Data aggregation for biodiversity metrics

Purpose

1.1. Automate the aggregation, standardization, and reporting of biodiversity metrics such as tree species richness, prevalence, health status, and genetic diversity across diverse data sources within an arboretum.
1.2. Automating data collection from sensor networks, manual surveys, remote devices, and external databases to create unified dashboards and exportable reports for ecosystem monitoring.
1.3. Enable automated integration, validation, and periodic updating of environmental and collection data relevant to parks & gardens' tree collections for regulatory, research, and operational purposes.

Trigger Conditions

2.1. Scheduled intervals (e.g., daily, weekly, monthly) for collecting new biodiversity data.
2.2. API-based triggers when new sensor or field survey data is uploaded.
2.3. Manual triggers from portal/UI for on-demand biodiversity metric aggregation.

Platform Variants

3.1. Microsoft Power Automate
• Feature: Scheduled Flow; sample config—recurring schedule to pull from Excel/SharePoint APIs.

3.2. Zapier
• Feature: Multi-step Zaps; config—trigger on new Google Sheets row, webhook to ecological API.

3.3. Make (formerly Integromat)
• Feature: Scenario builder; config—poll REST API, aggregate JSON, update dashboard.

3.4. Google Cloud Functions
• Feature: HTTP trigger; config—function activated on incoming webhook, writing to BigQuery.

3.5. Amazon Lambda
• Feature: Event-driven automation; config—automate data pull from S3 into DynamoDB.

3.6. Datawrapper
• Feature: API data sync; config—fetch biodiversity CSVs periodically, visualize changes.

3.7. Tableau Prep
• Feature: Automated data prep flow; config—scheduled ingestion from Excel & RESTful services.

3.8. ArcGIS Online
• Feature: Scheduled data imports; config—automate field data upload, trigger layer analysis.

3.9. Alteryx
• Feature: Automated workflows; config—set up connectors for field sensor FTP/sites.

3.10. KNIME
• Feature: Orchestration node; config—connect to biodiversity DB, automate species updating.

3.11. Qlik Sense
• Feature: Data load automation; config—scheduled extractor for environmental metrics.

3.12. Airbyte
• Feature: Connector automation; config—scheduled imports from Postgres and Google Drive.

3.13. ElasticSearch
• Feature: Logstash pipeline; config—automate indexing of incoming ecological data.

3.14. Apache NiFi
• Feature: Flow-based automation; config—aggregate and route data from CSV, API, MQTT.

3.15. Google Apps Script
• Feature: Time-driven triggers; config—automatedly fetch and process Sheets data.

3.16. Smartsheet
• Feature: Data shuttle automation; config—schedule imports from external biodiversity files.

3.17. Monday.com
• Feature: Integration automation; config—automate board updates from JSON data.

3.18. Salesforce
• Feature: Flow builder; config—automated case creation on new biodiversity entries.

3.19. HubSpot
• Feature: Workflow automation; config—aggregate external metrics, email report.

3.20. Python (with Cron)
• Feature: Scripted logics; config—cronjobs fetch, transform, push to PostgreSQL.

3.21. IBM Watson Orchestrate
• Feature: Data aggregation automator; config—automate metric collection from IoT sources.

3.22. Notion
• Feature: API integration; config—automate update of collection database via API.

3.23. Airtable
• Feature: Automated imports; config—schedule biodiversity CSV ingestion, script analysis.

Benefits

4.1. Centralizes and automates biodiversity data aggregation from varied sources.
4.2. Provides automated, real-time, and accurate reporting for ecosystem and compliance needs.
4.3. Reduces manual errors, enhances data reliability, and streamlines environmental auditing.
4.4. Supports scalable, automatable decision-making and research into arboretum biodiversity.
4.5. Enables automatedly triggered alerts, insights, and visualizations for management and stakeholders.