cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3187535.v3
title: Vehicle Collisions

description: This is a version of the vehicle collisions dataset used in the following paper:<br><i>Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets, F. Chirigati, H. Doraiswamy, T. Damoulas, and J. Freire. In Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2016</i><br>The dataset includes traffic collision data provided by NYPD, from 2012 to 2014.<br>The original data is available at the NYC Open Data portal: https://data.cityofnewyork.us/d/h9gi-nx95?category=Public-Safety&view_name=NYPD-Motor-Vehicle-Collisions
DOI: 10.6084/m9.figshare.3840153.v7
title: Marine Microbial Eukaryotic Transcriptome Sequencing Project, re-assemblies

description: The Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) data set contains cultured samples of pelagic and endosymbiotic marine eukaryotic species representing more than 40 phyla (Keeling et al. 2014).<br>Methods for the de novo transcriptome assembly are described in the Eel pond khmer protocols (Brown et al. 2015).<br>Scripts available on github: <br>https://github.com/dib-lab/dib-MMETSP<br><br>C. Titus Brown, Camille Scott, and Leigh Sheneman. 2015. The Eel Pond mRNAseq Protocol. https://khmer-protocols.readthedocs.io/en/ctb/mrnaseq/<br>Keeling et al. 2014. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing. http://dx.doi.org/10.1371/journal.pbio.1001889<br>
DOI: 10.17632/fr5dwz8ttz
title: A Model for Two-Digit Number Processing Based on a Joint Garner and System Factorial Technology Analysis

description: These data are from a manuscript by Fitousi and Algom Experiment 1 (The Garner paradigm) Name of the file: FitousiAlgom_Experiment1. txt Data Structure V1= subject number V2 = Counter V3 = Task (1 = decade, 2 = unit) V4 = Block (1= Filtering, 2 = Baseline1, 3= Baseline2, 4= Positive Correlation, 5 = Negative Correlation) V5= Stimulus (two-digit number) V6 = Decade digit V7 = Unit digit V8 = Reaction time (in milliseconds) V9 = keypress response (1 = “m”, 2 = ”z”) V10 = accuracy (0=correct, 1 = incorrect) Experiment 2 (The System Factorial paradigm) Name of the file: FitousiAlgom_Experiment2_sub1-4. txt Data Structure V1= subject number V2 = day (1= day one, 2 = day two) V3 = counter V4 = stimulus (two-digit number) V5= salience condition (1 = HH, 2 = LH, 3 = HL, 4 = LL) V6 = reaction time in milliseconds V7 = keypress response (1 = “m”, 2 = ”z”) V8 = accuracy (0=correct, 1 = incorrect) V9 = unit digit V10 = decade digit V11 = trial type (the numbers refer to type: 1 = double target, 2 = single target decade, 3 = single target unit, 4 = no-target; the letters refer to salience level (a = HH, b= LH, c =HL, and d = LL). So, for example, 3d refers to a single target unit trial with LL salience.
DOI: 10.6084/m9.figshare.18741095.v1
title: Dataset for: SPECIES SENSITIVITY DISTRIBUTIONS FOR USE IN ENVIRONMENTAL PROTECTION, ASSESSMENT AND MANAGEMENT OF AQUATIC ECOSYSTEMS FOR 12,386 CHEMICALS

description: The present paper considers the collection and use of ecotoxicity data for risk assessment with Species Sensitivity Distributions (SSDs) of chemical pollution in surface water. SSDs are used to quantify the likelihood that critical effect levels are exceeded. This fits to the European Water Framework Directive, which suggest using models to assess the likelihood that chemicals affect water quality for management prioritization. We derived SSDs based on chronic and acute ecotoxicity test data for 12,386 compounds. The log-normal SSDs are characterized by the median and the standard deviation of log-transformed ecotoxicity data and by a quality score. A case study illustrates the utility of SSDs for water quality assessment and management prioritization. We quantified the chronic and acute mixture toxic pressure of mixture exposures for &gt;22,000 water bodies in Europe for 1,760 chemicals for which we had both exposure and hazard data. Results show the likelihood of mixture exposures exceeding a negligible effect level and increasing species loss, respectively. The SSDs presented in this paper represent a versatile and comprehensive approach to prevent, assess and manage chemical pollution problems.
DOI: 10.6084/m9.figshare.26014042.v2
title: Datasets supporting article_Efficacy of Interceptor G2, Royal Guard and PermaNet 3.0

description: This research describ the efficacy of Interceptor G2, Royal Guard and PermaNet 3.0 against resistan <i>Anopheles gambiae s.l.</i> from Za-kpota southern Benin
DOI: 10.6084/m9.figshare.27144354.v1
title: Combined Analysis of Metabolomics and Proteomics in Immune-Related Dry Eye Disease

description: Dry eye disease (DED) is a chronic ocular surface disorder with immune factors significantly contributing to its pathogenesis. Traditional research methods, often focused on single-dimensional approaches, have limited the understanding of the complex mechanisms involved in DED. The advent of multi-omics technologies, however, offers a comprehensive framework to explore molecular mechanisms across multiple levels, presenting new possibilities for diagnosing and treating DED. This study specifically aimed to compare the proteomics and metabolomics profiles of individuals with Sjögren’s Syndrome (SS), a condition often associated with DED, to healthy controls, and to identify potential biomarkers and pathways that play a role in its development. Serum proteins and metabolites were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), followed by multivariate and univariate statistical analysis to pinpoint differentially expressed proteins (DEPs) and metabolites (DEMs). Functional annotations and pathway enrichment analyses were conducted using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to identify key molecular pathways. Bioinformatics tools were then applied to integrate the proteomics and metabolomics data, highlighting crucial differential proteins, metabolites, and biomarkers. The study identified 112 DEPs in individuals with SS, including 94 upregulated and 18 downregulated proteins. The most significantly enriched pathways in KEGG analysis included amino acid biosynthesis, glycolysis/gluconeogenesis, the Rap1 signaling pathway, carbon metabolism, focal adhesion, platelet activation, the pentose phosphate pathway, and actin cytoskeleton regulation. Key proteins identified as potentially related to SS development included GAPDH, CFL1, ENO1, VCL, MYH9, FLNA, TPI1, ITGA2B, TLN1, and TXN. In the metabolomics analysis, 372 DEMs were identified, with 268 upregulated and 104 downregulated metabolites, primarily enriched in glycine, serine, threonine metabolism, as well as taurine and hypotaurine metabolism. The integration of proteomics and metabolomics data underscored the significance of the Rap1 signaling pathway, suggesting its potential involvement in the development of DED. Overall, this study revealed several key differential proteins, metabolites, and potential biomarkers, providing valuable insights into the molecular mechanisms underlying DED and offering new avenues for further research into its pathogenesis.
DOI: 10.4121/19606378.v1
title: Data for the paper: ‘Effects of different factors on the efficacy of anti PD-(L)1 monotherapy in patients with advanced non-small cell lung cancer: An observational, retrospective cohort study’

description: Effects of sex, age, EGFR mutation status and smoking status on immunotherapy. we showed that the benefits for PFS and OS were similar in male and female, and in younger (&lt; 65 years of age) and older (≥65 years of age) patients. EGFR mutation status and smoking status are two independent influencing factors. Regardless of EGFR mutation status, the ORR and PFS of current-smokers were better than those of never-smokers; The benefits of immunotherapy for other mutant subtypes except 19del, L858R and T790M were similar to those of EGFR wild-type; Former-smokers are a unique population who benefit poor from immunotherapy than never-smokers or current-smokers.
DOI: 10.6084/m9.figshare.10058204.v2
title: Saturation

description: AA saturationPCGs_N saturation
DOI: 10.6084/m9.figshare.10058486.v1
title: Table S2

description: Table S2 Mobile genetic element and mobile ARG detected in all dust metagenome assembled contigs.
DOI: 10.6084/m9.figshare.1394600.v1
title: EEG and behavioural data for "The neurophysiology of language processing shapes the evolution of grammar: evidence from case-marking"

description: This data set makes available the EEG and behavioural data described in: Bickel, B., Witzlack-Makarevich, A., Choudhary, K.K., Schlesewsky, M., & Bornkessel-Schlesewsky, I. (2015). The neurophysiology of language processing shapes the evolution of grammar: evidence from case-marking. PLoS ONE 10(8): e0132819. doi:10.1371/journal.pone.0132819 Please see the README file for further information.
DOI: 10.6084/m9.figshare.24598488.v1
title: Transparency/Sunshine Act/Poland

description: In May 2023, the current research was carried out utilizing an online survey distributed through the Ariadna internet panel, with a quota sample of 1057 individuals.
DOI: 10.6084/m9.figshare.6445658.v1
title: Supplementary Material for: Preoperative Visceral Adiposity and Muscularity Predict Poor Outcomes after Hepatectomy for Hepatocellular Carcinoma

description: <b><i>Objective:</i></b> Visceral adiposity, defined as a high visceral-to-subcutaneous adipose tissue area ratio (VSR), has been shown to be associated with poor outcomes in several cancers. However, in the surgical field, the significance of visceral adiposity remains controversial. The present study investigated the impact of visceral adiposity as well as sarcopenic factors (low muscularity) on outcomes in patients undergoing hepatectomy for hepatocellular carcinoma (HCC). <b><i>Patients and Methods:</i></b> This retrospective study analyzed data from 606 patients undergoing hepatectomy for HCC at our institution between April 2005 and March 2016. Using preoperative plain computed tomography imaging at the level of the third lumbar vertebra, visceral adiposity, skeletal muscle mass, and muscle quality were evaluated by the VSR, skeletal muscle mass index (SMI), and intramuscular adipose tissue content (IMAC), respectively. The impact of these parameters on outcomes after hepatectomy for HCC was analyzed. <b><i>Results:</i></b> The overall survival rate was significantly lower among patients with a high VSR (<i>p</i> &lt; 0.001) than among patients with a normal VSR. Similarly, the recurrence-free survival rate was significantly lower among patients with a high VSR (<i>p</i> = 0.016). A high VSR, low SMI, and high IMAC contributed to an increased risk of death (<i>p</i> &lt; 0.001) and HCC recurrence (<i>p</i> &lt; 0.001) in an additive manner. Multivariate analysis showed that not only preoperative low muscularity but also visceral adiposity was a significant risk factor for mortality (hazard ratio [HR] = 1.566, <i>p</i> &lt; 0.001) and HCC recurrence (HR = 1.329, <i>p</i> = 0.020) after hepatectomy for HCC. <b><i>Conclusions:</i></b> Preoperative visceral adiposity, as well as low muscularity, was closely related to poor outcomes after hepatectomy for HCC. It is crucial to establish a new strategy including perioperative nutritional interventions with rehabilitation for better outcomes after hepatectomy for HCC.
DOI: 10.6084/m9.figshare.17125415.v1
title: Ethical and Legal Issues in Counseling Self-Efficacy Scale (ELICSES)

description: the raw data of the psychometric assessment of the Ethical and Legal Issues in Counseling Self-Efficacy Scale (ELICSES)
DOI: 10.6084/m9.figshare.22644295.v1
title: Zhao, M., & Abeysekera, I. (2023). Board Diversity and Intellectual Capital Disclosure of Chinese listed firms with Belt and Road Initiative projects. https://doi.org/10.6084/m9.figshare.22644295

description: The information contained relates to the journal paper titled Board Diversity and Intellectual Capital Disclosure of Chinese Listed Firms with Belt and Road Initiative Projects (https://doi.org 10.1108/JIC-10-2022-0209)
DOI: 10.6084/m9.figshare.25127243.v1
title: RMS single-cell consensus atlas (all subtypes)

description: This is data relevant to the publication "Single cell transcriptomic profiling identifies tumor-acquired and therapy-resistant cell states in pediatric rhabdomyosarcoma" [https://doi.org/10.1101/2023.10.13.562224]. This is an R object in Rds format that corresponds to Figures 1-3.
DOI: 10.6084/m9.figshare.26485783.v1
title: Inflorescence Color Variations of the three Royal Irises in Palestine

description: This Supplementary file depicts the inflorescence coloration variety within the three studied Royal Irises in Palestine, namely: <i>Iris haynei</i> (endemic), <i>Iris loretii</i> (sub-endemic), and <i>Iris atrofusca</i> (sub-endemic). This data is part of a comprehensive study that was conducted between the years 2020 and 2023. Collected data was summarized in a manuscript submitted to PLANT SOCIOLOGY under the title “Shedding light on the geographical distribution, population size and inflorescence coloration variety of Iris Sect. Oncocyclus”, which was accepted for publication on the 31st of July 2024.
DOI: 10.6084/m9.figshare.28195140.v1
title: Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data

description: Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with log-normal mixture kernels for count vectors. A group factor model is used to model the covariance matrix of the mixing kernel that describes microorganism interaction. We construct a Dirichlet-Horseshoe (Dir-HS) shrinkage prior and use it as a joint prior for factor loading vectors. Joint sparsity induced by a Dir-HS prior greatly improves the performance in high-dimensional applications. We further model the effects of covariates on microbial abundances using regression. The semiparametric model flexibly accommodates large variability in observed counts and excess zero counts and provides a basis for robust estimation of the interaction and covariate effects. We evaluate Sp-BGFM using simulation studies and real data analysis, comparing it to popular alternatives. Our results highlight the necessity of joint sparsity induced by the Dir-HS prior, and the benefits of a flexible DP model for baseline abundances. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.17632/6x37kcvvrp
title: QSWalk.jl: Julia package for quantum stochastic walks analysis

description: The paper describes QSWalk.jl package for Julia programming language, developed for the purpose of simulating the evolution of open quantum systems. The package enables the study of quantum procedures developed using stochastic quantum walks on arbitrary directed graphs. We provide a detailed description of the implemented functions, along with a number of usage examples. The package is compared with the existing software offering a similar functionality.
DOI: 10.6084/m9.figshare.3552537.v1
title: Appendix A. An adaptive Metropolis-Hastings scheme.

description: An adaptive Metropolis-Hastings scheme.
DOI: 10.6084/m9.figshare.19470599.v2
title: MPF.2021.2.8

description: This dataset contains the MPF.2021.2.8 data used to train the m3gnet model reported in `https://arxiv.org/abs/2202.02450` <br> I have split the dataset into two pickle files. To load the data, you can use example code as below. <br> ``` import pickle with open('block_0_cif.p', 'rb') as f: data = pickle.load(f) <br> with open('block_1_cif.p', 'rb') as f: data.update(pickle.load(f)) ``` <br> where `data` will be a dictionary with `material_id` as the key and an inner dictionary as the value. <br> The inner dictionary contains the snapshots of this `material_id`, with the following keys. ``` - structure - energy - force - stress - id ``` The `structure` is a list of cif files generated by pymatgen. <br> Each id in the `id` list is of format `material_id-calc_id-ionic_step_id`, where `calc_id` is 0 (second) or 1 (first) in the double relaxation process. <br> The `stress` here is the raw output from VASP, meaning that it is really the negative stress using the convention in our paper. Hence to train the model, please multiply stress with -0.1 (kBa to GPa and change sign) <br> The units for energy, force and stress in the data are eV, eV/A, and kBa. Remember to convert the stress to GPa and take the negative sign to work with m3gnet training.
DOI: 10.6084/m9.figshare.24136446.v5
title: Transcriptome data

description: mRNA sequencing data of lung tissue autopsy samples from 10 COVID-19 patients and 5 healthy donors.mRNA sequencing data of SARS-CoV-2 infected golden hamster at different time points.
DOI: 10.6084/m9.figshare.28590812.v1
title: Environmental exposure of terrestrial biomes to global climate change: an n-dimensional approach

description: Terrestrial biomes, defined by unique climatic conditions and evolutionary histories, are increasingly vulnerable to global climate change. This study evaluates the exposure of Earth’s 14 terrestrial biomes to climate change by characterizing their climatic boundaries using n-dimensional probabilistic methods. We analyzed precipitation and temperature data from the Global 200 project, combined with bioclimatic variables and climate models, to assess changes across historical, present, and future scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Using NicheROVER and NicheA, we quantified climatic overlap and projected non-overlapping regions under future climate scenarios. Our results reveal significant climatic specificity in temperature and precipitation ranges, with Flooded Grasslands & Savannas, Tropical & Subtropical Dry Broadleaf Forests, and Mangroves identified as the most exposed biomes. Geographic projections for 2040 highlight high-exposure regions near the equator, including the Neotropics, Central and Northern Africa, Southern Asia, Oceania, and Antarctica. These regions intersect with 2,230 and 9,091 protected areas under the SSP3-7.0 and SSP5-8.5 scenarios, respectively. This study underscores the importance of characterizing climatic boundaries to enhance ecosystem resilience estimates and inform biodiversity conservation strategies. By integrating niche modeling approaches, we provide a framework for identifying regions most vulnerable to climate change. Our findings emphasize the need for targeted conservation efforts, policy interventions, and fine-scale analyses to mitigate climate impacts on biodiversity, agriculture, and human health. We conclude that understanding biome-specific climatic boundaries is critical for addressing the global climate crisis and guiding adaptive management in the most exposed regions.<br>This data set contains the spatially balanced points of each of the 14 terrestrial biomes analyzed in the study.Description of the data and file structureFile structure:The files are divided into folders corresponding to each time period analyzed, including: Late_Holocene, Present, Future_ssp126, Future_ssp370, Future_ssp585In each folder are the .csv files for each biome separately, including the following information:ID: Biome identifier, POINT_X: Length, POINT_Y: Latitude, Bio_01: Values of the bio_01 variable,Bio_05: Values of the bio_05 variable, Bio_06: Values of the bio_06 variable, Bio_12: Values of the bio_12 variable, Bio_13: Values of the bio_13 variable, Bio_14: Values of the bio_14 variable.The polygons of each biome can be found at the following link:https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-worldOlson, D. M., & Dinerstein, E. (2002). The Global 200: Priority ecoregions for global conservation. Annals of the Missouri Botanical Garden, 199-224.
DOI: 10.6084/m9.figshare.7862216.v1
title: Supplementary Material for: What Do NICU Fellows Identify as Important for Achieving Competency in Neonatal Intubation?

description: <b><i>Background:</i></b> Tracheal intubation (TI) is one of the most important interventions for the stabilization of critically ill neonates. Competency in airway management is essential for neonatal fellows. No studies have assessed which educational models, techniques, or instructions are perceived by neonatal fellows as the most beneficial for achieving competency in TI. <b><i>Objectives:</i></b> This study identifies which factors are considered most helpful in achieving intubation competency. <b><i>Method:</i></b> This was a mixed-method study. Semi-structured phone interviews addressed training experience for neonatal intubation. Through qualitative analysis, common themes were identified. RedCap electronic surveys and procedure logs were used to assess procedural experience. <b><i>Results:</i></b> Forty-two fellows from 5 programs completed phone interviews. Fellows recalled 6–10 intubation attempts before fellowship. Independent statements related to achieving intubation competency were analyzed and coded into 5 main themes (Procedure, Practice, Perceptual Environment, Personnel, and Preparation). A large proportion of the statements focused on the use of video laryngoscopy. <b><i>Conclusions:</i></b> The themes identified by neonatal-perinatal medicine (NPM) fellows as being the most beneficial in achieving proficiency in neonatal TI are categorized as “The 5 Ps.” Careful review of these themes may be utilized to develop validated curriculums that enhance the teaching of TI and optimize the achievement of TI competency among NPM fellows.
DOI: 10.6084/m9.figshare.8300021.v2
title: Heart rate data (Oximeter PPG vs Kalman PPGI) for eighteen patient used for statistical analysis

description: In the first attached data, 'Subject' is the person identificator, 'Measurement_Number' is the sample number of total measurement, 'Measurement_Number_Subject' is the sample number per person, 'HR_Fingertip_Pulse_Oximeter' is the heart rate measured by the fingertip pulse oximeter method, and 'HR_PPGI_Kalman' is the heart rate measured by the PPGI Kalman method. Also, 'Subject' is a number assigned to classify eighteen patients. Each sample is the number of heart rate calculated over a 6 s sliding-windows size.<br>In the second attached data, 'Number' is the sample number of total measurement, 'ICA_CPU_Usage' is the CPU usage measured when PPGI ICA method estimated heart rate, and 'Kalman_CPU_Usage' s the CPU usage measured when PPGI Kalman method estimated heart rate.
DOI: 10.6084/m9.figshare.8320199.v2
title: Pangenome gene trees of 41 Pseudorhizobium and Neorhizobium strains

description: Gene trees were computed for each of the 6,714 homologous gene family of the 41-species pangenome with at least 4 sequences using MrBayes (Ronquist & Huelsenbeck 2003) under the GTR+4G+I model, running the Metropolis-coupled Markov chain Monte-Carlo (MCMCMC) for 2,000,000 generations, sampling a tree every 500 generations. Convergence of bipartition distribution in independent pairs of 4,000 sampled gene tree sets (‘chains’) was achieved for all gene families under these conditions.<br>Only the consensus of these tree chains (discarding the first 25% sampled trees as burn-in) are presented here.Correspondance of gene family and coding sequence identifiers with metadata (genome of origin, genome location, functional annotation, etc.) is available using the Pantagruel database provided in the related post (open with SQLite 3).