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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
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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?
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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
Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?
Title: | Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs? |
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Authors: | Felicitas Löffler, Valentin Wesp, Birgitta König-Ries, Friederike Klan |
Source: | PlosONE |
Date: | 2021-03-24 |
Type: | Journal Paper |
URL: | https://doi.org/10.1371/journal.pone.0246099 |
BibTex: |
@article{10.1371/journal.pone.0246099, author = {Löffler, Felicitas AND Wesp, Valentin AND König-Ries, Birgitta AND Klan, Friederike}, journal = {PLOS ONE}, publisher = {Public Library of Science}, title = {Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?}, year = {2021}, month = {03}, volume = {16}, url = {https://doi.org/10.1371/journal.pone.0246099}, pages = {1-36}, abstract = {The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. In particular, we focus on scholarly search interests and metadata, the primary source of data in a dataset retrieval system. We show that existing metadata currently poorly reflect information needs and therefore are the biggest obstacle in retrieving relevant data. Our findings indicate that for data seekers in the biodiversity domain environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important information categories. These interests are well covered in metadata elements of domain-specific standards. However, instead of utilizing these standards, large data repositories tend to use metadata standards with domain-independent metadata fields that cover search interests only to some extent. A second problem are arbitrary keywords utilized in descriptive fields such as title, description or subject. Keywords support scholars in a full text search only if the provided terms syntactically match or their semantic relationship to terms used in a user query is known.}, number = {3}, doi = {10.1371/journal.pone.0246099} } |