cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26805865
title: Supplementary Material for: Identification and validation of an invasion-related disease-free survival prognostic model for tongue squamous cell carcinoma

description: Abstract
Background:
Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients.
Methods:
We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using qRT‒PCR analysis.
Results:
A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer disease-free survival (DFS) than did those in the high-risk group. Furthermore, qRT‒PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line.
Conclusion:
This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.
Keywords: Invasion; Tongue squamous cell carcinoma; Disease-free survival prognostic risk model; Prognostic genes; qRT‒PCR
DOI: 10.1594/wdcc/cmip5.mxmrpc
title: CMIP5 simulations of the Max Planck Institute for Meteorology (MPI-M) based on the MPI-ESM-MR model: The piControl experiment, served by ESGF

description: Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: piControl is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 3.1 piControl (3.1 Pre-Industrial Control) - Version 1: Pre-Industrial coupled atmosphere/ocean control run. Imposes non-evolving pre-industrial conditions. Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .
DOI: 10.5281/zenodo.10908703
title: User participation in digital accessibility evaluations: reviewing methods and objectives

description: Although laws and standardization bodies promote user participation in digital accessibility evaluations, people with disabilities still consider themselves excluded from this process. One reason could be the lack of systematized knowledge about evaluation methods involving users. This article seeks to understand how and for what purpose digital accessibility evaluations with user participation were conducted in the scientific literature from 2018 to 2021. Three types of user participation emerged: 1) user-based usability testing to evaluate task accomplishment, user reactions and interface qualities; 2) interviewing users to assess the local and social factors impacting digital service accessibility; 3) using questionnaires or crowdsourcing to check the compliance of certain interfaces with accessibility standards. Participants are primarily chosen based on their functional impairments and, to a lesser degree, their project-related skills, biographical information, technology habits, among other criteria. The comprehensive user insights gained with these methods are judged to be positive whereas the lack of representativeness of the selected user samples is found to be regrettable. The article finally discusses the definitions of accessibility and disability that underpin these methodologies.
DOI: 10.5281/zenodo.11005458
title: Dataset of 'Radar Evidence of an Accessible Cave Conduit on the Moon below the Mare Tranquillitatis Pit'
DOI: 10.6084/m9.figshare.26334964
title: Data from: Genetic diversity of salmonid fishes in the eastern part of the Onega Peninsula, Northern European Russia.

description: <b>Supplementary material</b><b>Table S1. </b><i>COI </i>dataset of <i>Salmo salar</i> and <i>S. trutta</i> used for the TCS network, including information about species name, haplotype ID, specimen voucher, locality, GenBank accession number and references.<b>Table S2. </b><i>Cyt b</i> dataset of <i>Salmo salar</i> and <i>S. trutta</i> used for the TCS network, including information about species name, haplotype ID, specimen voucher, locality, GenBank accession number and references.<b>Table S3.</b> Evolutionary lineages of <i>Salmo trutta </i>used in this study, including information about each lineage, haplotype ID, specimen voucher, locality, GenBank accession number and references.
DOI: 10.17632/t64dbpv63x.3
title: Riparian buffer zone quantitative effectiveness review database

description: The quantitative effectiveness review of RBZ width yielded 79 studies. These were subdivided into 474 database rows, where studies generated multiple rows across differing experimental parameters of width, vegetation, rainfall intensity and slope.
DOI: 10.17638/datacat.liverpool.ac.uk/2669
title: Data for "Alkali metal cations enhance CO2 reduction by a Co molecular complex in a bipolar membrane electrolyzer" by Siritanaratkul et al.

description: Data in Figures of "Alkali metal cations enhance CO2 reduction by a Co molecular complex in a bipolar membrane electrolyzer"
DOI: 10.17909/gxm6-5x86
title: Data from Deepest limits on scattered light emission from the Epsilon Eridani inner debris disk with HST/STIS
DOI: 10.5281/zenodo.10600669
title: Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks-Dataset

description: The file contains environmental data for nine distinct sites, featuring measurements of Date, precipitation, water content, water potential, evapotranspiration, and an autoencoder value labeled "type." Additionally, it includes water content data derived from SMAP (Soil Moisture Active Passive) Level 3 and Level 4 products for the same nine sites.
DOI: 10.5281/zenodo.13684035
title: Understanding stakeholders' perspectives behind wildfires in Sicily: A Q-Methodology Approach
DOI: 10.5281/zenodo.6024064
title: ELF dielectric functions for various materials.

description: <strong>Data for the energy loss function (ELF) of selected materials compiled from various publications.</strong> The imaginary part of the inverse of the dielectric function, <em>Im</em>{−1/ε(<em>k</em>,hω)}, denoted the energy loss function (ELF), is the leading function that describes the material response to account for energy losses of swift electrons in matter. For this reason, it is a practical advantage to express the dielectric function in the form of a parametrized ELF, instead of the dielectric function itself, as input for the calculation of inelastic scattering cross sections. Here the ELF is expressed as a sum of Drude-Lindhard type oscillators (see e.g. Yubero F, Sanz JM, Ramskov B, Tougaard S. Model for quantitative analysis of reflection-electron-energy-loss spectra: Angular dependence. Phys Rev B. 1996;53:9719-9727) <strong>The ELF data describe the dielectric properties of materials.</strong><br> It may be applied to calculate energy loss processes of fast electrons moving in a material as well as in geometries met in REELS, XPS and AES where the effect of the surface and a static core hole are included (see e.g. QUEELS-software that can be downloaded at: doi: 10.5281/zenodo.6022426) <strong>The data structure is:</strong><br> For each material, a reference to the data for each material is given<br> After a line with @ , the ELF data follows in lines with the following meaning 1. Excitation parameters ac, bc to model core excitations (see Penn, J. Elec. Spectr., 9(1976)29).<br> When ac = 0.001 and bc = -2: no data was available. 2. Energy gap (eV) 3. Refractive Index in the next lines, are the parameters for the oscillators: one line for each oscillator 4. energy position (eV), intensity (eV^2), width (eV), alpha<br> 5.<br> 6.<br> .<br> . Examples of data files that can be directly read by QUEELS for Au and Si are seen in the files ELF_Au.txt and ELF_Si.txt respectively.
DOI: 10.6073/pasta/3ed3427f88733a8afd01dfd8767d0250
title: Analysis of aboveground biomass in longleaf pine forests in southeast United States, 2015 to 2019

description: I obtained inventory data from the USDA Forest Inventory and Analysis (FIA) program, covering 1999 to 2022, focusing on eight southeastern states in the U.S. (Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, and Louisiana). For this study, I filtered the data for 2015-2019, during which the FIA captures 20% of plots in each state annually. Using R, I converted aboveground biomass (AGB) from pounds to kg/ha, tree height from feet to meters, and diameter at breast height (DBH) from inches to centimeters to align with the metric system. Structural diversity of tree height and diameter was calculated using the Shannon diversity index based on tree height and diameter classes. Climate data from PRISM and soil carbon to nitrogen data from the World Soil Database were integrated with the FIA data using the nearest neighbors method in the SF package in R. The combined dataset was standardized for Structural Equation Modeling (SEM) analysis.
DOI: 10.6084/m9.figshare.24828399
title: Supplementary Material for: Performance of a diagnostic model for the presence of unruptured intracranial aneurysms in the general population

description: Introduction: The prevalence of unruptured intracranial aneurysms (UIAs) in the general population is 3%. Aneurysmal subarachnoid hemorrhage (aSAH) can be prevented by screening for UIAs followed by monitoring and, if needed, preventive neurosurgical or endovascular treatment of identified UIAs. Therefore, we developed a diagnostic model for presence of UIAs in the general population to help identify persons at high risk of having UIAs.
Methods: Between 2005-2015, participants from the population-based Rotterdam Study underwent brain magnetic resonance imaging at 1.5 Tesla, on which presence of incidental UIAs was evaluated. We developed a multivariable logistic regression model using candidate diagnostic markers that were selected based on the literature, including sex, age, hypertension, smoking, hypercholesterolemia, diabetes, alcohol, and their interactions. We corrected for overfitting using bootstrapping. Model performance was assessed with discrimination, calibration, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results: 5835 persons were included (55.0% women, mean age 64.9 ± 10.9 years) with a 2.2% UIA prevalence. Sex, age, hypertension, smoking, diabetes, and interactions of sex with age, hypertension, and smoking were independent diagnostic markers. The resulting model had a c-statistic of 0.65 (95% confidence interval [CI] 0.60 – 0.68) and 56% sensitivity, 52% specificity, 98% PPV, and 3% NPV for UIA presence at a cut-off value of 4%. Because of interactions with sex, additional models for men and women separately were developed. The model for men had a c-statistic of 0.70 (95% CI 0.62 – 0.78) with age, hypertension, and smoking as diagnostic markers and comparable additional performance values as for the full model. The model for women had a c-statistic of 0.58 (95% CI 0.52 – 0.63) with smoking as the only diagnostic marker.
Conclusion: Our diagnostic model had insufficient performance to help identify persons at high risk of having UIAs in the general population. Rather, it provides insight in risk factors contributing to UIA risk and shows that these may be in part sex-specific.
DOI: 10.6084/m9.figshare.26085898
title: Landward site raw data

description: Sampling was conducted at a monospecific Avicenna marina mangrove stand in Thuwal, Saudi Arabia, on the eastern coast of the Central Red Sea (22.340787°N, 39.087991°E). The sampling location was approximately 150 m inland from the coast with soil cores were collected over two years, from April 2021 to May 2023, on a monthly basis, except when this was prevented by logistical challenges. Two sets of cores were collected each month. The first set of cores comprised four large clear PVC cylinders inserted into the soil to a depth of 10 cm and retrieved without disturbing the soil layers. These were incubated for 24 hours, with measurements taken at 0 hours (control), 12 hours (under light conditions), and 24 hours (under dark conditions). CO<sub>2</sub>, CH<sub>4</sub>, δ<sup>13</sup>C-CO<sub>2</sub>, and δ<sup>13</sup>C-CH<sub>4</sub> was measured with G2201-i CRDS analyser (Picarro, Santa Clara, California USA). Fluxes were converted from ppm to μmol m<sup>-2</sup> d<sup>-1 </sup>by the equations shown the the raw datasheet. The second set of scores were collected immediately next to the large cores and used to assess the physical and chemical properties for the top 3 cm of soil, including conductivity, total carbon (TC), total organic carbon (C<sub>org</sub>), total nitrogen (TN), bulk density (BD), and water content (WC). Additional details of sample processing and analysis can be found in: J Breavington, A Steckbauer, C Fu, M Ennasri, and CM. Duarte. 'Dynamics of CO2 and CH4 fluxes in Red Sea mangrove soils'
DOI: 10.6084/m9.figshare.26085928
title: Landward and seaward fluxes and soil properties

description: Sampling was conducted at a two sites within a monospecific Avicenna marina mangrove stand in Thuwal, Saudi Arabia, on the eastern coast of the Central Red Sea (22.340787°N, 39.087991°E). The landward sampling location was approximately 150 m inland from the coast with soil cores were collected over two years, from April 2021 to May 2023, on a monthly basis, except when this was prevented by logistical challenges. The sampling site was either inundated with tidal waters (wet) and measured flux from the sea-air interface or without overlying water (dry) measuring flux from the soil-air interface. The seaward sampling location had cores collected in a narrower time window between September and October 2022, with weekly sampling for two consecutive weeks, followed by a two-week break and a further two weeks of sampling. All collection efforts were with tidal inundation (wet). Two sets of cores were collected during every sampling event. The first set of cores comprised four large clear PVC cylinders inserted into the soil to a depth of 10 cm and retrieved without disturbing the soil layers. These were incubated for 24 hours, with measurements taken at 0 hours (control), 12 hours (under light conditions), and 24 hours (under dark conditions). CO<sub>2</sub>, CH<sub>4</sub>, δ<sup>13</sup>C-CO<sub>2</sub>, and δ<sup>13</sup>C-CH<sub>4</sub> was measured with G2201-i CRDS analyser (Picarro, Santa Clara, California USA). Fluxes were converted from ppm to μmol m<sup>-2</sup> d<sup>-1 </sup>by the equations shown the the raw datasheet. The second set of scores were collected immediately next to the large cores and used to assess the physical and chemical properties for the top 3 cm of soil, including conductivity, total carbon (TC), total organic carbon (C<sub>org</sub>), total nitrogen (TN), bulk density (BD), and water content (WC).The dataset summarizes all variables, with the average, SD, SE, median, min and max values. Separate datasheets show variables across all sites, each site, and split the data into wet and dry sampling conditions. Additional details of sample processing and analysis can be found in: J Breavington, A Steckbauer, C Fu, M Ennasri, and CM. Duarte. 'Dynamics of CO2 and CH4 fluxes in Red Sea mangrove soils'.
DOI: 10.17603/ds2609n
title: University of Auckland High Axial Load Wall Tests

description: 4 large scale walls were tested at the University of Auckland Structural Testing Facility to assess effects of axial load and varied confinement detailing. This dataset summarizes the deformation, force and strain characteristics of four tested RC walls. Strain gauge, LVDT, portal gauge (displacement transducer), string potentiometer, inclinometer and load cell data is provided. Photos taken throughout the testing are also provided. All instrumentation data was collected autonomously in real-time throughout each test at a rate of 2-5Hz. This dataset is intended to serve as as a reference for future work, as well as an open resource to be used in future research.
DOI: 10.25592/uhhfdm.10047
title: GLips - German Lipreading Dataset

description: The German Lipreading dataset consists of 250,000 publicly available videos of the faces of speakers of the Hessian Parliament, which was processed for word-level lip reading using an automatic pipeline. The format is similar to that of the English language Lip Reading in the Wild (LRW) dataset, with each H264-compressed MPEG-4 video encoding one word of interest in a context of 1.16 seconds duration, which yields compatibility for studying transfer learning between both datasets. Choosing video material based on naturally spoken language in a natural environment ensures more robust results for real-world applications than artificially generated datasets with as little noise as possible. The 500 different spoken words ranging between 4-18 characters in length each have 500 instances and separate MPEG-4 audio- and text metadata-files, originating from 1018 parliamentary sessions. Additionally, the complete TextGrid files containing the segmentation information of those sessions are also included. The size of the uncompressed dataset is 16GB.
description: Copyright of original data: Hessian Parliament (https://hessischer-landtag.de). If you use this dataset, you agree to use it for research purpose only and to cite the following reference in any works that make any use of the dataset. Reference: Gerald Schwiebert, Cornelius Weber, Leyuan Qu, Henrique Siqueira, Stefan Wermter (2022). A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning. arXiv:2202.13403
description: {"references": ["Gerald Schwiebert, Cornelius Weber, Leyuan Qu, Henrique Siqueira, Stefan Wermter (2022). A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning", "arXiv:2202.13403"]}
DOI: 10.25739/03nt-4f29
title: Rohit_GlobalSurfaceWaterDataset_2024

description: This hand-labeled global surface water validation dataset generated by Social[Pixel] lab is a high-resolution dataset labeled based on 3m PlanetScope data. It contains manual labels based on PlanetScope data and a modified 8-bit version of PlanetScope data. It includes 100 scenes of globally sampled surface water samples collected between 2022-2023. It was created in March of 2024.
DOI: 10.48480/95ct-gn40
title: Dashboard biogene Rohstoffe in Deutschland
DOI: 10.5281/zenodo.10052205
title: Dual spectral reconstruction for nm-resolution microspectrometers
DOI: 10.5281/zenodo.10455822
title: Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances

description: Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.

See publication and ReadMe file for analysis description and further details.
DOI: 10.5281/zenodo.4421500
title: EMG-EPN-612 Dataset

description: EMG-EPN-612 Dataset This dataset, called EMG-EPN-612, contains EMG signals of 612 people for benchmarking of hand gesture recognition systems. This dataset has been created by the Artificial Intelligence and Computer Vision Research Lab from the Escuela Politécnica Nacional, Quito-Ecuador. The data was obtained by recording, with the Myo armband, EMG signals on the forearm while users were performing five hand gestures: wave-in, wave-out, pinch, open and fist. EMGs of the hand relaxed are also included. The dataset is divided into two groups of 306 people each. One group is for training or designing hand gesture recognition models and the other is intended for testing the classification and recognition accuracy of hand gesture recognition models. In each of these two groups, each person has 50 EMGs for each of the 5 gesture recorded and also 50 EMGs for the hand relaxed. More information about this dataset can be found at: https://laboratorio-ia.epn.edu.ec/en/resources/dataset/2020_emg_dataset_612
DOI: 10.5281/zenodo.7309045
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.