EcoGenoRisk: Developing a computational ecological risk assessment tool for synthetic biology

The expanding field of synthetic biology (synbio) supports new opportunities in the design of targeted bioproducts or modified microorganisms. However, this rapid development of synbio products raises concerns surrounding the potential risks of modified microorganisms contaminating unintended environments. These potential invasion risks require new bioinformatic tools to inform the design phase. EcoGenoRisk is a newly constructed computational risk assessment tool for invasiveness that aims to predict where synbio microorganisms may establish a population by screening for habitats of genetically similar microorganisms. The first module of the tool identifies genetically similar microorganisms and potential ecological relationships such as competition, mutualism, and inhibition. In total, 520 archaeal and 32,828 bacterial complete assembly genomes were analyzed to test the specificity and accuracy of the tool as well as to characterize the enzymatic profiles of different taxonomic lineages. Additionally, ecological relationships were analyzed to determine which would result in the greatest potential overlap between shared functional profiles. Notably, competition displayed the significantly highest overlap of shared functions between compared genomes. Overall, EcoGenoRisk is a flexible software pipeline that assists environmental risk assessors to query large databases of known microorganisms and prioritize follow-up bench scale studies.

How are exclusively data journals indexed in major scholarly databases? An examination of four databases

The data paper is becoming a popular way for researchers to publish their research data. The growing numbers of data papers and journals hosting them have made them an important data source for understanding how research data is published and reused. One barrier to this research agenda is a lack of knowledge as to how data journals and their publications are indexed in the scholarly databases used for quantitative analysis. To address this gap, this study examines how a list of 18 exclusively data journals (i.e., journals that primarily accept data papers) are indexed in four popular scholarly databases: the Web of Science, Scopus, Dimensions, and OpenAlex. We investigate how comprehensively these databases cover the selected data journals and, in particular, how they present the document type information of data papers. We find that the coverage of data papers, as well as their document type information, is highly inconsistent across databases, which creates major challenges for future efforts to study them quantitatively, which should be addressed in the future.

Data Science and Precision Oncology Nursing: Creating an Analytic Ecosystem to Support Personalized Supportive Care across the Trajectory of Illness

OBJECTIVES

The authors' objective is to present an overarching framework of an analytic ecosystem using diverse data domains and data science approaches that can be used and implemented across the cancer continuum. Analytic ecosystems can improve quality practices and offer enhanced anticipatory guidance in the era of precision oncology nursing.

DATA SOURCES

Published scientific articles supporting the development of a novel framework with a case exemplar to provide applied examples of current barriers in data integration and use.

CONCLUSION

The combination of diverse data sets and data science analytic approaches has the potential to extend precision oncology nursing research and practice. Integration of this framework can be implemented within a learning health system where models can update as new data become available across the continuum of the cancer care trajectory. To date, data science approaches have been underused in extending personalized toxicity assessments, precision supportive care, and enhancing end-of-life care practices.

IMPLICATIONS FOR NURSING PRACTICE

Nurses and nurse scientists have a unique role in the convergence of data science applications to support precision oncology across the trajectory of illness. Nurses also have specific expertise in supportive care needs that have been dramatically underrepresented in existing data science approaches thus far. They also have a role in centering the patient and family perspectives and needs as these frameworks and analytic capabilities evolve.

Open data-set identifier for open innovation and knowledge management

Purpose

This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals.

Design/methodology/approach

Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId).

Findings

This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data.

Originality/value

From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.

The Environmental Data Initiative: Connecting the past to the future through data reuse

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom-up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

The ATTAC guiding principles to openly and collaboratively share wildlife ecotoxicology data

The inability to quantitatively integrate scattered data regarding potential threats posed by the increasing total amount and diversity of chemical substances in our environment limits our ability to understand whether existing regulations and management actions sufficiently protect wildlife. Systematic literature reviews and meta-analyses are great scientific tools to build upon the current push for accessibility under the Open Science and FAIR movements. Despite the potential of such integrative analyses, the emergence of innovative findings in wildlife ecology and ecotoxicology is still too rare relative to the potential that is hidden within the entirety of the available scattered data. To promote the reuse of wildlife ecotoxicology data, we propose the ATTAC workflow which comprises five key steps (Access, Transparency, Transferability, Add-ons, and Conservation sensitivity) along the chain of collecting, homogenizing, and integrating data for subsequent meta-analyses. The ATTAC workflow brings together guidelines supporting both the data prime movers and re-users. As such, the ATTAC workflow could promote an open and collaborative wildlife ecotoxicology able to reach a major objective in this applied field, namely, providing strong scientific support for regulations and management actions to protect and preserve wildlife species.

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result's metadata. An ontology for Evidence Graphs, EVI ( https://w3id.org/EVI ), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

The data-index: An author-level metric that values impactful data and incentivizes data sharing

Author-level metrics are a widely used measure of scientific success. The h-index and its variants measure publication output (number of publications) and research impact (number of citations). They are often used to influence decisions, such as allocating funding or jobs. Here, we argue that the emphasis on publication output and impact hinders scientific progress in the fields of ecology and evolution because it disincentivizes two fundamental practices: generating impactful (and therefore often long-term) datasets and sharing data. We describe a new author-level metric, the data-index, which values both dataset output (number of datasets) and impact (number of data-index citations), so promotes generating and sharing data as a result. We discuss how it could be implemented and provide user guidelines. The data-index is designed to complement other metrics of scientific success, as scientific contributions are diverse and our value system should reflect that both for the benefit of scientific progress and to create a value system that is more equitable, diverse, and inclusive. Future work should focus on promoting other scientific contributions, such as communicating science, informing policy, mentoring other scientists, and providing open-access code and tools.

Lessons Learnt from Engineering Science Projects Participating in the Horizon 2020 Open Research Data Pilot

Trends in the sciences are indicative of data management becoming established as a feature of the mainstream research process. In this context, the European Commission introduced an Open Research Data pilot at the start of the Horizon 2020 research programme. This initiative followed the success of the Open Access pilot implemented in the prior (FP7) research programme, which thereafter became an integral component of Horizon 2020. While the Open Access phenomenon can reasonably be argued to be one of many instances of web technologies disrupting established business models (namely publication practices and workflows established over several centuries in the case of Open Access), initiatives designed to promote research data management have no established foundation on which to build. For Open Data to become a reality and, more importantly, to contribute to the scientific process, data management best practices and workflows are required. Furthermore, with the scientific community having operated to good effect in the absence of data management, there is a need to demonstrate the merits of data management. This circumstance is complicated by the lack of the necessary ICT infrastructures, especially interoperability standards, required to facilitate the seamless transfer, aggregation and analysis of research data. Any activity aiming to promote Open Data thus needs to overcome a number of cultural and technological challenges. It is in this context that this paper examines the data management activities and outcomes of a number of projects participating in the Horizon 2020 Open Research Data pilot. The result has been to identify a number of commonly encountered benefits and issues; to assess the utilisation of data management plans; and through the close examination of specific cases, to gain insights into obstacles to data management and potential solutions. Although primarily anecdotal and difficult to quantify, the experiences reported in this paper tend to favour developing data management best practices rather than doggedly pursue the Open Data mantra. While Open Data may prove valuable in certain circumstances, there is good reason to claim that managed access to scientific data of high inherent intellectual and financial value will prove more effective in driving knowledge discovery and innovation.

Digitalization and AI in European Agriculture: A Strategy for Achieving Climate and Biodiversity Targets?

This article analyzes the environmental opportunities and limitations of digitalization in the agricultural sector by applying qualitative governance analysis. Agriculture is recognized as a key application area for digital technologies, including artificial intelligence. This is not least because it faces major sustainability challenges, especially with regard to meeting the climate and biodiversity targets set out in the Paris Agreement and the Convention on Biological Diversity, as well as the water-related objectives of EU environmental legislation. Based on an overview of the possible applications of digital technologies in agriculture, the article offers a status quo analysis of legal acts with relevance to digitalization in the EU agricultural sector. It is found that a reliable legal framework with regard to product liability and product safety, as well as data privacy, data access, and data security is important in this context. In addition, the European Common Agricultural Policy, as the most important funding instrument for digital innovations in the agricultural sector, should be designed in such a way that it links digitalization-related objectives more closely with sustainability targets. So far, the existing EU governance does not fully exploit the potentials of digitalization for environmental protection, and sight is lost of possible negative side effects such as rebound and shifting effects. Therefore, the article also offers proposals for the optimization of EU governance.

A study of the roles of metadata standard and data repository in science, technology, engineering and mathematics researchers' data reuse

Purpose

This research investigates how the availabilities of both metadata standards and data repositories influence researchers' data reuse intentions either directly or indirectly as mediated by the norms of data reuse and their attitudes toward data reuse.

Design/methodology/approach

The theory of planned behavior (TPB) was employed to develop the research model of researchers' data reuse intentions, focusing on the roles of metadata standards, data repositories and norms of data reuse. The proposed research model was evaluated using the structural equation modeling (SEM) method based on the survey responses received from 811 STEM (science, technology, engineering and mathematics) researchers in the United States.

Findings

This research found that the availabilities of both metadata standards and data repositories significantly affect STEM researchers' norm of data reuse, which influences their data reuse intentions as mediated by their attitudes toward data reuse. This research also found that both the availability of data repositories and the norm of data reuse have a direct influence on data reuse intentions and that norm of data reuse significantly increases the effect of attitude toward data reuse on data reuse intention as a moderator.

Research limitations/implications

The modified model of TPB provides a new perspective in apprehending the roles of resource facilitating conditions such as the availabilities of metadata standards and data repositories in an individual's attitude, norm and their behavioral intention to conduct a certain behavior.

Practical implications

This study suggests that scientific communities need to develop more supportive metadata standards and data repositories by considering their roles in enhancing the community norm of data reuse, which eventually lead to data reuse behaviors.

Originality/value

This study sheds light on the mechanism of metadata standard and data repository in researchers' data reuse behaviors through their community norm of data reuse; this can help scientific communities and academic institutions to better support researchers in their data sharing and reuse behaviors.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0431

Connected Research: The Potential of the PID Graph

Persistent identifiers (PIDs) provide unique and long-lasting references to entities. They enable unique identification persistently over time and hence play a crucial role in supporting the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In this paper, we describe how the benefits of PIDs can be amplified by connecting them via their metadata. We are introducing the next step in PID infrastructure: the PID Graph. The PID Graph establishes connections between different entities within the research landscape, thereby enabling both researchers and institutions to access new information. The paper closes with three recommendations, which will help to optimize the use and value of PIDs within the research ecosystem.

Every fifth published metagenome is not available to science

Have you ever sought to use metagenomic DNA sequences reported in scientific publications? Were you successful? Here, we reveal that metagenomes from no fewer than 20% of the papers found in our literature search, published between 2016 and 2019, were not deposited in a repository or were simply inaccessible. The proportion of inaccessible data within the literature has been increasing year-on-year. Noncompliance with Open Data is best predicted by the scientific discipline of the journal. The number of citations, journal type (e.g., Open Access or subscription journals), and publisher are not good predictors of data accessibility. However, many publications in high-impact factor journals do display a higher likelihood of accessible metagenomic data sets. Twenty-first century science demands compliance with the ethical standard of data sharing of metagenomes and DNA sequence data more broadly. Data accessibility must become one of the routine and mandatory components of manuscript submissions-a requirement that should be applicable across the increasing number of disciplines using metagenomics. Compliance must be ensured and reinforced by funders, publishers, editors, reviewers, and, ultimately, the authors.

Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.

Social Data: CESSDA Best Practices

The European Commission report “Turning FAIR into reality” provides an index of 27 FAIR Action Plan recommendations. This index is used for a self-assessment on CESSDA, the Consortium of European Social Science Data Archives. CESSDA is performing well on “Concepts for FAIR implementation”, “Skills for FAIR”, and “Investment in FAIR”; there is work in progress on “FAIR culture”, and work to start up on “FAIR ecosystem” and especially on “Incentives and metrics for FAIR data and services”. Next, an analysis on the FAIR components, reveals that CESSDA has accomplished the “F”, is working on the “A” – considering the sensitivity and security requirements of social data, just started on “I”, and that there is lack of clarity on what should be in “R”. On Findability, the CESSDA Data Catalogue is explained, showing the building blocks that need to be in place before one can produce a catalogue. The article ends with a forward look on CESSDA's deployment on the FAIR principles.

FAIR Computational Workflows

Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.