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.

Graph Database Market Outlook

The global graph database market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 8.5 billion by 2032, growing at a CAGR of 21.2% from 2024 to 2032. The substantial growth of this market is driven primarily by increasing data complexity, advancements in data analytics technologies, and the rising need for more efficient database management systems.

One of the primary growth factors for the graph database market is the exponential increase in data generation. As organizations generate vast amounts of data from various sources such as social media, e-commerce platforms, and IoT devices, the need for sophisticated data management and analysis tools becomes paramount. Traditional relational databases struggle to handle the complexity and interconnectivity of this data, leading to a shift towards graph databases which excel in managing such intricate relationships.

Another significant driver is the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies. These technologies rely heavily on connected data for predictive analytics and decision-making processes. Graph databases, with their inherent ability to model relationships between data points effectively, provide a robust foundation for AI and ML applications. This synergy between AI/ML and graph databases further accelerates market growth.

Additionally, the increasing prevalence of personalized customer experiences across industries like retail, finance, and healthcare is fueling demand for graph databases. Businesses are leveraging graph databases to analyze customer behaviors, preferences, and interactions in real-time, enabling them to offer tailored recommendations and services. This enhanced customer experience translates to higher customer satisfaction and retention, driving further adoption of graph databases.

From a regional perspective, North America currently holds the largest market share due to early adoption of advanced technologies and the presence of key market players. However, significant growth is also anticipated in the Asia-Pacific region, driven by rapid digital transformation, increasing investments in IT infrastructure, and growing awareness of the benefits of graph databases. Europe is also expected to witness steady growth, supported by stringent data management regulations and a strong focus on data privacy and security.

Component Analysis

The graph database market can be segmented into two primary components: software and services. The software segment holds the largest market share, driven by extensive adoption across various industries. Graph database software is designed to create, manage, and query graph databases, offering features such as scalability, high performance, and efficient handling of complex data relationships. The growth in this segment is propelled by continuous advancements and innovations in graph database technologies. Companies are increasingly investing in research and development to enhance the capabilities of their graph database software products, catering to the evolving needs of their customers.

On the other hand, the services segment is also witnessing substantial growth. This segment includes consulting, implementation, and support services provided by vendors to help organizations effectively deploy and manage graph databases. As businesses recognize the benefits of graph databases, the demand for expert services to ensure successful implementation and integration into existing systems is rising. Additionally, ongoing support and maintenance services are crucial for the smooth operation of graph databases, driving further growth in this segment.

The increasing complexity of data and the need for specialized expertise to manage and analyze it effectively are key factors contributing to the growth of the services segment. Organizations often lack the in-house skills required to harness the full potential of graph databases, prompting them to seek external assistance. This trend is particularly evident in large enterprises, where the scale and complexity of data necessitate robust support services.

Moreover, the services segment is benefiting from the growing trend of outsourcing IT functions. Many organizations are opting to outsource their database management needs to specialized service providers, allowing them to focus on their core business activities. This shift towards outsourcing is further bolstering the demand for graph database services, driving market growth.

The dataset named “Dataset: Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction” consists of Raw time-averaged images, which are generated by sequence of 100 frames extracted from experimental videos captured at various voltages (5V, 10V, 15V, 20V, and 50V), and saved as .tif files. These images were analysed to produce the data used in figure 2 and 3 of the article. The dataset also includes two Excel files named as “Figure 2_Experimental data.xlsx” and “Figure 3_Experimental data.xlsx”. These excel files contain the data used to create the experimental plots shown in Figure 2C, and Figure 3 of the research article respectively.

In the “Figure 2C_Experimental Data.xlsx” excel file, each sheet corresponds to a different voltage value shown in the figure, and contains three columns: A, B, and C. which represents the X-location, Y-location, and orientation angle (in degrees) of the experimental plot (red rods in the figure) respectively. This plot is overlaid on the model data (black rods in the figure) and displayed in Figure 2C given in the article.

The “Figure 3_Experimental data.xlsx” file contains three sheets for each voltage (5V, 10V, 15V, 20V, and 50V) and each of these three sheets provide data at three different X-locations (X=579, X= 1079, and X= 1779) as a function of Y-location as shown in the Figure 3 of the article. Each sheet has five columns: A, B, C, D, and E. These columns represent the X-location, Y-location, Orientation angle (in degrees), Coherency, and Error in the orientation angle (in degrees), respectively. These data points are used to create the experimental scatter plot shown in Figure 3 of the article.

I put the machine in manual mode so I can change the T when I needed to and I would take data points at 30 seconds for melting and annealing temperatures and at 30s and 5 minutes for extending T. This is how long I would be doing that step during the PCR reaction so it seemed to make sense. Attached is a chart of the Set T and the recorded T at each interval, the raw data of recorded T, a converted recorded T excel file, and the plot of that data.

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

Numerical data (Excel spreadsheet) that underly the graphs in Figs 2G, 2H, 4E, 5E, 6I, 6J, 7C,

Introduction Preservation and management of semi-arid ecosystems requires understanding of the processes involved in soil erosion and their interaction with plant community. Rainfall simulations on natural plots provide an effective way of obtaining a large amount of erosion data under controlled conditions in a short period of time. This dataset contains hydrological (rainfall, runoff, flow velocity), erosion (sediment concentration and rate), vegetation (plant cover), and other supplementary information from 272 rainfall simulation experiments conducted on 23 rangeland locations in Arizona and Nevada between 2002 and 2013. The dataset advances our understanding of basic hydrological and biological processes that drive soil erosion on arid rangelands. It can be used to quantify runoff, infiltration, and erosion rates on a variety of ecological sites in the Southwestern USA. Inclusion of wildfire and brush treatment locations combined with long term observations makes it important for studying vegetation recovery, ecological transitions, and effect of management. It is also a valuable resource for erosion model parameterization and validation. Instrumentation Rainfall was generated by a portable, computer-controlled, variable intensity simulator (Walnut Gulch Rainfall Simulator). The WGRS can deliver rainfall rates ranging between 13 and 178 mm/h with variability coefficient of 11% across 2 by 6.1 m area. Estimated kinetic energy of simulated rainfall was 204 kJ/ha/mm and drop size ranged from 0.288 to 7.2 mm. Detailed description and design of the simulator is available in Stone and Paige (2003). Prior to each field season the simulator was calibrated over a range of intensities using a set of 56 rain gages. During the experiments windbreaks were setup around the simulator to minimize the effect of wind on rain distribution. On some of the plots, in addition to rainfall only treatment, run-on flow was applied at the top edge of the plot. The purpose of run-on water application was to simulate hydrological processes that occur on longer slopes (>6 m) where upper portion of the slope contributes runoff onto the lower portion. Runoff rate from the plot was measured using a calibrated V-shaped supercritical flume equipped with depth gage. Overland flow velocity on the plots was measured using electrolyte and fluorescent dye solution. Dye moving from the application point at 3.2 m distance to the outlet was timed with stopwatch. Electrolyte transport in the flow was measured by resistivity sensors imbedded in edge of the outlet flume. Maximum flow velocity was defined as velocity of the leading edge of the solution and was determined from beginning of the electrolyte breakthrough curve and verified by visual observation (dye). Mean flow velocity was calculated using mean travel time obtained from the electrolyte solution breakthrough curve using moment equation. Soil loss from the plots was determined from runoff samples collected during each run. Sampling interval was variable and aimed to represent rising and falling limbs of the hydrograph, any changes in runoff rate, and steady state conditions. This resulted in approximately 30 to 50 samples per simulation. Shortly before every simulation plot surface and vegetative cover was measured at 400 point grid using a laser and line-point intercept procedure (Herrick et al., 2005). Vegetative cover was classified as forbs, grass, and shrub. Surface cover was characterized as rock, litter, plant basal area, and bare soil. These 4 metrics were further classified as protected (located under plant canopy) and unprotected (not covered by the canopy). In addition, plant canopy and basal area gaps were measured on the plots over three lengthwise and six crosswise transects. Experimental procedure Four to eight 6.1 m by 2 m replicated rainfall simulation plots were established on each site. The plots were bound by sheet metal borders hammered into the ground on three sides. On the down slope side a collection trough was installed to channel runoff into the measuring flume. If a site was revisited, repeat simulations were always conducted on the same long term plots. The experimental procedure was as follows. First, the plot was subjected to 45 min, 65 mm/h intensity simulated rainfall (dry run) intended to create initial saturated condition that could be replicated across all sites. This was followed by a 45 minute pause and a second simulation with varying intensity (wet run). During wet runs two modes of water application were used as: rainfall or run-on. Rainfall wet runs typically consisted of series of application rates (65, 100, 125, 150, and 180 mm/h) that were increased after runoff had reached steady state for at least five minutes. Runoff samples were collected on the rising and falling limb of the hydrograph and during each steady state (a minimum of 3 samples). Overland flow velocities were measured during each steady state as previously described. When used, run-on wet runs followed the same procedure as rainfall runs, except water application rates varied between 100 and 300 mm/h. In approximately 20% of simulation experiments the wet run was followed by another simulation (wet2 run) after a 45 min pause. Wet2 runs were similar to wet runs and also consisted of series of varying intensity rainfalls and/or run-on inputs. Resulting Data The dataset contains hydrological, erosion, vegetation, and ecological data from 272 rainfall simulation experiments conducted on 12 sq. m plots at 23 rangeland locations in Arizona and Nevada. The experiments were conducted between 2002 and 2013, with some locations being revisited multiple times. Resources in this dataset:Resource Title: Appendix B. Lists of sites and general information. File Name: Rainfall Simulation Sites Summary.xlsxResource Description: The table contains list or rainfall simulation sites and individual plots, their coordinates, topographic, soil, ecological and vegetation characteristics, and dates of simulation experiments. The sites grouped by common geographic area.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix F. Site pictures. File Name: Site photos.zipResource Description: Pictures of rainfall simulation sites and plots.Resource Title: Appendix C. Rainfall simulations. File Name: Rainfall simulation.csvResource Description: Please see Appendix C. Rainfall simulations (revised) for data with errors corrected (11/27/2017). The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experimentsResource Software Recommended: MS Access,url: https://products.office.com/en-us/access Resource Title: Appendix C. Rainfall simulations. File Name: Rainfall simulation.csvResource Description: Please see Appendix C. Rainfall simulations (revised) for data with errors corrected (11/27/2017). The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experimentsResource Software Recommended: MS Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix E. Simulation sites map. File Name: Rainfall Simulator Sites Map.zipResource Description: Map of rainfall simulation sites with embedded images in Google Earth.Resource Software Recommended: Google Earth,url: https://www.google.com/earth/ Resource Title: Appendix D. Ground and vegetation cover. File Name: Plot Ground and Vegetation Cover.csvResource Description: The table contains ground (rock, litter, basal, bare soil) cover, foliar cover, and basal gap on plots immediately prior to simulation experiments. Resource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access Resource Title: Appendix D. Ground and vegetation cover. File Name: Plot Ground and Vegetation Cover.csvResource Description: The table contains ground (rock, litter, basal, bare soil) cover, foliar cover, and basal gap on plots immediately prior to simulation experiments. Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix A. Data dictionary. File Name: Data dictionary.csvResource Description: Explanation of terms and unitsResource Software Recommended: MS Excel,url: https://products.office.com/en-us/excel Resource Title: Appendix A. Data dictionary. File Name: Data dictionary.csvResource Description: Explanation of terms and unitsResource Software Recommended: MS Access,url: https://products.office.com/en-us/access Resource Title: Appendix C. Rainfall simulations (revised). File Name: Rainfall simulation (R11272017).csvResource Description: The table contains rainfall, runoff, sediment, and flow velocity data from rainfall simulation experiments (updated 11/27/2017)Resource Software Recommended: Microsoft Access,url: https://products.office.com/en-us/access

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