Dendro: Collaborative Research Data Management Built on Linked Open Data

Silva, João Rocha da; Castro, João Aguiar; Ribeiro, Cristina; Lopes, João Correia

doi:10.1007/978-3-319-11955-7_71

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…Several web tools have also been developed each addressing certain challenges in RDM, such as COPO, 3 and Dendro. 23 Adamant is equipped with a back-end layer whose main purpose is to ensure the possibility of seamless integration with such web applications. The back-end is written in Python (v3.8.7) using the Flask library (v2.0.2), which is advantageous given numerous web applications provide Python libraries for their API calls.…”

Section: Methodsmentioning

confidence: 99%

Adamant: a JSON schema-based metadata editor for research data management workflows

2022

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The web tool Adamant has been developed to systematically collect research metadata as early as the conception of the experiment. Adamant enables a continuous, consistent, and transparent research data management (RDM) process, which is a key element of good scientific practice ensuring the path to Findable, Accessible, Interoperable, Reusable (FAIR) research data. It simplifies the creation of on-demand metadata schemas and the collection of metadata according to established or new standards. The approach is based on JavaScript Object Notation (JSON) schema, where any valid schema can be presented as an interactive web-form. Furthermore, Adamant eases the integration of numerous available RDM methods and software tools into the everyday research activities of especially small independent laboratories. A programming interface allows programmatic integration with other software tools such as electronic lab books or repositories. The user interface (UI) of Adamant is designed to be as user friendly as possible. Each UI element is self-explanatory and intuitive to use, which makes it accessible for users that have little to no experience with JSON format and programming in general. Several examples of research data management workflows that can be implemented using Adamant are introduced. Adamant (client-only version) is available from: https://plasma-mds.github.io/adamant.

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Section: Methodsmentioning

confidence: 99%

Adamant: a JSON schema-based metadata editor for research data management workflows

2022

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“…There is a need to be able to capture the essence of knowhow and innovation embedded within a single or various knowledge sources (Frappaolo & Capshaw, 1999;Joshi et al, 2007;Meihami & Meihami, 2014), such as a KO or the know-how of a knowledge worker. Storing various metadata descriptions as well as related questions, presents the LL with a source of data which could be linked to other ontologies for semantic interoperability (da Silva et al, 2014), improved levels of integration and better exploitation of inherent knowledge and synergies (Wruck et al, 2014).…”

Section: Dolmentioning

confidence: 99%

Semantically Enriching the Knowledge Payload of Knowledge Objects Through the Utilization of Knowledge Object Wrappers

Buitendag

Hattingh

2020

InSITE Conference

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Aim/Purpose: In this research the authors present the designs of three different knowledge object meta-data wrapper models as a supportive technology to assist the knowledge intensive operations of a network of knowledge, such as a living lab. Background: Within any knowledge driven network environment there is a need to increase the corporate knowledge capacity of the network. The role of experts and knowledge brokers are emphasized, and the exchange of knowledge based on prior experiences informing corporate memories of the members, is the departure point of this research. Methodology: The primary research method applied is that of the design science research methodology supported by experience and application research and the literature. Contribution: Three different metadata models are presented that will when implemented support the informing process within the network of knowledge. The models are grounded on the utilization of metadata elements composing of various key descriptors as found in activity theory and normal means of heuristic enquiry which entail common questions. The elements are annotated and fur-ther enriched using standard JSON-LD IRI pairs. The presented models expand on the extant knowledge of the use of metadata annotations and present a novel way in encapsulating the corporate memories of knowledge workers in the form of knowledge object wrappers. Findings: The results of the evaluation process of the design science research methodolo-gy applied, showed that there is a consensus that the use of knowledge object wrappers as additional metadata, containers could enhance the knowledge ca-pacity and efficiency of a LL and in particular the knowledge brokers.

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“…Metadata captured in these wizards is then automatically transformed into the format specified by the repository, removing this task from the user. The design is distinct from that of Dendro 15 and CEDAR 16 , two other platforms with related capabilities. Dendro provides linear web forms.…”

Section: Researchers Need Help To Biocuratementioning

confidence: 99%

COPO: a metadata platform for brokering FAIR data in the life sciences

Etuk

Shaw

González-Beltrán

et al. 2019

Preprint

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Scientific innovation is increasingly reliant on data and computational resources. Much of today's life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share it with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible. Building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing. Availability:COPO is entirely open source and freely available on GitHub at https://github.com/collaborative-open-plant-omics . A public instance of the platform for use by the community, as well as more information, can be found at copo-project.org .

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Dendro: Collaborative Research Data Management Built on Linked Open Data

Cited by 12 publications

References 2 publications

Adamant: a JSON schema-based metadata editor for research data management workflows

Adamant: a JSON schema-based metadata editor for research data management workflows

Semantically Enriching the Knowledge Payload of Knowledge Objects Through the Utilization of Knowledge Object Wrappers

COPO: a metadata platform for brokering FAIR data in the life sciences

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