Machine-actionable Data Research Articles

Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans

Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans

ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph

AbstractThe purpose of this work is to describe theorkg-Leaderboard software designed to extractleaderboardsdefined astask–dataset–metrictuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG’s supported Semantic Web infrastructure of representing machine-actionable ‘resources’ on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on theleaderboardextraction task, thus provingorkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense,orkg-Leaderboards transforms theleaderboardextraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.

Creating lightweight FAIR Digital Objects with RO-Crate

Creating lightweight FAIR Digital Objects with RO-Crate

Proof of Concept and Horizons on Deployment of FAIR Data Points in the COVID-19 Pandemic

AbstractRapid and effective data sharing is necessary to control disease outbreaks, such as the current coronavirus pandemic. Despite the existence of data sharing agreements, data silos, lack of interoperable data infrastructures, and different institutional jurisdictions hinder data sharing and accessibility. To overcome these challenges, the Virus Outbreak Data Network (VODAN)-Africa initiative is championing an approach in which data never leaves the institution where it was generated, but, instead, algorithms can visit the data and query multiple datasets in an automated way. To make this possible, FAIR Data Points—distributed data repositories that host machine-actionable data and metadata that adhere to the FAIR Guidelines (that data should be Findable, Accessible, Interoperable and Reusable)—have been deployed in participating institutions using a dockerised bundle of tools called VODAN in a Box (ViB). ViB is a set of multiple FAIR-enabling and open-source services with a single goal: to support the gathering of World Health Organization (WHO) electronic case report forms (eCRFs) as FAIR data in a machine-actionable way, but without exposing or transferring the data outside the facility. Following the execution of a proof of concept, ViB was deployed in Uganda and Leiden University. The proof of concept generated a first query which was implemented across two continents. A SWOT (strengths, weaknesses, opportunities and threats) analysis of the architecture was carried out and established the changes needed for specifications and requirements for the future development of the solution.

Data Access, Control, and Privacy Protection in the VODAN-Africa Architecture

AbstractThe Virus Outbreak Data Network (VODAN)-Africa aims to contribute to the publication of Findable Accessible, Interoperable, and Reusable (FAIR) health data under well-defined access conditions. The next step in the VODAN-Africa architecture is to locally deploy the Center for Expanded Data Annotation and Retrieval (CEDAR) and arrange accessibility based on the ‘data visiting’ concept. Locally curated and reposited machine-actionable data can be visited by queries or algorithms, provided that the conditions of access are met. The goal is to enable the multiple (re)use of data with secure access functionality by clinicians (patient care), an idea aligned with the FAIR-based Personal Health Train (PHT) concept. The privacy and security requirements in relation to the FAIR Data Host and the FAIRification workspace (to produce metadata) or dashboard (for the patient) must be clear to design the IT architecture. This article describes a (first) practice, a reference implementation in development, within the VODAN-Africa and Leiden University Medical Center community.

DCSO: towards an ontology for machine-actionable data management plans

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

Making the collective knowledge of chemistry open and machine actionable.

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Automating Research Data Management Using Machine-Actionable Data Management Plans

Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability

The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.

FAIR Data Austria - Aligning the Implementation of FAIR Tools and Services

This article gives an overview of the FAIR Data Austria project objectives and current results. In collaboration with our project partners, we work on the development and establishment of tools for managing the lifecycle of research data, including machine-actionable Data Management Plans (maDMPs), repositories for long-term archiving of research results, RDM training and support services, models, and profiles for Data Stewards and FAIR Office Austria.

Interconnecting Systems Using Machine-Actionable Data Management Plans – Hackathon Report

The common standard for machine-actionable Data Management Plans (DMPs) allows for automatic exchange, integration, and validation of information provided in DMPs. In this paper, we report on the hackathon organised by the Research Data Alliance in which a group of 89 participants from 21 countries worked collaboratively on use cases exploring the utility of the standard in different settings. The work included integration of tools and services, funder templates mapping, and development of new serialisations. This paper summarises the results achieved during the hackathon and provides pointers to further resources.

Application Profile for Machine-Actionable Data Management Plans

This paper presents the application profile for machine-actionable data management plans that allows information from traditional data management plans to be expressed in a machine-actionable way. We describe the methodology and research conducted to define the application profile. We also discuss design decisions made during its development and present systems which have adopted it. The application profile was developed in an open and consensus-driven manner within the DMP Common Standards Working Group of the Research Data Alliance and is its official recommendation.

Updating the DCC Curation Lifecycle Model

The DCC Curation Lifecycle Model has played a vital role in the field of data curation for over a decade. During that time, the scale and complexity of data have changed dramatically, along with the contexts of data production and use. This paper reports on a study examining factors impacting data curation practices and presents recommendations for updating the DCC Curation Lifecycle Model. The study was grounded in a review of other lifecycle models and informed by a site visit to the Digital Curation Centre and consultation with expert practitioners and researchers. Framed by contemporary conditions impacting the conduct of research and provision of data services, the analysis and proposed recommendations account for the prominence of machine-actionable data, the importance of machine learning for data processing and analytics, growth of integrated research workflows, and escalating concerns with fairness, accountability, and transparency of data and algorithms.

Machine-actionable Data Management Plans Model Analysis and Improvement Direction

Machine-actionable Data Management Plans Model Analysis and Improvement Direction

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

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).

Ten principles for machine-actionable data management plans.

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP.This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.

Linked Serial Data: Mapping the CONSER Standard Record to BIBFRAME

ABSTRACTThe CONSER BIBFRAME mapping project began in December 2015, and completed a mapping between the elements of the CONSER Standard Record and BIBFRAME 1.0. Subsequently, the group has converted that mapping to BIBFRAME 2.0, developed sample RDF serializations for each element, and is developing recommendations for PCC best practices.This article summarizes CONSER BIBFRAME mapping outcomes, describes Valerie Bross' contributions to the project, and presents topics for further consideration within the PCC: BIBFRAME development, and serials cataloging communities, including: changes to the description, literal vs. machine actionable data, enumeration and chronology information, modeling issues, and relationships.

Information Integration for Machine Actionable Data Management Plans

Data management plans are free-form text documents describing the data used and produced in scientific experiments. The complexity of data-driven experiments requires precise descriptions of tools and datasets used in computations to enable their reproducibility and reuse. Data management plans fall short of these requirements. In this paper, we propose machine-actionable data management plans that cover the same themes as standard data management plans, but particular sections are filled with information obtained from existing tools. We present mapping of tools from the domains of digital preservation, reproducible research, open science, and data repositories to data management plan sections. Thus, we identify the requirements for a good solution and identify its limitations. We also propose a machine-actionable data model that enables information integration. The model uses ontologies and is based on existing standards.

Machine-actionable data management plans (maDMPs)

This report presents outputs of the International Digital Curation Conference 2017 workshop on machine-actionable data management plans. It contains community-generated use cases covering eight broad topics that reflect the needs of various stakeholders. It also articulates a consensus about the need for a common standard for machine-actionable data management plans to enable future work in this area.