cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gwswp5
title: Occurrence Download

description: A dataset containing 296 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Month is January",
 "Month is February",
 "Month is December",
 "Month is March",
 "Month is November"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Pipistrellus nathusii (Keyserling & Blasius, 1839)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 296 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000269-170822121629195/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sdc83d
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-69.2 -32.8,-68.8 -32.8,-68.8 -32.4,-69.2 -32.4,-69.2 -32.8))",
 "TaxonKey is Rhea pennata d'Orbigny, 1834"
 ]
}

 The dataset includes 48 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007037-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wa2qmk
title: Occurrence Download

description: A dataset containing 948 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Living Specimen"
 ]
 },
 "TaxonKey is Lobatus gigas (Linnaeus, 1758)"
 ]
}

 The dataset includes 948 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023465-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5chufv
title: Occurrence Download

description: A dataset containing 263037 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Menyanthes trifoliata L., Atropa belladonna L.)"
 ]
}

 The dataset includes 263037 records from 1072 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033362-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.14609287
title: Additional file 1 of In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

description: Additional file 1. Tables S1–11.
DOI: 10.6084/m9.figshare.14609287.v1
title: Additional file 1 of In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

description: Additional file 1. Tables S1–11.
DOI: 10.6084/m9.figshare.14609290
title: Additional file 2 of In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

description: Additional file 2. The R code to conduct single-cell clustering analysis.
DOI: 10.6084/m9.figshare.14609290.v1
title: Additional file 2 of In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

description: Additional file 2. The R code to conduct single-cell clustering analysis.
DOI: 10.6084/m9.figshare.14609279.v1
title: Additional file 2 of Half-leaf width symmetric distribution reveals buffering strategy of Cunninghamia lanceolata

description: Additional file 2: Table S1. Regression coefficients, R2, horizontal and longitudinal distance of apex, and the ratio between the horizontal distances of apex with total length of main relationships used to estimate the length and half-leaf width trajectories in the selected leaves. The best fitting on leaf length(X) and half-leaf width(Y) is obtained by quadratic-function models: Y=C + B1X + B2X2, and all regressions have p &lt; 0.0001.
DOI: 10.6084/m9.figshare.14609282
title: Additional file 3 of Half-leaf width symmetric distribution reveals buffering strategy of Cunninghamia lanceolata

description: Additional file 3.
DOI: 10.5281/zenodo.10631953
title: DustNet - structured data and Python code to reproduce the model, statistical analysis and figures

description: Data and Python code used for AOD prediction with DustNet model - a Machine Learning/AI based forecasting. 

Model input data and code

Processed MODIS AOD data (from Aqua and Terra) and selected ERA5 variables* ready to reproduce the DustNet model results or for similar forecasting with Machine Learning. These long-term daily timeseries (2003-2022) are provided as n-dimensional NumPy arrays. The Python code to handle the data and run the DustNet model** is included as Jupyter Notebook ‘DustNet_model_code.ipynb’. A subfolder with normalised and split data into training/validation/testing sets is also provided with Python code for two additional ML based models** used for comparison (U-NET and Conv2D). Pre-trained models are also archived here as TensorFlow files. 

Model output data and code

This dataset was constructed by running the ‘DustNet_model_code.ipynb’ (see above). It consists of 1095 days of forecased AOD data (2020-2022) by CAMS, DustNet model, naïve prediction (persistence) and gridded climatology. The ground truth raw AOD data form MODIS is provided for comparison and statystical analysis of predictions. It is intended for a quick reproduction of figures and statystical analysis presented in DustNet introducing paper. 

 

*datasets are NumPy arrays (v1.23) created in Python v3.8.18.

**all ML models were created with Keras in Python v3.10.10.
DOI: 10.5281/zenodo.10631954
title: DustNet - structured data and Python code to reproduce the model, statistical analysis and figures

description: Data and Python code used for AOD prediction with DustNet model - a Machine Learning/AI based forecasting. 

Model input data and code

Processed MODIS AOD data (from Aqua and Terra) and selected ERA5 variables* ready to reproduce the DustNet model results or for similar forecasting with Machine Learning. These long-term daily timeseries (2003-2022) are provided as n-dimensional NumPy arrays. The Python code to handle the data and run the DustNet model** is included as Jupyter Notebook ‘DustNet_model_code.ipynb’. A subfolder with normalised and split data into training/validation/testing sets is also provided with Python code for two additional ML based models** used for comparison (U-NET and Conv2D). Pre-trained models are also archived here as TensorFlow files. 

Model output data and code

This dataset was constructed by running the ‘DustNet_model_code.ipynb’ (see above). It consists of 1095 days of forecased AOD data (2020-2022) by CAMS, DustNet model, naïve prediction (persistence) and gridded climatology. The ground truth raw AOD data form MODIS is provided for comparison and statystical analysis of predictions. It is intended for a quick reproduction of figures and statystical analysis presented in DustNet introducing paper. 

 

*datasets are NumPy arrays (v1.23) created in Python v3.8.18.

**all ML models were created with Keras in Python v3.10.10.
DOI: 10.5281/zenodo.10722953
title: DustNet - structured data and Python code to reproduce the model, statistical analysis and figures

description: Data and Python code used for AOD prediction with DustNet model - a Machine Learning/AI based forecasting. 

Model input data and code

Processed MODIS AOD data (from Aqua and Terra) and selected ERA5 variables* ready to reproduce the DustNet model results or for similar forecasting with Machine Learning. These long-term daily timeseries (2003-2022) are provided as n-dimensional NumPy arrays. The Python code to handle the data and run the DustNet model** is included as Jupyter Notebook ‘DustNet_model_code.ipynb’. A subfolder with normalised and split data into training/validation/testing sets is also provided with Python code for two additional ML based models** used for comparison (U-NET and Conv2D). Pre-trained models are also archived here as TensorFlow files. 

Model output data and code

This dataset was constructed by running the ‘DustNet_model_code.ipynb’ (see above). It consists of 1095 days of forecased AOD data (2020-2022) by CAMS, DustNet model, naïve prediction (persistence) and gridded climatology. The ground truth raw AOD data form MODIS is provided for comparison and statystical analysis of predictions. It is intended for a quick reproduction of figures and statystical analysis presented in DustNet introducing paper. 

 

*datasets are NumPy arrays (v1.23) created in Python v3.8.18.

**all ML models were created with Keras in Python v3.10.10.
DOI: 10.15151/esrf-es-1442317543
title: Mapping the Rare Earth Magnetization profile in designed compositionally-graded GdCoFe ferrimagnetic alloy films for spintronic applicatio

description: This experiment aims to map the local variations of Gd magnetization profile through the thickness of GdFeCo ferrimagnetic films with a deliberately designed Gd variation through the film thickness. Hard X-ray resonant dichroism and magnetic reflectivity (XRMR) at the Gd L3 edge (7.24 keV) will be used to map the Gd magnetization profile through the film thickness. Temperature dependent XRMR measurements through compensation, combined with SIMS and SQUID magnetometry, will enable us to understand the evolution of the global and local compensation behaviour. This approach offers a comprehensive understanding of magnetization behaviour arising from compositional non-uniformities and their influence on the evolution of ferrimagnetic compensation in RE:TM alloys for spintronics
DOI: 10.6084/m9.figshare.23522276
title: Supplementary information for 'Intelligent Decision Method for Stability Assessment of Shield Tunnel Based on Multi-objective Data Mining' from Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining

description: Due to the improper operation of the shield construction process and unknown geological survey, shield construction faces many risks in passing through the complex strata, among which the excavation face instability is the most serious disaster accident. To solve these issues, this research focused on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, the analytical formula of limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is given to assess the excavation face stability quantitatively. Then the rough set algorithm was used to analyze the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The BP neural network was used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10<sup>−4</sup> was established. The prediction performance of BP was verified by comparing the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability.This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
DOI: 10.6084/m9.figshare.23522276.v1
title: Supplementary information for 'Intelligent Decision Method for Stability Assessment of Shield Tunnel Based on Multi-objective Data Mining' from Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining

description: Due to the improper operation of the shield construction process and unknown geological survey, shield construction faces many risks in passing through the complex strata, among which the excavation face instability is the most serious disaster accident. To solve these issues, this research focused on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, the analytical formula of limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is given to assess the excavation face stability quantitatively. Then the rough set algorithm was used to analyze the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The BP neural network was used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10<sup>−4</sup> was established. The prediction performance of BP was verified by comparing the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability.This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
DOI: 10.6084/m9.figshare.11987946
title: Seasonal Variation of Lipid Profile of Oyster <i>Crassostrea talienwhanensis</i> from the Yellow Sea Area

description: Lipid profile of the oyster <i>Crassostrea talienwhanensis</i> from the Yellow Sea was evaluated based on the seasonal changes in lipid categories, phospholipid (PL) categories, fatty acid (FA) composition, atherogenicity index (IA), thrombogenicity index (IT), and glycerophospholipid (GP) molecular species. We observed that the samples harvested in February contained higher levels of polyunsaturated fatty acid (PUFA) (48.77% of total FAs) and PL (52.12% of total lipids) and had lower IA (0.29). In addition, more than 290 GP molecular species of glycerophosphocholine (GPCho), glycerophosphoethanolamine (GPEtn), glycerophosphoserine (GPSer), and glycerophosphoinositol (GPIns) were determined. The molecular species containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) constituted 75.24% of total GPCho in the samples harvested in February. Considering the lipid profile, oyster <i>Crassostrea talienwhanensis</i> harvested in February had better nutritional value.
DOI: 10.6084/m9.figshare.11987946.v1
title: Seasonal Variation of Lipid Profile of Oyster <i>Crassostrea talienwhanensis</i> from the Yellow Sea Area

description: Lipid profile of the oyster <i>Crassostrea talienwhanensis</i> from the Yellow Sea was evaluated based on the seasonal changes in lipid categories, phospholipid (PL) categories, fatty acid (FA) composition, atherogenicity index (IA), thrombogenicity index (IT), and glycerophospholipid (GP) molecular species. We observed that the samples harvested in February contained higher levels of polyunsaturated fatty acid (PUFA) (48.77% of total FAs) and PL (52.12% of total lipids) and had lower IA (0.29). In addition, more than 290 GP molecular species of glycerophosphocholine (GPCho), glycerophosphoethanolamine (GPEtn), glycerophosphoserine (GPSer), and glycerophosphoinositol (GPIns) were determined. The molecular species containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) constituted 75.24% of total GPCho in the samples harvested in February. Considering the lipid profile, oyster <i>Crassostrea talienwhanensis</i> harvested in February had better nutritional value.
DOI: 10.57745/soolmd
title: Practice_Abstract_AUA.pdf

description: :unav
DOI: 10.5281/zenodo.10058983
title: HYADES

description: arcHive of Yearly mAxima of Daily prEcipitation recordS
DOI: 10.5281/zenodo.10058984
title: HYADES

description: arcHive of Yearly mAxima of Daily prEcipitation recordS
DOI: 10.5281/zenodo.10723025
title: HYADES

description: arcHive of Yearly mAxima of Daily prEcipitation recordS
DOI: 10.6084/m9.figshare.11988057
title: Era of molecular diagnosis for pathogen identification of unexplained pneumonia, lessons to be learned

description: Unexplained pneumonia (UP) caused by a novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) emerged in China in late December 2019 and has infected more than 9000 cases by 31 January 2020. Shanghai reported the first imported case of COVID-19 (Coronavirus Disease 2019) in 20 January 2020. A combinative approach of real-time RT–PCR, CRISPR-based assay and metagenomic next-generation sequencing (mNGS) were used to diagnose this unexplained pneumonia patient. Real-time RT–PCR and CRISPR-based assay both reported positive. This sample belonged to Betacoronavirus and shared a more than 99% nucleotide (nt) identity with the Wuhan SARS-CoV-2 isolates. We further compared pros and cons of common molecular diagnostics in UP. In this study, we illustrated the importance of combining molecular diagnostics to rule out common pathogens and performed mNGS to obtain unbiased potential pathogen result for the diagnosis of UP.
DOI: 10.6084/m9.figshare.11988057.v1
title: Era of molecular diagnosis for pathogen identification of unexplained pneumonia, lessons to be learned

description: Unexplained pneumonia (UP) caused by a novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) emerged in China in late December 2019 and has infected more than 9000 cases by 31 January 2020. Shanghai reported the first imported case of COVID-19 (Coronavirus Disease 2019) in 20 January 2020. A combinative approach of real-time RT–PCR, CRISPR-based assay and metagenomic next-generation sequencing (mNGS) were used to diagnose this unexplained pneumonia patient. Real-time RT–PCR and CRISPR-based assay both reported positive. This sample belonged to Betacoronavirus and shared a more than 99% nucleotide (nt) identity with the Wuhan SARS-CoV-2 isolates. We further compared pros and cons of common molecular diagnostics in UP. In this study, we illustrated the importance of combining molecular diagnostics to rule out common pathogens and performed mNGS to obtain unbiased potential pathogen result for the diagnosis of UP.
DOI: 10.6084/m9.figshare.15133900
title: <i>In silico</i> analysis revealing CsrA roles in motility-sessility switching and tuning VBNC cells in <i>Vibrio parahaemolyticus</i>

description: The formation of biofilms is a survival strategy employed by bacteria to help protect them from changing or unfavourable environments. In this research, 319 genes which govern biofilm formation in <i>V. parahaemolyticus,</i> as reported in 1,625 publications, were analysed using protein-protein-interaction (PPI) network analysis. CsrA was identified as a motility-sessility switch and biofilm formation regulator. Through robust rank aggregation (RRA) analysis of GSE65340, the generation of viable but non-culturable (VBNC) cells that may enhance cell tolerance to stress, was found to be associated with the TCA cycle and carbon metabolism biological pathways. The finding that CsrA is likely to play a role in the development of VBNC cells improves understanding of the molecular mechanisms of VBNC formation in <i>V. parahaemolyticus</i> and contributes to on-going efforts to reduce the hazard posed by this foodborne pathogen.
DOI: 10.6084/m9.figshare.15133900.v1
title: <i>In silico</i> analysis revealing CsrA roles in motility-sessility switching and tuning VBNC cells in <i>Vibrio parahaemolyticus</i>

description: The formation of biofilms is a survival strategy employed by bacteria to help protect them from changing or unfavourable environments. In this research, 319 genes which govern biofilm formation in <i>V. parahaemolyticus,</i> as reported in 1,625 publications, were analysed using protein-protein-interaction (PPI) network analysis. CsrA was identified as a motility-sessility switch and biofilm formation regulator. Through robust rank aggregation (RRA) analysis of GSE65340, the generation of viable but non-culturable (VBNC) cells that may enhance cell tolerance to stress, was found to be associated with the TCA cycle and carbon metabolism biological pathways. The finding that CsrA is likely to play a role in the development of VBNC cells improves understanding of the molecular mechanisms of VBNC formation in <i>V. parahaemolyticus</i> and contributes to on-going efforts to reduce the hazard posed by this foodborne pathogen.
DOI: 10.6084/m9.figshare.19852479
title: Additional file 1 of Genome-wide characterization and identification of candidate ERF genes involved in various abiotic stress responses in sesame (Sesamum indicum L.)

description: Additional file 1: Table S1. Detailed information of Sesamun indicum ERF (SiERF) genes; Table S2. Sequence Characteristics of SiERF genes; Table S3. Number of each cis-acting element in the promoter region of SiERF genes; Table S4. Information related to the cis-acting elements identified in the SiERF genes; Table S5. List of primers used for the qRT-PCR analysis.
DOI: 10.6084/m9.figshare.19852479.v1
title: Additional file 1 of Genome-wide characterization and identification of candidate ERF genes involved in various abiotic stress responses in sesame (Sesamum indicum L.)

description: Additional file 1: Table S1. Detailed information of Sesamun indicum ERF (SiERF) genes; Table S2. Sequence Characteristics of SiERF genes; Table S3. Number of each cis-acting element in the promoter region of SiERF genes; Table S4. Information related to the cis-acting elements identified in the SiERF genes; Table S5. List of primers used for the qRT-PCR analysis.
DOI: 10.6084/m9.figshare.15133927
title: Dual inhibition of EGFR and IL-6-STAT3 signalling by miR-146b: a potential targeted therapy for epithelial ovarian cancer

description: Epidermal growth factor receptor (EGFR) signalling and the interleukin-6 (IL-6)/signal transducer and activator of transcription 3 (STAT3) are aberrantly activated in ovarian cancer. However, inhibition of EGFR signalling in ovarian cancer patients resulted in a disappointing clinical benefit. In this study, we found that EGFR could activate IL-6-STAT3 pathway in ovarian cancer cells. However, we also demonstrated that EGFR knockdown could increase STAT3 phosphorylation in HO8910 and OVCAR-3 ovarian cancer cells. Interestingly, we further demonstrated that the non-coding RNA miR-146b could simultaneously block both the EGFR and IL-6-STAT3 pathways. Finally, our data demonstrated that miR-146b overexpression resulted in a greater suppression of cell migration than STAT3 pathway inhibition alone.These results suggest a complex and heterogeneous role of EGFR in ovarian cancer. Combined blockade of EGFR and IL-6-STAT3 pathways by miR-146b might be a strategy for improving the clinical benefit of targeting the EGFR pathway in ovarian cancer patients in the future.
DOI: 10.6084/m9.figshare.15133927.v1
title: Dual inhibition of EGFR and IL-6-STAT3 signalling by miR-146b: a potential targeted therapy for epithelial ovarian cancer

description: Epidermal growth factor receptor (EGFR) signalling and the interleukin-6 (IL-6)/signal transducer and activator of transcription 3 (STAT3) are aberrantly activated in ovarian cancer. However, inhibition of EGFR signalling in ovarian cancer patients resulted in a disappointing clinical benefit. In this study, we found that EGFR could activate IL-6-STAT3 pathway in ovarian cancer cells. However, we also demonstrated that EGFR knockdown could increase STAT3 phosphorylation in HO8910 and OVCAR-3 ovarian cancer cells. Interestingly, we further demonstrated that the non-coding RNA miR-146b could simultaneously block both the EGFR and IL-6-STAT3 pathways. Finally, our data demonstrated that miR-146b overexpression resulted in a greater suppression of cell migration than STAT3 pathway inhibition alone.These results suggest a complex and heterogeneous role of EGFR in ovarian cancer. Combined blockade of EGFR and IL-6-STAT3 pathways by miR-146b might be a strategy for improving the clinical benefit of targeting the EGFR pathway in ovarian cancer patients in the future.