<p dir="ltr">There is a critical need for improvement in scientific data management when the big-data era arrives. Motivated by the evolution and significance of FAIR principles in contemporary research, the study focuses on the development and evaluation of a FAIR-compliant data resource portal. The challenge lies in translating the abstract FAIR principles into actionable, technological implementations and the evaluation. After baseline selection, the study aims to benchmark standards and outperform existing FAIR compliant data resource portals. The proposed approach includes an assessment of existing portals, the interpretation of FAIR principles into practical considerations, and the integration of modern technologies for the implementation. With a FAIR-ness evaluation framework designed and applied to the implementation, this study evaluated and improved the FAIR-compliance of data resource portal. Specifically, the study identified the need for improved persistent identifiers, comprehensive descriptive metadata, enhanced metadata access methods and adherence to community standards and formats. The evaluation of the FAIR-compliant data resource portal with FAIR implementation, showed a significant improvement in FAIR compliance, and eventually enhanced data discoverability, usability, and overall management in academic research.</p>

With the increasing amounts of data generated in life science, methods for data storage and sharing are being developed and implemented. Online data repositories are more and more commonly used for data sharing. The national Swedish platform Science of Life Laboratory has decided to use an institutional data repository as a mean to address the increasing amounts of data generated at the platform. In this project, the system used for the institutional repository at SciLifeLab was studied and compared to implementations of the same system at other institutions to create user documentation for the repository. This documentation was created with the FAIR principles as a guidance. Feedback on the guidelines were then sought from users and based on the received feedback, the user documentation was improved. Using a FAIR evaluation tool called FAIR evaluation services, items published on the repository were evaluated. Investigation of these results and their correlation to the items record on the repository were carried out. Out of ten evaluated datasets all except one scored exactly the same on the FAIR evaluation services tests. This could indicate that the test used is not evaluating aspects needed to encounter the differences in these published items. Based on this, conclusions as to in what extent user documentation can increase the FAIRness of data cannot be drawn.

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of "dynamic data citation". The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.

Introduction. Research data management is a topic of ongoing discussion, particularly in academic institutions, where researchers strive to effectively handle diverse types of data. This study examines the practices of research data management in selected digital humanities projects at Uppsala University. The objective is to as- sess the impact that data management plans (DMPs) on these interdisciplinary projects and evaluate the applica- tion of the FAIR guiding principles. It is crucial to consider the researchers’ perspective on this matter. Universi- ties could invest in robust data management practices by taking into account the needs and skills of researchers. Method. Semi-structured interviews were conducted using purposive sampling targeting researchers from various departments within the Faculty of Arts who were involved in interdisciplinary digital humanities pro- jects. Eight interviews were carried out with principal investigators (PIs) and researchers. Analysis. The interviews, along with the provided DMPs, were thematically analysed to address the re- search questions regarding the effect of DMPs in the selected projects. Results. The study findings indicate that the PIs and researchers do not perceive the DMP as an integral part of their research work in digital humanities projects. Nonetheless, most participants recognise its signifi- cance and its role could be enhanced in research projects. Challenges typically arise during stages of the research data life cycle, such as data analysis, rather than in the development of the DMP itself. Moreover, the practical implementation of the FAIR principles often poses difficulties due to variations in data types and project goals. Conclusion. The results of this study highlight the need for more actionable DMPs in digital humanities projects and further training for researchers on data management issues. The interdisciplinary nature of these projects facilitates collaboration among researchers in the development of DMPs.

Die sog. FAIR-Prinzipien haben sich mittlerweile als Standard-Anforderung im Forschungsdatenmanagement etabliert. In Förderanträgen und -berichten müssen Wissenschaftler:innen darlegen, wie sie Forschungsdaten gemäß den FAIR-Prinzipien verwalten und veröffentlichen. Auch immer mehr Fachzeitschriften bzw. Verlage fordern von ihren Autor:innen, dass sie ihre Forschungsdaten gemäß den FAIR-Prinzipien teilen, um die Reproduzierbarkeit und Überprüfbarkeit ihrer Ergebnisse zu gewährleisten. Was das Akronym FAIR eigentlich bedeutet und worauf Forschende in diesem Zusammenhang achten sollten, fasst dieser Beitrag kurz zusammen.

Background: Objectives: To produce and synthesize reliable data, systematic reviews need to adhere to rigorous methodological standards. This living meta-review aims to investigate risk of bias and open science practices in systematic reviews from the educational field published between 2022-2023. The aim of this meta-review is to get a better understanding of the current state of educational research regarding the aforementioned topics.  Methods: Eligibility criteria: We included all systematic reviews of educational interventions, instructions, and methods for all K-12 student populations with experimental or quasi-experimental designs where the outcome variables were academic performance of any kind.  Information sources: We searched through the Education Resources Information Centre (ERIC) for systematic reviews published between 2022-2023. In addition, we also hand searched four scholarly databases. Risk of Bias and FAIR principles: To assess systematic reviews risk of bias and open science practices in systematic reviews two tools were used, ROBIS - Risk of bias in systematic reviews, as well as a FAIR assessment tool.  Results: 44 studies that matched our PICOS were included in this meta-review. Out of these studies four (9%) were deemed as having a low risk of bias. The remaining 40 studies were deemed as either having high risk of bias (89%) or unclear risk of bias (2%).  Among the 44 studies included, only four studies (9%) had their data available, and none of them adhered to all of the items regarding the FAIR principles.  Discussion: This meta-review shows that only a small part of systematic reviews in education can be considered low risk of bias, and an even smaller part can be considered adhering to open science principles. Therefore, much needs to change to adapt to new scientific guidelines.

[ES] Las ontologías son artefactos computacionales con una amplia gama de aplicaciones. Estos artefactos representan el conocimiento con la mayor precisión posible y brindan a los humanos un marco para representar y aclarar el conocimiento. Además, las ontologías se pueden implementar y procesar agregando semántica a los datos que deben intercambiarse entre sistemas. En los sistemas, los datos transportan información y deben seguir los Principios FAIR para cumplir su propósito. Sin embargo, los dominios del conocimiento pueden ser vastos, complejos y sensibles, lo que hace que la interoperabilidad sea un desafío. Además, el diseño y desarrollo de ontologías no es una tarea sencilla, y debe seguir metodologías y estándares, además de cumplir una serie de requisitos. De hecho, las ontologías se han utilizado para producir FAIRness de datos debido a sus características, aplicaciones y competencias semánticas. Con la creciente necesidad de interoperar datos surgió la necesidad de interoperar ontologías para garantizar la correcta transmisión e intercambio de información. Para satisfacer esta demanda de ontologías interoperativas y, al mismo tiempo, conceptualizar dominios amplios y complejos, surgieron las Redes de Ontologías. Además, las ontologías comenzaron a presentar conceptualizaciones a través de la fragmentación del conocimiento de diferentes maneras, dependiendo de requisitos como el alcance de la ontología, su propósito, si es procesable o para uso humano, su contexto, entre otros aspectos formales, haciendo que la Ingeniería Ontológica sea también un dominio complejo. El problema es que en el Proceso de Ingeniería de Ontologías, las personas responsables toman diferentes perspectivas sobre las conceptualizaciones, provocando que las ontologías tengan sesgos a veces más ontológicos y otras más relacionados con el dominio. Estos problemas dan como resultado ontologías que carecen de fundamento o bien implementaciones de ontologías sin un modelo de referencia previo. Proponemos una (meta)ontología basada en la Ontología Fundacional Unicada (UFO, del inglés, Unified Foundational Ontology) y respaldada por estándares de clasificación ontológica reconocidos, guías y principios FAIR para resolver este problema de falta de consenso en las conceptualizaciones. La Ontología para el Análisis Ontológico (O4OA, del inglés, Ontology for Ontological Analysis) considera perspectivas, conocimientos, características y compromisos, que son necesarios para que la ontología y el dominio faciliten el proceso de Análisis Ontológico, incluyendo el análisis de las ontologías que conforman una red de ontologías. Utilizando O4OA, proponemos el Marco para la Caracterización Ontológica (F4OC, del inglés, Framework for Ontology Characterization) para proporcionar pautas y mejores prácticas a los responsables, a la luz de O4OA. F4OC proporciona un entorno estable y homogéneo para facilitar el análisis ontológico, abordando simultáneamente las perspectivas ontológicas y de dominio de los involucrados. Además, aplicamos O4OA y F4OC a varios estudios de casos en el Dominio de Ciberseguridad, el cual es complejo, extremadamente regulado y sensible, y propenso a dañar a personas y organizaciones. El principal objetivo de esta tesis doctoral es proporcionar un entorno sistemático y reproducible para ingenieros en ontologías y expertos en dominios, responsables de garantizar ontologías desarrolladas de acuerdo con los Principios FAIR. Aspiramos a que O4OA y F4OC sean contribuciones valiosas para la comunidad de modelado conceptual, así como resultados adicionales para la comunidad de ciberseguridad a través del análisis ontológico de nuestros estudios de caso. / [CA] Les ontologies són artefactes computacionals amb una àmplia gamma d'aplicacions. Aquests artefactes representen el coneixement amb la major precisió possible i brinden als humans un marc per a representar i aclarir el coneixement. A més, les ontologies es poden implementar i processar agregant semàntica a les dades que han d'intercanviar-se entre sistemes. En els sistemes, les dades transporten informació i han de seguir els Principis FAIR per a complir el seu propòsit. No obstant això, els dominis del coneixement poden ser vastos, complexos i sensibles, la qual cosa fa que la interoperabilitat siga un desafiament. A més, el disseny i desenvolupament d'ontologies no és una tasca senzilla, i ha de seguir metodologies i estàndards, a més de complir una sèrie de requisits. De fet, les ontologies s'han utilitzat per a produir FAIRness de dades a causa de les seues característiques, aplicacions i competències semàntiques. Amb la creixent necessitat de inter operar dades va sorgir la necessitat de inter operar ontologies per a garantir la correcta transmissió i intercanvi d'informació. Per a satisfer aquesta demanda d'ontologies inter operatives i, al mateix temps, conceptualitzar dominis amplis i complexos, van sorgir Xarxes d'Ontologies. A més, les ontologies van començar a presentar conceptualitzacions a través de la fragmentació del coneixement de diferents maneres, depenent de requisits com l'abast de l'ontologia, el seu propòsit, si és procesable o per a ús humà, el seu context i diversos altres aspectes formals, fent que el Enginyeria Ontològica també és un domini complex. El problema és que en Procés d'Enginyeria d'Ontologies, les persones responsables prenen diferents perspectives sobre les conceptualitzacions, provocant que les ontologies tinguen biaixos a vegades més ontològics i altres més relacionats amb el domini. Aquests problemes donen com a resultat ontologies que manquen de fonament i implementacions d'ontologies sense un model de referència previ. Proposem una (meta)ontologia basada en la Ontologia Fundacional Unificada (UFO, de le inglés, Unified Foundational Ontology) i recolzada per coneguts estàndard de classificació ontològica, guies i principis FAIR per a resoldre aquest problema de falta de consens en les conceptualitzacions. La Ontologia per a l'Anàlisi Ontològica (O4OA, de le inglés, Ontology for Ontological Analysis) considera perspectives, coneixements, característiques i compromisos, que són necessaris perquè l'ontologia i el domini faciliten el procés de Anàlisi Ontològica, incloent-hi l'anàlisi de les ontologies que conformen una xarxa d'ontologies. Utilitzant O4OA, proposem el Marco per a la Caracterització Ontològica (F4OC, de le inglés, Framework for Ontology Characterization) per a proporcionar pautes i millors pràctiques als responsables, a la llum d'O4OA. F4OC proporciona un entorn estable i homogeni per a facilitar l'anàlisi ontològica, abordant simultàniament les perspectives ontològiques i de domini dels involucrades. A més, apliquem O4OA i F4OC a diversos estudis de casos en el Domini de Seguretat Cibernètica, que és complex, extremadament regulat i sensible, i propens a danyar a persones i organitzacions. L'objectiu principal d'aquesta tesi és proporcionar un entorn sistemàtic, reproduïble i escalable per a engineers en ontologies i experts in dominis encarregats de garantir les ontologies desenvolupades d'acord amb els Principis FAIR. Aspirem a fer que O4OA i F4OC aportin valuoses contribucions a la comunitat de modelització conceptual, així com resultats addicionals per a la comunitat de ciberseguretat mitjançant l'anàlisi ontològica dels nostres estudis de cas. / [EN] Ontologies are computational artifacts with a wide range of applications. They represent knowledge as accurately as possible and provide humans with a framework for knowledge representation and clarification. Additionally, ontologies can be implemented and processed by adding semantics to data that needs to be exchanged between systems. In systems, data is the carrier of information and needs to comply with the FAIR Principles to fulfill its purpose. However, knowledge domains can be vast, complex, and sensitive, making interoperability challenging. Moreover, ontology design and development are not easy tasks; they must follow methodologies and standards and comply with a set of requirements. Indeed, ontologies have been used to provide data FAIRness due to their characteristics, applications, and semantic competencies. With the growing need to interoperate data came the need to interoperate ontologies to guarantee the correct transmission and exchange of information. To meet the need to interoperate ontologies and at the same time conceptualize complex and vast domains, Ontology Networks emerged. Moreover, ontologies began to carry out conceptualizations, fragmenting knowledge in different ways depending on requirements, such as the ontology scope, purpose, whether it is processable or for human use, its context, and several other formal aspects, making Ontology Engineering also a complex domain. The problem is that in the Ontology Engineering Process, stakeholders take different perspectives of the conceptualizations, and this causes ontologies to have biases that are sometimes more ontological and sometimes more related to the domain. These problems result in ontologies that lack grounding and ontology implementations without a previous reference model. We propose a (meta)ontology grounded over the Unified Foundational Ontology (UFO) and supported by well-known ontological classification standards, guides, and FAIR Principles to address this problem of lack of consensual conceptualization. The Ontology for Ontological Analysis (O4OA) considers ontological-related and domain-related perspectives, knowledge, characteristics, and commitment that are needed to facilitate the process of Ontological Analysis, including the analysis of ontologies composing an ontology network. Using O4OA we propose the Framework for Ontology Characterization (F4OC) to provide guidelines and best practices in the light of O4OA for stakeholders. The F4OC fosters a stable and uniform environment for ontological analysis, integrating stakeholder perspectives. Moreover, we applied O4OA and F4OC to several case studies in the Cybersecurity Domain, which is intricate, highly regulated, and sensitive to causing harm to people and organizations. The main objective of this doctoral thesis is to provide a systematic and reproducible environment for ontology engineers and domain specialists responsible for ensuring ontologies developed according to the FAIR Principles. We aspire that O4OA and F4OC be valuable contributions to the conceptual modeling community as well as the additional outcomes for the cybersecurity community through the ontological analysis in our case studies. / Franco Martins Souza, B. (2024). A Framework for Conceptual Characterization of Ontologies and its Application in the Cybersecurity Domain [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/204584