This integrated dataset represents the large-scale transcriptomics cell atlas of pons and medulla, compiled from 8 independent single-cell/single-nucleus RNA sequencing (sc/sn-RNA seq) datasets. This integration was performed using a standardized bioinformatic workflow, clustering, and marker-based annotation. This dataset comprised 317 985 quality-passed cells with 45 cell types.This dataset offers a valuable source for single-cell neuroscience to understand region-specific molecular insights and their cellular diversity.To aid interpretability, annotations are structured hierarchically into three levels:Level 1: Broad categories (e.g., Neurons, Astrocytes, Oligodendrocytes)Level 2: Subtypes within broad categories (e.g., Excitatory neurons, Inhibitory neurons)Level 3: Fine-grained subtypes (e.g., Glut1, Glut2, GABA1, GABA2).In addition, we also incorporated label transfer from the Yao et al. (https://doi.org/10.1038/s41586-023-06812-z) to benchmark and validate our annotations. Transferred Yao labels are included in the metadata as predicted.Class, predicted.Subclass, and predicted.Neurotransmitter, together with associated confidence scores.We provide the following datasets to facilitate reuse:Final_Integrated_Pons_Medulla.rdsContains all neuronal and non-neuronal populations.The cell_type column includes Level 1, Level 2, and Level 3 annotations combined.Pons_Medulla_Neurons_level_3.rdsSubset of neuronal populations.Includes Level 2 and Level 3 annotations for neurons in the cell_type column.GABA_Gluta.rdsSubset of excitatory (glutamatergic) and inhibitory (GABAergic) neuronal populations.Includes Level 3 annotations for detailed analysis of these key neurotransmitter systems.

RDS files contain "dgCMatrix" class objects for sparse raw counts of the downsampled datasets: GSE97179 (Luo et al, Science, 2017)

RDS files contain named "factor" class objects for cluster assignment of each dataset, obtained from the original study.

These datasets are prepared primarily for the use of presenting LIGER integration method.

Stakeholder pressure and public awareness of environmental protection drive organizations to improve environmental practices in the supply chain (SC), such as green supply chain integration (GSCI) and green innovation (GI). The use of information technology (IT) is crucial to manufacturing organizations’ GSCI and performance. However, the research on the relationship between IT capabilities, GSCI, GI and organizational performance is still limited. Therefore, empirical research is needed on the cognitive thinking of employees using IT capabilities to improve GSCI and organizational performance. The data for this study comes from SC personnel in manufacturing organizations through a structured questionnaires and was analyzed by employing structural equation modeling. Based on the results, this paper concludes that organizational IT capabilities positively affect the GSCI and improve organizational performance (environmental and operational performance). Furthermore, the study discovered that GI increases organizational performance and acts as a positive mediator in the link between GSCI and performance. The findings contribute to existing GSCI and GI knowledge, which can provide a bird’s eye-view to develop an organization’s IT capabilities to achieve competitive performance goals.

These contour datasets were generated from the National Elevation Dataset (NED) and the National Hydrography Dataset (NHD) in a fully automated process. The input raster data source was the 1/3 arc-second version of the NED. The NED data were modified by the NHD flow lines, areas, and water bodies to facilitate improved integration between the hypsography and hydrography. These contour datasets were generated from the National Elevation Dataset (NED) and the National Hydrography Dataset (NHD) in a fully automated process. The input raster data source was the 1/3 arc-second version of the NED. The NED data were modified by the NHD flow lines, areas, and water bodies to facilitate improved integration between the hypsography and hydrography. These datasets are not the ones that appear on the U.S. Geological Survey's USTopo GeoPDFs.

Abstract Celebrating the 25 years of existence of the Journal Ciência & Saúde Coletiva (C&SC), this paper analyzed 375 documents published between 2000-2019 as an integral part of the editorial of collective oral health. The production analysis aimed to understand how oral health core appears in publications and how it could have contributed to knowledge on the population’s health-disease, specific public policies, education, and management of oral health services in the SUS. The process employed bibliometric and documental analysis. We could show the authors’ territorial distribution, their extensive collaboration network, and the dimension of citations in publications, including the international plan. The Brazilian states most present in the publications were São Paulo and Minas Gerais, followed by authors from Pernambuco, Rio Grande do Sul, and Santa Catarina. Citations were more frequent in Brazil (85.14%), followed by the United States (2.31%), Portugal (1.34%), and Australia (1.34%). We concluded that, despite the limitations, the C&SC showed unequivocally a powerful instrument for the dissemination of scientific production from the perspective of collective oral health, enabling the exchange of information and facilitating the integration between researchers and enabling a path to its consolidation.

Additional file 2: SnRNA-seq cluster ontology list.

Additional file 6: CellChat inferred ligand receptor interactions.

Servicio integral de administracion de personal y proceso de nomina, mediante un sistema especializado para recursos humanos y de nomina de gobierno