Unlocking crop protection models for decision support: Web infrastructure and integration

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.

Putting FAIR into practice for ecologists: How to make ecological data more reusable

ZusammenfassungGesundheitsdaten haben in der heutigen datenorientierten Welt einen hohen Stellenwert. Durch automatisierte Verarbeitung können z. B. Prozesse im Gesundheitswesen optimiert und klinische Entscheidungen unterstützt werden. Dabei sind Aussagekraft, Qualität und Vertrauenswürdigkeit der Daten wichtig. Nur so kann garantiert werden, dass die Daten sinnvoll nachgenutzt werden können.Konkrete Anforderungen an die Beschreibung und Kodierung von Daten werden in den FAIR-Prinzipien beschrieben. Verschiedene nationale Forschungsverbünde und Infrastrukturprojekte im Gesundheitswesen haben sich bereits klar zu den FAIR-Prinzipien positioniert: Sowohl die Infrastrukturen der Medizininformatik-Initiative als auch des Netzwerks Universitätsmedizin operieren explizit auf Basis der FAIR-Prinzipien, ebenso die Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten oder das Deutsche Zentrum für Diabetesforschung.Um eine FAIRe Ressource bereitzustellen, sollte zuerst in einem Assessment der FAIRness-Grad festgestellt werden und danach die Priorisierung für Verbesserungsschritte erfolgen (FAIRification). Seit 2016 wurden zahlreiche Werkzeuge und Richtlinien für beide Schritte entwickelt, basierend auf den unterschiedlichen, domänenspezifischen Interpretationen der FAIR-Prinzipien.Auch die europäischen Nachbarländer haben in die Entwicklung eines nationalen Rahmens für semantische Interoperabilität im Kontext der FAIR-Prinzipien investiert. So wurden Konzepte für eine umfassende Datenanreicherung entwickelt, um die Datenanalyse beispielsweise im Europäischen Gesundheitsdatenraum oder über das Netzwerk der Observational Health Data Sciences and Informatics zu vereinfachen. In Kooperation mit internationalen Projekten, wie z. B. der European Open Science Cloud, wurden strukturierte FAIRification-Maßnahmen für Gesundheitsdatensätze entwickelt.

The European Community Directive 128/2009 on the Sustainable Use of Pesticides establishes a strategy for the use of plant protection products (PPPs) in the European Community so as to reduce risks to human health and the environment. Integrated Pest Management (IPM) is a key component of this strategy, which will become mandatory in 2014. IPM is based on dynamic processes and requires decision-making at strategic, tactical, and operational levels. Relative to decision makers in conventional agricultural systems, decision makers in IPM systems require more knowledge and must deal with greater complexity. Different tools have been developed for supporting decision-making in plant disease control and include warning services, on-site devices, and decision support systems (DSSs). These decision-support tools operate at different spatial and time scales, are provided to users both by public and private sources, focus on different communication modes, and can support multiple options for delivering information to farmers. Characteristics, weaknesses, and strengths of these tools are described in this review. Also described are recently developed DSSs, which are characterised by: i) holistic treatment of crop management problems (including pests, diseases, fertilisation, canopy management and irrigation); ii) conversion of complex decision processes into simple and easy-to-understand ‘decision supports’; iii) easy and rapid access through the Internet; and iv) two-way communication between users and providers that make it possible to consider context-specific information. These DSSs are easy-to-use tools that perform complex tasks efficiently and effectively. The delivery of these DSSs via the Internet increases user accessibility, allows the DSSs to be updated easily and continuously (so that new knowledge can be rapidly and efficiently provided to farmers), and allows users to maintain close contact with providers.

The housing industry in the Developing World and for long time has suffered from underinvestment, the lack of knowhow and the lack of sufficient strategies and policies. This in turn, led to a total failure in performance, accumulative massive housing demand and underachieving. Consequently, and because of the massive growth in the world’s population, especially the Islamic World, people in the poorest countries have been the most affected and forced to live in slums and shanty towns which some worldwide have millions of occupants. This research paper presents a scientific approach to assist governments and decision makers in the Islamic World setting up most sufficient and effective strategies and policies on the mega-level (country level) for the housing industry. The final outcome of this research will produce a Decision Support System Model (DSS) which could be used by decision makers to setting up holistic, realistic and achievable strategies and policies based on the scientific interpretation of the interface of the DSS Model. The DSS Model operates using five engines and one interface to identify, calculate and compare between Financial Sources (Government, PFI, International Fund and Grants) and Total Cost of several variables such as, Know How (Local and Foreign), Labour (Local and Foreign), Training (Local PM and Skilled Labour), Building Materials (Local and Import), Land (Urban and Rural). This in turn gives a clear idea to governments on their financial sources, the total cost of the whole housing project, regulation and legislations necessary and required to facilitate and support the housing industry, etc. The research methodology will consist of two parts; literature review which shed light on DSS Model in terms of definition, stages, purposes, mechanism, how it functions, etc. The second will introduce Interpretive Structural Model (ISM), which is used previously in a different stage of research to identify and prioritise Housing Industry Variables and DSS Model. Finally, the DSS Model will be examined and tested using different scenarios for validation. The findings will be stated in the concluding section.

Advancements in information technology have supported the open availability of environmental monitoring datasets to aid global initiatives such as the United Nations Sustainable Development Goals (UN SDGs). Despite these efforts, challenges concerning data quality and adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles continue to restrict the effective reuse of such datasets, particularly for secondary applications. This study uses the F-UJI assessment tool and a set of eight established DQ dimensions to evaluate the FAIRness and Data Quality (DQ) of four publicly available urban air quality monitoring datasets from international agencies. Each dataset was assessed against 17 FAIR metrics and scored accordingly. The FAIR assessments revealed moderate to low levels of compliance across datasets, with Reusable scores ranging from 2 to 3 out of 10, and Interoperability often being the weakest dimension. DQ analysis showed recurring issues in consistency, completeness, interpretability, and traceability, particularly where metadata was poorly structured or lacked semantic depth. While the scope is limited to four datasets, the results highlight common structural and semantic deficiencies hindering data reuse. Based on these findings, the study offers targeted recommendations to support improved metadata practices and better alignment with FAIR principles within the air quality monitoring subdomain.

Data science increasingly employs cloud-based Web application programming interfaces (APIs). However, automatically discovering and connecting suitable APIs for a given application is difficult due to the lack of explicit knowledge about the structure and datatypes of Web API inputs and outputs. To address this challenge, we conducted a survey to identify the metadata elements that are crucial to the description of Web APIs and subsequently developed the smartAPI metadata specification and associated tools to capture their domain-related and structural characteristics using the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This paper presents the results of the survey, provides an overview of the smartAPI specification and a reference implementation, and discusses use cases of smartAPI. We show that annotating APIs with smartAPI metadata is straightforward through an extension of the existing Swagger editor. By facilitating the creation of such metadata, we increase the automated interoperability of Web APIs. This work is done as part of the NIH Commons Big Data to Knowledge (BD2K) API Interoperability Working Group.

The FAIR (Findable, Accessible, Interoperable, Reusable) principles are increasingly important for research data management (RDM). Implementing the principles effectively requires appropriate training, yet little is known about available educational resources that trainers can (re)use. This review systematically identified open educational resources (OERs) on FAIR data stewardship reported in scientific literature. Four databases (MEDLINE, Web of Science, ACM Digital Library, IEEE Xplore) were searched, and all records were independently assessed by two researchers. Data on resource type, target audience, delivery method, discipline, accessibility, and availability were extracted, resulting in 15 included articles published after the FAIR principles were introduced. The identified OERs comprised training platforms and registries, handbooks and toolkits, curricula, and guidelines. Most were openly accessible, delivered online, multidisciplinary in scope, and primarily targeted at trainers and educators. Expanding disciplinary reach, embedding FAIR in education, and making existing OERs easier to find and reuse will help scale up FAIR training and improve its quality.

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.

BackgroundThe clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. MethodsA conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles. ResultsThe framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels. ConclusionOur study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

BackgroundThe FAIR (Findable, Accessible, Interoperable, Reusable) principles were proposed in 2016 to set a path towards reusability of research datasets. In this systematic review, we assessed the FAIRness of datasets associated with peer-reviewed articles in veterinary epidemiology research published since 2017, specifically looking at salmonids and dairy cattle. We considered the differences in practices between molecular epidemiology, the branch of epidemiology using genetic sequences of pathogens and hosts to describe disease patterns, and non-molecular epidemiology.ResultsA total of 152 articles were included in the assessment. Consistent with previous assessments conducted in other disciplines, our results showed that most datasets used in non-molecular epidemiological studies were not available (i.e., neither findable nor accessible). Data availability was much higher for molecular epidemiology papers, in line with a strong repository base available to scientists in this discipline. The available data objects generally scored favourably for Findable, Accessible and Reusable indicators, but Interoperability was more problematic.ConclusionsNone of the datasets assessed in this study met all the requirements set by the FAIR principles. Interoperability, in particular, requires specific skills in data management which may not yet be broadly available in the epidemiology community. In the discussion, we present recommendations on how veterinary research could move towards greater reusability according to FAIR principles. Overall, although many initiatives to improve data access have been started in the research community, their impact on the availability of datasets underlying published articles remains unclear to date.

Descriptive metadata are vital for reporting, discovering, leveraging, and mobilizing research datasets. However, resolving metadata issues as part of a data management plan can be complex for data producers. To organize and document data, various descriptive metadata must be created. Furthermore, when sharing data, it is important to ensure metadata interoperability in line with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Given the practical nature of these challenges, there is a need for management tools that can assist data managers effectively. Additionally, these tools should meet the needs of data producers and be user-friendly, requiring minimal training. We developed Maggot (Metadata Aggregation on Data Storage), a web-based tool to locally manage a data catalog using high-level metadata. The main goal was to facilitate easy data dissemination and deposition in data repositories. With Maggot, users can easily generate and attach high-level metadata to datasets, allowing for seamless sharing in a collaborative environment. This approach aligns with many data management plans as it effectively addresses challenges related to data organization, documentation, storage, and the sharing of metadata based on FAIR principles within and beyond the collaborative group. Furthermore, Maggot enables metadata crosswalks (i.e., generated metadata can be converted to the schema used by a specific data repository or be exported using a format suitable for data collection by third-party applications). The primary purpose of Maggot is to streamline the collection of high-level metadata using carefully chosen schemas and standards. Additionally, it simplifies data accessibility via metadata, typically a requirement for publicly funded projects. As a result, Maggot can be utilized to promote effective local management with the goal of facilitating data sharing while adhering to the FAIR principles. Furthermore, it can contribute to the preparation of the future EOSC FAIR Web of Data within the European Open Science Cloud framework.

Background:The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice.Methods:A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure the compliance with FAIR principles.Results:The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion and frame levels.Conclusion:Our study shows that the proposed framework facilitates the storage of the visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

Since 2016, with the publication of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson, M., et al.), FAIR data management and stewardship in the life sciences have been at the forefront of numerous publications, talks, research projects and policies at public institutions. Yet, for researchers working with data, applying the FAIR principles to their projects has been a hurdle. A skill gap in the life sciences workforce also hinders the progress toward FAIR data and its benefits in sharing, reusing and safeguarding life sciences data.This workshop will offer attendees the opportunity to learn about the existing tools to implement FAIR principles tailored to a life sciences user, with toolkits and step-by-step guides. In addition, participants will also discover different training opportunities for personal use and further implementation in their organisations.

Driven by the scientific need for global exchange of data to study earthquakes and related phenomena, community standards and best practices have evolved in seismology for decades. These developments are largely driven by operational and scientific requirements coming directly from the community of academic research and seismological monitoring, and have resulted in standardised data formats, data models and services for data access and exchange.Initial developments, promotion and further evolution of these standards are coordinated mainly within the International Federation of Digital Seismic Networks (FDSN, https://fdsn.org), a commission of IASPEI (International Association of Seismology and Physics of the Earth's Interior, httwww.iaspei.org) that is one of eight associations of the IUGG (International Union of Geodesy and Geophysics, https://iugg.org).   With the introduction of the FAIR (Findable, Accessible, Interoperable, Reusable) principles in 2016 and the subsequent appearance of FAIR assessment methods and tools it became clear that these seismological community standards only cover parts of the FAIR principles. Interoperability remains challenging, for example, due to the lack of community standardised FAIR vocabularies, and the lack of a harmonised and consistently applied data license policy impacts Reproducibility.The emergence of new data types and the drastic increase in data volumes due to new measurement techniques require updates and evolution of the existing community standards, highlighting another general challenge:  Who are the recognised and appropriate governance bodies for curation and further development of 'relevant community standards' (as required by the FAIR principles)?In this presentation we describe the current status of FAIRness for seismological waveform data and beyond, also looking towards seismology in general, geodesy and some other fields of geophysics. Based on our assessment of current challenges we discuss open questions and possible ways forward. We look at FAIR-relevant development and governance of standards, the potential role of existing international organisations like FDSN, IASPEI and IUGG, and the possibility and need to coordinate across domains for harmonisation as well as demarcation.