scholarly journals Towards FAIR protocols and workflows: the OpenPREDICT use case

2020 ◽  
Vol 6 ◽  
pp. e281
Author(s):  
Remzi Celebi ◽  
Joao Rebelo Moreira ◽  
Ahmed A. Hassan ◽  
Sandeep Ayyar ◽  
Lars Ridder ◽  
...  
Keyword(s):  
Drug Repurposing ◽  
Research Data ◽  
Use Case ◽  
Semantic Technologies ◽  
Semantic Model ◽  
Execution Traces ◽  
Fair Principles ◽  
Digital Objects ◽  
High Degree ◽  
Highly Cited

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

scholarly journals Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

2021 ◽  
Author(s):  
Núria Queralt-Rosinach ◽  
Rajaram Kaliyaperumal ◽  
César H. Bernabé ◽  
Qinqin Long ◽  
Simone A. Joosten ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Management ◽  
Research Data ◽  
Patient Data ◽  
Research Data Management ◽  
Hospitalised Patients ◽  
The World ◽  
Data Points ◽  
Fair Principles ◽  
Digital Objects

AbstractBackgroundThe COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.ResultsIn this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.ConclusionsOur work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR digital objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.

scholarly journals FAIRifying Clinical Studies Metadata: A Registry for the Biomedical Research

2021 ◽  
Author(s):  
Vittorio Meloni ◽  
Alessandro Sulis ◽  
Cecilia Mascia ◽  
Francesca Frexia
Keyword(s):  
Clinical Trials ◽  
User Interface ◽  
Clinical Studies ◽  
Source Code ◽  
Use Case ◽  
Semantic Model ◽  
Current Implementation ◽  
Fair Principles ◽  
Rest Api ◽  
Clinical Domain

The data produced during a research project are too often collected for the sole purpose of the study, therefore hindering profitable reuse in similar contexts. The growing need to counteract this trend has recently led to the formalization of the FAIR principles that aim to make (meta)data Findable, Accessible, Interoperable and Reusable, for humans and machines. Since their introduction, efforts are ongoing to encourage FAIR principles adoption and to implement solutions based on them. This paper reports on the FAIR-compliant registry we developed to collect and serve metadata describing clinical trials. The design of the registry is based on the FAIR Data Point (FDP) specifications, the state-of-the-art reference for FAIRified metadata sharing. To map the metadata relevant to our use case, we have extended the DCAT-based semantic model of the FDP adopting well-established ontologies in the biomedical and clinical domain, like the Semanticscience Integrated Ontology (SIO). Current implementation is based on the Molgenis software and provides both a user interface and a REST API for metadata discovering. At present the registry is being loaded with the metadata of the 18 clinical studies included in the ‘I FAIR Program’, a project finalised to the dissemination of FAIR best practices among the clinical researchers in Sardinia (Italy). After a testing phase, the registry will be publicly available, while the new model and the source code will be released open source.

scholarly journals Data Papers as a New Form of Knowledge Organization in the Field of Research Data

2019 ◽  
Vol 46 (8) ◽  
pp. 622-638
Author(s):  
Joachim Schöpfel ◽  
Dominic Farace ◽  
Hélène Prost ◽  
Antonella Zane
Keyword(s):  
Business Models ◽  
Open Science ◽  
Research Data ◽  
Data Driven ◽  
Academic Publishing ◽  
Science Data ◽  
Research Data Management ◽  
Fair Principles ◽  
New Form ◽  
Machine Readable

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.

scholarly journals Grain of narrow-leaved lupine with inclusion of organic micronutrient complex OMC as an alternative to soybeans in protein concentrates

2021 ◽  
Vol 901 (1) ◽  
pp. 012022
Author(s):  
Z N Fedorova ◽  
Yu G Tkachenko ◽  
V G Bliadze
Keyword(s):  
Protein Content ◽  
Research Data ◽  
Fiber Content ◽  
High Protein ◽  
Protein Concentrate ◽  
Cattle Farm ◽  
Protein Concentrates ◽  
Kaliningrad Region ◽  
High Degree ◽  
Competitive Import

Abstract The article presents the research data on the use of high-protein extruded concentrates based on narrow-leaved lupine in the compound feed in combination with organic microelement complex OMEK-7 M (complex, microelement additive produced by CJSC “Bioamid”, Saratov) in order to replace soybean. The studies were carried out on a cattle farm in the settlement of Novgorodskoe, Guryevskii district, Kaliningrad region (Temp LLC). The object of the research were calves of black-and-wheat breed. It was found that due to the extrusion of lupine grain in combination with OMEC premix, a competitive, import-substituting soybean-based protein concentrate with a high degree of bioavailability of feed was obtained. It contains a sufficient protein content of 26% and a low fiber content of 4.05%, which is very important for calves in the dairy period.

scholarly journals DataverseNO: A National, Generic Repository and its Contribution to the Increased FAIRness of Data from the Long Tail of Research

Ravnetrykk ◽  
2020 ◽  
Author(s):  
Philipp Conzett
Keyword(s):  
Open Source Software ◽  
Crucial Role ◽  
Research Data ◽  
Background Information ◽  
Data Repositories ◽  
Long Tail ◽  
Open Research ◽  
Research Organizations ◽  
Digital Objects ◽  
Organizational Features

Research data repositories play a crucial role in the FAIR (Findable, Accessible, Interoperable, Reusable) ecosystem of digital objects. DataverseNO is a national, generic repository for open research data, primarily from researchers affiliated with Norwegian research organizations. The repository runs on the open-source software Dataverse. This article presents the organization and operation of DataverseNO, and investigates how the repository contributes to the increased FAIRness of small and medium sized research data. Sections 1 to 3 present background information about the FAIR Data Principles (section 1), how FAIR may be turned into reality (section 2), and what these principles and recommendations imply for data from the so-called long tail of research, i.e. small and medium-sized datasets that are often heterogenous in nature and hard to standardize (section 3). Section 4 gives an overview of the key organizational features of DataverseNO, followed by an evaluation of how well DataverseNO and the repository application Dataverse as such support the FAIR Data Principles (section 5). Section 6 discusses how sustainable and trustworthy the repository is. The article is rounded up in section 7 by a brief summary including a look into the future of the repository.

scholarly journals Promoting Semantic Annotation of Research Data by Their Creators: A Use Case with B2NOTE at the End of the RDM Workflow

2017 ◽  
pp. 112-122 ◽  
Author(s):  
Yulia Karimova ◽  
João Aguiar Castro ◽  
João Rocha da Silva ◽  
Nelson Pereira ◽  
Cristina Ribeiro
Keyword(s):  
Semantic Annotation ◽  
Research Data ◽  
Use Case

scholarly journals Activity profiling and crystal structures of inhibitor-bound SARS-CoV-2 papain-like protease: A framework for anti–COVID-19 drug design

2020 ◽  
Vol 6 (42) ◽  
pp. eabd4596 ◽  
Author(s):  
Wioletta Rut ◽  
Zongyang Lv ◽  
Mikolaj Zmudzinski ◽  
Stephanie Patchett ◽  
Digant Nayak ◽  
...  
Keyword(s):  
Substrate Specificity ◽  
Crystal Structures ◽  
Drug Repurposing ◽  
Activity Profiling ◽  
Irreversible Inhibitors ◽  
Inhibitory Mechanisms ◽  
Promising Target ◽  
Therapeutic Value ◽  
Positional Scanning ◽  
High Degree

Viral papain-like cysteine protease (PLpro, NSP3) is essential for SARS-CoV-2 replication and represents a promising target for the development of antiviral drugs. Here, we used a combinatorial substrate library and performed comprehensive activity profiling of SARS-CoV-2 PLpro. On the scaffold of the best hits from positional scanning, we designed optimal fluorogenic substrates and irreversible inhibitors with a high degree of selectivity for SARS PLpro. We determined crystal structures of two of these inhibitors in complex with SARS-CoV-2 PLpro that reveals their inhibitory mechanisms and provides a molecular basis for the observed substrate specificity profiles. Last, we demonstrate that SARS-CoV-2 PLpro harbors deISGylating activity similar to SARSCoV-1 PLpro but its ability to hydrolyze K48-linked Ub chains is diminished, which our sequence and structure analysis provides a basis for. Together, this work has revealed the molecular rules governing PLpro substrate specificity and provides a framework for development of inhibitors with potential therapeutic value or drug repurposing.

scholarly journals FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Publications ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 21 ◽  
Author(s):  
Koenraad De Smedt ◽  
Dimitris Koureas ◽  
Peter Wittenburg
Keyword(s):  
Data Science ◽  
Open Science ◽  
Research Data ◽  
Use Cases ◽  
Digital Object ◽  
Data Interoperability ◽  
Actionable Knowledge ◽  
The Past ◽  
Research Communities ◽  
Digital Objects

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

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