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.

Using the User Manual as a guide and the Excel Graph Input Data Example file as a reference, the user enters the semantics of the graph model in this file.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

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.

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).

In "Sample Student Data", there are 6 sheets. There are three sheets with sample datasets, one for each of the three different exercise protocols described (CrP Sample Dataset, Glycolytic Dataset, Oxidative Dataset). Additionally, there are three sheets with sample graphs created using one of the three datasets (CrP Sample Graph, Glycolytic Graph, Oxidative Graph). Each dataset and graph pairs are from different subjects. · CrP Sample Dataset and CrP Sample Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the creatine phosphate system. Here, the subject was a track and field athlete who threw the shot put for the DeSales University track team. The NIRS monitor was placed on the right triceps muscle, and the student threw the shot put six times with a minute rest in between throws. Data was collected telemetrically by the NIRS device and then downloaded after the student had completed the protocol. · Glycolytic Dataset and Glycolytic Graph: This is an example of a dataset and graph created from an exercise protocol designed to stress the glycolytic energy system. In this example, the subject performed continuous squat jumps for 30 seconds, followed by a 90 second rest period, for a total of three exercise bouts. The NIRS monitor was place on the left gastrocnemius muscle. Here again, data was collected telemetrically by the NIRS device and then downloaded after he had completed the protocol. · Oxidative Dataset and Oxidative Graph: In this example, the dataset and graph are from an exercise protocol designed to stress the oxidative system. Here, the student held a sustained, light-intensity, isometric biceps contraction (pushing against a table). The NIRS monitor was attached to the left biceps muscle belly. Here, data was collected by a student observing the SmO2 values displayed on a secondary device; specifically, a smartphone with the IPSensorMan APP displaying data. The recorder student observed and recorded the data on an Excel Spreadsheet, and marked the times that exercise began and ended on the Spreadsheet.

Mobile Application Coverage: The 30% Curse and Ways Forward## Purpose In this artifact, we provide the information about our benchmarks used for manual and tool exploration. We include coverage results achieved by tools and human analysts as well as plots of the coverage progression over time for analysts. We further provide manual analysis results for our case study, more specifically extracted reasons for unreachability for the case study apps and extracted code-level properties, which constitute a ground truth for future work in coverage explainability. Finally, we identify a list of beyond-GUI exploration tools and categorize them for future work to take inspiration from. We are claiming available and reusable badges; the artifact is fully aligned with the results described in our paper and comprehensively documented.## ProvenanceThe paper preprint is available here: https://people.ece.ubc.ca/mjulia/publications/Mobile_Application_Coverage_ICSE2025.pdf## Data The artifact submission is organized into five parts:- 'BenchInfo' excel sheet describing our experiment dataset- 'Coverage' folder containing coverage results for tools and analysts (RQ1) - 'Reasons' excel sheet describing our manually extracted reasons for unreachability (RQ2)- 'ActivationProperties' excel sheet describing our manually extracted code properties of unreached activities (RQ3)- 'ActivationProperties-Graph' pdf which presents combinations of the extracted code properties in a graph format.- 'BeyondGUI' folder containing information about identified techniques which go beyond GUI exploration.The artifact requires about 15MB of storage.### Dataset: 'BenchInfo.xlsx'This file list the full application dataset used for experiments into three tabs: 'BenchNotGP' (apps from AndroTest dataset which are not on Google Play), 'BenchGP' (apps from AndroTest which are also on Google Play) and 'TopGP' (top ranked free apps from Google Play). Each tab contains the following information:- Application Name- Package Name- Version Used (Latest)- Original Version- # Activities- Minimum SDK- Target SDK- # Permissions (in Manifest)- List of Permissions (in Manifest)- # Features (in Manifest)- List of Features (in Manifest)The 'TopGP' sheet also includes Google-Play-specific information, namely:- Category (one of 32 app categories)- Downloads- Popularity RankThe 'BenchGP' and 'BenchNotGP' sheets also include the original version (included in the AndroTest benchmark) and the source (one of F-Droid, Github or Google Code Archives).### RQ1: 'Coverage'The 'Coverage' folder includes coverage results for tools and analysts, and is structured as follows:- 'CoverageResults.xlsx": An excel sheet containing the coverage results achieved by each human analysts and tool. - The first tab described the results over all apps for analysts combined, tools combined, and analysts + tools, which map to Table II in the paper. - Each of the following 42 tab, one per app in TopGP, marks the activities reached by Analyst 1, Analyst 2, Tool 1 (ape) and Tool 2 (fastbot), with an 'x' in the corresponding column to indicate that the activity was reached by the given agent.- 'Plots': A folder containing plots of the progressive coverage over time of analysts, split into one folder for 'Analyst1' and one for 'Analyst2'. - Each of the analysts' folder includes a subfolder per benchmark ('BenchNotGP', 'BenchGP' and 'TopGP'), containing as many png files as applications in the benchmark (respectively 47, 14 and 42 image files) named 'ANALYST_[X]_[APP_PACKAGE_NAME]'.png.### RQ2: 'Reasons.xslx'This file contains the extracted reasons for unreachability for the 11 apps manually analyzed. - The 'Summary' tab provides an overview of unreached activities per reasons over all apps and per app, which corresponds to Table III in the paper. - The following 11 tabs, each corresponding to and named after a single application, describe the reasons associated with each activity of that application. Each column corresponds to a single reason and 'x' indicates that the activity is unreached due to the reason in that column. The top row sums up the total number of activities unreached due to a given reason in each column.- The activities at the bottom which are greyed out correspond to activities that were reached during exploration, and are thus excluded from the reason extraction.### RQ3: 'ActivationProperties.xslx'This file contains the full list of activation properties extracted for each of the 185 activities analyzed for RQ2.The first half of the columns (columns C-M) correspond to the reasons (excluding Transitive, Inconclusive and No Caller) and the second half (columns N-AD) correspond to properties described in Figure 5 in the paper, namely:- Exported- Activation Location: - Code: GUI/lifecycle, Other Android or App-specific - Manifest- Activation Guards: - Enforcement: In Code or In Resources - Restriction: Mandatory or Discretionary- Data: - Type: Parameters, Execution Dependencies - Format: Primitive, Strings, ObjectsThe rows are grouped by applications, and each row correspond to an activity of that application. 'x' in a given column indicates the presence of the property in that column within the analyzed path to the activity. The third and fourth rows sums up the numbers and percentages for each property, as reported in Figure 5.### RQ3: 'ActivationProperties-Graph.pdf'This file shows combinations of the individual properties listed in 'ActivationProperties.xlsx' in a graph format, extending the combinations described in Table IV with data (types and format) and reasons for unreachability.### BeyondGUIThis folder includes:- 'ToolInfo.xlsx': an excel sheet listing the identified 22 beyond-GUI papers, the date of publication, availability, invasiveness (Source code, Bytecode, framework, OS) and their targeting strategy (None, Manual or Automated).- ToolClassification.pdf': a pdf file describing our paper selection methodology as well as a classication of the techniques in terms of Invocation Strategy, Navigation Strategy, Value Generation Strategy, and Value Generation Types. We fully introduced these categories in the pdf file.## Requirements & technology skills assumed by the reviewer evaluating the artifactThe artifact entirely consists of Excel sheets which can be opened with common Excel visualization software, i.e., Microsoft Excel, coverage plots as PNG files and PDF files. It requires about 15MB of storage in total.No other specific technology skills are required of the reviewer evaluating the artifact.

Graph Database for Security Market Outlook

According to our latest research, the global graph database for security market size reached USD 2.1 billion in 2024. This dynamic sector is expanding rapidly, supported by a robust compound annual growth rate (CAGR) of 22.7% from 2025 to 2033. By the end of the forecast period in 2033, the market is expected to attain a value of USD 16.3 billion. This impressive trajectory is primarily driven by escalating cyber threats, the proliferation of complex digital ecosystems, and the increasing demand for advanced analytics in security operations.

One of the most significant growth factors for the graph database for security market is the exponential rise in cyberattacks and sophisticated threat vectors targeting organizations worldwide. As digital transformation accelerates across industries, enterprises are generating vast volumes of interconnected data, creating new vulnerabilities and attack surfaces. Traditional relational databases struggle to effectively manage and analyze such complex, highly connected datasets. In contrast, graph databases excel at mapping relationships and patterns, making them invaluable for identifying suspicious activities, tracking threat actors, and correlating diverse security events in real-time. The ability to visualize and traverse connections at scale empowers security teams to detect advanced persistent threats, insider attacks, and fraud schemes that would otherwise go unnoticed.

Another pivotal driver is the increasing regulatory pressure and compliance requirements faced by organizations in sectors such as BFSI, healthcare, and government. Regulations including GDPR, HIPAA, and PCI DSS demand robust data protection, rigorous access controls, and comprehensive audit trails. Graph database technologies enable organizations to model complex access hierarchies, monitor user behaviors, and ensure compliance with evolving legal frameworks. By providing granular visibility into user roles, permissions, and interactions, these solutions facilitate proactive risk management and timely incident response. The integration of artificial intelligence and machine learning with graph databases further enhances predictive analytics and automation in security operations, reducing the burden on human analysts and improving overall resilience.

The rapid adoption of cloud computing, IoT devices, and remote work models is reshaping the security landscape and fueling demand for graph database solutions. As organizations migrate workloads to multi-cloud and hybrid environments, the complexity of managing identities, access rights, and network flows increases exponentially. Graph databases provide a unified view of assets, users, and their interdependencies, enabling security teams to identify misconfigurations, detect lateral movement, and enforce zero-trust principles. The scalability and flexibility of cloud-based graph database offerings are particularly attractive to enterprises seeking to modernize their security infrastructure without incurring significant capital expenditures. Strategic investments in research and development, partnerships with cybersecurity vendors, and the emergence of managed graph database services are further propelling market growth.

Regionally, North America dominates the graph database for security market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major technology providers, high cybersecurity spending, and early adoption of advanced analytics solutions. Europe follows closely, driven by stringent data privacy regulations and a strong focus on digital sovereignty. The Asia Pacific region is witnessing the fastest growth, supported by rapid digitalization, government initiatives, and increased awareness of cybersecurity risks. Latin America and the Middle East & Africa are emerging as promising markets, although challenges such as limited infrastructure and skills gaps persist. Overall, regional dynamics are shaped by varying regulatory landscapes, industry maturity, and investment levels in digital security.

Component Analysis

The graph database for security market is segmented by component into software and services, each playing a critical role in the adoption and effectiveness of graph database solutions. The software segment comprises graph database management systems, visualization tools, analytics engines, and integration platforms. Thes

The semantic knowledge graph market is experiencing robust growth, driven by the increasing need for organizations to derive actionable insights from complex, unstructured data. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of big data necessitates efficient data management and knowledge extraction tools; semantic knowledge graphs excel in this arena by organizing information into easily understandable and interlinked structures. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of semantic knowledge graphs, improving their ability to process and analyze ever-increasing volumes of data. Thirdly, the growing adoption of cloud-based solutions is simplifying deployment and accessibility, further driving market growth. Key players like Microsoft, Google, and Yandex are heavily investing in this technology, creating a competitive yet innovative landscape. However, challenges remain, including the complexity of implementing these systems, high initial investment costs, and the need for skilled professionals to manage and interpret the resulting knowledge graphs. Despite these restraints, the long-term prospects for the semantic knowledge graph market are incredibly positive. The increasing demand for improved data governance, enhanced business intelligence, and personalized customer experiences will continue to fuel adoption across various sectors, including finance, healthcare, and manufacturing. The market segmentation is expected to evolve, with increasing specialization in specific industry verticals and the development of more sophisticated analytics tools built on top of semantic knowledge graph technologies. The focus will likely shift towards the integration of semantic knowledge graphs with other emerging technologies such as blockchain and the Internet of Things (IoT) to unlock even greater value from data. This convergence will lead to the emergence of smarter and more autonomous systems capable of decision-making based on comprehensive, contextualized knowledge. Regions like North America and Europe are anticipated to maintain significant market shares, though Asia-Pacific is projected to witness substantial growth driven by increasing digitalization and technological advancements.

With the user manual provided at the end of the research manuscript, and the Graph Input Data Example.xlsx as a reference, the user provides all the graph semantic data required to evaluate all the performance criteria for the system.These criteria include the probability that the principal target can be reached, and the costs, elapsed times and total vulnerability resulting from a penetration attempt by one or more intruders.This performance computation is accurate and efficient, requiring an insignificant amount of computation time.It also resolves all the statistical dependencies and probabilistic uncertainties believed to be an important challenge to a risk manager and his or her analysts.User enters the Graph Topological data in this excel file, thereby creating a topological model.

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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1wwwd-o-tgoogled-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/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://res1productsd-o-tofficed-o-tcom.vcapture.xyz/en-us/access