
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2015

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 LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2015. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2014</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.34740/kaggle/dsv/4975505
title: Elon Musk Tweets
DOI: 10.5281/zenodo.7628040
title: Raw Data for the article: Continuous Renal Replacement Therapy after Liver Transplantation: Peri-Operative Associated Factors and Impact on Survival

description: Continuous renal replacement therapy (CRRT) following orthotopic liver transplantation (OLT) is usually started for multifactorial reasons, with variable incidence among series. This paper presents a single-center retrospective observational study on the early use (within one week) of CRRT after consecutive cadaveric OLT from January 2008 to December 2016. Preoperative patient characteristics and intraoperative data were collected, and patients were divided into two groups (CRRT and no CRRT) to explore the factors associated with the use of CRRT. Repeated measurements of postoperative creatinine were analyzed with generalized estimating equation (GEE) models. Among 528 OLT patients, 75 (14.2%) were treated with CRRT at least once in the first week. Patients treated with CRRT showed lower survival in a Kaplan-Meier curve (log-rank <em>p</em> value &lt; 0.01). Patients treated with CRRT had a more severe preoperative profile, with a significantly higher age, MELD, BUN, creatinine, and total bilirubin, as well as a longer surgery time and a higher number of transfusions of red blood cells, plasma, and platelets (all <em>p</em> values &lt; 0.05). In a stepwise multiple analysis, the following characteristics remained independently associated with the use of CRRT: the MELD score OR 1.12 (95% CL: 1.07-1.16), <em>p</em> value &lt; 0.001, and the preoperative value for blood urea nitrogen OR 1.016 (95% CL: 1.010-1.023), <em>p</em> value &lt; 0.001. The early use of CRRT after OLT occurred at a low rate in this large cohort; however, it was associated with worse outcomes. Apart from the preoperative severity, repeated intraoperative hypotension episodes, which were likely modifiable or preventable, were associated with the increased use of CRRT and higher postoperative creatinine.
DOI: 10.5281/zenodo.7628039
title: Raw Data for the article: Continuous Renal Replacement Therapy after Liver Transplantation: Peri-Operative Associated Factors and Impact on Survival

description: Continuous renal replacement therapy (CRRT) following orthotopic liver transplantation (OLT) is usually started for multifactorial reasons, with variable incidence among series. This paper presents a single-center retrospective observational study on the early use (within one week) of CRRT after consecutive cadaveric OLT from January 2008 to December 2016. Preoperative patient characteristics and intraoperative data were collected, and patients were divided into two groups (CRRT and no CRRT) to explore the factors associated with the use of CRRT. Repeated measurements of postoperative creatinine were analyzed with generalized estimating equation (GEE) models. Among 528 OLT patients, 75 (14.2%) were treated with CRRT at least once in the first week. Patients treated with CRRT showed lower survival in a Kaplan-Meier curve (log-rank <em>p</em> value &lt; 0.01). Patients treated with CRRT had a more severe preoperative profile, with a significantly higher age, MELD, BUN, creatinine, and total bilirubin, as well as a longer surgery time and a higher number of transfusions of red blood cells, plasma, and platelets (all <em>p</em> values &lt; 0.05). In a stepwise multiple analysis, the following characteristics remained independently associated with the use of CRRT: the MELD score OR 1.12 (95% CL: 1.07-1.16), <em>p</em> value &lt; 0.001, and the preoperative value for blood urea nitrogen OR 1.016 (95% CL: 1.010-1.023), <em>p</em> value &lt; 0.001. The early use of CRRT after OLT occurred at a low rate in this large cohort; however, it was associated with worse outcomes. Apart from the preoperative severity, repeated intraoperative hypotension episodes, which were likely modifiable or preventable, were associated with the increased use of CRRT and higher postoperative creatinine.
DOI: 10.15783/c78c7k
title: CRAWDAD dataset novay/cosphere (v. 2009-05-01)

description: The CoSphere (Communication Context for Adaptive Mobile Applications) trial gathered 
network traces on the personal mobile devices of 12 trial participants over a period 
of approximately one month in the February/March 2007 time frame.
description: Changelog: the initial version
DOI: 10.15783/c7zk53
title: CRAWDAD dataset upmc/rollernet (v. 2009-02-02)

description: This data includes traces of any opportunistic sighting of Bluetooth devices 
by groups of rollerbladers carrying iMotes in the roller tour in Paris, France.
description: Changelog: the initial version
DOI: 10.6084/m9.figshare.19525708
title: Table_S3_summary_bacteria_sample_features

description: <strong>Table S3</strong>. Key features of the bacterial samples. Fraction is the fraction of all genes that were plasmid-encoded. Plasmid_Plasmid_PPI is the number of PPIs exclusively between plasmid-encoded proteins. Plasmid_PPI is the number of PPIs between a chromosomal protein and a plasmid protein. Chrom_PPI is the number of PPIs between chromosomal proteins. PPIs is the total number of PPIs. CTri indicates the number of chromosomal protein trios. Loops indicates the number of indirect connections for chromosomal proteins. Ccs indicates the number of connected components for chromosomal proteins. CTri_all indicates the number of all protein trios. Loops_all indicates the number of indirect connections for all proteins. Ccs_all indicates the number of connected components for all proteins. APMNLE indicates the chromosomal proteins' aPMNLE (indirect connectivity). APMNLE_all indicates the aPMNLE for all proteins. DiffPMNLE stands for the % difference in aPMNLE when plasmid proteins are added to the chromosomal ones. Proteins_ccs_all is the number of connected components per protein for all proteins. Proteins_ccs_chrom is the number of connected components per protein for chromosomal proteins. Indirect_all is the number of indirect connections per protein for all proteins. indirect_chrom is the number of indirect connections per protein for chromosomal proteins. <br> Table S3 for n=4432 contains samples for which data was not accessible are included here, but can be removed for analysis. Data with no value is marked "NA", which means either the calculation was not possible, and/or there was no information on that feature.
DOI: 10.6084/m9.figshare.19525708.v9
title: Table_S3_summary_bacteria_sample_features

description: <strong>Table S3</strong>. Key features of the bacterial samples. Fraction is the fraction of all genes that were plasmid-encoded. Plasmid_Plasmid_PPI is the number of PPIs exclusively between plasmid-encoded proteins. Plasmid_PPI is the number of PPIs between a chromosomal protein and a plasmid protein. Chrom_PPI is the number of PPIs between chromosomal proteins. PPIs is the total number of PPIs. CTri indicates the number of chromosomal protein trios. Loops indicates the number of indirect connections for chromosomal proteins. Ccs indicates the number of connected components for chromosomal proteins. CTri_all indicates the number of all protein trios. Loops_all indicates the number of indirect connections for all proteins. Ccs_all indicates the number of connected components for all proteins. APMNLE indicates the chromosomal proteins' aPMNLE (indirect connectivity). APMNLE_all indicates the aPMNLE for all proteins. DiffPMNLE stands for the % difference in aPMNLE when plasmid proteins are added to the chromosomal ones. Proteins_ccs_all is the number of connected components per protein for all proteins. Proteins_ccs_chrom is the number of connected components per protein for chromosomal proteins. Indirect_all is the number of indirect connections per protein for all proteins. indirect_chrom is the number of indirect connections per protein for chromosomal proteins. <br> Table S3 for n=4432 contains samples for which data was not accessible are included here, but can be removed for analysis. Data with no value is marked "NA", which means either the calculation was not possible, and/or there was no information on that feature.
DOI: 10.6084/m9.figshare.19525708.v8
title: Table_S3_summary_bacteria_sample_features

description: <strong>Table S3</strong>. Key features of the bacterial samples. Fraction is the fraction of all genes that were plasmid-encoded. Plasmid_Plasmid_PPI is the number of PPIs exclusively between plasmid-encoded proteins. Plasmid_PPI is the number of PPIs between a chromosomal protein and a plasmid protein. Chrom_PPI is the number of PPIs between chromosomal proteins. PPIs is the total number of PPIs. CTri indicates the number of chromosomal protein trios. Loops indicates the number of indirect connections for chromosomal proteins. Ccs indicates the number of connected components for chromosomal proteins. CTri_all indicates the number of all protein trios. Loops_all indicates the number of indirect connections for all proteins. Ccs_all indicates the number of connected components for all proteins. APMNLE indicates the chromosomal proteins' aPMNLE (indirect connectivity). APMNLE_all indicates the aPMNLE for all proteins. DiffPMNLE stands for the % difference in aPMNLE when plasmid proteins are added to the chromosomal ones. Proteins_ccs_all is the number of connected components per protein for all proteins. Proteins_ccs_chrom is the number of connected components per protein for chromosomal proteins. Indirect_all is the number of indirect connections per protein for all proteins. indirect_chrom is the number of indirect connections per protein for chromosomal proteins. Samples for which data was not accessible are included here, but can be removed for analysis. Data with no value is marked "NA", which means either the calculation was not possible, and/or there was no information on that feature.
DOI: 10.5519/qd.t146n4zr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (61 records)
DOI: 10.5257/iea/co2/2022
title: Greenhouse Gas Emissions from Energy (2022 Edition)

description: In recognition of the fundamental importance of understanding energy-related environmental issues, the IEA Greenhouse Gasses from Energy provides a full analysis of emissions stemming from energy use. This annual publication has become an essential tool for analysts and policy makers in many international fora. The data in this dataset are designed to assist in understanding the evolution of the emissions of Greenhouse Gases from 1971 for over 100 countries and regions by sector and by fuel. Emissions were calculated using IEA energy databases and the default methods and emission factors from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
DOI: 10.5281/zenodo.7625939
title: A Building Imagery Database for the Calibration of Machine Learning Algorithms

description: An open database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This open database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. <strong>Full Changelog</strong>: https://github.com/vsilva028/ML/commits/v1.0.0
DOI: 10.6084/m9.figshare.16587311.v20
title: AREAdata updating output files

description: AREAdata output files for automatically updating climate averages. These are daily climate estimates based on the CDS ERA-5 reanalysis, see pearselab.github.io/areadata/.<br>File names begin with the climate variable given:temp = Temperaturespechumid = Specific Humidityrelhumid = Relative Humidityprecip = Precipiationuv = Ultraviolet<br>File names contain the administrative division level:<br>countries - highest administrative division<br>GID1 - lower administrative division, equivalent to states<br>GID2 - lower administrative division, equivalent to counties<br>Files downloadable as RDS files for direct use in the R statistical programming environment (appended .RDS), or as zip-compressed tab delimited files for other applications (appended .zip).<br>
DOI: 10.7910/dvn/4q7a9f
title: Chang et al. Predicting EMI learning outcomes through academic motivation and English proficiency

description: This data set measures the effects of academic motivation on EMI learning outcomes.