The Q-Herilearn scale is a probabilistic scale of summative estimates that measures different aspects of the learning process in Heritage Education. It consists of seven factors (Knowing, Understanding, Respecting, Valuing, Caring, Enjoying and Transmitting). Each dimension is measured by means of seven indicators scored on a 4-point frequency response scale (1 = Never or almost never; 2 = Sometimes; 3 = Quite often; 4 = Always or almost always). Sufficient evidence of content validity has been obtained through a concordance analysis —which employed multi-facet logistic models (Many Facet Rasch Model MFRM)— of the scores of 40 judges, who estimated the relevance, adequacy, and clarity of each item. The metric properties of the scores were determined using ESEM —Exploratory Structural Equation Modeling—, EGA Exploratory Graph Analysis and Network Analysis. The scale was calibrated using Item Response Theory models: the Nominal Response Model and the Graded Response Model.

The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

Asia Pacific Hyper-scale Data Center market USD 32544.7 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031. Expanding IT infrastructure and growing presence of major players is expected to aid the sales to USD 55207.4 million by 2031

North America Hyper-scale Data Center market size will be USD 56616.8 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.4% from 2024 to 2031. North America has emerged as a prominent participant, and its sales revenue is estimated to reach USD 83242.5 Million by 2031. This growth is mainly attributed to the region's widespread use of digital services, from streaming and social media to cloud computing and IoT.

Les écailles ont été prélevées sur le saumon dans l'océan Pacifique Nord-Est et analysées pour obtenir des informations sur l'âge. Ces données ont été recueillies dans le cadre de l'expédition en haute mer du golfe d'Alaska de l'Année internationale du saumon (IYS) menée en mars et avril 2020, afin d'améliorer encore la compréhension des facteurs ayant une incidence sur la survie hivernale du saumon en début de mer.

The Latin America Hyper-scale Data Center market will be USD 7077.1 million in 2024 and is estimated to grow at a compound annual growth rate (CAGR) of 5.6% from 2024 to 2031. The market is foreseen to reach USD 11429.7 million by 2031 owing to Investments in high-speed internet and telecommunications networks.

The Atlas of Canada National Scale Data 1:1,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas large scale (1:1,000,000 to 1:4,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

Hyper Scale Data Centres Market Outlook

The global market size for Hyper Scale Data Centres was valued at USD 35.6 billion in 2023 and is projected to reach USD 92.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 11.1% during the forecast period. The market is being driven by the increasing demand for scalable and efficient data handling capabilities, as well as the rising adoption of cloud services by enterprises worldwide.

One of the primary growth factors for the Hyper Scale Data Centres market is the exponential increase in data generation across various sectors. The proliferation of Internet of Things (IoT) devices, the rise of big data analytics, and the advancement in artificial intelligence and machine learning technologies have necessitated sophisticated data storage and processing solutions. Hyper Scale Data Centres, with their ability to scale resources seamlessly, offer a robust solution to manage these vast amounts of data efficiently, thereby fueling market growth.

Another significant growth driver is the increasing adoption of cloud computing services. As businesses continue to transition from traditional on-premises data centers to cloud-based solutions, the demand for Hyper Scale Data Centres has surged. Cloud service providers are investing heavily in hyper-scalable infrastructure to meet the growing needs of enterprises for high-performance computing, data storage, and network capabilities. This shift towards cloud-centric operations is expected to sustain the growth of the Hyper Scale Data Centres market over the forecast period.

The need for enhanced data security and regulatory compliance is also contributing to the market's expansion. Businesses are increasingly focusing on ensuring the security and integrity of their data amidst a growing number of cyber threats. Hyper Scale Data Centres offer advanced security features, including encryption, access controls, and multi-factor authentication, which are critical for industries such as BFSI, healthcare, and government. The ability of Hyper Scale Data Centres to provide robust security measures while maintaining operational efficiency is a key factor driving their adoption.

From a regional perspective, North America holds a significant share of the Hyper Scale Data Centres market, driven by the presence of major cloud service providers and technological advancements in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, owing to the rapid digital transformation across emerging economies, increasing investments in data center infrastructure, and the growing demand for cloud-based services. Europe, Latin America, and the Middle East & Africa are also anticipated to contribute to market growth, albeit at varying growth rates.

Component Analysis

The Hyper Scale Data Centres market is segmented by component into hardware, software, and services. Each of these components plays a critical role in the overall functioning and efficiency of hyper-scale data centers. The hardware segment includes servers, storage devices, networking equipment, and other physical infrastructure essential for building and operating data centers. Due to the need for high-performance and reliable hardware, this segment is expected to hold a substantial market share.

Servers are the backbone of Hyper Scale Data Centres, providing the computational power required to process and analyze large datasets. With advancements in server technology, including higher processing power, energy efficiency, and scalability, the hardware segment continues to evolve. Additionally, the growing emphasis on environmentally sustainable data center operations has led to the adoption of energy-efficient servers and cooling systems, further driving the hardware market.

Software plays an equally important role in the Hyper Scale Data Centres ecosystem. This segment encompasses data center management software, virtualization software, and security solutions. Effective software solutions are crucial for managing the complex operations of hyper-scale data centers, ensuring optimal resource allocation, and maintaining high levels of security and compliance. With increasing cyber threats and the need for streamlined operations, the demand for advanced software solutions is on the rise.

The services component includes consulting, implementation, and maintenance services. As businesses continue to adopt hyper-scale data center solutions, the need for expert guidance and support becomes para

The Atlas of Canada National Scale Data 1:15,000,000 Series consists of boundary, coast and coastal islands, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas small scale (1:15,000,000 and 1:30,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

Les écailles ont été prélevées sur le saumon dans l'océan Pacifique Nord-Est et analysées pour obtenir des informations sur l'âge. Ces données ont été recueillies dans le cadre de l'expédition en haute mer du golfe d'Alaska de l'Année internationale du saumon (IYS) menée en février et mars 2019, afin d'améliorer encore la compréhension des facteurs ayant une incidence sur la survie hivernale du saumon en début de mer.

Genomics is narrowing uncertainty in the phylogenetic structure for many amniote groups. For one of the most diverse and species-rich groups, the squamate reptiles (lizards and snakes, amphisbaenians), an inverse correlation between the number of taxa and loci sampled still persists across all publications using DNA sequence data and reaching a consensus on the relationships among them has been highly problematic. Here, we use high-throughput sequence data from 289 samples covering 75 families of squamates to address phylogenetic affinities, estimate divergence times, and characterize residual topological uncertainty in the presence of genome scale data. Importantly, we address genomic support for the traditional taxonomic groupings Scleroglossa and Macrostomata using novel machine-learning techniques. We interrogate genes using various metrics inherent to these loci, including parsimony-informative sites, phylogenetic informativeness, length, gaps, number of substitutions, and site con...

In Mexico Hyper Scale Data Center Market, The cloud and IT sector is expected to remain the largest consumer as cloud adoption grows.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Lake. The data include parameters of paleolimnology with a geographic location of Ecuador. The time period coverage is from 15090 to -26 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

In China Hyper Scale Data Center Market, The cloud and IT sector is expected to remain the largest consumer as cloud adoption grows.

Untargeted lipidomics data

Europe's Hyper-scale Data Center market USD 42462.6 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031. European digital traffic increased tremendously due to the surge in electronic transactions, systems, and digital information is expected to aid sales to USD 61245.7 million by 2031

The identified I-O relationships, parameters of the Hill equation, and gain and time constant calculated from the linear ARX model in Fig 4.

We characterized coastal wetland responses to flooding stress by measuring vegetation cover, wetland elevation and water elevation in healthy and degrading wetlands dominated by Spartina patens. Wetland elevation was measured using real-time kinematic survey methods. Vegetation cover was determined by visual estimation methods, and water elevation was measured using in situ continuous recorders. In addition to these local-scale responses, we also measured landscape-scale patterns of land and water aggregation or fragmentation using remotely sensed data (Jones et al., 2018). Associated products: Jones, W.R., Hartley, S.B., Stagg, C.L., and Osland, M.J. 2018. Land-water classification for selected sites in McFaddin NWR and J.D. Murphree WMA: U.S. Geological Survey data release, https://doi.org/10.5066/F7736Q51.

##### CD40 inhibiton in AMI on d5, seq on d7 and d14

# Load necessary libraries for data manipulation, analysis, and visualization

library(dplyr)

library(Seurat)

library(patchwork)

library(plyr)

# Set the working directory to the folder containing the data

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/938-2_cellranger_count/outs")

# Read the M0 dataset from the 10X Genomics format

pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

RNA <- pbmc.data$`Gene Expression`

ADT <- pbmc.data$`Antibody Capture`

HST <- pbmc.data$`Multiplexing Capture`

# Load the Matrix package

library(Matrix)

# Hashtag 1, 2 and 3 are marking the mouse replicates per condition

# Subset the rows based on row names

subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")

animals_data <- HST[subsetted_rows, , drop = FALSE]

# Hashtag 4, 5, 6, 7 are representing DMSO d7, TS d7, DMSO d14 and TS d14

subsetted_rows <- c(""TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")

treatment_data <- HST[subsetted_rows, , drop = FALSE]

#Create a Seurat obeject and more assays to combine later

RNA <- CreateSeuratObject(counts = RNA)

ADT <- CreateAssayObject(counts = ADT)

Mice <- CreateAssayObject(counts = animals_data)

Treatment <- CreateAssayObject(counts = treatment_data)

seurat <- RNA

#Add the Assays

seurat[["ADT"]] <- ADT

seurat[["HST_Mice"]] <- Mice

seurat[["HST_Treatment"]] <- Treatment

#Check for AK Names

rownames(seurat[["ADT"]])

#Cluster cells on the basis of their scRNA-seq profiles

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

FeaturePlot(seurat, features = "Col1a1", order = T)

# Normalize ADT data,

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

#Demultiplex cells based on Mouse_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.60)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Mice_classification.global)

DimPlot(seurat, group.by = "HST_Mice_classification")

#Demultiplex cells based on Treatment_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.60)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Treatment_classification.global)

DimPlot(seurat, group.by = "HST_Treatment_classification")

Idents(seurat) <- seurat$HST_Treatment_classification

pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)

Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification

pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

#Redo the clssification to remove the doublettes

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)

table(pbmc.singlet$HST_Treatment_classification.global)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)

table(pbmc.singlet$HST_Mice_classification.global)

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")

seurat <- pbmc.singlet

seurat$mice <- seurat$HST_Mice_maxID

seurat$treatment <- seurat$HST_Treatment_maxID

library(plyr)

seurat$treatment <- revalue(seurat$treatment, c(

"TotalSeq-B0304" = "DMSO_d7",

"TotalSeq-B0305" = "TS_d7",

"TotalSeq-B0306" = "DMSO_d14",

"TotalSeq-B0307" = "TS_d14"

))

library(plyr)

seurat$mice <- revalue(seurat$mice, c(

"TotalSeq-B0301" = "1",

"TotalSeq-B0302" = "2",

"TotalSeq-B0303" = "3"

))

#Cluster cells on the basis of their scRNA-seq profiles without doublettes

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/Analyse")

saveRDS(seurat, file= "TSd5.v0.1.RDS")

##### CD40 activation and the effect on Neutrophils

# Load necessary libraries for data manipulation, analysis, and visualization

library(dplyr)

library(Seurat)

library(patchwork)

library(plyr)

# Set the working directory to the folder containing the data

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/938-1_cellranger_count/outs")

# Read the M0 dataset from the 10X Genomics format

pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

RNA <- pbmc.data$`Gene Expression`

ADT <- pbmc.data$`Antibody Capture`

HST <- pbmc.data$`Multiplexing Capture`

# Load the Matrix package

library(Matrix)

# Hashtag 1, 2 and 3 are marking the organs (heart, blood, spleen)

# Subset the rows based on row names

subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")

animals_data <- HST[subsetted_rows, , drop = FALSE]

# Hashtag 4, 5, 6, 7 are representing IgG_1, IgG_1, FGK45_1 and FGK45_1

subsetted_rows <- c("TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")

treatment_data <- HST[subsetted_rows, , drop = FALSE]

#Create a Seurat obeject and more assays to combine later

RNA <- CreateSeuratObject(counts = RNA)

ADT <- CreateAssayObject(counts = ADT)

Organ <- CreateAssayObject(counts = animals_data)

Treatment <- CreateAssayObject(counts = treatment_data)

seurat <- RNA

#Add the Assays

seurat[["ADT"]] <- ADT

seurat[["HST_Mice"]] <- Organ

seurat[["HST_Treatment"]] <- Treatment

#Check for AK Names

rownames(seurat[["ADT"]])

#Cluster cells on the basis of their scRNA-seq profiles

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

FeaturePlot(seurat, features = "S100a9", order = T)

# Normalize ADT data,

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

#Demultiplex cells based on Mouse_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.99)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Mice_classification.global)

DimPlot(seurat, group.by = "HST_Mice_classification")

#Demultiplex cells based on Treatment_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.99)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Treatment_classification.global)

DimPlot(seurat, group.by = "HST_Treatment_classification")

Idents(seurat) <- seurat$HST_Treatment_classification

pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)

Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification

pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

#Redo the clssification to remove the doublettes

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)

table(pbmc.singlet$HST_Treatment_classification.global)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)

table(pbmc.singlet$HST_Mice_classification.global)

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")

seurat <- pbmc.singlet

seurat$organ <- seurat$HST_Mice_maxID

seurat$mouse <- seurat$HST_Treatment_maxID

seurat$treatment <- seurat$HST_Treatment_maxID

library(plyr)

seurat$treatment <- revalue(seurat$treatment, c(

"TotalSeq-B0304" = "IgG",

"TotalSeq-B0305" = "IgG",

"TotalSeq-B0306" = "FGK45",

"TotalSeq-B0307" = "FGK45"

))

library(plyr)

seurat$organ <- revalue(seurat$organ, c(

"TotalSeq-B0301" = "heart",

"TotalSeq-B0302" = "blood",

"TotalSeq-B0303" = "spleen"

))

seurat$mouse <- revalue(seurat$mouse, c(

"TotalSeq-B0304" = "1",

"TotalSeq-B0305" = "2",

"TotalSeq-B0306" = "3",

"TotalSeq-B0307" = "4"

))

#Cluster cells on the basis of their scRNA-seq profiles without doublettes

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/Analyse")

saveRDS(seurat, file = "FGK45_heart_blood_spleen.v0.1.RDS")