Essential Annotation Schema for Ecology (EASE)—A framework supporting the efficient data annotation and faceted navigation in ecology

Pfaff, Claas-Thido; Eichenberg, David; Liebergesell, Mario; König‐Ries, Birgitta; Wirth, Christian

doi:10.1371/journal.pone.0186170

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Species are still an important category scholars are interested in, however, further important topics for the acquisition and description of ecosystem services are emerging. Our results confirm some of the defined dimensions in EASE [ 43 ], a metadata schema developed for search purposes. Common categories are ORGANISM, PROCESS, TIME and LOCATION (Space).…”

Section: D—discussionsupporting

confidence: 82%

“…Other approaches favor an improved metadata schema. Pfaff et al [ 43 ] introduce the Essential Annotation Schema for Ecology (EASE). The schema was primarily developed in workshops and intensive discussions with scholars and aims to support scientists in search tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The naming was adapted to domain-specific denotations and ontologies. Furthermore, the categories were compared to EASE [ 43 ], a metadata schema which was primarily developed for an improved dataset retrieval in the field of ecology. This comparison revealed that there is an overlap with EASE but that we discovered further relevant categories [ 51 ].…”

Section: A—information Needs In the Biodiversity Domainmentioning

confidence: 99%

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Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

et al. 2021

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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.

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Section: D—discussionsupporting

confidence: 82%

Section: Related Workmentioning

confidence: 99%

Section: A—information Needs In the Biodiversity Domainmentioning

confidence: 99%

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Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

et al. 2021

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“…, ABCD for collection data or Darwin Core for occurrence data), they are still lacking for more complex BEF data. The Easy Annotation Scheme for Ecology (Pfaff et al, 2017) or BioSchemas (http://bioschemas.org; Gray et al, 2017), for instance, aim to alleviate this problem. In addition to better annotation schemes, better tools to reduce the human effort in creating these annotations are needed.…”

Section: What Are the Key Challenges Of Future Bef Research?mentioning

confidence: 99%

A multitrophic perspective on biodiversity–ecosystem functioning research

Eisenhauer

Schielzeth

Barnes

et al. 2019

Advances in Ecological Research

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134

102

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Concern about the functional consequences of unprecedented loss in biodiversity has prompted biodiversity-ecosystem functioning (BEF) research to become one of the most active fields of ecological research in the past 25 years. Hundreds of experiments have manipulated biodiversity as an independent variable and found compelling support that the functioning of ecosystems increases with the diversity of their ecological communities. This research has also identified some of the mechanisms underlying BEF relationships, some context-dependencies of the strength of relationships, as well as implications for various ecosystem services that mankind depends upon. In this paper, we argue that a multitrophic perspective of biotic interactions in random and nonrandom biodiversity change scenarios is key to advance future BEF research and to address some of its most important remaining challenges. We discuss that the study and the quantification of multitrophic interactions in space and time facilitates scaling up from small-scale biodiversity manipulations and ecosystem function assessments to management-relevant spatial scales across ecosystem boundaries. We specifically consider multitrophic conceptual frameworks to understand and predict the context-dependency of BEF relationships. Moreover, we highlight the importance of the eco-evolutionary underpinnings of multitrophic BEF relationships. We outline that FAIR data (meeting the standards of findability, accessibility, interoperability, and reusability) and reproducible processing will be key to advance this field of research by making it more integrative. Finally, we show how these BEF insights may be implemented for ecosystem management, society, and policy. Given that human well-being critically depends on the multiple services provided by diverse, multitrophic communities, integrating the approaches of evolutionary Eisenhauer et al.

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INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

Heger¹,

Zarrieß²,

Algergawy³

et al. 2022

RIO

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Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

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Essential Annotation Schema for Ecology (EASE)—A framework supporting the efficient data annotation and faceted navigation in ecology

Cited by 3 publications

References 18 publications

Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

A multitrophic perspective on biodiversity–ecosystem functioning research

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

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