cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23922381.v1
title: Supplementary Material for: Childhood-onset Refractory Hypertension Results from Neurofibromatosis type 1 Caused by a Splicing NF1 mutation

description: Background: Neurofibromatosis type 1 (NF-1) is caused by mutations in the NF1 gene that encodes neurofibromin, a negative regulator of RAS proto-oncogene. Approximately one-third of the reported pathogenic mutations in NF1 are splicing mutations, but most consequences are unclear. The objective of this study was to identify the pathogenicity of splicing mutation in a Chinese family with NF-1 and determine the effects of the pre-mRNA splicing mutation by in vitro functional analysis. Methods: Next-generation sequencing was used to screen candidate mutations. We performed a minigene splicing assay to determine the effect of the splicing mutation on NF1 expression and three-dimensional structure models of neurofibromin were generated using SWISS-MODEL and PROCHECK method, respectively. Results: A pathogenic splicing mutation c.479+1G&gt;C in NF1 was found in the proband characterized by childhood-onset refractory hypertension. In vitro analysis demonstrated that c.479+1G&gt;C mutation caused skipping of exon 4, leading to a Glutamine to Valine substitution at position 97 in neurofibromin and an open reading frame shift terminating at codon 108. Protein modelling showed that several major domains were missing in the truncated neurofibromin protein. Conclusion: The splicing mutation c.479+1G&gt;C identified in a Chinese patient with NF-1 and childhood-onset refractory hypertension caused skipping of exon 4 and a truncated protein. Our findings offered new evidence for the molecular diagnosis of NF-1.
DOI: 10.17909/gdm2-0q65
title: JWST ERS NIRSpec G395H data of WASP-39b

description: This data is part of ERS-1366 from the JWST Transiting Exoplanet Community observations of the transiting hot Jupiter WASP-39b with the NIRSpec G395H instrument mode
DOI: 10.5281/zenodo.7385880
title: Dataset for "Three-dimensional modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm", Marshalko et al. (2023), Space Weather

description: Results of 3-D modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm in 2003 (29-31 October). Electric_field_YYYYMMDDHHMMSS_YYYYMMDDHHMMSS.h5 files (in hdf5 format) contain horizontal electric field components Ex and Ey (in mV/km) corresponding to 6 h of data and latitude and longitude grids corresponding to Ex and Ey arrays. Ex and Ey are 2160x567x543 arrays (temporal resolution is 10 s, thus, 2160 time steps). Latitude and Longitude are 567x543 arrays. All values are in single-precision floating-point format. Electric field values were obtained with the use of the conductivity-based inducing source following Marshalko et al. (2023). Files Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat contain the ground electric field time series (in mV/km) modeled during the Halloween geomagnetic storm in 2003 (29-31 October) at IMAGE magnetometers' locations, Mäntsälä Finnish natural gas pipeline GIC recording point (MAN), and Point X located 0.5 degrees north of MAN. The files are in plain-text (column-based) format. Electric field values in Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat were obtained with the use of the conductivity-based inducing source, MT intersite impedance method, and Spherical Elementary Current Systems (SECS) based approach, correspondingly, following Marshalko et al. (2023).
DOI: 10.5281/zenodo.7385879
title: Dataset for "Three-dimensional modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm", Marshalko et al. (2023), Space Weather

description: Results of 3-D modeling of the ground electric field in Fennoscandia during the Halloween geomagnetic storm in 2003 (29-31 October). Electric_field_YYYYMMDDHHMMSS_YYYYMMDDHHMMSS.h5 files (in hdf5 format) contain horizontal electric field components Ex and Ey (in mV/km) corresponding to 6 h of data and latitude and longitude grids corresponding to Ex and Ey arrays. Ex and Ey are 2160x567x543 arrays (temporal resolution is 10 s, thus, 2160 time steps). Latitude and Longitude are 567x543 arrays. All values are in single-precision floating-point format. Electric field values were obtained with the use of the conductivity-based inducing source following Marshalko et al. (2023). Files Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat contain the ground electric field time series (in mV/km) modeled during the Halloween geomagnetic storm in 2003 (29-31 October) at IMAGE magnetometers' locations, Mäntsälä Finnish natural gas pipeline GIC recording point (MAN), and Point X located 0.5 degrees north of MAN. The files are in plain-text (column-based) format. Electric field values in Electric_field_CB_20031029000000_20031031235950.dat, Electric_field_MT_20031029000000_20031031235950.dat, and Electric_field_SECS_20031029000000_20031031235950.dat were obtained with the use of the conductivity-based inducing source, MT intersite impedance method, and Spherical Elementary Current Systems (SECS) based approach, correspondingly, following Marshalko et al. (2023).
DOI: 10.17617/4.dg
title: Passively Stabilized Phase-Resolved Collinear SFG Spectroscopy Using a Displaced Sagnac Interferometer

description: Lukas, M., Backus, E. H. G., Bonn, M., & Grechko, M. (2022). Passively Stabilized Phase-Resolved Collinear SFG Spectroscopy Using a Displaced Sagnac Interferometer. The Journal of Physical Chemistry A, 126(6), 951-956. doi:10.1021/acs.jpca.1c10155.
DOI: 10.6084/m9.figshare.23866962.v38
title: Untitled Item

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset: 7 CMIP6 GCMs (1850-2014, ACCESS-CM2 to GFDL-ESM4)Future dataset: 7 CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.5281/zenodo.8226797
title: Test-time training for deep MS/MS spectrum prediction improves peptide identification

description: In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, with the similarity scores of predicted and experimental spectra being integrated into peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard datasets and uses the model for prediction on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To address this issue, we proposed a Test-Time Training paradigm that adapts the pre-trained model to experimental data-specific models, namely PepT3. PepT3 results in a 10-40\% increase in peptide identification, depending on the distinctness of training and experimental data. Intriguingly, PepT3 improves the identification of tumor-specific neo-epitopes when applied to a complex patient-derived immunopeptidomic sample, with two-thirds of these neo-epitopes predicted to bind to the patient's human leukocyte antigen isoforms
DOI: 10.5281/zenodo.8226798
title: Test-time training for deep MS/MS spectrum prediction improves peptide identification

description: In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, with the similarity scores of predicted and experimental spectra being integrated into peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard datasets and uses the model for prediction on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To address this issue, we proposed a Test-Time Training paradigm that adapts the pre-trained model to experimental data-specific models, namely PepT3. PepT3 results in a 10-40\% increase in peptide identification, depending on the distinctness of training and experimental data. Intriguingly, PepT3 improves the identification of tumor-specific neo-epitopes when applied to a complex patient-derived immunopeptidomic sample, with two-thirds of these neo-epitopes predicted to bind to the patient's human leukocyte antigen isoforms
DOI: 10.5281/zenodo.8231084
title: Test-time training for deep MS/MS spectrum prediction improves peptide identification

description: In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, with the similarity scores of predicted and experimental spectra being integrated into peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard datasets and uses the model for prediction on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To address this issue, we proposed a Test-Time Training paradigm that adapts the pre-trained model to experimental data-specific models, namely PepT3. PepT3 results in a 10-40\% increase in peptide identification, depending on the distinctness of training and experimental data. Intriguingly, PepT3 improves the identification of tumor-specific neo-epitopes when applied to a complex patient-derived immunopeptidomic sample, with two-thirds of these neo-epitopes predicted to bind to the patient's human leukocyte antigen isoforms
DOI: 10.5517/ccdc.csd.cc2fct1p
title: CCDC 2245672: Experimental Crystal Structure Determination

description: Related Article: Tong Xiao, Dong-Dong Yang, Yong-Sheng Shi, Han-Wen Zheng, Zhong-Gang Xia, Xiang-Jun Zheng|2023|Cryst.Growth Des.|23|5957|doi:10.1021/acs.cgd.3c00536
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: 10.5517/ccdc.csd.cc2fct2q
title: CCDC 2245673: Experimental Crystal Structure Determination

description: Related Article: Tong Xiao, Dong-Dong Yang, Yong-Sheng Shi, Han-Wen Zheng, Zhong-Gang Xia, Xiang-Jun Zheng|2023|Cryst.Growth Des.|23|5957|doi:10.1021/acs.cgd.3c00536
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.23910102
title: <b>Table 1</b>. Populations’ General Characteristics and Its Chi Square Analysis with MoCA-Ina, <b>Table 2</b>. Sensitivity, Specificity, and Youden’s index value from olfactory function test score, <b>Table 3</b>. Crosstabulation of olfactory function test with optimal cut off value with MoCA-Ina categories, <b>Table 4</b>. Diagnostic values of olfactory function test in several studies - Supplemental material for Olfactory Test as Early Diagnosis of Cognitive Impairment of Older Adults

description: Supplemental material, <b>Table 1</b>. Populations’ General Characteristics and Its Chi Square Analysis with MoCA-Ina, <b>Table 2</b>. Sensitivity, Specificity, and Youden’s index value from olfactory function test score, <b>Table 3</b>. Crosstabulation of olfactory function test with optimal cut off value with MoCA-Ina categories, <b>Table 4</b>. Diagnostic values of olfactory function test in several studies, for <b>Olfactory Test as Early Diagnosis of Cognitive Impairment of Older Adults</b> by Prof. Dr. Yvonne Suzy Handajani, MPH<sup> 1</sup>*, Prof. Dr. dr. Yuda Turana Sp.N(K) <sup>1</sup>, Antoninus Hengky, MD<sup>2</sup>, Kevin Kristian, MD<sup>1</sup>
DOI: 10.6084/m9.figshare.23910102.v1
title: <b>Table 1</b>. Populations’ General Characteristics and Its Chi Square Analysis with MoCA-Ina, <b>Table 2</b>. Sensitivity, Specificity, and Youden’s index value from olfactory function test score, <b>Table 3</b>. Crosstabulation of olfactory function test with optimal cut off value with MoCA-Ina categories, <b>Table 4</b>. Diagnostic values of olfactory function test in several studies - Supplemental material for Olfactory Test as Early Diagnosis of Cognitive Impairment of Older Adults

description: Supplemental material, <b>Table 1</b>. Populations’ General Characteristics and Its Chi Square Analysis with MoCA-Ina, <b>Table 2</b>. Sensitivity, Specificity, and Youden’s index value from olfactory function test score, <b>Table 3</b>. Crosstabulation of olfactory function test with optimal cut off value with MoCA-Ina categories, <b>Table 4</b>. Diagnostic values of olfactory function test in several studies, for <b>Olfactory Test as Early Diagnosis of Cognitive Impairment of Older Adults</b> by Prof. Dr. Yvonne Suzy Handajani, MPH<sup> 1</sup>*, Prof. Dr. dr. Yuda Turana Sp.N(K) <sup>1</sup>, Antoninus Hengky, MD<sup>2</sup>, Kevin Kristian, MD<sup>1</sup>
DOI: 10.17617/4.dh
title: Toward Understanding Bacterial Ice Nucleation

description: Lukas, M., Schwidetzky, R., Eufemio, R. J., Bonn, M., & Meister, K. (2022). Toward Understanding Bacterial Ice Nucleation. The Journal of Physical Chemistry B, 126(9), 1861-1867. doi:10.1021/acs.jpcb.1c09342.
DOI: 10.5281/zenodo.4681359
title: Dataset Comprehensive volatilome and metabolome signatures of colorectal cancer: A systematic review and meta-analysis

description: Tables summarizing the data used for the systematic review and meta-analysis. Up to 17 tables, and a list of the included studies is provided.
description: This research was supported by the Spanish MINECO coordinated project TensorChrom (Total2DChrom, ref. RTI2018-098577-B-C21 and TENSOMICS RTI2018-098577-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 798038. Catalan AGAUR project (2018LLAV00072). MLL is thankful for her graduate fellowship from the URV PMF-PIPF program (ref. 2019PMF-PIPF-37). We would like to acknowledge the Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (expedient 2017 SGR 1721 and 2017 SGR 1119); the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; and the European Social Fund (ESF). Additional financial support was provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC and IISPV are members of the CERCA Programme/Generalitat de Catalunya. CM is thankful for her pre-doctoral fellowship from the UB APIF program (ref. APIF_18_19).
DOI: 10.5281/zenodo.4681360
title: Dataset Comprehensive volatilome and metabolome signatures of colorectal cancer: A systematic review and meta-analysis

description: Tables summarizing the data used for the systematic review and meta-analysis. Up to 17 tables, and a list of the included studies is provided.
description: This research was supported by the Spanish MINECO coordinated project TensorChrom (Total2DChrom, ref. RTI2018-098577-B-C21 and TENSOMICS RTI2018-098577-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 798038. Catalan AGAUR project (2018LLAV00072). MLL is thankful for her graduate fellowship from the URV PMF-PIPF program (ref. 2019PMF-PIPF-37). We would like to acknowledge the Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (expedient 2017 SGR 1721 and 2017 SGR 1119); the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; and the European Social Fund (ESF). Additional financial support was provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC and IISPV are members of the CERCA Programme/Generalitat de Catalunya. CM is thankful for her pre-doctoral fellowship from the UB APIF program (ref. APIF_18_19).
DOI: 10.5281/zenodo.8195894
title: oral insulin nanoparticle based on PLGA and PEG

description: the project contain: - data sets - Ethical approval - The ARRIVE guidelines 2.0: author checklist - figures 1-3 - Flow chart
DOI: 10.5281/zenodo.8195893
title: oral insulin nanoparticle based on PLGA and PEG

description: the project contain: - data sets - Ethical approval - The ARRIVE guidelines 2.0: author checklist - figures 1-3 - Flow chart
DOI: 10.5519/qd.4c3go149
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6 records)
DOI: 10.6084/m9.figshare.23922108
title: <b>Genomic analysis reveals complex population structure within the smooth newt, </b><i><b>Lissotriton vulgaris</b></i><b> in Central Europe</b>

description: .vcf file with filtered SNP data used in the paper Genomic analysis reveals complex population structure within the smooth newt, <i>Lissotriton vulgaris</i> in Central Europe.
DOI: 10.6084/m9.figshare.23925018
title: Capurso, Carroll, Stewart 2023

description: Questionnaire and literature review raw data for Capurso, Carroll, Stewart 2023
DOI: 10.6084/m9.figshare.23925018.v1
title: Capurso, Carroll, Stewart 2023

description: Questionnaire and literature review raw data for Capurso, Carroll, Stewart 2023
DOI: 10.5258/soton/d1943
title: Development of childhood asthma prediction models using machine learning and data integration
DOI: 10.4121/d2e0953c-3ac6-4547-9fb4-8ceaa0ede4d7
title: Supporting data for the article "3D geomechanical modeling of induced seismic slips considering realistic reservoir geometry with intersecting faults"

description: The dataset includes the code and data used for generating the figures in the article. The simulation results are stored in hdf5 files. Python codes are used to plot the simulation data.
DOI: 10.4121/d2e0953c-3ac6-4547-9fb4-8ceaa0ede4d7.v1
title: Supporting data for the article "3D geomechanical modeling of induced seismic slips considering realistic reservoir geometry with intersecting faults"

description: The dataset includes the code and data used for generating the figures in the article. The simulation results are stored in hdf5 files. Python codes are used to plot the simulation data.
DOI: 10.34740/kaggle/dsv/6281383
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.23925015
title: <b>Table 1. </b>Clerkship Student’s Characteristics, <b>Table 2. </b>Comparative Results of QOL and its Domains According to The Clinical Rotation Department, <b>Table 3. </b>Multiple Linear Regression Analysis on Factors Affecting QOL- Supplemental materials for <i>Factors on Clerkship Students’ Quality of Life</i>

description: Supplemental material, <b>Table 1. </b>Clerkship Student’s Characteristics, <b>Table 2. </b>Comparative Results of QOL and its Domains According to The Clinical Rotation Department, <b>Table 3. </b>Multiple Linear Regression Analysis on Factors Affecting QOL, for <i>Factors on Clerkship Students’ Quality of Life</i> by Yunisa Astiarani<sup>1</sup>, Kevin Kristian<sup>1</sup>, Anthony Ekaputra,<sup>1</sup>, Nicholas Hardi<sup>2</sup>
DOI: 10.6084/m9.figshare.23925015.v1
title: <b>Table 1. </b>Clerkship Student’s Characteristics, <b>Table 2. </b>Comparative Results of QOL and its Domains According to The Clinical Rotation Department, <b>Table 3. </b>Multiple Linear Regression Analysis on Factors Affecting QOL- Supplemental materials for <i>Factors on Clerkship Students’ Quality of Life</i>

description: Supplemental material, <b>Table 1. </b>Clerkship Student’s Characteristics, <b>Table 2. </b>Comparative Results of QOL and its Domains According to The Clinical Rotation Department, <b>Table 3. </b>Multiple Linear Regression Analysis on Factors Affecting QOL, for <i>Factors on Clerkship Students’ Quality of Life</i> by Yunisa Astiarani<sup>1</sup>, Kevin Kristian<sup>1</sup>, Anthony Ekaputra,<sup>1</sup>, Nicholas Hardi<sup>2</sup>
DOI: 10.6084/m9.figshare.23925039
title: 20230810_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23925039.v1
title: 20230810_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23866962.v39
title: Untitled Item

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset: 7 CMIP6 GCMs (1850-2014, ACCESS-CM2 to GFDL-ESM4)Future dataset: 7 CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.6084/m9.figshare.20789203.v2
title: Data used in the article <em>Different methods in assessing El Niño flavors lead to opposite results.</em>

description: Data used in the article <em>Different methods in assessing El Niño flavors lead to opposite results.</em>
DOI: 10.20375/0000-0010-05c0-f
title: Alexei_Kouprianov.2023.An_extended_description_of_the_repository.pdf
DOI: 10.20375/0000-0010-05c1-e
title: Daily_data.2020-01-31_2023-05-15.csv
DOI: 10.20375/0000-0010-05c2-d
title: Daily_data.2020-01-31_2023-05-15.json
DOI: 10.20375/0000-0010-05c3-c
title: Daily_data.2020-01-31_2023-05-15.xlsx
DOI: 10.20375/0000-0010-05c4-b
title: Data_scripts_plots.zip
DOI: 10.48320/9fd27aca-7865-4e75-9c59-582a7cbb7a39
title: COVID-19 in the Russian Federation
DOI: 10.6084/m9.figshare.23766477.v1
title: Reader study data of dermatologists' diagnoses, confidence, and trust in an AI system on dermoscopic images of melanomas and nevi

description: The datasets encompass the outcomes of the reader studies as presented in the publication available at the following link: https://arxiv.org/abs/2303.12806.
DOI: 10.6084/m9.figshare.23766477.v2
title: Reader study data of dermatologists' diagnoses, confidence, and trust in an AI-based support system on dermoscopic images of melanomas and nevi

description: The datasets encompass the outcomes of the reader studies as presented in the publication available at the following link: https://arxiv.org/abs/2303.12806.
DOI: 10.6084/m9.figshare.23925072
title: Key_words_RG_and_UP.xlsx

description: List of keywords found with "R" in articles about the Russo-Ukrainian war published by Rossiyskaya Gazeta and Ukrayinska Pravda
DOI: 10.6084/m9.figshare.23766477.v3
title: Reader study data of dermatologists' diagnoses, confidence, and trust in an AI-based support system on dermoscopic images of melanomas and nevi

description: The datasets encompass the outcomes of the reader studies as presented in the publication available at the following link: https://arxiv.org/abs/2303.12806.