Ecoinformatics: supporting ecology as a data-intensive science

Michener, William K.; Jones, Matthew B.

doi:10.1016/j.tree.2011.11.016

Cited by 358 publications

(353 citation statements)

References 54 publications

(64 reference statements)

Supporting

Mentioning

324

Contrasting

Unclassified

Order By: Relevance

“…Metadata is information about the data, including how it was collected, what the units of measurement are, and descriptions of how to best use the data (Michener and Jones 2012). Clear metadata makes it easier to figure out if a dataset is appropriate for a project.…”

Section: Provide Metadatamentioning

confidence: 99%

“…Good metadata should provide the following information (Michener et al 1997, Zimmerman 2003, Strasser et al 2012 (Reichman et al 2011, Whitlock 2011, Michener and Jones 2012. These standards are designed to provide consistency in metadata across different datasets and also to allow computers to interpret the metadata auto-matically.…”

Section: Provide Metadatamentioning

confidence: 99%

“…More advanced quality control can include automated checks on data as it is entered, and double-entry of data (Lampe and Weiler 1998, Michener and Jones 2012, Paulsen et al 2012). This additional effort can be time consuming but is valuable because it increases data accuracy by catching typographical errors, reader/recorder error, out-of-range values, and questionable data in general (Lampe andWeiler 1998, Paulsen et al 2012).…”

Section: Perform Basic Quality Controlmentioning

confidence: 99%

“…Many of these recommendations can be implemented at any point during a project, but the best time to think about how to handle your data is before the project even starts (Michener and Jones 2012). A few hours of thought about how the data will be documented, structured, and shared at the beginning of a project can prevent the need to restructure data or recall old information.…”

Section: Data That Are Shared In Established Repositoriesmentioning

confidence: 99%

See 3 more Smart Citations

Nine simple ways to make it easier to (re)use your data

White

Baldridge²,

Brym³

et al. 2013

IEE

View full text Add to dashboard Cite

Section: Provide Metadatamentioning

confidence: 99%

Section: Provide Metadatamentioning

confidence: 99%

Section: Perform Basic Quality Controlmentioning

confidence: 99%

Section: Data That Are Shared In Established Repositoriesmentioning

confidence: 99%

See 2 more Smart Citations

Nine simple ways to make it easier to (re)use your data

White

Baldridge²,

Brym³

et al. 2013

IEE

View full text Add to dashboard Cite

“…While the subject of semantic mediation in biodiversity informatics has been addressed as an architecture component (e.g. [17][18]), few examples of practical applications exist.…”

Section: Semantics In Biodiversity Informaticsmentioning

confidence: 99%

A Case-Study of Ontology-Driven Semantic Mediation of Flower-Visiting Data from Heterogeneous Data-Stores in Three South African Natural History Collections

Coetzer

Moodley

Gerber

2013

Advanced Information Systems Engineering

View full text Add to dashboard Cite

Abstract. The domain complexity and structural-and semantic heterogeneity of biodiversity data, as well as idiosyncratic legacy data-creation processes, present significant integration and interoperability challenges. In this paper we describe a casestudy of ontology-driven semantic mediation using records of flower-visiting insects from three natural history collections in South Africa. We establish a conceptual domain model for flower-visiting, expressed in an OWL ontology, and use it to semantically enrich the three data-stores. We show how this enrichment allows for the creation of an integrated flower visiting data set. We discuss how this ontology captures both implicit and explicit knowledge, how it can be used to identify and analyze highlevel flower-visiting behaviour, and ultimately to construct flower-visiting and pollination networks.Keywords: biodiversity information, semantic mediation, ontology, plant-insect interactions, pollination IntroductionThe challenges of integrating, or making interoperable, distributed, heterogeneous sources of biodiversity-and ecological data have been described. Biodiversity is a complex domain and is no different from other domains in that users encode different definitions of the same concepts [3], which frustrates efforts to integrate data.We present a case study of three data-stores of flower-visiting insect specimens. All three data-stores consistently contained the names of the plant species, termed hostplants, with which both flower-visiting and non-flower-visiting insect specimens were associated. Whereas flower-visiting records were not explicit in most records of two data-stores, most records of the third data-store contained explicit, easily distinguishable flower-visiting data. To develop a semantic mediation solution, we created the first version of an OWL ontology containing concepts related to flower-visiting and the utilization of flower products, as well as the bearing of pollen by insect vectors. Our work will facilitate the construction of a system to bring about interoperability between distributed and heterogeneous biodiversity data-stores and systems. This will enable biodiversity scientists to more easily extract and analyze the behaviour of flower-visiting insects. Such a system would allow flower-visiting and pollination networks to be automatically assembled and compared.Outline. In Section 2 we sketch the background against which the need for our study emerged, discuss previous work in biodiversity semantics, and introduce our casestudy of interoperability of flower-visiting data. Section 3 begins by describing the domain of flower-visiting and pollination, including our scope, before explaining the process of ontology construction. Expert-and implicit knowledge is highlighted. The usefulness of the concepts in the ontology is discussed in Section 4, by linking data from the data-stores to classes in the ontology. Finally we discuss our approach to a potential solution, including areas where future work is required, and conclude. Background 2

show abstract

Water quality data for national‐scale aquatic research: The Water Quality Portal

Read

Carr

DeCicco

et al. 2017

Water Resources Research

148

116

View full text Add to dashboard Cite

Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third‐party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.

show abstract

Ecoinformatics: supporting ecology as a data-intensive science

Cited by 358 publications

References 54 publications

Nine simple ways to make it easier to (re)use your data

Nine simple ways to make it easier to (re)use your data

A Case-Study of Ontology-Driven Semantic Mediation of Flower-Visiting Data from Heterogeneous Data-Stores in Three South African Natural History Collections

Water quality data for national‐scale aquatic research: The Water Quality Portal

Contact Info

Product

Resources

About