The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Civil and geological engineers have used field variable-head permeability tests (VH tests or slug tests) for over one century to assess the local hydraulic conductivity of tested soils and rocks. The water level in the pipe or riser casing reaches, after some rest time, a static position or elevation, z2. Then, the water level position is changed rapidly, by adding or removing some water volume, or by inserting or removing a solid slug. Afterward, the water level position or elevation z1(t) is recorded vs. time t, yielding a difference in hydraulic head or water column defined as Z(t) = z1(t) - z2. The water level at rest is assumed to be the piezometric level or PL for the tested zone, before drilling a hole and installing test equipment. All equations use Z(t) or Z*(t) = Z(t) / Z(t=0). The water-level response vs. time may be a slow return to equilibrium (overdamped test), or an oscillation back to equilibrium (underdamped test). This document deals exclusively with overdamped tests. Their data may be analyzed using several methods, known to yield different results for the hydraulic conductivity. The methods fit in three groups: group 1 neglects the influence of the solid matrix strain, group 2 is for tests in aquitards with delayed strain caused by consolidation, and group 3 takes into account some elastic and instant solid matrix strain. This document briefly explains what is wrong with certain theories and why. It shows three ways to plot the data, which are the three diagnostic graphs. According to experience with thousands of tests, most test data are biased by an incorrect estimate z2 of the piezometric level at rest. The derivative or velocity plot does not depend upon this assumed piezometric level, but can verify its correctness. The document presents experimental results and explains the three-diagnostic graphs approach, which unifies the theories and, most important, yields a user-independent result. Two free spreadsheet files are provided. The spreadsheet "Lefranc-Test-English-Model" follows the Canadian standards and is used to explain how to treat correctly the test data to reach a user-independent result. The user does not modify this model spreadsheet but can make as many copies as needed, with different names. The user can treat any other data set in a copy, and can also modify any copy if needed. The second Excel spreadsheet contains several sets of data that can be used to practice with the copies of the model spreadsheet. En génie civil et géologique, on a utilisé depuis plus d'un siècle les essais in situ de perméabilité à niveau variable (essais VH ou slug tests), afin d'évaluer la conductivité hydraulique locale des sols et rocs testés. Le niveau d'eau dans le tuyau ou le tubage prend, après une période de repos, une position ou élévation statique, z2. Ensuite, on modifie rapidement la position du niveau d'eau, en ajoutant ou en enlevant rapi-dement un volume d'eau, ou en insérant ou retirant un objet solide. La position ou l'élévation du niveau d'eau, z1(t), est alors notée en fonction du temps, t, ce qui donne une différence de charge hydraulique définie par Z(t) = z1(t) - z2. Le niveau d'eau au repos est supposé être le niveau piézométrique pour la zone testée, avant de forer un trou et d'installer l'équipement pour un essai. Toutes les équations utilisent Z(t) ou Z*(t) = Z(t) / Z(t=0). La réponse du niveau d'eau avec le temps peut être soit un lent retour à l'équilibre (cas suramorti) soit une oscillation amortie retournant à l'équilibre (cas sous-amorti). Ce document ne traite que des cas suramortis. Leurs données peuvent être analysées à l'aide de plusieurs méthodes, connues pour donner des résultats différents pour la conductivité hydraulique. Les méthodes appartiennent à trois groupes : le groupe 1 néglige l'influence de la déformation de la matrice solide, le groupe 2 est pour les essais dans des aquitards avec une déformation différée causée par la consolidation, et le groupe 3 prend en compte une certaine déformation élastique et instantanée de la matrice solide. Ce document explique brièvement ce qui est incorrect dans les théories et pourquoi. Il montre trois façons de tracer les données, qui sont les trois graphiques de diagnostic. Selon l'expérience de milliers d'essais, la plupart des données sont biaisées par un estimé incorrect de z2, le niveau piézométrique supposé. Le graphe de la dérivée ou graphe des vitesses ne dépend pas de la valeur supposée pour le niveau piézomé-trique, mais peut vérifier son exactitude. Le document présente des résultats expérimentaux et explique le diagnostic à trois graphiques, qui unifie les théories et donne un résultat indépendant de l'utilisateur, ce qui est important. Deux fichiers Excel gratuits sont fournis. Le fichier"Lefranc-Test-English-Model" suit les normes canadiennes : il sert à expliquer comment traiter correctement les données d'essai pour avoir un résultat indépendant de l'utilisateur. Celui-ci ne modifie pas ce...

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Graph Data Integration Platform Market Outlook

According to our latest research, the global graph data integration platform market size reached USD 2.1 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 18.4% from 2025 to 2033, reaching approximately USD 10.7 billion by 2033. This significant growth is fueled by the increasing need for advanced data management and analytics solutions that can handle complex, interconnected data across diverse organizational ecosystems. The rapid digital transformation and the proliferation of big data have further accelerated the demand for graph-based data integration platforms.

The primary growth factor driving the graph data integration platform market is the exponential increase in data complexity and volume within enterprises. As organizations collect vast amounts of structured and unstructured data from multiple sources, traditional relational databases often struggle to efficiently process and analyze these data sets. Graph data integration platforms, with their ability to map, connect, and analyze relationships between data points, offer a more intuitive and scalable solution. This capability is particularly valuable in sectors such as BFSI, healthcare, and telecommunications, where real-time data insights and dynamic relationship mapping are crucial for decision-making and operational efficiency.

Another significant driver is the growing emphasis on advanced analytics and artificial intelligence. Modern enterprises are increasingly leveraging AI and machine learning to extract actionable insights from their data. Graph data integration platforms enable the creation of knowledge graphs and support complex analytics, such as fraud detection, recommendation engines, and risk assessment. These platforms facilitate seamless integration of disparate data sources, enabling organizations to gain a holistic view of their operations and customers. As a result, investment in graph data integration solutions is rising, particularly among large enterprises seeking to enhance their analytics capabilities and maintain a competitive edge.

The surge in regulatory requirements and compliance mandates across various industries also contributes to the expansion of the graph data integration platform market. Organizations are under increasing pressure to ensure data accuracy, lineage, and transparency, especially in highly regulated sectors like finance and healthcare. Graph-based platforms excel in tracking data provenance and relationships, making it easier for companies to comply with regulations such as GDPR, HIPAA, and others. Additionally, the shift towards hybrid and multi-cloud environments further underscores the need for robust data integration tools capable of operating seamlessly across different infrastructures, further boosting market growth.

From a regional perspective, North America currently dominates the graph data integration platform market, accounting for the largest share due to early adoption of advanced data technologies, a strong presence of key market players, and significant investments in digital transformation initiatives. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid industrialization, expanding IT infrastructure, and increasing adoption of cloud-based solutions among enterprises in countries like China, India, and Japan. Europe also remains a significant contributor, supported by stringent data privacy regulations and a mature digital economy.

Component Analysis

The component segment of the graph data integration platform market is bifurcated into software and services. The software segment currently commands the largest market share, reflecting the critical role of robust graph database engines, visualization tools, and integration frameworks in managing and analyzing complex data relationships. These software solutions are designed to deliver high scalability, flexibility, and real-time proces

The Hadith Isnad narrators data may be useful for the researcher to better adapt these techniques for particular problems. This data is developed for future research on the public repository for all the Research Institutes, Scientific and Islamic communities who want to work on Hadith's domain. This dataset contains two types of excel documents: Hadith_SahihMuslim_CoreInfo.xlsx file (7748 records) and Hadith_SahihMuslim_DetailsInfo_Sanad_Narrators.xlsx document (77797 records). The data contains 7748 Hadiths and 2092 unique records of Narrators of All Sahih Muslim Hadith

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains time series figures (shown in the report) generated for baseline and crdp mine footprints , which represent the footprints used in the surface water modelling. The footprints are contained within a single shapefile (HUN Mine footprints for timeseries) and the timelines contained within the the spreadhseet (HUN mine time series tables v01). ## Dataset History The footprints are contained within a single shapefile (HUN Mine footprints for timeseries) and the timelines contained within the the spreadsheet (HUN mine time series tables v01). Timelines for all mines were assembled into the spreadsheet Mine_files_summary_Final.xlsx. The script MineFootprint_TimeSeries_Final.m reads the data from the spreadsheet and creates the time series figures in png format which form the dataset. ## Dataset Citation Bioregional Assessment Programme (XXXX) HUN Mine Footprints Timeseries Graph v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/11493517-df5f-49ed-84dc-23afdbe00c5e. ## Dataset Ancestors * Derived From HUN Groundwater footprint polygons v01 * Derived From HUN mine time series tables v01 * Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 * Derived From HUN Historical Landsat Images Mine Foot Prints v01 * Derived From Historical Mining footprints DTIRIS HUN 20150707 * Derived From HUN Mine footprints for timeseries * Derived From Climate model 0.05x0.05 cells and cell centroids * Derived From HUN Historical Landsat Derived Mine Foot Prints v01 * Derived From HUN SW footprint shapefiles v01 * Derived From Mean Annual Climate Data of Australia 1981 to 2012

Snapshot img

Graph Database Market Size 2025-2029

The graph database market size is valued to increase by USD 11.24 billion, at a CAGR of 29% from 2024 to 2029. Open knowledge network gaining popularity will drive the graph database market.

Market Insights

North America dominated the market and accounted for a 46% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 1.51 billion in 2023
By Type - RDF segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 670.01 million 
Market Future Opportunities 2024: USD 11235.10 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth due to the increasing demand for low-latency query capabilities and the ability to handle complex, interconnected data. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures. One real-world business scenario where graph databases excel is in supply chain optimization. In this context, graph databases can help identify the shortest path between suppliers and consumers, taking into account various factors such as inventory levels, transportation routes, and demand patterns. This can lead to increased operational efficiency and reduced costs.
However, the market faces challenges such as the lack of standardization and programming flexibility. Graph databases, while powerful, require specialized skills to implement and manage effectively. Additionally, the market is still evolving, with new players and technologies emerging regularly. Despite these challenges, the potential benefits of graph databases make them an attractive option for businesses seeking to gain a competitive edge through improved data management and analysis.

What will be the size of the Graph Database Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market is an evolving landscape, with businesses increasingly recognizing the value of graph technology for managing complex and interconnected data. According to recent research, the adoption of graph databases is projected to grow by over 20% annually, surpassing traditional relational databases in certain use cases. This trend is particularly significant for industries requiring advanced data analysis, such as finance, healthcare, and telecommunications. Compliance is a key decision area where graph databases offer a competitive edge. By modeling data as nodes and relationships, organizations can easily trace and analyze interconnected data, ensuring regulatory requirements are met. Moreover, graph databases enable real-time insights, which is crucial for budgeting and product strategy in today's fast-paced business environment.
Graph databases also provide superior performance compared to traditional databases, especially in handling complex queries involving relationships and connections. This translates to significant time and cost savings, making it an attractive option for businesses seeking to optimize their data management infrastructure. In conclusion, the market is experiencing robust growth, driven by its ability to handle complex data relationships and offer real-time insights. This trend is particularly relevant for industries dealing with regulatory compliance and seeking to optimize their data management infrastructure.

Unpacking the Graph Database Market Landscape

In today's data-driven business landscape, the adoption of graph databases has surged due to their unique capabilities in handling complex network data modeling. Compared to traditional relational databases, graph databases offer a significant improvement in query performance for intricate relationship queries, with some reports suggesting up to a 500% increase in query response time. Furthermore, graph databases enable efficient data lineage tracking, ensuring regulatory compliance and enhancing data version control. Graph databases, such as property graph models and RDF databases, facilitate node relationship management and real-time graph processing, making them indispensable for industries like finance, healthcare, and social media. With the rise of distributed and knowledge graph databases, organizations can achieve scalability and performance improvements, handling massive datasets with ease. Security, indexing, and deployment are essential aspects of graph databases, ensuring data integrity and availability. Query performance tuning and graph analytics libraries further enhance the value of graph databases in data integration and business intelligence applications. Ultimately, graph databases offer a powerful alternative to NoSQL databases, providing a more flexible and efficient approach to managing complex data relationships.

Key Market Drivers Fueling Growth

The growing popularity o

This dataset contains both a PDF report of the LEAR in question, and an Excel spreadsheet of data that sits behind the graphs and maps in the report. The report can be found under Additional Documention section below, and the spreadsheet of backing under Raw Files.

Graph Database Vector Search Market Outlook

According to our latest research, the global Graph Database Vector Search market size reached USD 2.35 billion in 2024, exhibiting robust growth driven by the increasing demand for advanced data analytics and AI-powered search capabilities. The market is expected to expand at a CAGR of 21.7% during the forecast period, propelling the market size to an anticipated USD 16.8 billion by 2033. This remarkable growth trajectory is primarily fueled by the proliferation of big data, the widespread adoption of AI and machine learning, and the growing necessity for real-time, context-aware search solutions across diverse industry verticals.

One of the primary growth factors for the Graph Database Vector Search market is the exponential increase in unstructured and semi-structured data generated by enterprises worldwide. Organizations are increasingly seeking efficient ways to extract meaningful insights from complex datasets, and graph databases paired with vector search capabilities are emerging as the preferred solution. These technologies enable organizations to model intricate relationships and perform semantic searches with unprecedented speed and accuracy. Additionally, the integration of AI and machine learning algorithms with graph databases is enhancing their ability to deliver context-rich, relevant results, thereby improving decision-making processes and business outcomes.

Another significant driver is the rising adoption of recommendation systems and fraud detection solutions across various sectors, particularly in BFSI, retail, and e-commerce. Graph database vector search platforms excel at identifying patterns, anomalies, and connections that traditional relational databases often miss. This capability is crucial for detecting fraudulent activities, building sophisticated recommendation engines, and powering knowledge graphs that underpin intelligent digital experiences. The growing need for personalized customer engagement and proactive risk mitigation is prompting organizations to invest heavily in these advanced technologies, further accelerating market growth.

Furthermore, the shift towards cloud-based deployment models is catalyzing the adoption of graph database vector search solutions. Cloud platforms offer scalability, flexibility, and cost-effectiveness, making it easier for organizations of all sizes to implement and scale graph-powered applications. The availability of managed services and API-driven architectures is reducing the complexity associated with deployment and maintenance, enabling faster time-to-value. As more enterprises migrate their data infrastructure to the cloud, the demand for cloud-native graph database vector search solutions is expected to surge, driving sustained market expansion.

Geographically, North America currently dominates the Graph Database Vector Search market, owing to its advanced IT infrastructure, high adoption rate of AI-driven technologies, and presence of leading technology vendors. However, rapid digital transformation initiatives across Europe and the Asia Pacific are positioning these regions as high-growth markets. The increasing focus on data-driven decision-making, coupled with supportive regulatory frameworks and government investments in AI and big data analytics, is expected to fuel robust growth in these regions over the forecast period.

Component Analysis

The Component segment of the Graph Database Vector Search market is broadly categorized into software and services. The software sub-segment commands the largest share, driven by the relentless innovation in graph database technologies and the integration of advanced vector search functionalities. Organizations are increasingly deploying graph database software to manage complex data relationships, power semantic search, and enhance the performance of AI and machine learning applications. The software market is characterized by the proliferation of both open-source and proprietary solutions, with vendors

Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?:

This data set contains all the experimental raw data, analysis and source files for the final figures reported in the manuscript: "Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?". It is divided into five (1-5) zipped folders, named as the technique used to obtain the data. Each of them, where applicable, consists of three different subfolders (raw data, analysed data, final graph). Read below for more details.

1) ConfocalMicroscopy

1a) Raw_Data: the raw images are reported as .dat and .tif formats, divided into folders (according to date first yymmdd, and within the same day according to composition). Each folder contains a .txt file reporting the experimental details

1b) GUVs_Statistics - GUVs_Statistics.txt explains how we generated the bar plot shown in Fig. 1E

1c) Final_Graph - Figure_1B_1D.png is the figure representing figure 1B and 1D - Figure1E_%ofGUVswithCaMAdsorbptions.csv is the source file x-y of the bar plot shown in figure 1E (% of GUVs which showed adsorption of CaM over the total amount of measured GUVs) - Where_To_Find_Representative_Images.txt states the folders where the raw images chosen for figure 1 can be found

2) FCS 2a) Raw_Data: - 1_points: .ptu files - 2_points: .ht3 files - Raw_Data_Description.docx which compositions and conditions correspond to which point in the two data sets 2b) Final_Graphs: - Figure_2A.xlsx contains the x-y source file for figure 2A

2c) Analysis: - FCS_Fits.xlsx outcome of the global fitting procedure described in the .docx below (each group of points represents a certain composition and calcium concentration, read the Raw_Data_Description.docx in the FCS > Raw_Data) - Notes_for_FCS_Analysis.docx contains a brief description of the analysis of the autocorrelation curves

3) GPLaurdan 3a) Raw Data: all the spectra are stored in folders named by date (yymmdd_lipidcomposition_Laurdan) and are in both .FS and .txt formats

3b) GP calculations: contains all the .xlsx files calculating the GP values from the raw emission and excitation spectra

3c) Final_Graphs - Data_Processing_For_Fig_2D.csv contains the data processing from the GP values calculated from the spectra to the DeltaGP (GP with- GP without CaM) reported in fig. 2D - Figure_2C_2D.xlsx contains the x-y source file for the figure 2C and 2D

4) LiveCellsImaging

3a) Intensity_Protrusions_vs_Cell_Body: - contains all the .xlsx files calculating the intensity of the various images. File renamed by date (yymmdd) - All data in all excel sheets gathered in another Excel file to create a final graph

3b) Final_Graphs - Figure_S2B.xlsx contains the x-y source file for the figure S2B

5) LiveCellImaging_Raw_Data: it contains some of the images, which are given in .tif. They are divided by date (yymmdd) and each contains subfolders renamed by sample name, concentration of ionomycin. Within the subfolders, the images are divided into folders distinguishing the data acquired before and after the ionomycin treatment and the incubation time.

6) 211124_BioCev_Imaging_1 folder has the .jpg files of the time laps, these are shown in fig 1A and S2.

7) 211124_BioCev_Imaging_2 and 8) 211124_BioCev_Imaging_3 contain the images of HeLa cells expressing EGFP-CaM after treatment with ionomycin 200 nM (A1) and 1 uM (A2), respectively.

9) SPR

9a) Raw Data: - SPR_Raw_Data.xlsx x/y exported sensorgrams - the .jpg files of the software are also reported and named by lipid composition

9b) Final_Graph: - Fig.2B.xlsx contains the x-y source file for the figure 2B

9c) Analysis - SPR_Analysis.xlsx: excel file containing step-by-step (sheet by sheet) how we processed the raw data to obtain the final figure (details explained in the .docx below) - Analysis of SPR data_notes.docx: read me for detailed explanation

Graph Neural Network Platform Market Outlook

According to our latest research, the global Graph Neural Network (GNN) Platform market size is valued at USD 1.08 billion in 2024, underscoring its rapid ascent in the artificial intelligence domain. The market is projected to expand at a robust CAGR of 32.4% from 2025 to 2033, reaching an estimated USD 13.5 billion by 2033. This remarkable growth trajectory is fueled by the increasing adoption of graph-based deep learning for complex data analytics, especially in sectors such as BFSI, healthcare, and IT & telecommunications, where traditional AI models fall short in capturing intricate data relationships.

One of the primary growth drivers for the Graph Neural Network Platform market is the exponential increase in connected data and the need for advanced analytics to derive actionable insights from it. With the proliferation of IoT devices, social networks, and enterprise systems, organizations are accumulating vast volumes of data with complex interdependencies. GNN platforms excel in analyzing these intricate networks, enabling businesses to uncover hidden patterns, detect anomalies, and optimize decision-making processes. The ability of GNNs to model relationships in data far surpasses conventional machine learning algorithms, making them indispensable for applications like fraud detection, recommendation systems, and knowledge graph construction.

Moreover, the growing emphasis on personalized customer experiences and targeted marketing strategies is accelerating the adoption of Graph Neural Network Platforms in retail, e-commerce, and financial services. Enterprises are leveraging GNNs to enhance recommendation engines, predict customer behavior, and deliver hyper-personalized offerings, thereby increasing customer engagement and retention. In the healthcare sector, GNNs are revolutionizing drug discovery and patient care by facilitating the analysis of biological networks, protein interactions, and disease pathways. This technological edge, combined with increasing investments in AI research and development, is propelling the market forward at an unprecedented pace.

Another significant factor contributing to the market’s growth is the rapid evolution of cloud computing and scalable infrastructure. Cloud-based deployment modes are making GNN platforms more accessible to organizations of all sizes, eliminating the need for heavy upfront investments in hardware and specialized personnel. The integration of GNNs with big data analytics, edge computing, and other AI technologies is further expanding their use cases across industries. As regulatory frameworks mature and data privacy concerns are addressed, adoption rates are expected to soar, particularly in regions with strong digital transformation initiatives.

From a regional perspective, North America currently dominates the Graph Neural Network Platform market due to its robust technological ecosystem, high concentration of AI startups, and significant R&D investments. However, the Asia Pacific region is emerging as a formidable contender, driven by rapid digitization, government support for AI initiatives, and the presence of large-scale enterprises in countries like China, India, and Japan. Europe also represents a substantial share, bolstered by stringent data regulations and a focus on innovation in healthcare and finance. Latin America and the Middle East & Africa are gradually catching up, fueled by growing awareness and adoption of advanced analytics solutions.

Component Analysis

The Component segment of the Graph Neural Network Platform market is bifurcated into Software and Services, each playing a pivotal role in the ecosystem. The Software sub-segment dominates the market, accounting for over 68% of the total revenue in 2024. This dominance is attributed to the increasing demand for robust, scalable, and easy-to-integrate GNN frameworks and libraries that can be tailored for diverse use cases. Software solutions are continuously evolving to offer greater flexibility, interoperability with existing data systems, and user-friendly interfaces that cater to both data scientists and business analysts. The proliferation of open-source GNN libraries and the integration of proprietary features by leading vendors are further enhancing the value proposition for enterprises.<br

This dataset contains both a PDF report of the LEAR in question, and an Excel spreadsheet of data that sits behind the graphs and maps in the report. The report can be found under Additional Documention section below, and the spreadsheet of backing under Raw Files.

This dataset contains both a PDF report of the LEAR in question, and an Excel spreadsheet of data that sits behind the graphs and maps in the report. The report can be found under Additional Documention section below, and the spreadsheet of backing under Raw Files.

About Datasets: - Domain : Finance - Project: Bank loan of customers - Datasets: Finance_1.xlsx & Finance_2.xlsx - Dataset Type: Excel Data - Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revol_bal 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains Power Query, Power Pivot, Merge data, Clustered Bar Chart, Clustered Column Chart, Line Chart, 3D Pie chart, Dashboard, slicers, timeline, formatting techniques.

This dataset contains both a PDF report of the LEAR in question, and an Excel spreadsheet of data that sits behind the graphs and maps in the report. The report can be found under Additional Documention section below, and the spreadsheet of backing under Raw Files.

This dataset contains both a PDF report of the LEAR in question, and an Excel spreadsheet of data that sits behind the graphs and maps in the report. The report can be found under Additional Documention section below, and the spreadsheet of backing under Raw Files.

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

About Datasets:

Domain : Sales Project: McDonalds Sales Analysis Project Dataset: START-Dashboard Dataset Type: Excel Data Dataset Size: 100 records

KPI's: 1. Customer Satisfaction 2. Sales by Country 2022 3. 2021-2022 Sales Trend 4. Sales 5. Profit 6. Customers

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains dashboard, hyperlink, shapes, icons, map, radar chart, line chart, doughnut chart, KPIs, formatting.

This is a collection of Excel spreadsheet tables in support of the article "A pan-genome method to determine core regions of the Bacillus subtilis and Escherichia coli genomes" version 2. The tables show characteristics of pan-genome graph (PGG) determined core genes for Bacillus subtilis and Escherichia coli.Table 1. Selection of complete genomes for B. subtilis and E. coli PGGs. Table 2. Pan-genome graph statistics for B. subtilis and E. coli.Table 3. The number of deleted genes from B. subtilis reduced strains which are noncore versus core.Table 4. Large noncore regions which have not been deleted from any of the strains delta 6, IIG-Bs27-47-24, or PG10, PS38.Supplementary Table 1. All B. subtilis genes compared to the PGG based annotation of core regions for the type strain genome used by Kobayashi et al and Koo et al. (strain 168, GenBank sequence AL009126.3, BioSample SAMEA3138188, Assembly ASM904v1/GCF_000009045.1). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of synonyms for B. subtilis genes associated with the Koo and Kobayashi genes. Column 13 is the Koo et al. gene symbol/name. Columns 14-15 are the Kobayashi et al. gene symbol/name and evidence type (from Supporting Table 4 “RB, reference to study with Bacillus subtilis; RO, reference to study with other bacteria; TW, this work; TW*, inactivation failed but IPTG mutant could not be made”). Columns 16-17 are the GenBank protein product accession and name. Column 18 is the PGG core or non-core region the gene is contained in. Column 19 indicates if the gene is in MiniBacillus. Columns 20-23 show genes deleted for strains delta 6, IIG-Bs27-47-24, PG10, and PS38 respectively.Supplementary Table 2. The 305 B. subtilis genes deemed essential by either Kobayashi et al. or Koo et al. These genes are compared to the PGG based annotation of core regions for the type strain genome used by Kobayashi et al. and Koo et al. (strain 168, GenBank sequence AL009126.3, BioSample SAMEA3138188, Assembly ASM904v1/GCF_000009045.1). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of synonyms for B. subtilis genes associated with the Koo and Kobayashi genes. Column 13 is the Koo et al. gene symbol/name. Columns 14-15 are the Kobayashi et al. gene symbol/name and evidence type (from Supporting Table 4 “RB, reference to study with Bacillus subtilis; RO, reference to study with other bacteria; TW, this work; TW*, inactivation failed but IPTG mutant could not be made”). Columns 16-17 are the GenBank protein product accession and name. Column 18 is the PGG core or non-core region the gene is contained in.Supplementary Table 3. The 258 B. subtilis core regions identified through the pan-genome graph.Supplementary Table 4. All B. subtilis tRNA and rRNA genes for the type strain genome (strain 168, GenBank sequence AL009126.3, BioSample SAMEA3138188, Assembly ASM904v1/GCF_000009045.1) compared to the refined PGG. Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation.Supplementary Table 5. The 108 B. subtilis genomes used in the study. Data is from GenBank RefSeq: BioSample ID, Assembly ID, GenBank Species, GenBank Strain, Genome Size, and whether the genome is a type strain.Supplementary Table 6. The 34 protein coding genes from MiniBacillus 3 which were not in all 108 B. subtilis genomes.Supplementary Table 7. The 414 E. coli genes deemed essential by Goodall et al., Baba et al., or Yamazaki et al. These genes are compared to the PGG based annotation of core regions for the K-12 BW25113 strain used by Goodall (GenBank sequence CP009273.1, Assembly ASM75055v1/GCA_000750555.1, BioSample SAMN03013572). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of gene synonyms for the gene from GenBank. Columns 13-21 are from Goodall et al.: 13-15 from Table S1 (normal essentiality), 16-18 from Table S4 (essentiality after outgrowth), 19-20 from Table S3 (outlier discrepancies), and 21 from Table S2 (comparison of data sets). Column 22 is the PGG core or non-core region the gene is contained in.Supplementary Table 8. The 521 E. coli core regions identified through the pan-genome graph.Supplementary Table 9. All E. coli genes compared to the PGG based annotation of core regions for the K-12 BW25113 strain used by Goodall (GenBank sequence CP009273.1, Assembly ASM75055v1/GCA_000750555.1, BioSample SAMN03013572). Columns 1-5 are the start, stop, strand, OGC, and OGC size for the PGG annotation. Columns 6-11 are the gene type, start, stop, strand, locus tag, and gene symbol/name for the GenBank annotation. Column 12 is a list of gene synonyms for the gene from GenBank. Columns 13-21 are from Goodall et al.: 13-15 from Table S1 (normal essentiality), 16-18 from Table S4 (essentiality after outgrowth), 19-20 from Table S3 (outlier discrepancies), and 21 from Table S2 (comparison of data sets). Column 22 is the PGG core or non-core region the gene is contained in.Supplementary Table 10. The 971 E. coli genomes used in the study. Data is from GenBank RefSeq: BioSample ID, Assembly ID, GenBank Species, GenBank Strain, Genome Size, and whether the genome is a type strain.