The Web Analytics Market in Retail and CPG is experiencing robust growth, projected to reach $1.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.19% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing need for data-driven decision-making within retail and CPG companies is paramount. Businesses are leveraging web analytics to gain deeper insights into customer behavior, optimize marketing campaigns, and personalize the shopping experience. The rise of e-commerce and omnichannel strategies further intensifies the demand for sophisticated web analytics solutions. Specifically, the ability to track customer journeys across multiple touchpoints, analyze real-time data, and measure the effectiveness of online marketing initiatives are crucial factors driving market growth. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more predictive analytics, empowering businesses to anticipate customer needs and proactively address potential challenges. Competitive pressures are also pushing companies to adopt advanced web analytics technologies to gain a competitive edge and improve operational efficiency. Segmentation reveals a strong demand across both SMEs and large enterprises, with significant application in search engine optimization (SEO), online marketing automation, customer profiling, application performance management, and social media management. Major players like Google, IBM, Meta, and Salesforce are strategically positioned to capitalize on this expanding market. The market's growth trajectory is expected to be consistent throughout the forecast period, driven by continued digital transformation within the retail and CPG sectors. While challenges such as data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive. The North American market is anticipated to hold a significant share, given the region's advanced digital infrastructure and high adoption of web analytics technologies. However, other regions, particularly Asia Pacific, are expected to show significant growth due to the rapid expansion of e-commerce and increasing internet penetration. The market's future success hinges on the continued development of innovative analytics solutions that address the specific needs of retail and CPG companies, providing actionable insights that drive revenue growth, customer loyalty, and operational efficiency. Recent developments include: April 2024 - IBM Consulting and Microsoft have unveiled the opening of the IBM-Microsoft Experience Zone in Bangalore, India. The Experience Zone is designed as an exclusive venue where clients can delve into the potential of generative AI, hybrid cloud solutions, and other advanced Microsoft offerings. The goal is to expedite their business transformations and secure a competitive edge., January 2024 - Microsoft Corp. announced a suite of generative AI and data solutions tailored for retailers. These solutions cover every touchpoint of the retail shopper journey, from crafting personalized shopping experiences and empowering store associates to harness and consolidating retail data, ultimately aiding brands in better connecting with their target audiences. Microsoft's initiatives include introducing copilot templates on Azure OpenAI Service, enhancing retailers' ability to craft personalized shopping experiences, and streamlining store operations. Microsoft Fabric hosts advanced retail data solutions, while Microsoft Dynamics 365 Customer Insights boasts new copilot features. Microsoft also rolled out the Retail Media Creative Studio within the Microsoft Retail Media Platform. These advancements collectively bolster Microsoft Cloud for Retail, providing retailers with diverse tools to integrate copilot experiences across the entire shopper journey seamlessly.. Key drivers for this market are: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Potential restraints include: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Notable trends are: Search Engine Optimization and Ranking Sector Significantly Driving the Market Growth.

5000 Brand-level and 1500 category-level segments. Can be distributed daily, weekly, or monthly. Free samples provided. Also can append key Demographic and Location data to the same MAID or HEM data records. Supplemental Identity data available. 1st-Party and 1st-party sourced, direct from consumer mobile device, in-app, SDK and/or Point of Sale (POS API). US only.

These datasets are continuous parameter grids (CPG) of soil component percentages (clay, sand, silt) in the Pacific Northwest. Source data come from the Digital General Soil Map of the United States, produced by the Natural Resources Conservation Service, United States Department of Agriculture.

This repository contains data released accompanying the manuscript "Germline CpG methylation signatures in the human population inferred from genetic polymorphism".

Data source 1Analysis of correlation between the CpG methylation (450K arrays) and RNA transcripts (RNAseq) levels within the same gene in tumour samples.Data source 2The multi-omics driver scores for all ERGs

Supplementary Table 1. CSV file describing genomic data sources and acquisition dates. (CSV 1 kb)

This dataset is a continuous parameter grid (CPG) of mean depth to water table in the Pacific Northwest. Source data come from the Digital General Soil Map of the United States, produced by the Natural Resources Conservation Service, United States Department of Agriculture.

IntroductionType 2 diabetes (T2D) is considered as a risk factor for kidney cancer (KC). However, so far, there is no study in the literature that has explored genetic factors through which T2D drive the development and progression of KC. Therefore, this study attempted to explore T2D- and KC-causing shared key genes (sKGs) for revealing shared pathogenesis and therapeutic drugs as their common treatments.MethodsThe integrated bioinformatics and system biology approaches were utilized in this study. The statistical LIMMA approach was used based web-tool GEO2R to detect differentially expressed genes (DEGs) through transcriptomics analysis. Then upregulated and downregulated DEGs for T2D and KC were combined to obtained shared DEGs (sDEGs) between T2D and KC. The STRING database was used to construct the protein-protein interaction (PPI) network of sDEGs. Then Cytohubba plugin-in Cytoscape were used in the PPI network to disclose the sKGs based on different topological measures. The RegNetwork database was used in NetworkAnalyst to analyze co-regulatory networks of sKGs with transcription factors (TFs) and micro-RNAs to identify key TFs and miRNAs as the transcriptional and post-transcriptional regulators of sKGs, respectively. AutoDock Vina is a tool used for molecular docking. ADME/T properties were 24 assessed using pkCSM and SwissADME.ResultsAt first, 74 shared DEGs (sDEGs) were identified that can distinguish both KC and T2D patients from control samples. Through protein-protein interaction (PPI) network analysis, top-ranked 6 sDEGs (CD74, TFRC, CREB1, MCL1, SCARB1 and JUN) were detected as the sKGs that drive both KC and T2D development and progression. The most common sKG ‘CD74’ is associated with key pathways, such as NF-κB signaling transduction, apoptotic processes, B cell proliferation. Differential expression patterns of sKGs validated by independent datasets of NCBI database for T2D and TCGA and GTEx databases for KC. Furthermore, sKGs were found to be significant at several CpG sites in DNA methylation studies. Regulatory network analysis identified three TFs proteins (SMAD5, ATF1 and NR2F1) and two miRNAs (hsa-mir-1-3p and hsa-mir-34a-5p) as the regulators of sKGs. The enrichment analysis of sKGs with KEGG-pathways and Gene Ontology (GO) terms revealed some crucial shared pathogenetic mechanisms (sPM) between two diseases. Finally, sKGs-guided four potential therapeutic drug molecules (Imatinib, Pazopanib hydrochloride, Sorafenib and Glibenclamide) were recommended as the common therapies for KC with T2D.ConclusionThe results of this study may be useful resources for the diagnosis and therapy of KC with the co-existence of T2D.

This dataset is a continuous parameter grid (CPG) of topographic wetness index in the Pacific Northwest. Source data come from the U.S. Geological Survey National Elevation Dataset and NHDPlus Version 2.

North America Source-to-Pay (S2P) Market size was valued at USD 437.06 Million in 2023 and is projected to reach USD 1195.08 Million by 2031, growing at a CAGR of 14.78% from 2024 to 2031.

North America Source-to-Pay (S2P) Market Dynamics

The key market dynamics that are shaping the North America Source-to-Pay (S2P) Market include:

Key Market Drivers

Digital Transformation: As North American businesses embrace digital transformation, there is an increased demand for S2P solutions to automate and expedite procurement processes. This move not only improves efficiency but also increases spending visibility and control, allowing for better decision-making.

Cost Reduction Pressures: In an economic environment when cost control is critical, North American enterprises use S2P systems to find cost-cutting possibilities and optimize spending. These systems give extensive spend analytics, allowing businesses to make informed decisions to minimize costs and enhance their bottom line.

Supply Chain Optimization: The demand for effective supply chain management is driving the use of S2P solutions in North America. These solutions provide complete capabilities for managing supplier relationships and performance, ensuring that supply chains are both efficient and resilient to disturbances.

Key Challenges:

Integration Complexity: Integrating S2P solutions with existing enterprise resource planning (ERP) systems is complicated and expensive. Aligning historical systems with new S2P solutions presents problems for organizations, impedes seamless data flow and process efficiency, and has an impact on market growth.

Change Management: Implementing S2P solutions necessitates considerable shifts in company processes and culture. Convincing stakeholders to embrace new systems and adapt established workflows is tough, resulting in opposition and delays in implementation.

Supplier Adoption: Getting all vendors on board with adopting an S2P platform is difficult. Smaller suppliers lack the resources and technical expertise to properly connect with these digital platforms, resulting in inefficiencies and fragmented procurement procedures.

Key Trends:

Artificial Intelligence and Machine Learning: AI and machine learning are increasingly being used in S2P applications to improve predictive analytics, automate repetitive operations, and make better decisions. These technologies aid in estimating demand, optimizing inventory, and finding possible cost savings, resulting in more effective procurement methods.

Sustainability Focus: There is an increasing trend for sustainable procurement methods within S2P operations. Companies prioritize suppliers who demonstrate environmental responsibility and sustainable operations by incorporating sustainability criteria into their sourcing and procurement choices.

Cloud-Based Solutions: The adoption of cloud-based S2P systems is growing due to their scalability, flexibility, and lower IT overhead. These platforms provide remote cooperation, real-time data access, and continual system updates, so making procurement procedures more agile.

Enhanced Supplier Collaboration: Modern S2P solutions are increasingly focused on improving supplier collaboration. Platforms now include more tools for managing supplier relationships, such as real-time communication, performance tracking, and collaborative problem-solving, which improves overall supply chain resilience and efficiency.

Genome assembly for the California condor, genotype data for two California condors and two related species (Andean condor and turkey vulture), and supporting files.

See "Genome-wide diversity in the California condor tracks its prehistoric abundance and decline," by Robinson et al. (2021) for full details. Also see https://10.5281/zenodo.4680034 for processing and analysis code.

Samples: CRW1112 California condor (Studbook #593) CYW1141 California condor (Studbook #309) VulGry1 Andean condor (ISIS 417) BGI_N323 Turkey vulture (SAMN02319050, from https://doi.org/10.1126/science.1251385)

Files: gc_PacBio_HiC.fasta California condor genome sequence.

gc_PacBio_HiC_scaffold_chr_key.txt Key giving the chromosomal identity of scaffolds in the California condor genome assembly (where known).

gc_PacBio_HiC_repeats*.bed Coordinates of repeats in the California condor genome identified with Tandem Repeats Finder (TRF, https://tandem.bu.edu/trf/trf.html) and WindowMasker (WM, https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/src/app/winmasker).

*.vcf.gz, *.vcf.gz.tbi Raw VCF files plus indexes for each sample aligned to reference gc_PacBio_HiC.fasta.

cpgIslands.bed Coordinates of CpG islands in gc_PacBio_HiC.fasta. Coordinates including and excluding CpG islands in repeats are provided.

*.over.chain.gz, *.rbest.chain.gz Chain files for liftOver (https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64). Named as FROM.TO.TYPE.chain.gz. The "rbest" chains represent the reciprocal best alignments between both genomes. ASM69994v1 is the turkey vulture genome assembly, galGal6 is the chicken genome assembly.

ismc_CYW1141.rho.*.bed Bed files containing coordinate ranges and rho/bp inferred with iSMC (https://github.com/gvbarroso/iSMC) using California condor #309. Intervals of 1 kb and 1 Mb are provided.

*.psmc, *.msmc, *.msmc2 Output files from PSMC (https://github.com/lh3/psmc), MSMC (https://github.com/stschiff/msmc), and MSMC2 (https://github.com/stschiff/msmc2).

ROH*.bed Coordinates of runs of homozygosity (ROH) >=1 Mb in each California condor sample, identified with Plink (v1.9, https://www.cog-genomics.org/plink/).

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This dataset is a continuous parameter grid (CPG) of baseflow index values (percent of discharge as baseflow) estimated at U.S. Geological Survey (USGS) streamgages in the Pacific Northwest. Source data was produced by David Wolock of USGS.

This dataset should be used for the practical of the "Advanced Forensic Biology" lesson. It has been slightly modified from the initial source (see below). It consists of two parts:

1) CpG methylation beta values: tabulated separated methylation beta values in a file called cpg_methylation_beta_values.tsv.

2) Samples to chronological age : the relationship between sample identifiers and their chronological age in a tabulated separated file called cpg_methylation_sample_age.tsv.

3) CpG identifier to gene symbol and annotation: a tabulated separated file containing the relationship between CpG island identifier, the corresponding gene and its functional annotation. cpg_methylation_cpg_to_annotation.tsv

Dataset provenance:

Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E. Epigenetic predictor of age. PLoS One. 2011;6(6):e14821. doi: 10.1371/journal.pone.0014821. Epub 2011 Jun 22. PMID: 21731603; PMCID: PMC3120753.

GEO link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28746

These datasets are continuous parameter grids (CPG) of irrigated agriculture data (percent of basin classified as irrigated) for the years 2002, 2007, and 2012 in the Pacific Northwest. Source data was the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US), produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center.

These datasets are continuous parameter grids (CPG) of permeability (and impermeability) of surface geology in the Pacific Northwest. Source data come from work by Chris Konrad, U.S. Geological Survey (USGS), and geologic map databases produced by USGS scientists.

methyl CpG binding protein 2 Enables several functions, including DNA binding activity; histone deacetylase binding activity; and promoter-specific chromatin binding activity. Involved in several processes, including central nervous system development; positive regulation of dense core granule transport; and regulation of dendrite extension. Located in chromatin. Part of protein-containing complex. Is active in glutamatergic synapse. Used to study Rett syndrome. Biomarker of fetal alcohol spectrum disorder; hepatocellular carcinoma; and transient cerebral ischemia. Human ortholog(s) of this gene implicated in Rett syndrome; autistic disorder; gastrointestinal system disease; severe congenital encephalopathy due to MECP2 mutation; and syndromic X-linked intellectual disability (multiple). Orthologous to human MECP2 (methyl-CpG binding protein 2). Enables several functions, including DNA binding activity; histone deacetylase binding activity; and promoter-specific chromatin binding activity. Involved in several processes, including central nervous system development; positive regulation of dense core granule transport; and regulation of dendrite extension. Located in chromatin. Part of protein-containing complex. Is active in glutamatergic synapse. Used to study Rett syndrome. Biomarker of fetal alcohol spectrum disorder; hepatocellular carcinoma; and transient cerebral ischemia. Human ortholog(s) of this gene implicated in Rett syndrome; autistic disorder; gastrointestinal system disease; severe congenital encephalopathy due to MECP2 mutation; and syndromic X-linked intellectual disability (multiple). Orthologous to human MECP2 (methyl-CpG binding protein 2); PARTICIPATES IN DNA modification pathway; INTERACTS WITH 17alpha-ethynylestradiol; 17beta-estradiol; 2,2',4,4'-Tetrabromodiphenyl ether.

Lifesight specializes in alternative consumer data collected from mobile audiences to provide intelligence covering consumer identity, behavior and interests. The company’s massive consumer datasets of over 2.3 billion profiles across the globe, in over 36 markets, and are designed to help users leverage alternative data for various use cases in industries such as CPG, retail, financial services and travel.

Lifesight collects high quality persistent human mobility data from location aware mobile apps using an embedded SDK. Lifesight complies with global privacy regulations including CCPA and GDPR, and adheres to strict data collection (opt-in) and opt-out processes.

This dataset provides aggregated data from all sources and it is delivered at quadkey grid level 17 (circa 300x300m cells)

While resource selection varies according to the scale and context of study, gathering data representative of multiple scales and contexts can be challenging especially when a species is small, elusive, and threatened. We explore resource selection in a small, nocturnal, threatened species—the greater bilby (Macrotis lagotis)—to test (a) which resources best predict bilby occupancy, and (b) whether responses are sex-specific and/or vary over time. We tracked a total of 20 bilbies and examined within home range resource selection over multiple seasons in a large (110ha) fenced sanctuary in temperate Australia. We tested a set of plausible models for bilby resource selection, showing that food biomass (terrestrial and subterranean invertebrates, and subterranean plants) and soil textures (% sand, clay and silt) best predicted bilby resource selection for all sampling periods. Selection was also sex-specific; female resource use, relative to males, was more closely linked to the location o..., Data includes terrestrial invertebrate, and subterranean invertebrate and plant biomass data collected over four seasons (summer 2020, winter 2020, spring 2020, and summer 2021). We have also provided the R code used for generating the interpolation rasters and the necessary shapefiles (generated in QGIS) and rasters for this interpolation. The R code used for construction of resource selection functions and for generating 'Figure 4' in the main paper is also provided. The GPS data associated with this analysis can be requested from the corresponding author upon reasonable request., , # Data from: Digging deeper: habitat selection within the home ranges of a threatened marsupial

This README file lists the supplementary datasets, shapefiles, rasters, and R code used in the associated paper. It also provides a brief description of how each file was used in generating the results, and definitions for any abbreviated variables within datasets. Note: dates are all in the format ‘date-month-year’.Â

Description of the data and file structure

Shapefiles.zip (and auxiliary files):

Sanctuary fenceline.shp

• Sanctuary fenceline.qpj
• Sanctuary fenceline.prj
• Sanctuary fenceline.dbf
• Sanctuary fenceline.cpg
• Sanctuary fenceline.shx

water sources.shp

• water sources.shx
• water sources.prj
• water sources.dbf
• water sources.cpg

Roads.shp

• Roads.shx
• Roads.prj
• Roads.dbf
• Roads.cpg

Individual Minimum Convex Polygons (MCPs):

Minimum convex polygons (MCPs) were generated for individual bilbies tracked in each season u...

DNA methylation can cause stable changes in neuronal gene expression, but we know little about its role in individual differences in the wild. In this study, we focus on the vasopressin 1a receptor (avpr1a), a gene extensively implicated in vertebrate social behaviour, and explore natural variation in DNA methylation, genetic polymorphism and neuronal gene expression among 30 wild prairie voles (Microtus ochrogaster). Examination of CpG density across 8 kb of the locus revealed two distinct CpG islands overlapping promoter and first exon, characterized by few CpG polymorphisms. We used a targeted bisulfite sequencing approach to measure DNA methylation across approximately 3 kb of avpr1a in the retrosplenial cortex, a brain region implicated in male space use and sexual fidelity. We find dramatic variation in methylation across the avrp1a locus, with pronounced diversity near the exon–intron boundary and in a genetically variable putative enhancer within the intron. Among our wild voles, differences in cortical avpr1a expression correlate with DNA methylation in this putative enhancer, but not with the methylation status of the promoter. We also find an unusually high number of polymorphic CpG sites (polyCpGs) in this focal enhancer. One polyCpG within this enhancer (polyCpG 2170) may drive variation in expression either by disrupting transcription factor binding motifs or by changing local DNA methylation and chromatin silencing. Our results contradict some assumptions made within behavioural epigenetics, but are remarkably concordant with genome-wide studies of gene regulation.