OSU_SnowCourse Summary: Manual snow course observations were collected over WY 2012-2014 from four paired forest-open sites chosen to span a broad elevation range. Study sites were located in the upper McKenzie (McK) River watershed, approximately 100 km east of Corvallis, Oregon, on the western slope of the Cascade Range and in the Middle Fork Willamette (MFW) watershed, located to the south of the McKenzie. The sites were designated based on elevation, with a range of 1110-1480 m. Distributed snow depth and snow water equivalent (SWE) observations were collected via monthly manual snow courses from 1 November through 1 April and bi-weekly thereafter. Snow courses spanned 500 m of forested terrain and 500 m of adjacent open terrain. Snow depth observations were collected approximately every 10 m and SWE was measured every 100 m along the snow courses with a federal snow sampler. These data are raw observations and have not been quality controlled in any way. Distance along the transect was estimated in the field. OSU_SnowDepth Summary: 10-minute snow depth observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meterological stations were located in the approximate center of each forest or open snow course transect. These data have undergone basic quality control. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN flags for missing data to NA, and added site attributes such as site name and cover. We replaced positive values with NA, since snow depth values in raw data are negative (i.e., flipped, with some correction to use the height of the sensor as zero). Thus, positive snow depth values in the raw data equal negative snow depth values. Second, the sign of the data was switched to make them positive. Then, the smooth.m (MATLAB) function was used to roughly smooth the data, with a moving window of 50 points. Third, outliers were removed. All values higher than the smoothed values +10, were replaced with NA. In some cases, further single point outliers were removed. OSU_Met Summary: Raw, 10-minute meteorological observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meteorological stations were located in the approximate center of each forest or open snow course transect. These stations were deployed to collect numerous meteorological variables, of which snow depth and wind speed are included here. These data are raw datalogger output and have not been quality controlled in any way. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN and 7999 flags for missing data to NA, and added site attributes such as site name and cover. OSU_Location Summary: Location Metadata for manual snow course observations and meteorological sensors. These data are compiled from GPS data for which the horizontal accuracy is unknown, and from processed hemispherical photographs. They have not been quality controlled in any way.

SoilExcel workflow, a tool designed to optimize soil data analysis. It covers data preparation, statistical analysis methods, and result visualization. SoilExcel integrates various environmental data types and applies advanced techniques to enhance accuracy in soil studies. The results demonstrate its effectiveness in interpreting complex data, aiding decision-making in environmental management projects. Background Understanding the intricate relationships and patterns within soil samples is crucial for various environmental and agricultural applications. Principal Component Analysis (PCA) serves as a powerful tool in unraveling the complexity of multivariate soil datasets. Soil datasets often consist of numerous variables representing diverse physicochemical properties, making PCA an invaluable method for: ∙Dimensionality Reduction: Simplifying the analysis without compromising data integrity by reducing the dimensionality of large soil datasets. ∙Identification of Dominant Patterns: Revealing dominant patterns or trends within the data, providing insights into key factors contributing to overall variability. ∙Exploration of Variable Interactions: Enabling the exploration of complex interactions between different soil attributes, enhancing understanding of their relationships. ∙Interpretability of Data Variance: Clarifying how much variance is explained by each principal component, aiding in discerning the significance of different components and variables. ∙Visualization of Data Structure: Facilitating intuitive comprehension of data structure through plots such as scatter plots of principal components, helping identify clusters, trends, and outliers. ∙Decision Support for Subsequent Analyses: Providing a foundation for subsequent analyses by guiding decision-making, whether in identifying influential variables, understanding data patterns, or selecting components for further modeling. Introduction The motivation behind this workflow is rooted in the imperative need to conduct a thorough analysis of a diverse soil dataset, characterized by an array of physicochemical variables. Comprising multiple rows, each representing distinct soil samples, the dataset encompasses variables such as percentage of coarse sands, percentage of organic matter, hydrophobicity, and others. The intricacies of this dataset demand a strategic approach to preprocessing, analysis, and visualization. To lay the groundwork, the workflow begins with the transformation of an initial Excel file into a CSV format, ensuring improved compatibility and ease of use throughout subsequent analyses. Furthermore, the workflow is designed to empower users in the selection of relevant variables, a task facilitated by user-defined parameters. This flexibility allows for a focused and tailored dataset, essential for meaningful analysis. Acknowledging the inherent challenges of missing data, the workflow offers options for data quality improvement, including optional interpolation of missing values or the removal of rows containing such values. Standardizing the dataset and specifying the target variable are crucial, establishing a robust foundation for subsequent statistical analyses. Incorporating PCA offers a sophisticated approach, enabling users to explore inherent patterns and structures within the data. The adaptability of PCA allows users to customize the analysis by specifying the number of components or desired variance. The workflow concludes with practical graphical representations, including covariance and correlation matrices, a scree plot, and a scatter plot, offering users valuable visual insights into the complexities of the soil dataset. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of diverse soil samples: ∙Data transformation: Efficiently convert the initial Excel file into a CSV format to enhance compatibility and ease of use. ∙Variable selection: Empower users to extract relevant variables based on user-defined parameters, facilitating a focused and tailored dataset. ∙Data quality improvement: Provide options for interpolation or removal of missing values to ensure dataset integrity for downstream analyses. ∙Standardization and target specification: Standardize the dataset values and designate the target variable, laying the groundwork for subsequent statistical analyses. ∙PCA: Conduct PCA with flexibility, allowing users to specify the number of components or desired variance for a comprehensive understanding of data variance and patterns. ∙Graphical representations: Generate visual outputs, including covariance and correlation matrices, a scree plot, and a scatter plot, enhancing the interpretability of the soil dataset. Scientific questions This workflow addresses critical scientific questions related to soil analysis: ∙Variable importance: Identify variables contributing significantly to principal components through the covariance matrix and PCA. ∙Data structure: Explore correlations between variables and gain insights from the correlation matrix. ∙Optimal component number: Determine the optimal number of principal components using the scree plot for effective representation of data variance. ∙Target-related patterns: Analyze how selected principal components correlate with the target variable in the scatter plot, revealing patterns based on target variable values.