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A Test Collection for Dataset Retrieval in Biodiversity Research
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BiodivOnto: Towards a Core Ontology for Biodiversity
Building high-quality merged ontologies from multiple sources with requirements customization
Capturing and Semantically Describing Provenance to Tell the Story of R Scripts
Comprehensive leaf size traits dataset for seven plant species from digitised herbarium specimen images covering more than two centuries
Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?
Deep leaf: Mask R-CNN based leaf detection and segmentation from digitized herbarium specimen images
ISTMINER: Interactive Spatiotemporal Co-occurrence Pattern Extraction: A Biodiversity case study
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles.
PhenoDeep: A Deep Learning-Based Approach for Detecting Reproductive Organs from Digitized Herbarium Specimen Images
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Towards Scientific Data Synthesis Using Deep Learning and Semantic Web
Towards Tracking Provenance from Machine Learning Notebooks
Understanding experiments and research practices for reproducibility: an exploratory study
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ISTMINER: Interactive Spatiotemporal Co-occurrence Pattern Extraction: A Biodiversity case study
Title: | ISTMINER: Interactive Spatiotemporal Co-occurrence Pattern Extraction: A Biodiversity case study |
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Authors: | Dina Sharafeldeen, Mohamed Bakli, Alsayed Algergawy and Birgitta König-Ries |
Source: | Computer Science for Biodiversity (CS4BioDiversity) Workshop at INFORMATIK2021 |
Date: | 2021-09-27 |
Type: | Workshop Paper |
Abstract: |
In recent years, the exponential growth of spatiotemporal data has led to an increasing need for new interactive methods for accessing and analyzing this data. In the biodiversity domain, species co-occurrence models are critical to gain a mechanistic understanding of the processes underlying biodiversity and supporting its maintenance. This paper introduces a new framework that allows users to explore species occurrences datasets at different spatial and temporal periods to extract co-occurrence patterns. As a real-world case study, we conducted several experiments on a subset of the Global Biodiversity Information Facility (GBIF) occurrences dataset to extract species co-occurrence patterns interactively. For better understanding, these co-occurrence patterns are visualized in a map view and as a graph. Also, the user can export these patterns in CSV format for further use. For any queries, runtimes are in a range that allows for interaction already. Further optimizations are on our research agenda. |