Navigation
A Data-driven Approach for Core Biodiversity Ontology Development.
A deep learning-based approach for segmenting and counting reproductive organs from digitized herbarium specimen images using refined Mask Scoring R-CNN
A Test Collection for Dataset Retrieval in Biodiversity Research
BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data
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
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Results of the Ontology Alignment Evaluation Initiative 2021
Towards an Ontology Network for the reproducibility of scientific studies
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
[Dai:Si] – A Modular Dataset Retrieval Framework with a Semantic Search for Biological Data
A Test Collection for Dataset Retrieval in Biodiversity Research
Title: | A Test Collection for Dataset Retrieval in Biodiversity Research |
---|---|
Authors: | Felicitas Löffler, Andreas Schuldt, Birgitta König-Ries, Helge Bruelheide, Friederike Klan |
Source: | RIO - Research Ideas and Outcomes |
Date: | 2021-05-26 |
Type: | Journal Paper |
URL: | https://doi.org/10.3897/rio.7.e67887 |
BibTex: |
@article{10.3897/rio.7.e67887, author = {Felicitas Löffler and Andreas Schuldt and Birgitta König-Ries and Helge Bruelheide and Friederike Klan}, title = {A Test Collection for Dataset Retrieval in Biodiversity Research}, volume = {7}, number = {}, year = {2021}, doi = {10.3897/rio.7.e67887}, publisher = {Pensoft Publishers}, abstract = {Searching for scientific datasets is a prominent task in scholars' daily research practice. A variety of data publishers, archives and data portals offer search applications that allow the discovery of datasets. The evaluation of such dataset retrieval systems requires proper test collections, including questions that reflect real world information needs of scholars, a set of datasets and human judgements assessing the relevance of the datasets to the questions in the benchmark corpus. Unfortunately, only very few test collections exist for a dataset search. In this paper, we introduce the BEF-China test collection, the very first test collection for dataset retrieval in biodiversity research, a research field with an increasing demand in data discovery services. The test collection consists of 14 questions, a corpus of 372 datasets from the BEF-China project and binary relevance judgements provided by a biodiversity expert.}, issn = {}, pages = {e67887}, URL = {https://doi.org/10.3897/rio.7.e67887}, eprint = {https://doi.org/10.3897/rio.7.e67887}, journal = {Research Ideas and Outcomes} } |