cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/dfhdfm8gmy
title: Broadly recognized, cross-reactive SARS-CoV-2 CD4 T cell epitopes are highly conserved across human coronaviruses and presented by common HLA alleles. Becerra-Artiles et al.

description: Sequence homology between SARS-CoV-2 and common-cold human coronaviruses (HCoVs) raises the possibility that memory T cells elicited to prior HCoV infection can impact response in COVID-19. We studied T cell cross-reactivity between SARS-CoV-2 and HCoVs by measuring responses to SARS-CoV-2 S protein in convalescent COVID-19 donors, mRNA COVID-19 vaccine recipients, and individuals not exposed to SARS-CoV-2. We expanded T cell lines from SARS-CoV-2 donors in vitro by stimulation with HCoV S peptides, which enriches for cross-reactive T cells. We found that SARS-CoV-2 spike sequence S811-831 dominated the cross-reactive response. S811-831 is highly conserved across SARS-CoV-2 variants, and human and animal coronaviruses. Most donors tested recognized S811-831, including severe and mild COVID-19 donors, individuals receiving mRNA vaccines, and previously unexposed donors, although most unexposed donors required in vitro expansion to detect these cross-reactive cells.
We defined minimal epitopes within S811-831 as well as the presenting HLA alleles. We identified two major reactivity patterns, with an N-terminal epitope RSFIEDLLF S815-823 presented by DQ5 (DQA1*01:01/DQB1*05:01), and a partially overlapping C-terminal epitope IEDLLFNKV S818-826 presented by DP2 (DPA1*01:03 DPB1*02:01) and DP4 (DPA1*01:03/DPB1*04:01). The broad recognition of these epitopes is driven in part by the prevalence of the presenting HLA alleles, particularly DP4, which is the most common allele worldwide.
T cells recognizing S811-831 were cross-reactive for HCoV homologous sequences. The key to this cross-reactive recognition seems to be the remarkable conservation across coronaviruses of amino acids at expected T cell contact positions for both the DQ5 and DP4 binding frames. The selection for DP4 and DQ5 as preferred presenting elements for cross-reactive recognition may be related to their binding motifs, which accommodate peptide sequence variability while still presenting identical residues for TCR recognition. 
T cells cross-reactive for both SARS-CoV-2 and HCoVs comprise a small fraction of the overall response in both pre-pandemic and COVID-19 donors. The responses were characterized by a highly diverse TCR repertoire, mainly specific to individual donors with a few public clonotypes. 
Table S1 shows the demographic, clinical, serological, and HLA typing of the donors used in the study. Table S2 shows a summary of immunological assays performed on T cell clones. Table S3 summarizes the MHC binding prediction on 3 candidate cross-reactive epitopes. Table S4 lists the selected coronaviruses used for the conservation analysis. Table S5 provides information on the TCR clonotypes identified in different samples and the analysis of sequence sharing among samples. Table S6 provides the results from the GLIPH algorithm applied to our data. Table S7 lists sequences of synthetic peptides used throughout the study.
DOI: 10.17632/r3tbsgtpzg
title: CT Volume Samples for Lung Adenocarcinoma Classification

description: Major content: This dataset consists of lung adenocarcinoma samples locally cropped from 3D CT images. Samples are discriminated by 3 pathological categories, respectively Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), and Minimally Invasive Adenocarcinoma (MIA). This dataset totally contains 21 samples of AAH, 444 samples of AIS, and 585 samples of MIA. Usage: Samples are stored with .npy files, as can be loaded in Python by the package of NumPy, and introduced in deep learning on the task of lung adenocarcinoma classification. Each sample is a 3D volume with 128*128*128 voxel resolution, where the lung adenocarcinoma is located at the center of the volume, and the volume can be cropped with any scale (e.g. 64*64*64) to fit the input size of 3D convolutional neural networks.
DOI: 10.6084/m9.figshare.24616491.v2
title: Inland water bodies of 2020 over Tibetan Plateau (WBTP)

description: This is a high-resolution (2m) map of water bodies on the Tibetan Plateau. The dataset is produced by visual transformer model (Swin-UNet) based on Gaofen-1 data with overall accuracy at 98%. In addition, Water area, abundance, size and landscape indices of WBs are included in the dataset.
DOI: 10.6084/m9.figshare.25633887.v1
title: DEAP Dataset Label Value Processing

description: DEAP dataset label value processing
DOI: 10.6084/m9.figshare.26952631.v3
title: Lee et al., 2024, <b>Social Status as a Latent Variable in the Amygdala of Observers of Social Interactions</b>, Neural Data

description: Neural dataset.
DOI: 10.6084/m9.figshare.28673351.v1
title: m5-ipx-ml380

description: This folder has the input files, topology file and pdb file for iperoxo-ML380 bound M5 mAChR mini-G complex
DOI: 10.6084/m9.figshare.28927928.v1
title: 第一组视频

description: data
DOI: 10.17632/34rpmsxc4z
title: Wastewater Treatment Plant Data for Nutrient Removal System

description: The dataset is collected from Denmark's Agtrup (BlueKolding) wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high-frequency Supervisory Control and Data Acquisition (SCADA) system data collection process, which captures a wide range of variables related to the operational dynamics of nutrient removal. It comprises time-series data featuring measurements sampled to a frequency of two minutes across various control, process, and environmental variables. The comprehensive dataset aims to foster significant advancements in wastewater management by supporting the development of sophisticated predictive models and optimizing operational strategies. By providing detailed insights into the interactions and efficiencies of chemical and biological phosphorus removal processes, the dataset serves as a vital resource for environmental researchers and engineers focused on improving the sustainability and effectiveness of wastewater treatment operations. The ultimate goal of this dataset is to facilitate the creation of digital twins and the application of machine learning techniques, such as deep reinforcement learning, to predict and enhance system performance under varying operational conditions.
DOI: 10.6084/m9.figshare.27020455.v7
title: <b>Assessment of computational methods in predicting TCR-epitope binding recognition</b>

description: T-cell receptors (TCRs) are critical for the immune system's ability to recognize specific epitopes. Accurate prediction of TCR-epitope interactions is fundamental for understanding and enhancing immune responses. Despite significant advances in computational methods for TCR-epitope binding prediction, a thorough evaluation of these tools remains lacking. Here, we assessed 50 state-of-the-art TCR-epitope prediction models using 21 datasets covering 762 epitopes and hundreds of thousands corresponding binding TCR sequences. Our analysis revealed that while the ratio of positive to negative samples subtly influences performance, the source of negative TCR samples significantly impacts model accuracy. External negative data may introduce uncontrolled confounders, compromising evaluation reliability. A positive correlation between the number of TCRs per epitope and model performance highlights the importance of large, diverse datasets. Multi-feature models generally outperform single CDR3β models, but generalization to unseen epitopes remains a challenge across all evaluated models. Using independently sourced test sets, which offer practical predictions for real-world applications, for both seen and unseen epitopes is crucial for objective performance assessment. These insights would guide the development of more accurate and robust computational tools for TCR-epitope interaction prediction, accelerating advancements in this evolving field.
DOI: 10.17632/jvs782kvw8
title: Health-related quality of life (EuroQol 5D-5L) in patients with autoimmunity in the context of immunotherapy: a large dataset comprising cancer patients after cessation of checkpoint inhibitor therapy and patients with autoimmune diseases

description: This dataset contains demographic, clinical, and health-related quality of life (HRQoL) data from 2905 patients including 200 cancer patients after immune checkpoint inhibitor (ICI) cessation and 2705 patients with a wide variety of autoimmune diseases.
DOI: 10.6084/m9.figshare.24830790.v1
title: English Super League Rugby (League) 1st Team Force Plate Data

description: This spreadsheet contains anonymised force plate data for the following tests: countermovement jump and isometric mid-thigh pull.Following a dynamic warm-up, three trials of each of these tests were performed bilaterally and with maximum-effort by male first team rugby league players from one English Super League Club at the beginning of pre-season training period (2021-22 and 2022-23 seasons). The tests were performed in a randomised order with around 30-60 seconds of rest between trials and at least 3-5 minutes of rest given between tests.The data were collected on Hawkin Dynamics force plates and analysed by their software. Here is the link to the Hawkin Dynamics metric database that explains how each metric included in the spreadsheet was measured: https://www.hawkindynamics.com/hawkin-metric-databaseEthics approval was granted from the author's institution and informed consent was provided by each player for their anonymised data to be uploaded to this repository for research use.
DOI: 10.6084/m9.figshare.28238927.v1
title: <b>Academic Performance in Online Learning</b>

description: The Open University Learning Analytics Dataset (OULAD) will be used for the performance evaluation of the suggested method utilizing f1-score, accuracy, precision, and recall metrics.
DOI: 10.17632/527r8g8rpp
title: ClassSTRONG: Classical simulations of strong field processes

description: Abstract A set of Mathematica functions is presented to model classically two of the most important processes in strong field physics, namely high-order harmonic generation (HHG) and above-threshold ionization (ATI). Our approach is based on the numerical solution of the Newton–Lorentz equation of an electron moving on an electric field and takes advantage of the symbolic languages features and graphical power of Mathematica. Like in the Strong Field Approximation (SFA), the effects of atomic potentia... Title of program: ClassSTRONG Catalogue Id: AEQV_v1_0 Nature of problem The Mathematica functions model high-order harmonic generation (HHG) and above-threshold ionization (ATI) using the classical equations of motion of an electron moving in an oscillating electric field. In Strong Field Physics HHG and ATI represent two of the most prominent examples of the nonperturbative interaction between strong laser sources and matter. In HHG an atomic or molecular bound electron is put into the continuum by the external laser electric field. Due to the oscillatory nature of ... Versions of this program held in the CPC repository in Mendeley Data AEQV_v1_0; ClassSTRONG; 10.1016/j.cpc.2013.09.009 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.6084/m9.figshare.16383600.v1
title: Average annual precipitation above the stations in the Yellow River Basin

description: The original data is the data interpolation product of China Meteorological Website: Surface precipitation monthly value grid data set in China.
DOI: 10.6084/m9.figshare.23796105.v2
title: DATASET - Impact of variable fault geometries and slip rates on earthquake catalogues from physics-based simulations of a normal fault

description: This dataset contains input and output files for the RSQSim earthquake simulations performed in this study: Delogkos, E., Howell, A., Seebeck, H., Shaw, B., Nicol, A., Liao, M. Y.-W., Walsh, J.J. (2023) <i>Impact of variable fault geometries and slip rates on earthquake catalogues from physics-based simulations of a normal fault</i>, Journal of Geophysical Research: Solid Earth, doi: https://doi.org/10.1029/2023JB026746.
DOI: 10.6084/m9.figshare.26089341.v1
title: HiGTS_2020s1

description: HiGTS is a high temporal resolution global gridded dataset of human thermal stress metrics. Derived using climate variables from ERA5-Land and ERA5, the HiGTS consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily Thermal Stress Duration (TSD) at 0.1° × 0.1° spatial resolution over the global land surface.HiGTS_2020s1 contains the data from January 1, 2020 to March 31, 2020.<br>
DOI: 10.17632/rzzwcwc4rs
title: Data for: In-vitro estrogenic activity of binary and multicomponent mixtures with bisphenol A

description: Bisphenol A and its analogs are well known endocrine disruptors with estrogenic and anti-androgenic activities. In studies of human biomonitoring, simultaneous exposure to multiple bisphenols was shown in different biological samples, at picomolar to low nanomolar concentrations. Evaluation of their combined toxicities will therefore be a more realistic and reliable predictor for estimation of health risks than evaluation of only the single chemicals. In the present study, estrogenic activities of individual bisphenols were evaluated, along with their binary and multicomponent mixtures, using the Organisation for Economic Co-operation and Development validated transactivation assay with the hERα-Hela9903 cell line. Concentration-dependent estrogenic activity was confirmed for all of the tested bisphenols, in the nanomolar to micromolar range. Estrogenic activities of binary and multicomponent mixtures followed a concentration addition model. Although exposure to individual bisphenols remains below their effective doses, we demonstrate that as a mixture, they can contribute additively to toxicity. This study thus emphasizes the importance of mixture toxicity evaluation for risk assessment of compounds that act like the bisphenols.
DOI: 10.25416/ntr.16559355.v1
title: Communicating Impact and Value.pdf

description: A compilation of advice to support colleagues as they communicate the impact and value of their learning and teaching practice. Particularly useful for individuals who are building a claim for fellowship.
DOI: 10.6084/m9.figshare.19787326.v2
title: CDFLOW - A 30-year dataset on CO2 in flowing freshwaters in the United States

description: CDFLOW consists of CO2 estimates in flowing freshwaters across the lower 48 US states from 1990 through 2020. CO2 estimations were generated by downloading US water quality data, including pH, temperature, and total alkalinity measuments and using the USGS program PHREEQC to estimate CO2. Site data for CDFLOW was generated by spatially joining lat/long data from the dowloaded water quality data with the EPAs NHDPlus dataset to the nearest streamcatchment feature. NHDPlus streamcatchment features are geometric features of unique stream sections denoted by a unique COMID code. For a more detailed look at the creation of this dataset view (link in progrees). <br> To download data generating code and supplemental R scripts use github link below: <br> https://github.com/ttoavs/CDFLOW
DOI: 10.6084/m9.figshare.28388111.v1
title: Predicting initiation and failure of static liquefaction landslides in loess sediment: A laboratory data-driven sliding-block model

description: Static liquefaction landslides are particularly prevalent in loose saturated granular soils. Generally, they are catastrophic geological processes due to their high-speed and long-runout mobility. However, here can be challenging to accurately predict the initiation and failure of the static liquefaction landslides. Here, I compiled a database of undrained triaxial compression tests of 152 saturated loess at 15 sampling sites in the Chinese Loess Plateau. Meanwhile, I collected the landslide monitoring data from six flume tests and three field landslides. These data help to build and verify the laboratory data-driven sliding-block model. The model can serve as an effective tool for static liquefaction landslide prediction.
DOI: 10.6084/m9.figshare.15241017.v1
title: 54节点配电网络规划参数

description: 54节点配电网络，只考虑部分线路存在故障的情况（10条），包括线路、变压器、变电站等参数
DOI: 10.6084/m9.figshare.27015307.v1
title: DataSet

description: Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific disciplines, which led to the emergence of machine learning techniques, has presented significant opportunities for the development of antimicrobial peptides. Machine learning and deep learning are used to predict antimicrobial peptide efficacy in the study. The main purpose is to overcome traditional experimental method constraints. Gram-negative bacterium Escherichia coli is the model organism in this study. The investigation assesses 1,360 peptide sequences that exhibit anti-E. coli activity. These peptides' minimal inhibitory concentrations have been observed to be correlated with a set of 34 physicochemical characteristics. Two distinct methodologies are implemented. The initial method involves utilizing the pre-computed physicochemical attributes of peptides as the fundamental input data for a machine-learning classification approach. In the second method, these fundamental peptide features are converted into signal images, which are then transmitted to a deep learning neural network. The first and second methods have combined accuracy of 74% and 92.9%, respectively. The proposed methods were developed to target single microorganism (gram negative <i>E.coli</i>), however, it can be applied for all types of antimicrobial, antiviral and anticancer peptides. Furthermore, they have the potential to result in significant time and cost reductions, as well as the development of innovative AMP-based treatments. This research contributes to the advancement of deep learning-based AMP drug discovery methodologies by generating potent peptides for drug development and application. This discovery has significant implications for the processing of biological data and the computation of pharmacology.
DOI: 10.6084/m9.figshare.5284378.v1
title: Prostate_Cancer_onc607_Data.xls

description: Exosomes may play a role in the disparity between african american and white american prostate cancer incidence and aggression.
DOI: 10.6084/m9.figshare.22086218.v1
title: Raw sequence data

description: Datasets and metadata analysed during the study of the Northern muriqui microbiome.
DOI: 10.6084/m9.figshare.25904014.v1
title: Weld bead images and masks

description: Weld bead images and masks by Hyun Soo Kim(Professor, School of Mechanical Engineering, Ulsan college)-. Version 1.0 : masks for binary segmentation-. # of images: 1,394-. Dataset    Original_data          ∟ images : 1,394 images(448*448, 3 channels PNG image)          ∟ masks : 1,394 masks(448*448, 3 channels PNG image)          ∟ train.csv : Connection information between image and mask
DOI: 10.6084/m9.figshare.28682824.v1
title: Artículo de divulgación científica

description: El zooplancton está constituido por diminutos organismos que viven a merced de las aguas, y en él se en-cuentran representados todos los grupos de la fauna marina; estos pequeños animales son un vínculo vital en la ca-dena trófica, entre los productores primarios y los consumidores superiores. Aquí se relata la travesía de un grupo de exploradores marinos, quienes interactúan durante su viaje con el variado universo del zooplancton, admirando las ca-racterísticas morfoecológicas que le permiten sobrevivir a la deriva en los mares del mundo
DOI: 10.6084/m9.figshare.7726454.v3
title: Data Carpentry Genomics Curriculum Example Data

description: p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 16.0px 'Andale Mono'; color: #29f914; background-color: #000000} span.s1 {font-variant-ligatures: no-common-ligatures} These files are intended for use with the Data Carpentry Genomics curriculum (https://datacarpentry.org/genomics-workshop/). Files will be useful for instructors teaching this curriculum in a workshop setting, as well as individuals working through these materials on their own. <br> This curriculum is normally taught using Amazon Web Services (AWS). Data Carpentry maintains an AWS image that includes all of the data files needed to use these lesson materials. For information on how to set up an AWS instance from that image, see https://datacarpentry.org/genomics-workshop/setup.html. Learners and instructors who would prefer to teach on a different remote computing system can access all required files from this FigShare dataset. <br> This curriculum uses data from a long term evolution experiment published in 2016: Tempo and mode of genome evolution in a 50,000-generation experiment (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988878/) by Tenaillon O, Barrick JE, Ribeck N, Deatherage DE, Blanchard JL, Dasgupta A, Wu GC, Wielgoss S, Cruveiller S, Médigue C, Schneider D, and Lenski RE. (doi: 10.1038/nature18959). All sequencing data sets are available in the NCBI BioProject database under accession number PRJNA294072 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA294072). <br> backup.tar.gz: contains original fastq files, reference genome, and subsampled fastq files. Directions for obtaining these files from public databases are given during the lesson https://datacarpentry.org/wrangling-genomics/02-quality-control/index.html). On the AWS image, these files are stored in <code>~/.backup</code> directory. 1.3Gb in size. <br> Ecoli_metadata.xlsx: an example Excel file to be loaded during the R lesson. <br> shell_data.tar.gz: contains the files used as input to the Introduction to the Command Line for Genomics lesson (https://datacarpentry.org/shell-genomics/). <br> sub.tar.gz: contains subsampled fastq files that are used as input to the Data Wrangling and Processing for Genomics lesson (https://datacarpentry.org/wrangling-genomics/). 109Mb in size. <br> solutions: contains the output files of the Shell Genomics and Wrangling Genomics lessons, including <code>fastqc</code> output, sam, bam, bcf, and vcf files. <br> vcf_clean_script.R: converts vcf output in .solutions/wrangling_solutions/variant_calling_auto to single tidy data frame.<br> <br> combined_tidy_vcf.csv: output of vcf_clean_script.R <br>
DOI: 10.26188/5da5583906307
title: EPiC database - Steel pipe

description: This material is part of the free Environmental Performance in Construction (EPiC) Database. The EPiC Database contains embodied environmental flow coefficients for 250+ construction materials using a comprehensive hybrid life cycle inventory approach.<br><br>Steel is a ferrous metal and is an alloy of iron and carbon, as well as potential other elements. It has a very high tensile strength. Steel has been used in the construction industry for over a century.<br><br>The core material for making steel is iron, which is found in iron ore. Iron is extracted from iron ore in blast furnaces through the smelting process, while controlling for the content of carbon. The molten steel is usually further processed before being extruded into circular pipes.<br><br>Steel pipes are often used in pile foundations, plumbing and as columns (usually filled with concrete).
DOI: 10.6084/m9.figshare.20453622.v1
title: economic indicators

description: The main economic indicators of labor in the People's Republic of China and the Russian Federation
DOI: 10.6084/m9.figshare.25557009.v1
title: Data and code for IJGIS publications

description: On ignoring the heterogeneity in spatial autocorrelation: consequences and solutions
DOI: 10.6084/m9.figshare.26170117.v2
title: GDGT data for HGY

description: GDGT data for HGY
DOI: 10.6084/m9.figshare.28639718.v1
title: Mangrove sample plot survey data

description: Mangrove sample plot survey data, including information on the height and diameter at breast height of individual trees in the sample plot