cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48420/22116443
title: Table 1. Proteome Discoverer settings for shotgun proteomic analysis.

description: Settings used for Proteome Discoverer label-free quantification analysis workflows to analyse raw mass spectra from Nasosorption FX-i (nasal) samples from the thesis entitled: "The Nasal Proteome as a Source of Biomarkers for SARS-CoV-2 and Lung Cancer". <br> Nasal mucus from Nasosorption FX-i devices was eluted and processed for mass spectrometry analysis using S-Trap. Samples were analysed in a data-dependent manner and subsequently analysed using Proteome Discoverer software with the settings described in this file.
DOI: 10.48420/22116443.v1
title: Table 1. Proteome Discoverer settings for shotgun proteomic analysis.

description: Settings used for Proteome Discoverer label-free quantification analysis workflows to analyse raw mass spectra from Nasosorption FX-i (nasal) samples from the thesis entitled: "The Nasal Proteome as a Source of Biomarkers for SARS-CoV-2 and Lung Cancer". <br> Nasal mucus from Nasosorption FX-i devices was eluted and processed for mass spectrometry analysis using S-Trap. Samples were analysed in a data-dependent manner and subsequently analysed using Proteome Discoverer software with the settings described in this file.
DOI: 10.48420/22116932
title: Table 1. Proteins identified in COPD cohort Nasosorption FX-i samples

description: Excel file with tabs containing proteins identified across COPD and no COPD Nasosorption FX-i samples and proteins unique to COPD and no COPD samples described in Chapter 5 of the thesis entitled: "The Nasal Proteome as a Source of Biomarkers for SARS-CoV-2 and Lung Cancer". <br> Nasal mucus from Nasosorption FX-i devices from patients with and without COPD was eluted and processed for mass spectrometry analysis using S-Trap. Samples were analysed in a data-dependent manner and subsequently analysed using Proteome Discoverer software.
DOI: 10.48420/22116932.v1
title: Table 1. Proteins identified in COPD cohort Nasosorption FX-i samples

description: Excel file with tabs containing proteins identified across COPD and no COPD Nasosorption FX-i samples and proteins unique to COPD and no COPD samples described in Chapter 5 of the thesis entitled: "The Nasal Proteome as a Source of Biomarkers for SARS-CoV-2 and Lung Cancer". <br> Nasal mucus from Nasosorption FX-i devices from patients with and without COPD was eluted and processed for mass spectrometry analysis using S-Trap. Samples were analysed in a data-dependent manner and subsequently analysed using Proteome Discoverer software.
DOI: 10.5519/qd.8hqm6uqp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2852 records)
DOI: 10.48420/22116824
title: Table 2. Proteins identified in lung cancer cohort Nasosorption FX-i samples.

description: Excel file containing proteins identified in the lung cancer cohort described in Chapter 5 of the thesis entitled: "The Nasal Proteome as a Source of Biomarkers for SARS-CoV-2 and Lung Cancer". Tabs containing proteins identified in all samples, unique to individual disease groups and present in two of three disease groups. <br> Nasal mucus from Nasosorption FX-i devices from patients with lung cancer, post-lung cancer resection and matched controls was eluted and processed for mass spectrometry analysis using S-Trap. Samples were analysed in a data-dependent manner and subsequently analysed using Proteome Discoverer software. <br>
DOI: 10.48420/22116824.v1
title: Table 2. Proteins identified in lung cancer cohort Nasosorption FX-i samples.

description: Excel file containing proteins identified in the lung cancer cohort described in Chapter 5 of the thesis entitled: "The Nasal Proteome as a Source of Biomarkers for SARS-CoV-2 and Lung Cancer". Tabs containing proteins identified in all samples, unique to individual disease groups and present in two of three disease groups. <br> Nasal mucus from Nasosorption FX-i devices from patients with lung cancer, post-lung cancer resection and matched controls was eluted and processed for mass spectrometry analysis using S-Trap. Samples were analysed in a data-dependent manner and subsequently analysed using Proteome Discoverer software. <br>
DOI: 10.6084/m9.figshare.22147508
title: Statistical Process Control as a Tool for Quality Improvement A Case Study in Denim Pant Production

description: In this paper, we show that concept of Statistical Process Control tools was thoroughly examined and the definitions of quality control concepts were presented. This is significant because of it is anticipated that this study will contribute to the literature as an exemplary application that demonstrates the role of statistical process control (SPC) tools in quality improvement in the evaluation and decision-making phase. <br> This is significant because of this study is to investigate applications of quality control, to clarify statistical control methods and problem-solving procedures, to generate proposals for problem-solving approaches, and to disseminate improvement studies in the ready-to-wear industry. The basic Statistical Process Control tools used in the study, the most repetitive faults were detected and these faults were divided into sub-headings for more detailed analysis. In this way, it was tried to prevent the repetition of faults by going down to the root causes of any detected fault. With this different perspective, it is expected that the study will contribute to other fields. <br> We give consent for the publication of identifiable details, which can include photograph(s) and case history and details within the text (“Material”) to be published in the Journal of Quality Technology. We confirm that have seen and been given the opportunity to read both the Material and the Article (as attached) to be published by Taylor & Francis.
DOI: 10.3886/e185341v1
title: A Fully Bayesian Approach to Model Process Data in a Newly Developed Computer-based Assessment

description: Writing requires substantial conscious effort compared to other literacy skills (e.g., reading). Cognitive models of writing have been proposed to reflect the underlying mechanisms of the writing process. In a computer-based writing assessment, keystroke log data can capture a student’s writing process (e.g., their typing behavior) as the student responds to a writing assignment prompt. Quantification of the writing process may contribute to establishing a writing profile for each student and influence the ongoing writing instruction. Extracting meaningful features from writing process data remains challenging. In this research, for interpretability purposes, we endorse the idea that cognitive models should be the foundation to extract process features. Specifically, we have (1) designed a computer-based assessment to capture students’ writing process; (2) administered the assessment to students to collect response process data; (3) parsed the raw data into pause data; and (4) modeled the pause data with a hierarchical mixture model using a fully Bayesian approach. Under the Bayesian model’s assumption of exchangeability, essays (students) are treated as similar data units, meaning that the model borrows strength from longer essays to enhance the efficiency of estimation for shorter essays. In addition, we provided the source code for designing the keystroke logging system. Therefore, if future researchers intend to develop their own keystroke logging system, they can modify our callback functions, instead of creating the code from scratch.
DOI: 10.5281/zenodo.15205034
title: Data for "Energetic Electron Enhancements Near the Dayside Magnetopause: Outward Radial Transport Due to Asymmetric Drift-Orbit Bifurcation"

description: Here we provide a dataset that includes test-particle simulation results (test_particles.zip) used in this study, as well as data from the THEMIS spacecraft (A, C, E) during the magnetopause crossing on 1st October 2018.

The test-particle data is given in separate .txt files for each particle and includes time in c/R_E units (where c is the speed of light and R_E is Earth’s radius), azimuthal coordinate (mlt) in hours, guiding center coordinates (x, y, z), parallel momentum ppar in m_e c units (m_e is the electron rest mass), relativistic gamma factor gamma, and magnetic field strength at the mirror point B_m in B0 = 31000 nT units.

THEMIS-A and THEMIS-D data (tha_20181001_080000.txt and thd_20181001_080000.txt) are formatted as: UT|T|L|MLT|X|Y|Z|Bx|By|Bz|LB_ts04|…, where UT is Universal Time, MLT is Magnetic Local Time, (X, Y, Z) are spacecraft coordinates in GSM, (Bx, By, Bz) are magnetic field components in GSM, and LB_ts04 is the L* parameter from the Tsyganenko & Sitnov (2005) model, numbers after "LB_tso4|..." refer to energy chanels (in keV) mesured by the Solid State Telescope.

ARTEMIS (THEMIS-C) data (thc_20181001_080000.txt) is formatted as: Tdat, T, Bx, By, Bz, Btot, theta, pdyn, X_th, Y_th, Z_th, where Tdat is Universal Time, T is time in seconds, (Bx, By, Bz) are magnetic field components in GSM, Btot is the total magnetic field, theta is the IMF rotation angle, pdyn is the solar wind dynamic pressure, and (X_th, Y_th, Z_th) are spacecraft coordinates in GSM.
DOI: 10.5281/zenodo.15205035
title: Data for "Energetic Electron Enhancements Near the Dayside Magnetopause: Outward Radial Transport Due to Asymmetric Drift-Orbit Bifurcation"

description: Here we provide a dataset that includes test-particle simulation results (test_particles.zip) used in this study, as well as data from the THEMIS spacecraft (A, C, E) during the magnetopause crossing on 1st October 2018.

The test-particle data is given in separate .txt files for each particle and includes time in c/R_E units (where c is the speed of light and R_E is Earth’s radius), azimuthal coordinate (mlt) in hours, guiding center coordinates (x, y, z), parallel momentum ppar in m_e c units (m_e is the electron rest mass), relativistic gamma factor gamma, and magnetic field strength at the mirror point B_m in B0 = 31000 nT units.

THEMIS-A and THEMIS-D data (tha_20181001_080000.txt and thd_20181001_080000.txt) are formatted as: UT|T|L|MLT|X|Y|Z|Bx|By|Bz|LB_ts04|…, where UT is Universal Time, MLT is Magnetic Local Time, (X, Y, Z) are spacecraft coordinates in GSM, (Bx, By, Bz) are magnetic field components in GSM, and LB_ts04 is the L* parameter from the Tsyganenko & Sitnov (2005) model, numbers after "LB_tso4|..." refer to energy chanels (in keV) mesured by the Solid State Telescope.

ARTEMIS (THEMIS-C) data (thc_20181001_080000.txt) is formatted as: Tdat, T, Bx, By, Bz, Btot, theta, pdyn, X_th, Y_th, Z_th, where Tdat is Universal Time, T is time in seconds, (Bx, By, Bz) are magnetic field components in GSM, Btot is the total magnetic field, theta is the IMF rotation angle, pdyn is the solar wind dynamic pressure, and (X_th, Y_th, Z_th) are spacecraft coordinates in GSM.
DOI: 10.15468/dl.y2frxr
title: Occurrence Download

description: A dataset containing 758 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chusquea culeou É.Desv."
 ]
}

 The dataset includes 758 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021259-250402121839773/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.23812464
title: Additional file 13 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 13. Table S8. The deconvolution results for bulk RNA-seq in GLASS and CGGA.
DOI: 10.6084/m9.figshare.23812464.v1
title: Additional file 13 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 13. Table S8. The deconvolution results for bulk RNA-seq in GLASS and CGGA.
DOI: 10.6084/m9.figshare.23812467
title: Additional file 14 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 14. Table S9. DEG list for each GBM cell subpopulation.
DOI: 10.6084/m9.figshare.23812467.v1
title: Additional file 14 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 14. Table S9. DEG list for each GBM cell subpopulation.
DOI: 10.6084/m9.figshare.23812470
title: Additional file 15 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 15. Table S10. Signatures used in this study.
DOI: 10.6084/m9.figshare.23812470.v1
title: Additional file 15 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 15. Table S10. Signatures used in this study.
DOI: 10.6084/m9.figshare.23812473
title: Additional file 16 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 16. Table S11. The proportion of GBM cell subpopulations labeled by several signatures.
DOI: 10.6084/m9.figshare.23812473.v1
title: Additional file 16 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 16. Table S11. The proportion of GBM cell subpopulations labeled by several signatures.
DOI: 10.6084/m9.figshare.23812476
title: Additional file 17 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 17. Table S12. The list for the three patterns of genes from monocle heatmap.
DOI: 10.6084/m9.figshare.23812476.v1
title: Additional file 17 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 17. Table S12. The list for the three patterns of genes from monocle heatmap.
DOI: 10.6084/m9.figshare.23812479.v1
title: Additional file 18 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 18. Table S13. Enriched GO terms for each gene group identified in monocle.
DOI: 10.6084/m9.figshare.23812479
title: Additional file 18 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 18. Table S13. Enriched GO terms for each gene group identified in monocle.
DOI: 10.6084/m9.figshare.23812482
title: Additional file 19 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 19. Table S14. Enriched GO terms for upregulated genes in each non-tumor cell type at GBM recurrence.
DOI: 10.6084/m9.figshare.23812482.v1
title: Additional file 19 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 19. Table S14. Enriched GO terms for upregulated genes in each non-tumor cell type at GBM recurrence.
DOI: 10.6084/m9.figshare.23812485
title: Additional file 20 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 20. Table S15. The expression modules of myeloid identified in GBM.
DOI: 10.6084/m9.figshare.23812485.v1
title: Additional file 20 of Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

description: Additional file 20. Table S15. The expression modules of myeloid identified in GBM.
DOI: 10.6084/m9.figshare.23812551
title: Additional file 2 of Enrichment of human nasopharyngeal bacteriome with bacteria from dust after short-term exposure to indoor environment: a pilot study

description: Additional file 2. Bacterial phyla in the dust and nasopharyngeal samples. * Number of occurrences of the specific phylum in the dust (n = 44)/nasopharyngeal samples (n = 44). One nasopharyngeal sample collected in the afternoon was excluded from the analysis. ** Values are based on the relative abundances of bacterial phyla in the dust/nasopharyngeal samples. One nasopharyngeal sample collected in the afternoon was excluded from the analysis.
DOI: 10.6084/m9.figshare.23812551.v1
title: Additional file 2 of Enrichment of human nasopharyngeal bacteriome with bacteria from dust after short-term exposure to indoor environment: a pilot study

description: Additional file 2. Bacterial phyla in the dust and nasopharyngeal samples. * Number of occurrences of the specific phylum in the dust (n = 44)/nasopharyngeal samples (n = 44). One nasopharyngeal sample collected in the afternoon was excluded from the analysis. ** Values are based on the relative abundances of bacterial phyla in the dust/nasopharyngeal samples. One nasopharyngeal sample collected in the afternoon was excluded from the analysis.
DOI: 10.6084/m9.figshare.23812554.v1
title: Additional file 3 of Enrichment of human nasopharyngeal bacteriome with bacteria from dust after short-term exposure to indoor environment: a pilot study

description: Additional file 3. Bacterial genera in the dust and nasopharyngeal samples. * Number of occurrences of the specific genus in the dust (n = 44)/nasopharyngeal samples (n = 44). One nasopharyngeal sample collected in the afternoon was excluded from the analysis. ** Values are based on the relative abundances of bacterial genera in the dust/nasopharyngeal samples.
DOI: 10.6084/m9.figshare.20011166.v1
title: Estimating ignition delay times of nitroglycerin: A chemical kinetic modeling study

description: Nitroglycerin (NG) is commonly used as an ingredient in propellant formulations. In this study, a first automatically generated detailed kinetic model of this energetic material has been developed. The construction of this model was made possible by performing computations with the open source software package Reaction Mechanism Generator (RMG). To enable a faster convergence, significant intermediate species of NG decomposition and optimized operating conditions were indicated in the RMG input parameters. Thermochemical data related to significant decomposition species were derived from ab initio calculations at the DFT B3LYP/6-31 G(d,p) level of theory. To validate the RMG-built mechanism, simulations were performed with CHEMKIN-Pro. Computed species profiles from simulations were compared with flash pyrolysis measurements from the literature. Sensitivity analyses were performed on species mole fraction, and the most significant elementary reactions were identified. Some rate constant parameters were adjusted within the reaction rate uncertainty to improve the predictability of the model. Ignition delay times were computed for NG, and consistent trends were obtained when compared against to calculations for RDX and TNT using referenced model available in the literature. Although experimental data are scarce, this automated kinetic generation approach, applied to energetic materials, is to be highly promising.
DOI: 10.57841/casdc.0003325
title: 呼包鄂地区格点化城市污染物排放清单数据集
DOI: 10.17632/ywvfccyy9y.1
title: A NEW BENZOPHENONE FROM Dilochopentas decora (S. Moore) AND IT’S ANTIBACTERIAL, ANTIOXIDANT AND SUN PROTECTION POTENTIAL

description: Supplementary information of the bioassay isolation of compounds from Dilochopentas decora (S. Moore), raw data for the antibacterial and antioxidant activity of the extracts and isolated compounds. Sun protection potential of the isolated compounds, spectroscopic data of the isolated compounds and NMR spectrums of compound 6
DOI: 10.17632/ywvfccyy9y.2
title: A NEW BENZOPHENONE FROM Dilochopentas decora (S. Moore) AND IT’S ANTIBACTERIAL, ANTIOXIDANT AND SUN PROTECTION POTENTIAL

description: Supplementary information of the bioassay isolation of compounds from Dilochopentas decora (S. Moore), raw data for the antibacterial and antioxidant activity of the extracts and isolated compounds. Sun protection potential of the isolated compounds, spectroscopic data of the isolated compounds and NMR spectrums of compound 6
DOI: 10.17632/m7s79xyy6z.1
title: Retrospective analysis of ten year import data of Pacific white shrimp brooders to India

description: The Pacific white shrimp, Penaeus vannamei is an exotic shrimp species cultured in India and a predominant item in the Indian export basket. Commercial culture of P. vannamei was introduced in India, in the year 2009 with the establishment of a dedicated Centralized Shrimp Quarantine unit, called the Aquatic Quarantine Facility (AQF). The role of this facility is to quarantine the specific pathogen free (SPF) shrimp brooders imported by the shrimp hatcheries in the country from empanelled overseas broodstock suppliers. The dataset provided in the article is on the shrimp imports into the country during each quarter period, in the span of last ten years. Imports by different broodstock suppliers recorded during the ten year period are furnished. The data can be utilized for modelling sustainable vannamei farming in the country without compromising loss in shrimp production and export revenue.
DOI: 10.17632/ny25vx9cbm.1
title: Data for height and leadership

description: Data for height and leadership
DOI: 10.17632/dvkfc4c4jb.1
title: ARDUINO

description: Electronic components on ARDUINO Boards
DOI: 10.17632/4s2zfg6mg9.1
title: Data set and models to select algorithms for a serial-batch scheduling problem

description: For selecting an algorithm from a set of algorithms to solve a serial-batch scheduling problem, we propose in our working paper "Machine Learning based Algorithm Selection and Genetic Algorithms for serial-batch scheduling" to use Machine Learning models for predicting an algorithm based on characteristics of the problem instance.
To get an accurate Machine Learning model for this task, a large data set of instances with different "complexities" is needed.
This repository contains a data set of serial-batch scheduling problems (with their feature vectors) and several Machine Learning models developed in ["Learning-augmented heuristics for scheduling parallel serial-batch processing machines" (Computers & Operations Research, 2022, 10.1016/j.cor.2022.106122)], and [Uzunoglu, A., Gahm, C. & Tuma, A. A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05541-w]. 
These Machine Learning models are used to perform an efficient grid search in the parameter space of a heuristic. Each Machine Learning-based grid search (MLPP-methods in the first paper and MLRP-methods in the second paper) represents a solution method to solve the serial-batch scheduling problem.

The data set comprises two feature vector representations (CF12 with 12 features and AF85 with 85 features) and 17 objective values computed by the solution methods of the aforementioned papers for each problem instance. Please note that the ordering of the features must be maintained when applying the Machine Learning models. The provided serial-batch scheduling problem instances are a subset of the instance set in [Gahm, Christian (2022), “Extended instance sets for the parallel serial-batch scheduling problem with incompatible job families, sequence-dependent setup times, and arbitrary sizes”, Mendeley Data, V2, doi: 10.17632/rxc695hj2k.2] excluding the S (small) instances.
DOI: 10.17632/zgzy4cnpcf.1
title: Topography and flow data concerning subaqueous dunes under combined flows

description: This file contains 2 types of data:
1- Topography raw data along the five transect along the flume and during a 3hr measurement (every 15min)
2- Flow raw data, acoustic data above the migrating dune in equilibrium (30 min measurement)
Each file contain the backscattered signal used to define the dune bathymetry, water column, streamwise velocity vertical velocity