FAIR Principles Research Articles

Who is keeping an eye on FAIR principles?

Who is keeping an eye on FAIR principles?

ECOTOXr: An R package for reproducible and transparent retrieval of data from EPA's ECOTOX database

The US EPA ECOTOX database provides key ecotoxicological data that are crucial in environmental risk assessment. It can be used for computational predictions of toxicity or indications of hazard in a wide range of situations. There is no standardised or formalised method for extracting and subsetting data from the database for these purposes. Consequently, results in such meta-analyses are difficult to reproduce. The present study introduces the software package ECOTOXr, which provides the means to formalise data retrieval from the ECOTOX database in the R scripting language. Three cases are presented to evaluate the performance of the package in relation to earlier data extractions and searches on the website. These cases demonstrate that the package can reproduce data sets relatively well. Furthermore, they illustrate how future studies can further improve traceability and reproducibility by applying the package and adhering to some simple guidelines. This contributes to the FAIR principles, credibility and acceptance of research that uses data from the ECOTOX database.

ONCO-FAIR Project: Improving Data Interoperability in Oncology Chemotherapy Treatments for Data Reuse.

The ONCO-FAIR project's initial experimentation aims to enhance data interoperability in oncology chemotherapy treatments, adhering to the FAIR principles. This study focuses on integrating the HL7 FHIR standard to address interoperability challenges within chemotherapy data exchange. Collaborating with healthcare institutions in Rennes, the research team assessed the limitations of current standards such as PN13, mCODE, and OSIRIS, leading to the customization of twelve FHIR resources complemented by two chemotherapy-specific extensions. The methodological approach follows the Integrating the Healthcare Enterprise (IHE) framework, organizing the process into four key stages to ensure the effectiveness and relevance of health data reuse for research. This framework facilitated the identification of chemotherapy-specific needs, the evaluation of existing standards, and data modeling through a FHIR implementation guide. The article underscores the importance of upstream interoperability for aligning chemotherapy software with clinical data warehouse infrastructure, showcasing the proposed solution's capability to overcome interoperability barriers and promote data reuse in line with FAIR principles. Furthermore, it discusses future directions, including extending this approach to other oncology data categories and enhancing downstream interoperability with health data sharing platforms.

Sustainability in Secondary Use of Health Data - A Scoping Review.

This scoping review investigates sustainability in the reuse of health data on a technological, intra-organizational, inter-organizational, and regulatory level. Thereby, it focuses on the evolutionary, relational, and durational perspective of sustainability. The study highlights various challenges in achieving data sustainability, from regulatory norms such as FAIR principles towards data governance processes and responsibilities in organizations that facilitate data sharing. By highlighting the need for economic sustainability of health data sharing platforms and adapted principles for data sharing, this study aims to analyze current practices that aim for sustainability in the secondary use of health data.

Unlocking the Power of Real-World Data: A Framework for Sustainable Healthcare.

Real-world data (RWD) has the potential to revolutionize healthcare by offering valuable insights into patient outcomes and treatment efficacy. However, leveraging RWD effectively presents challenges, including its inherent limitations, diverse stakeholders, and insufficient data management pipelines. A proposed framework advocates three essential elements: adherence to FAIR principles (Findable, Accessible, Interoperable, and Reusable), stakeholder engagement and education, and highlighting the need for inclusive, pragmatic federated hybrid pipelines. By employing these strategies, healthcare organizations can overcome obstacles to RWD utilization and foster sustainable progress in patient care.

How Data Infrastructure Deals with Bias Problems in Medical Imaging.

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.

Modeling Rare Disease Datasets with ART-DECOR.

This paper presents ongoing work on the modeling of different datasets using the ART-DECOR modeling tool, with a focus on adherence to the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The successful modeling of the French minimal dataset for rare diseases (Set de donnees minimal des maladies rares (SDM-MR.fr)) should provide inspiration for the development of the German minimal dataset for rare diseases (Minimalbasisdatensatz fur Seltene Erkrankungen (MBDS-SE.de)).

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Kolmogorov–Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning models (PIKANs) and deep operator models (DeepOKANs) for solving differential equations for forward and inverse problems. In particular, we compare them with physics-informed neural networks (PINNs) and deep operator networks (DeepONets), which are based on the standard MLP representation. We find that although the original KANs based on the B-splines parameterization lack accuracy and efficiency, modified versions based on low-order orthogonal polynomials have comparable performance to PINNs and DeepONet, although they still lack robustness as they may diverge for different random seeds or higher order orthogonal polynomials. We visualize their corresponding loss landscapes and analyze their learning dynamics using information bottleneck theory. Our study follows the FAIR principles so that other researchers can use our benchmarks to further advance this emerging topic.

GeoFRESH – an online platform for freshwater geospatial data processing

ABSTRACT Freshwater ecosystems are characterized by their unique longitudinal and lateral habitat connectivity. As a result, spatial units in freshwater-specific analyses can often not be considered independent of each other. Accounting for this connectivity in modelling analyses requires advanced skills in Geographic Information Systems (GIS) for adequately processing and managing the data. To address this challenge, we developed the GeoFRESH online platform, which is available at https://geofresh.org . The platform provides a graphical, easy-to-use interface to create freshwater-specific analysis-ready data for any given location in the world, based on a high-resolution stream network (https://hydrography.org/hydrography90m/hydrography90m_layers). Users can (i) upload and visualize point coordinates, (ii) automatically assign points to the closest stream network segment, (iii) annotate the point data with a suite of 104 local and/or upstream-aggregated topographic, climatic, land-cover and soil variables, (iv) visualize summary plots, and (v) download the data in csv-format for further analyses. The platform can be expanded given its modular structure and it can serve as a key element to support freshwater science and management relying on high-resolution geospatial analyses. GeoFRESH provides a low-entry interface while being complementary to the hydrographr R-package, and contributes importantly to the re-usability of data as an important aspect of the FAIR principles.

BUILDING AN AI COMPACT TO UPHOLD ARTIFICIAL INTEGRITY

AbstractThe author is Group Vice President of Digital Marketing and Digital Transformation at Thales, and a Lecturer at INSEAD and elsewhere. He discusses a concept he originated, Artificial Integrity. This involves how society can build fair, just, and equitable principles into the fabric of artificial intelligence, as the latter continues to move and evolve at lightning speed, in ways most people cannot understand. He describes his holistic framework of principles “for continuous vigilance and collaborative efforts in shaping AI’s role in society, ensuring it contributes positively to human progress.” He details 17 different principles, which “provide a framework aimed at establishing a global, constitution‐like foundation to govern AI.” They are, in his words: Protection of Human Identity and Dignity, Safety and Well‐being, Obedience to Human Orders, Transparency and Explainability, Confidentiality and Data Protection, Regulation and Human Decision‐Making, Responsibility in Case of Failure, Self‐protection and Updating, Shared Responsibility, Fairness and Inclusion, Protection of Meaningful and Significant Jobs, Ethical and Cultural Respect, Environmental Impact, International Collaboration, Respect for State Sovereignty, Economic Incentive for Societal Development, and Education and Awareness.

Dados Abertos para Sustentabilidade Ambiental

Sustainability has been a topic with increasing focus in all areas of scientific knowledge, including Information Science. Open datasets have the potential to be used for sustainability. The study´s aim was to evaluate open datasets made available by Brazilian federal environmental agencies regarding their adherence to the FAIR principles and the W3C Good Practices for Data on the Web. The method consisted of preparing a protocol in the form of a checklist, composed of questions based on these principles and good practices, which was applied to the 166 data sets located on Brazilian government data portals. The results indicate that, although the government makes a substantial amount of this type of data available under open licenses, the potential for its location, access and reuse is compromised by the low use of permanent identifiers and good quality metadata. Conclusion: The use of data can boost and assist social and environmental causes in the Brazilian context, as, for action, arguments evidenced with data are necessary to support advocacy and public policies that aim to disseminate information to raise awareness.

Sharing practices of software artefacts and source code for reproducible research

AbstractWhile source code of software and algorithms depicts an essential component in all fields of modern research involving data analysis and processing steps, it is uncommonly shared upon publication of results throughout disciplines. Simple guidelines to generate reproducible source code have been published. Still, code optimization supporting its repurposing to different settings is often neglected and even less thought of to be registered in catalogues for a public reuse. Though all research output should be reasonably curated in terms of reproducibility, it has been shown that researchers are frequently non-compliant with availability statements in their publications. These do not even include the use of persistent unique identifiers that would allow referencing archives of code artefacts at certain versions and time for long-lasting links to research articles. In this work, we provide an analysis on current practices of authors in open scientific journals in regard to code availability indications, FAIR principles applied to code and algorithms. We present common repositories of choice among authors. Results further show disciplinary differences of code availability in scholarly publications over the past years. We advocate proper description, archiving and referencing of source code and methods as part of the scientific knowledge, also appealing to editorial boards and reviewers for supervision.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

ANALISIS HUKUM MENGENAI TATA KELOLA PAJAK BUMI DAN BANGUNAN PERKOTAAN DAN PERDESAAN DALAM RANGKA PEMBANGUNAN DAN KESEJAHTERAAN MASYARAKAT DESA

This study aims to learn about and analyze legal studies on rural and urban land governance and building tax in the context of development and well-being of rural communities and to know the role of information technology in the management of rural and urban areas of the United Nations in order to improve community obedience and discipline of the tax system. The method used is to make the field of legal science the foundation of its mother science, so the research used is normative legal research. The results showed that the Rural and Urban Property and Construction Tax (PBB-P2) should be collected on the basis of laws and regulations from the top to the implementing regulations, namely village regulations and information technology optimization, as it plays an important role in the management of the Rural and Urban Property and Real Estate Tax (PBB-P2). In terms of increased public obedience, information technology can be used to facilitate and speed up the tax payment process. For this reason, it is important to guide UN-P2 governance to village governments on the basis of fair and transparent legal principles, in order to foster the development and well-being of rural communities.

Desiderata for discoverability and FAIR adoption of health data hubs

BackgroundThe future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens. ObjectiveThis study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best.practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded. MethodsA survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation. ResultsThe study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs. ConclusionsThe study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.

Managing macromolecular crystallographic data with a laboratory information management system.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of thehome laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

The Meise Botanic Garden Herbarium Data Management Plan

This Data Management Plan outlines a comprehensive strategy for handling, storing, and sharing of data generated by digitisation projects of the herbarium at Meise Botanic Garden with Index Herbarium code BR. Its purpose is to establish clear guidelines for both staff and external users, specifying the terms governing data usage and storage. It aims to prioritise the FAIR principles (Findable, Accessible, Interoperable and Reusable), ensure responsible data management, facilitate long-term preservation, uphold legal and ethical obligations, all while aligning with the research excellence mission of Meise Botanic Garden. This plan serves as a guiding document to effectively and efficiently achieve these goals.

The Jimmie Barker corpus: A Muruwari man’s documentation of Aboriginal languages, history and culture between 1968 and 1972

ABSTRACT Jimmie Barker (1900–1972) was a Muruwari Elder, Cultural Knowledge Holder, linguist, historian, ethnographer, inventor and sound engineer who produced over 113 h of audio recordings using reel-to-reel tape recorders between 1968 and 1972. Jimmie was supported in his endeavours by Janet Mathews and the Australian Institute of Aboriginal and Torres Strait Islander Studies (AIATSIS) (formerly, the Australian Institute of Aboriginal Studies). The “Barker Collection” of audio recordings is now housed in the AIATSIS Collection. Jimmie initially set out to record a Muruwari-English dictionary, but this expanded into documenting Muruwari language and culture, as well as personal, family, domestic and international history. Much of the collection comprises self-elicitation, metalinguistic analyses, oral histories and reflections. It includes some of the earliest recordings and language documentation by an Aboriginal person of other Aboriginal people, observations of typological properties and language change in Aboriginal languages, and critical analyses of linguistic and anthropological research. Since 2021, Roy J. Barker, grandson of Jimmie and Muruwari Cultural Knowledge Holder, has overseen a team of linguists designing a time-aligned ELAN corpus of Jimmie’s recordings for language revival outcomes. The recordings are transcribed, annotated with metadata and coded with cultural and language tags with consideration to the FAIR and CARE Principles.

PHIRI: lessons for an extensive reuse of sensitive data in federated health research.

The extensive and continuous reuse of sensitive health data could enhance the role of population health research on public decisions. This paper describes the design principles and the different building blocks that have supported the implementation and deployment of Population Health Information Research Infrastructure (PHIRI), the strengths and challenges of the approach and some future developments. The design and implementation of PHIRI have been developed upon: (i) the data visiting principle-data does not move but code moves; (ii) the orchestration of the research question throughout a workflow that ensured legal, organizational, semantic and technological interoperability and (iii) a 'master-worker' federated computational architecture that supported the development of four uses cases. Nine participants nodes and 28 Euro-Peristat members completed the deployment of the infrastructure according to the expected outputs. As a consequence, each use case produced and published their own common data model, the analytical pipeline and the corresponding research outputs. All the digital objects were developed and published according to Open Science and FAIR principles. PHIRI has successfully supported the development of four use cases in a federated manner, overcoming limitations for the reuse of sensitive health data and providing a methodology to achieve interoperability in multiple research nodes.