Phytosociological Inventory Import: Excel to Database

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.<div><br></div><div>Background</div><div>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.</div><div><br></div><div>Introduction</div><div>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.</div><div><br></div><div>Aims</div><div>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:</div><div>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.</div><div><br></div><div>Scientific Questions</div><div>- Data Format Compatibility: How effectively does the workflow convert Excel-based phytosociological inventories to the JSON format for MongoDB integration?</div><div>- 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?</div>

Date ( Publication): 2023-12-31T00:00:00

Status: On going / operational

Principal investigator

University of Malaga - José Francisco Aldana Montes

Publisher

LifeWatch ERIC ICT Core - Francisco Manuel SÁNCHEZ-CANO

Custodian

LifeWatch ERIC ICT Core - Antonio José SÁENZ-ALBANÉS

Principal investigator

LifeWatch ERIC ICT Core - ICT Core Group

Keywords: Phytosociological inventory

Keywords: Excel format

Keywords: JSON conversion

Keywords: MongoDB database

Keywords: Data integration

Keywords: Duplicate check

Keywords: Data integrity

Keywords: Virtual Research Environment (VRE)

Keywords: Inventory count increment

Keywords: Data parsing

Access constraints: Copyright

Other constraints: Copyright 2023 Khaos Research Group

Protocol: DOI

Service Name: Import file Excel

Service Description: Extract phytosociological inventory data from Excel files

Service Reference (id): https://gitlab.lifewatch.dev/lfw002-khaos/wrapper-library/-/tree/develop/core/ImportFile/0.0.5

Service Name: XLSX to JSON

Service Description: Convert the extracted data from Excel format to JSON format for compatibility with MongoDB. Verify the JSON data against existing entries in the MongoDB database to identify and handle any duplicates, ensuring data integrity.