Controlled vocabularies for scientific data: Users and desired functionalities

Zhang, Yue; Ogletree, Adrian; Greenberg, Jane; Rowell, Chelcie

doi:10.1002/pra2.2015.145052010054

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…While STARDIT is a novel element set, where possible it will also incorporate element sets from established data standards and map them where possible (see Table 5 in appendix for examples of data standards which could be incorporated). This includes standard elements and value sets and controlled vocabularies 68 . The terms used in this paper are working terms, which will be progressively standardised over the lifetime of the project.…”

Section: Using and Developing Data Standardsmentioning

confidence: 99%

Standardised Data on Initiatives – STARDIT: Beta Version

Nunn

Shafee

Chang

et al. 2021

Preprint

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BackgroundThere is currently no standardised way to share information across disciplines about initiatives, including fields such as health, environment, basic science, manufacturing, media and international development. All problems, including complex global problems such as air pollution and pandemics require reliable data sharing between disciplines in order to respond effectively. Current reporting methods also lack information about the ways in which different people are involved in initiatives, making it difficult to collate and appraise data about the most effective ways to involve different people. ObjectiveSTARDIT (Standardised Data on Initiatives) was created to help everyone in the world find and understand information about collective human actions, which are referred to as ‘initiatives’. STARDIT enables multiple categories of data to be reported in a standardised way across disciplines, facilitating appraisal of initiatives and aiding synthesis of evidence for the most effective ways for people to be involved in initiatives. MethodSTARDIT is the first open access web-based data-sharing system which standardises the way that information about initiatives is reported across diverse fields and disciplines, including information about which tasks were done by which stakeholders. STARDIT data is licensed as public domain (CC0) and integrated into Wikidata; it works across multiple languages and is both human and machine readable. Reports can be updated throughout the lifetime of an initiative, from planning to evaluation, allowing anyone to be involved in reporting impacts and outcomes. STARDIT development is guided by participatory action research paradigms, and has been co-created with people from multiple disciplines around the world in multiple ways, working with Indigenous people, cancer patients, health researchers, environmental researchers, economists, librarians and academic publishers. ResultsOver 100 people from multiple disciplines have been involved in co-designing STARDIT. STARDIT reports have been created for peer-reviewed research in multiple journals and other research projects. A working Beta version was publicly released in February 2021 (ScienceforAll.World/STARDIT).

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Section: Using and Developing Data Standardsmentioning

confidence: 99%

Standardised Data on Initiatives – STARDIT: Beta Version

Nunn

Shafee

Chang

et al. 2021

Preprint

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“…A curator can work with both historical and contemporary ontologies in a single automatic indexing sequence while also including ontologies representing different domains. For instance, a curator may seek to use both the contemporary LCSH and a geospatial ontology [22].…”

Section: B Helping Interdisciplinary Vocabulary Engineering (Hive)mentioning

confidence: 99%

A Computational Approach to Historical Ontologies

Kelly

Greenberg

Rauch

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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This paper presents a use case exploring the application of the Archival Resource Key (ARK) persistent identifier for promoting and maintaining ontologies. In particular we look at improving computation with an in-house ontology server in the context of temporally aligned vocabularies. This effort demonstrates the utility of ARKs in preparing historical ontologies for computational archival science.

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“…Despite strong uptake of these ideas for some environmental applications (Zhang et al. ), however, the potential of semantic mapping for clarifying and resolving ecological debates remains underdeveloped.…”

Section: Future Directionsmentioning

confidence: 99%

The difficulties of systematic reviews

Westgate

Lindenmayer

2017

Conservation Biology

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The need for robust evidence to support conservation actions has driven the adoption of systematic approaches to research synthesis in ecology. However, applying systematic review to complex or open questions remains challenging, and this task is becoming more difficult as the quantity of scientific literature increases. We drew on the science of linguistics for guidance as to why the process of identifying and sorting information during systematic review remains so labor intensive, and to provide potential solutions. Several linguistic properties of peer-reviewed corpora-including nonrandom selection of review topics, small-world properties of semantic networks, and spatiotemporal variation in word meaning-greatly increase the effort needed to complete the systematic review process. Conversely, the resolution of these semantic complexities is a common motivation for narrative reviews, but this process is rarely enacted with the rigor applied during linguistic analysis. Therefore, linguistics provides a unifying framework for understanding some key challenges of systematic review and highlights 2 useful directions for future research. First, in cases where semantic complexity generates barriers to synthesis, ecologists should consider drawing on existing methods-such as natural language processing or the construction of research thesauri and ontologies-that provide tools for mapping and resolving that complexity. These tools could help individual researchers classify research material in a more robust manner and provide valuable guidance for future researchers on that topic. Second, a linguistic perspective highlights that scientific writing is a rich resource worthy of detailed study, an observation that can sometimes be lost during the search for data during systematic review or meta-analysis. For example, mapping semantic networks can reveal redundancy and complementarity among scientific concepts, leading to new insights and research questions. Consequently, wider adoption of linguistic approaches may facilitate improved rigor and richness in research synthesis.

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Controlled vocabularies for scientific data: Users and desired functionalities

Cited by 5 publications

References 12 publications

Standardised Data on Initiatives – STARDIT: Beta Version

Standardised Data on Initiatives – STARDIT: Beta Version

A Computational Approach to Historical Ontologies

The difficulties of systematic reviews

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