This workflow aims to streamline the integration of phytosociological inventory data stored in Excel format into a MongoDB database. This process is essential for the project's Virtual Research Environment (VRE), facilitating comprehensive data analysis. Key components include converting Excel files to JSON format, checking for duplicate inventories to ensure data integrity, and uploading the JSON files to the database. This workflow promotes a reliable, robust dataset for further exploration and utilization within the VRE, enhancing the project's inventory database. Background Efficient data management in phytosociological inventories requires seamless integration of inventory data. This workflow facilitates the importation of phytosociological inventories in Excel format into the MongoDB database, connected to the project's Virtual Research Environment (VRE). The workflow comprises two components: converting Excel to JSON and checking for inventory duplicates, ultimately enhancing the inventory database. Introduction Phytosociological inventories demand efficient data handling, especially concerning the integration of inventory data. This workflow focuses on the pivotal task of importing phytosociological inventories, stored in Excel format, into the MongoDB database. This process is integral to the VRE of the project, laying the groundwork for comprehensive data analysis. The workflow's primary goal is to ensure a smooth and duplicate-free integration, promoting a reliable dataset for further exploration and utilization within the project's VRE. Aims The primary aim of this workflow is to streamline the integration of phytosociological inventory data into the MongoDB database, ensuring a robust and duplicate-free dataset for further analysis within the project's VRE. To achieve this, the workflow includes the following key components: 1. Excel to JSON Conversion: Converts phytosociological inventories stored in Excel format to JSON, preparing the data for MongoDB compatibility. 2. Duplicate Check and Database Upload: Checks for duplicate inventories in the MongoDB database and uploads the JSON file, incrementing the inventory count in the database. Scientific Questions - Data Format Compatibility: How effectively does the workflow convert Excel-based phytosociological inventories to the JSON format for MongoDB integration? - Database Integrity Check: How successful is the duplicate check component in ensuring data integrity by identifying and handling duplicate inventories? - Inventory Count Increment: How does the workflow contribute to the increment of the inventory count in the MongoDB database, and how is this reflected in the overall project dataset?

This workflow focuses on enhancing spatial interpolation of meteorological variables by incorporating altitude. The process involves importing geolocated meteorological data from shapefiles, downloading a Digital Elevation Model (DEM) for elevation data, and utilizing 3D Kriging for interpolation. This method improves the accuracy of meteorological data interpolation across various elevations, providing comprehensive spatial coverage. Key components include precise data import, effective DEM integration, and accurate 3D Kriging, addressing scientific questions about data import precision, DEM integration, Kriging accuracy, and spatial coverage enhancement. Background Interpolating geolocated meteorological variables is crucial for obtaining comprehensive insights into environmental conditions. This workflow, comprising three components, focuses on importing shapefile data containing geolocated meteorological variables. The primary objective is to perform a 3D interpolation, considering altitude as a significant factor. To achieve this, the workflow downloads a Digital Elevation Model (DEM) to incorporate elevation information and utilizes 3D Kriging for interpolation. Introduction Interpolating meteorological variables in geospatial datasets is essential for understanding environmental conditions. This workflow aims to enhance the accuracy of such interpolations by importing shapefile data, obtaining elevation data from a DEM, and performing a 3D interpolation using Kriging. The resulting dataset provides interpolated meteorological values for locations not covered by the original sampling. Aims The primary aim of this workflow is to achieve accurate 3D interpolation of meteorological variables, considering altitude, to enhance spatial coverage. The workflow includes the following key components: ∙Shapefile Data Import: Imports geolocated meteorological variables from a shapefile, preparing the data for 3D interpolation. ∙Digital Elevation Model (DEM) Download: Downloads a Digital Elevation Model (DEM) to obtain elevation information for the interpolation process. ∙3D Kriging Interpolation: Utilizes 3D Kriging to interpolate meteorological variables, incorporating altitude information for enhanced accuracy. Scientific questions ∙Data Import Precision: How precise is the workflow in importing geolocated meteorological variables from the shapefile data? ∙DEM Download and Integration: How effectively does the workflow download the DEM and integrate elevation information into the interpolation process? ∙3D Kriging Accuracy: How accurate is the 3D Kriging interpolation in providing reliable meteorological values, considering altitude as a key factor? ∙Enhancement of Spatial Coverage: To what extent does the 3D interpolation process enhance spatial coverage, providing interpolated values for locations not originally sampled?

Land Use and Land Cover (LULC) maps are crucial for environmental monitoring. This workflow uses Remote Sensing (RS) and Artificial Intelligence (AI) to automatically create LULC maps by estimating the relative abundance of LULC classes. Using MODIS data and ancillary geographic information, an AI model was trained and validated in Andalusia, Spain, providing a tool for accurate and efficient LULC mapping. Background Land Use and Land Cover (LULC) maps are of paramount importance to provide precise information for dynamic monitoring, planning, and management of the Earth. Regularly updated global LULC datasets provide the basis for understanding the status, trends, and pressures of human activity on carbon cycles, biodiversity, and other natural and anthropogenic processes. Because of that, being able to automatically create these maps without human labor by using new Remote Sensing (RS) and Artificial Intelligence (AI) technologies is a great avenue to explore. Introduction In the last few decades, LULC maps have been created using RS images following the "raster data model", where the Earth's surface is divided in squares of a certain spatial resolution called pixels. Then, each of these pixels is assigned a "LULC class" (e.g., forest, water, urban...) that represents the underlying type of the Earth surface in each pixel. The number of different classes of a LULC map is referred to as thematic resolution. Frequently, the spatial and thematic resolutions do not match, which leads to the mixed pixel problem, i.e., pixels are not pure but contain several LULC classes. Under a "hard" classification approach, a mixed pixel would be assigned just one LULC class (e.g., the dominant class) while under a "soft" classification approach (also called spectral unmixing or abundance estimation) the relative abundance of each LULC class is provided per pixel. Moreover, ancillary information regarding the geographic, topographic, and climatic information of the studied area could also be useful to classify each pixel to its corresponding LULC class. Concretely, the following ancillary variables are studied: GPS coordinates, altitude, slope, precipitation, potential evapotranspiration, mean temperature, maximum temperature, and minimum temperature. Aims To estimate the relative abundance of LULC classes in Andalusia and develop an AI model to automatically perform the task, a new labeled dataset of Andalusia of pixels from MODIS at 460m resolution was built. Each pixel is a multi-spectral time series and includes the corresponding ancillary information. Also, each pixel is labeled with its corresponding LULC class abundances inside that pixel. The label is provided at two hierarchical levels, namely N1 (coarser) and N2 (finer). To create these labels, the SIPNA (Sistema de Información sobre el Patrimonio Natural de Andalucía) product was used, which aims to build an information system on the natural heritage of Andalusia. The first level "N1" contains four high-level LULC classes, whereas the second level "N2" contains ten finer LULC classes. Thus, this model was mainly trained and validated in the region of Andalusia in Spain. Once the dataset was created, the AI model was trained using about 80% of the data and then validated with the remaining 20% following a carefully spatial block splitting strategy to avoid spatial autocorrelation. The AI model processes the multi-spectral time series from MODIS at 460m and the ancillary information to predict the LULC abundances in that pixel. Both the RS dataset with the ancillary data used to create the AI model and the AI model itself are the deliverables of this project. In summary, we provide an automatic tool to estimate the LULC classes abundances of MODIS pixels from Andalusia using a soft classification approach and set a methodology that could be applied to other satellites where a better spatial resolution allows the use of more fine LULC classes in the future. Also, the AI model could serve as a starting point for researchers interested in applying the model in other locations, i.e., they can fine-tune the existing model with data for the new region of interest requiring far less training data thanks to transferring the learned patterns of our model. Scientific Questions Through the development of this workflow, we aim at addressing three main scientific questions: 1. Can we predict LULC abundances in a particular place through remote sensing and ancillary data and AI technologies?

Accurately mapping vegetation is crucial for environmental monitoring. Traditional methods for identifying shrubs are labor-intensive and impractical for large areas. This workflow uses remote sensing and deep learning to detect Juniperus shrubs from high-resolution RGB satellite images, making shrub identification more efficient and accessible to non-experts in machine learning. Background In a dynamic climate, accurately mapping vegetation distribution is essential for environmental monitoring, biodiversity conservation, forestry, and urban planning. One important application of vegetation mapping is the identification of shrub individuals. We term by shrub identification, detection of shrub location and segmentation of shrub morphology. Introduction Yet, shrub species monitoring is a challenging task. Ecologists used to identify shrubs using classical field surveying methods, however, this process poses a significant challenge since the shrubs are often distributed in large areas that are most of the time inaccessible. Thus, these methods are considered labor-intensive, costly, time-consuming, unsustainable, limited to a small spatial and temporal scale, and their data are often not publicly available. Combining remote sensing and deep learning, however, can play a significant role in tackling these challenges providing a great opportunity to improve plant surveying. First, remote sensing can offer highly detailed spatial resolution granting exceptional flexibility in data acquisition. Then, these data can be afterward processed by deep learning models for automatic identification of shrubs. Aims The objective of this workflow is to help scientists, non-expert in machine learning, detect Juniperus shrubs from RGB very-high resolution satellite images using deep learning and remote sensing tools. Scientific Questions Can we accurately detect high-mountain Juniperus shrubs from RGB very-high resolution satellite images using deep learning?

This workflow aims to compare plant species across different natural spaces. The workflow involves downloading and filtering phytosociological inventories, preprocessing data, and unifying it for comparative analysis. The main outputs are a Venn diagram displaying shared and unique species, and a CSV table detailing common and uncommon species. The workflow addresses filter application effectiveness, Venn diagram clarity, species table accuracy, and overall efficiency in processing and visualization, supporting ecological studies of plant distribution. Background Comparative analysis of phytosociological inventories across different natural spaces is essential for understanding plant distribution. This workflow focuses on downloading inventories stored in the database, applying distinct filters for each natural space, and conducting a comparative analysis of shared and unique plant species. The primary output includes a Venn diagram representing species intersections and a CSV table detailing common and uncommon plant species across the selected natural spaces. Introduction In ecological studies, understanding the overlap and uniqueness of plant species across different natural spaces is crucial. This workflow employs phytosociological inventories stored in the database, downloading them separately for each natural space using specific filters. The workflow then conducts a comparative analysis, identifying shared and unique plant species. The visualization includes a Venn diagram for easy interpretation and a CSV table highlighting the common and uncommon species across the selected natural spaces. Aims The primary aim of this workflow is to facilitate the comparison of phytosociological inventories from different natural spaces, emphasizing shared and unique plant species. The workflow includes the following key components: - Inventory Download and Preprocessing: Downloads phytosociological inventories from the database, applies specific filters for each natural space, and preprocesses the data to retain only the species present in each zone. - Data Unification: Unifies the processed data into a single dataset, facilitating comparative analysis. - Venn Diagram Representation: Generates a Venn diagram to visually represent the overlap and uniqueness of plant species across the selected natural spaces. - Species Table Generation: Creates a CSV table showcasing common and uncommon plant species in the selected natural spaces. Scientific Questions - Filter Application Effectiveness: How effectively does the workflow apply distinct filters to download inventories for each natural space? - Venn Diagram Interpretation: How intuitive and informative is the Venn diagram representation of shared and unique plant species across the selected natural spaces? - Species Table Accuracy: How accurate is the CSV table in presenting common and uncommon plant species in the comparative analysis? - Workflow Efficiency: How efficiently does the workflow streamline the entire process, from data download to visualization, for comparative phytosociological analysis?

This workflow streamlines the export, preprocessing, and analysis of phytosociological inventories from a project database. The workflow's goals include exporting and preprocessing inventories, conducting statistical analyses, and using interactive graphs to visualize species dominance, altitudinal distribution, average coverage, similarity clusters, and species interactions. It also calculates and visualizes the fidelity index for species co-occurrence. This workflow addresses key scientific questions about dominant species, distribution patterns, species coverage, inventory similarity, species interactions, and co-occurrence probabilities, aiding efficient vegetation management in environmental projects. Background Efficient vegetation management in environmental projects necessitates a detailed analysis of phytosociological inventories. This workflow streamlines the export and preprocessing of vegetation inventories from the project database. Subsequently, it conducts various statistical analyses and graphical representations, offering a comprehensive view of plant composition and interactions. Introduction In the realm of vegetation research, the availability of phytosociological data is paramount. This workflow empowers users to specify parameters for exporting vegetation inventories, performs preprocessing, and conducts diverse statistical analyses. The resulting insights are visually represented through interactive graphs, highlighting predominant species, altitudinal ranges of plant communities, average species coverage, similarity clusters, and interactive species interactions. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of phytosociological inventories: 1. Export and Preprocess Inventories: Enable the export and preprocessing of phytosociological inventories stored in the project database. 2. Statistical Analyses of Species and Plant Communities: Conduct detailed statistical analyses on the species and plant communities present in the inventories. 3. Interactive Graphical Representation: Utilize interactive graphs to represent predominant species, altitudinal ranges of plant communities, and average species coverage. 4. Similarity Dendrogram: Generate a dendrogram grouping similar phytosociological inventories based on the similarity of their species content. 5. Interactive Species Interaction Analysis: Visualize species interactions through interactive graphs, facilitating the identification of species that tend to coexist. 6. Calculation and Visualization of Fidelity Index: Calculate the fidelity index between species and visually represent the probability of two or more species co-occurring in the same inventory. Scientific Questions This workflow addresses critical scientific questions related to the analysis of phytosociological inventories: - Dominant Species Identification: Which species emerge as predominant in the phytosociological inventories, and what is their frequency of occurrence? - Altitudinal Distribution Patterns: How are plant communities distributed across altitudinal ranges, and are there discernible patterns? - Average Species Coverage Assessment: What is the average coverage of plant species, and how does it vary across different inventories? - Similarity in Inventory Content: How are phytosociological inventories grouped based on the similarity of their species content? - Species Interaction Dynamics: Which species exhibit notable interactive dynamics, and how can these interactions be visualized? - Fidelity Between Species: What is the likelihood that two or more species co-occur in the same inventory, and how does this fidelity vary across species pairs?

The workflow "Pollen Trends Analysis with AeRobiology" leverages the AeRobiology library to manage and analyze time-series data of airborne pollen particles. Aimed at understanding the temporal dynamics of different pollen types, this workflow ensures data quality, profiles seasonal trends, and explores temporal variations. It integrates advanced features for analyzing pollen concentrations and their correlation with meteorological variables, offering comprehensive insights into pollen behavior over time. The workflow enhances data accessibility, facilitating broader research and public health applications. Background In the dynamic landscape of environmental research and public health, the AeRobiology library (https://cran.r-project.org/web/packages/AeRobiology/index.html) emerges as a potent instrument tailored for managing diverse airborne particle data. As the prevalence of airborne pollen-related challenges intensifies, understanding the nuanced temporal trends in different pollen types becomes imperative. AeRobiology not only addresses data quality concerns but also offers specialized tools for unraveling intricate insights into the temporal dynamics of various pollen types. Introduction Amidst the complexities of environmental research, particularly in the context of health studies, the meticulous analysis of airborne particles—specifically various pollen types—takes center stage. This workflow, harnessing the capabilities of AeRobiology, adopts a holistic approach to process and analyze time-series data. Focused on deciphering the temporal nuances of pollen seasons, this workflow aims to significantly contribute to our understanding of the temporal dynamics of different airborne particle types. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of time series pollen samples: - Holistic Data Quality Assurance: Conduct a detailed examination of time-series data for various pollen types, ensuring completeness and accuracy to establish a robust foundation for subsequent analysis. - Pollen-Specific Seasonal Profiling: Leverage AeRobiology's advanced features to calculate and visually represent key parameters of the seasonal trends for different pollen types, offering a comprehensive profile of their temporal dynamics. - Temporal Dynamics Exploration: Investigate the temporal trends in concentrations of various pollen types, providing valuable insights into their evolving nature over time. - Enhanced Accessibility: Employ AeRobiology's interactive tools to democratize the exploration of time-series data, making complex information accessible to a broader audience of researchers and professionals. Scientific Questions This workflow addresses critical scientific questions related to pollen analysis: - Distinct Temporal Signatures: What are the discernible patterns and trends in the temporal dynamics of different airborne pollen types, especially during peak seasons? - Pollen-Specific Abundance Variability: How does the abundance of various pollen types vary throughout their respective seasons, and what environmental factors contribute to these fluctuations? - Meteorological Correlations: Are there statistically significant correlations between the concentrations of different pollen types and specific meteorological variables, elucidating the influencing factors unique to each type? - Cross-Annual Comparative Analysis: Through the lens of AeRobiology, how do the temporal trends of different pollen types compare across different years, and what contextual factors might explain observed variations?

This workflow aims to enhance water resource management by combining temporal precipitation and temperature data from various sources. It performs essential hydroclimatic calculations, including potential evapotranspiration (ETP), useful rainfall (LLU), runoff (ESC), and infiltration (INF). Using data integration and interactive HTML graph generation, the workflow provides dynamic visual representations of precipitation trends, ETP dynamics, and correlations between temperature and precipitation. This comprehensive approach facilitates a deeper understanding of hydroclimatic patterns and supports effective water management decisions. Background Water resource management necessitates a comprehensive understanding of hydroclimatic patterns. This series of workflows addresses the amalgamation of temporal precipitation and temperature data from distinct sources to facilitate an integrated analysis. By unifying these datasets, the workflows perform initial processing and calculations, including the determination of potential evapotranspiration (ETP), useful rainfall (LLU), runoff (ESC), and infiltration (INF). The subsequent components generate interactive HTML graphs, providing valuable insights into hydroclimatic dynamics. Introduction Effective water resource management hinges on the ability to synthesize disparate datasets into a cohesive analysis. This series of workflows not only consolidates temporal precipitation and temperature data from various locations but also performs essential calculations to derive key hydroclimatic parameters. The resulting interactive graphs offer a dynamic visual representation of the cumulative deviation from the mean precipitation, temporal trends in precipitation (including ESC, INF, LLU, and total precipitation), ETP, daily and cumulative precipitation, temperature (maximum and minimum), and monthly precipitation. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of ETP: ∙Data Integration: Unify temporal precipitation and temperature data from various sources into a coherent dataset for subsequent analysis. ∙Hydroclimatic Calculations: Calculate potential evapotranspiration (ETP), useful rainfall (LLU), runoff (ESC), and infiltration (INF) based on the integrated dataset. Note: ETP is calculated using formulas from different authors, including Hargreaves, Hamon, Jensen–Haise, Makkink, Taylor, Hydro Quebec, Oudin, and Papadakis. ∙Interactive Graph Generation: Utilize HTML to create interactive graphs representing cumulative deviation from the mean precipitation, temporal trends in precipitation (including ESC, INF, LLU, and total precipitation), ETP, daily and cumulative precipitation, temperature (maximum and minimum), and monthly precipitation. Scientific questions This workflow addresses critical scientific questions related to ETP analysis: ∙Temporal Precipitation Trends: Are there discernible patterns in the temporal trends of precipitation, and how do they relate to runoff, infiltration, and useful rainfall? ∙Potential Evapotranspiration (ETP) Dynamics: How does ETP vary over time using different authors' methods, and what are the implications for potential water loss? ∙Relationship Between Precipitation and Temperature: Are there significant correlations between variations in temperature (maximum and minimum) and the quantity and type of precipitation? ∙Seasonal Distribution of Precipitation: How is precipitation distributed across months, and are there seasonal patterns that may influence water management?

This workflow aims to streamline the integration of phytosociological inventory data stored in multiple XML files within a ZIP archive into a MongoDB database. This process is crucial for effective data management within the project's Virtual Research Environment (VRE). The workflow involves extracting XML files from the ZIP archive, converting them to JSON format for MongoDB compatibility, checking for duplicates to ensure data integrity, and uploading the data to increment the inventory count. This enhances the robustness and reliability of the inventory dataset for comprehensive analysis. Background Efficient data management is crucial in phytosociological inventories, necessitating seamless integration of inventory data. This workflow addresses a key aspect by facilitating the importation of phytosociological inventories stored in multiple XML files within a ZIP archive into the MongoDB database. This integration is vital for the project's Virtual Research Environment (VRE), providing a foundation for robust data analysis. The workflow comprises two essential components: converting XML to JSON and checking for inventory duplicates, ultimately enhancing the integrity and expansiveness of the inventory database. Introduction In phytosociological inventories, effective data handling is paramount, particularly concerning the integration of inventory data. This workflow focuses on the pivotal task of importing phytosociological inventories, stored in multiple XML files within a ZIP archive, into the MongoDB database. This process is integral to the VRE of the project, laying the groundwork for comprehensive data analysis. The workflow's primary goal is to ensure a smooth and duplicate-free integration, promoting a reliable dataset for further exploration and utilization within the project's VRE. Aims The primary aim of this workflow is to streamline the integration of phytosociological inventory data, stored in multiple XML files within a ZIP archive, into the MongoDB database. This ensures a robust and duplicate-free dataset for further analysis within the project's VRE. To achieve this, the workflow includes the following key components: - ZIP Extraction and XML to JSON Conversion: Extracts XML files from the ZIP archive and converts each phytosociological inventory stored in XML format to JSON, preparing the data for MongoDB compatibility. - Duplicate Check and Database Upload: Checks for duplicate inventories in the MongoDB database and uploads the JSON files, incrementing the inventory count in the database. Scientific Questions - ZIP Archive Handling: How effectively does the workflow handle ZIP archives containing multiple XML files with distinct phytosociological inventories? - Data Format Compatibility: How successful is the conversion of XML-based phytosociological inventories to the JSON format for MongoDB integration? - Database Integrity Check: How effective is the duplicate check component in ensuring data integrity by identifying and handling duplicate inventories? - Inventory Count Increment: How does the workflow contribute to the increment of the inventory count in the MongoDB database, and how is this reflected in the overall project dataset?

The Ecological Niche Modelling Workflows offer an extensible framework for analyzing or predicting the impact of environmental changes on the distribution of biodiversity. Especially in combination with data aggregation workflows like the Taxonomic Data Refinement Workflow, the Ecological Niche Modelling workflows facilitate the analysis of species distribution patterns over large geo-temporal, taxonomic, and environmental scales. Examples for applications are studies of species adaptations to climate change, dynamic modeling of ecologically related species, identification of regions with accumulated risk for invasion, potential for restoration, or natural protected areas. Developed by: The Biodiversity Virtual e-Laboratoy (BioVeL) (EU FP7 project) Technology or platform: These workflows have been developed to be run in the Taverna automated workflow environment (https://incubator.apache.org/projects/taverna.html). In their current form, the workflow files (with the .t2flow extension) can be loaded and executed in the workbench variant of Taverna. They have been tested with Taverna Workbench version 2.4. These workflows can also be run in BioVeL Portal, a light weight user interface which allows browsing, reviewing and running Taverna Workflows without the need of installing any software.