The FAIR guiding principles for data stewardship: fair enough?

Harmonizing the Generation and Pre-publication Stewardship of FAIR bioimage data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured bioimage data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable bioimage data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing bioimage data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). Here, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse bioimage data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made generating community standard practices for imaging Quality Control (QC) and metadata (Faklaris et al., 2022; Hammer et al., 2021; Huisman et al., 2021; Microscopy Australia, 2016; Montero Llopis et al., 2021; Rigano et al., 2021; Sarkans et al., 2021). We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study

BACKGROUND

Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse.

OBJECTIVE

To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications.

METHODS

The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya.

RESULTS

The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website.

CONCLUSIONS

The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research

BACKGROUND

Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that "health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements".

OBJECTIVE

To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR).

DATA AND METHODS

This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project.

RESULTS

The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained.

CONCLUSION

The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information

In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

BACKGROUND

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.

OBJECTIVE

This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.

METHODS

We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.

RESULTS

Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.

CONCLUSIONS

The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

DS-PACK: Tool assembly for the end-to-end support of controlled access human data sharing

The EU General Data Protection Regulation (GDPR) requirements have prompted a shift from centralised controlled access genome-phenome archives to federated models for sharing sensitive human data. In a data-sharing federation, a central node facilitates data discovery; meanwhile, distributed nodes are responsible for handling data access requests, concluding agreements with data users and providing secure access to the data. Research institutions that want to become part of such federations often lack the resources to set up the required controlled access processes. The DS-PACK tool assembly is a reusable, open-source middleware solution that semi-automates controlled access processes end-to-end, from data submission to access. Data protection principles are engraved into all components of the DS-PACK assembly. DS-PACK centralises access control management and distributes access control enforcement with support for data access via cloud-based applications. DS-PACK is in production use at the ELIXIR Luxembourg data hosting platform, combined with an operational model including legal facilitation and data stewardship.

Posttraumatic stress disorder in people with dementia: study protocol

Background: Posttraumatic stress disorder (PTSD) is considered an independent risk factor for dementia. Despite the (clinical) evidence that PTSD is associated with neuropsychiatric symptoms in people with dementia, studies on its prevalence and clinical manifestation are limited, and their quality is affected by the lack of a structured method to diagnose PTSD in this population. The primary aim of the current study is to validate the 'TRAuma and DEmentia' interview as a diagnostic tool for PTSD in people with dementia and to test feasibility of EMDR treatment for people with PTSD and dementia.Methods: This prospective multi-centre study is divided into two parts. In study A, 90 participants with dementia will be included to test the criterion validity, inter-rater reliability and feasibility of the 'TRAuma and DEmentia' interview. In study B, 29 participants with dementia and PTSD will receive eye movement desensitisation and reprocessing therapy by a trained psychologist, and 29 participants with dementia and PTSD will be placed on the waiting list control group.Conclusion: This study aims to improve the diagnostic process of PTSD and to assess the effects of eye movement desensitisation and reprocessing treatment in people with dementia living in Dutch care facilities.Trial registration: NL70479.068.20 / METC 20-063 / OSF registration: https://doi.org/10.17605/OSF.IO/AKW4F.

Joint controllers in large research consortia: a funnel model to distinguish controllers in the sense of the GDPR from other partners in the consortium

Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps. Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects' ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.

Behind every good research there are data. What are they and their importance to forensic science

Data underpinning science have become one of the most precious assets in research, and while the principles of FAIR (Findable, Accessible, Interoperable and Reusable) have been put forward as a guide to how to approach data handling, data sharing and long-term storage still remain a challenge for many research areas including forensic science. The reporting and the sharing of data can be made easier by giving them structure, the use of suitable labels and the inclusion of descriptors collated into metadata prior to their deposition in repositories with persistent identifiers. Such a systematic approach would strengthen the quality and the integrity of research while providing greater transparency to published materials.

Data-Driven Design for Metamaterials and Multiscale Systems: A Review

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.

Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences

Objective

Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience.

Methods

We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted.

Results

The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users.

Conclusion

The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.

Data-Sharing Across Otolaryngology: Comparing Journal Policies and Their Adherence to the FAIR Principles

OBJECTIVE

Data-sharing plays an essential role in advancing scientific understanding. Here, we aim to identify the commonalities and differences in data-sharing policies endorsed by otolaryngology journals and to assess their adherence to the FAIR (findable, accessible, interoperable, reusable) principles.

METHODS

Data-sharing policies were searched for among 111 otolaryngology journals, as listed by Scimago Journal & Country Rank. Policy extraction of the top biomedical journals as ranked by Google Scholar metrics were used as a comparison. The FAIR principles for scientific data management and stewardship were used for the extraction framework. This occurred in a blind, masked, and independent fashion.

RESULTS

Of the 111 ranked otolaryngology journals, 100 met inclusion criteria. Of those 100 journals, 79 provided data-sharing policies. There was a clear lack of standardization across policies, along with specific gaps in accessibility and reusability which need to be addressed. Seventy-two policies (of 79; 91%) designated that metadata should have globally unique and persistent identifiers. Seventy-one (of 79; 90%) policies specified that metadata should clearly include the identifier of the data they describe. Fifty-six policies (of 79; 71%) outlined that metadata should be richly described with a plurality of accurate and relevant attributes.

CONCLUSION

Otolaryngology journals have varying data-sharing policies, and adherence to the FAIR principles appears to be moderate. This calls for increased data transparency, allowing for results to be reproduced, confirmed, and debated.

Accelerating CO2 electrochemical conversion towards industrial implementation

Despite significant progress in CO2 conversion field, there remains a significant gap between fundamental research and the industrial demands. This Comment discusses key performance parameters for industrial applications and outlines current limitations in the field.

Data management and protection in occupational and environmental exposome research - A case study from the EU-funded EXIMIOUS project

Within collaborative projects, such as the EU-funded Horizon 2020 EXIMIOUS project (Mapping Exposure-Induced Immune Effects: Connecting the Exposome and the Immunome), collection and analysis of large volumes of data pose challenges in the domain of data management, with regards to both ethical and legal aspects. However, researchers often lack the right tools and/or accurate understanding of the ethical/legal framework to independently address such challenges. With the guidance and support within and between the partner institutes (the researchers and the ethical and legal teams) in the EXIMIOUS project, we have been able to understand and solve most challenges during the first two project years. This has fed into the development of a Data Management Plan and the establishment of data management platforms in accordance with the ethical and legal framework laid down by the EU and the different national regulations of the partners involved. Through this elaborate exercise, we have acquired tools which allow us to make our research data FAIR (Findable, Accessible, Interoperable, and Reusable), while at the same time ensuring data privacy and security (GDPR compliant). Herein we share our experience of creating and managing the data workflow through an open research communication, with the aim of helping other researchers build their data management framework in their own projects. Based on the measures adopted in EXIMIOUS to ensure FAIR data management, we also put together a checklist "DMP CHECK" containing a series of recommendations based on our experience.

Facilitating Harmonization of Variables in Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository

BACKGROUND

High-quality research in cardiovascular prevention, as in other fields, requires inclusion of a broad range of data sets from different sources. Integrating and harmonizing different data sources are essential to increase generalizability, sample size, and representation of understudied populations-strengthening the evidence for the scientific questions being addressed.

METHODS

Here, we describe an effort to build an open-access repository and interactive online portal for researchers to access the metadata and code harmonizing data from 4 well-known cohort studies-the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study, FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities) study. We introduce a methodology and a framework used for preprocessing and harmonizing variables from multiple studies.

RESULTS

We provide a real-case study and step-by-step guidance to demonstrate the practical utility of our repository and interactive web page. In addition to our successful development of such an open-access repository and interactive web page, this exercise in harmonizing data from multiple cohort studies has revealed several key themes. These themes include the importance of careful preprocessing and harmonization of variables, the value of creating an open-access repository to facilitate collaboration and reproducibility, and the potential for using harmonized data to address important scientific questions and disparities in cardiovascular disease research.

CONCLUSIONS

By integrating and harmonizing these large-scale cohort studies, such a repository may improve the statistical power and representation of understudied cohorts, enabling development and validation of risk prediction models, identification and investigation of risk factors, and creating a platform for racial disparities research.

REGISTRATION

URL: https://precision.heart.org/duke-ninds.

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research

BACKGROUND

Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.

METHODS

Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated.

RESULTS

Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation.

CONCLUSIONS

The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggest that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

Sharing FAIR monitoring program data improves discoverability and reuse

Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.

The metabolomics workbench file status website: a metadata repository promoting FAIR principles of metabolomics data

BACKGROUND

An updated version of the mwtab Python package for programmatic access to the Metabolomics Workbench (MetabolomicsWB) data repository was released at the beginning of 2021. Along with updating the package to match the changes to MetabolomicsWB's 'mwTab' file format specification and enhancing the package's functionality, the included validation facilities were used to detect and catalog file inconsistencies and errors across all publicly available datasets in MetabolomicsWB.

RESULTS

The MetabolomicsWB File Status website was developed to provide continuous validation of MetabolomicsWB data files and a useful interface to all found inconsistencies and errors. This list of detectable issues/errors include format parsing errors, format compliance issues, access problems via MetabolomicsWB's REST interface, and other small inconsistencies that can hinder reusability. The website uses the mwtab Python package to pull down and validate each available analysis file and then generates an html report. The website is updated on a weekly basis. Moreover, the Python website design utilizes GitHub and GitHub.io, providing an easy to replicate template for implementing other metadata, virtual, and meta- repositories.

CONCLUSIONS

The MetabolomicsWB File Status website provides a metadata repository of validation metadata to promote the FAIR use of existing metabolomics datasets from the MetabolomicsWB data repository.

Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing

The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.

Identifying and sharing per-and polyfluoroalkyl substances hot-spot areas and exposures in drinking water

Exposure to per- and polyfluoroalkyl substances (PFAS) in drinking water is widely recognized as a public health concern. Decision-makers who are responsible for managing PFAS drinking water risks lack the tools to acquire the information they need. In response to this need, we provide a detailed description of a Kentucky dataset that allows decision-makers to visualize potential hot-spot areas and evaluate drinking water systems that may be susceptible to PFAS contamination. The dataset includes information extracted from publicly available sources to create five different maps in ArcGIS Online and highlights potential sources of PFAS contamination in the environment in relation to drinking water systems. As datasets of PFAS drinking water sampling continue to grow as part of evolving regulatory requirements, we used this Kentucky dataset as an example to promote the reuse of this dataset and others like it. We incorporated the FAIR (Findable, Accessible, Interoperable, and Reusable) principles by creating a Figshare item that includes all data and associated metadata with these five ArcGIS maps.

A proposed FAIR approach for disseminating geospatial information system maps

We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.

Toward a global virus genomic surveillance network

The COVID-19 pandemic galvanized the field of virus genomic surveillance, demonstrating its utility for public health. Now, we must harness the momentum that led to increased infrastructure, training, and political will to build a sustainable global genomic surveillance network for other epidemic and endemic viruses. We suggest a generalizable modular sequencing framework wherein users can easily switch between virus targets to maximize cost-effectiveness and maintain readiness for new threats. We also highlight challenges associated with genomic surveillance and when global inequalities persist. We propose solutions to mitigate some of these issues, including training and multilateral partnerships. Exploring alternatives to clinical sequencing can also reduce the cost of surveillance programs. Finally, we discuss how establishing genomic surveillance would aid control programs and potentially provide a warning system for outbreaks, using a global respiratory virus (RSV), an arbovirus (dengue virus), and a regional zoonotic virus (Lassa virus) as examples.

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.

Robber flies and hover flies (Insecta, Diptera, Asilidae and Syrphidae) in beech forests of the central Apennines: a contribution to the inventory of insect biodiversity in Italian State Nature Reserves

Background

The present paper describes a sampling-event dataset on species belonging to two families of Diptera (Syrphidae and Asilidae) collected between 2012 and 2019 in two Italian beech forests located in the central Apennines. The reference dataset consists of an annotated checklist and has been published on Zenodo. Syrphidae and Asilidae are two widespread and key ecological groups, including predator, pollinator and saproxylic species. Despite their pivotal role in both natural and man-made ecosystems, these families are still poorly known in terms of local distribution and open-access sampling-event data are rare in Italy.

New information

This open-access dataset includes 2,295 specimens for a total of 21 Asilidae and 65 Syrphidae species. Information about the collection (e.g. place, date, methods applied, collector) and the identification (e.g. species name, author, taxon ID) of the species is provided. Given the current biodiversity crisis, the publication of checklists, sampling-event data and datasets on insect communities in open-access repositories is highly recommended, as it represents the opportunity to share biodiversity information amongst different stakeholders. Moreover, such data are also a valuable source of information for nature reserve managers responsible for monitoring the conservation status of protected and endangered species and habitats and for evaluating the effects of conservation actions over time.

Schema Playground: a tool for authoring, extending, and using metadata schemas to improve FAIRness of biomedical data

BACKGROUND

Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects.

RESULTS

Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema-a large multi-class schema for harmonizing various COVID-19 related resources.

CONCLUSIONS

We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR.

Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research

Background

Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.

Methods

Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 31st, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated.

Results

Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation.

Conclusions

The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggests that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

What is data stewardship? Towards a comprehensive understanding

Data stewardship is a term that is understood in heterogenous ways. In recent organisational developments and efforts to build infrastructures and hire professional staff for research data management in various scientific fields in Europe, data stewardship is understood as mainly aiming at optimising data management in line with the FAIR principles (findability, accessibility, interoperability, reusability) forpurposes of reuse in the interests of the scientific community and the public. In addition, especially in the health and biomedical sciences some understandings of data stewardship mainly focus on the responsibility to respect the informational rights of data subjects. Following on from these different understandings and from recent developments to include ever more stakeholders in data stewardship, we propose a comprehensive understanding of data stewardship. According to this comprehensive understanding, data stewardship includes responsibilities towards all pertinent stakeholders and to equally consider and respect their legitimate rights and interests in order to build and maintain an efficient, trusted and fair data ecosystem. We also point out some of the practical challenges implied in such a comprehensive understanding.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

Complementary and integrative interventions for PTSD

ABSTRACTTo treat the impact of trauma, most current evidence supports the use of trauma-focused psychotherapy as the first line approach. However, millions of individuals exposed to trauma worldwide seek Complementary and Integrative Health (CIH) therapies in hopes of achieving wellness above and beyond reducing symptoms. But what is the evidence for CIH interventions? What are potential pitfalls? Given the growing popularity of and strong interest in CIH, EJPT is featuring research on these approaches in this special issue. The papers range from common interventions such as mindfulness to the use of service dogs and scuba diving to alleviate trauma related symptoms. A featured editorial highlights the importance of defining when, where, and how placebo responses work. Nonspecific elements of treatment such as positive expectations, therapeutic rituals, healing symbols, and social interactions are identified as factors influencing treatment response and scientists looking to add to the CIH evidence base are encouraged to consider the impact and methodological challenges these elements present. CIH interventions more specifically recognize and harness some of these factors in addition to intervention-specific factors such as attention or emotion regulation along with focus on overall wellbeing. The body of work in this special issue supports the emerging evidence for meditative and relaxation-based interventions and illustrates a creative but nascent state of the field. Cross-intervention mechanisms that may play a role in achieving wellness, such as arousal reduction, emotion regulation, posttraumatic growth, and positive affect are highlighted. The trauma field would benefit from accumulation of evidence for promising CIH interventions, evaluation of potential mechanisms, and examination of health and wellbeing outcomes. With the paucity of high-quality trials, it would be premature to recommend CIH interventions as first-line treatments. However, the emerging literature on CIH continues to advance our understanding of what works and how these interventions exert their effects.

FAIR Aspects of a Health Information Protection and Management System

BACKGROUND

 Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects.

OBJECTIVES

 The two main objectives of the presented work are the definition of a secure and interoperable modular architecture to manage different kinds of medical content (xIPAMS [x, for Any kind of content, Information Protection And Management System] and HIPAMS [Health Information Protection And Management System]), and the application of FAIR principles to that architecture in such a way that privacy and security are compatible with FAIR.

METHODS

 We propose the concept of xIPAMS as a modular architecture, following standards for interoperability, which defines mechanisms for privacy, protection, storage, search, and access to health-related information.

RESULTS

 xIPAMS provides FAIR principles and preserves patient's privacy. For each module, we identify how FAIR principles apply.

CONCLUSIONS

 We have analyzed how xIPAMS, and in particular HIPAMS (Health content), support the FAIR principles focusing on security and privacy. We have identified the FAIR principles supported by the different xIPAMS modules, concluding that the four principles are supported. Our analysis has also considered a possible implementation based on the concept of DACS (Document Access and Communication System), a system storing medical documents in a private and secure way. In addition, we have analyzed security aspects of the FAIRification process and how they are provided by xIPAMS modules.

One Digital Health for more FAIRness

BACKGROUND

 One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.

OBJECTIVES

 This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.

METHODS

 An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.

RESULTS

 A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.

CONCLUSIONS

 ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.

Digital data and practice change: the impact of innovative web portals on user knowledge building and decision-making processes

Purpose

The impact of innovative web portals on users, from access to application, is gaining interest as the global call for increased data availability gains momentum. This study reports on the perceptions of portal end users about usage and access to digital data across a range of fields of practice.

Design/methodology/approach

Data were collected and analysed from interviews (n = 132) and email feedback (n = 235) from end users of interoperable spatial knowledge web portals.

Findings

Data reveal that users attribute importance to ease of access and applicability, and to confidence and trust in data. The acquisition of data assists with reducing knowledge silos, facilitates knowledge sharing and decision-making. Digital data portals enable the building of stronger collaborations between different groups of individuals and communities leading to improved outcomes and more positive developments across varied discipline and practice areas.

Practical implications

Recommendations for developing online portals to optimise knowledge transfer and associated benefits, for users, are offered.

Originality/value

By collecting extensive qualitative data drawn from the experiences of end users of digital data portals, this paper provides new insights, thereby addressing a knowledge gap in the published literature about the use of technology uptake and the application of online data for practice and industry benefit.

A mixed-methods protocol to develop and validate a stewardship maturity matrix for human genomic data in the cloud

This article describes a mixed-methods protocol to develop and test the implementation of a stewardship maturity matrix (SMM) for repositories which govern access to human genomic data in the cloud. It is anticipated that the cloud will host most human genomic and related health datasets generated as part of publicly funded research in the coming years. However, repository managers lack practical tools for identifying what stewardship outcomes matter most to key stakeholders as well as how to track progress on their stewardship goals over time. In this article we describe a protocol that combines Delphi survey methods with SMM modeling first introduced in the earth and planetary sciences to develop a stewardship impact assessment tool for repositories that manage access to human genomic data. We discuss the strengths and limitations of this mixed-methods design and offer points to consider for wrangling both quantitative and qualitative data to enhance rigor and representativeness. We conclude with how the empirical methods bridged in this protocol have potential to improve evaluation of data stewardship systems and better align them with diverse stakeholder values in genomic data science.

From FAIR to FAIRS: Data security by design for the global burden of animal diseases

Solving complex global problems involving data and data analysis can require data from both the public and private sectors. The sharing of data has traditionally been restricted to open data. To facilitate the use of both open and private data, a new data-sharing framework has been constructed as an extension to the popular Findable-Accessible-Interoperable-Reusable (FAIR) framework. The "Secure by Design" approach has been taken to define the FAIRS data-sharing framework where S stands for Secure. A Cloud infrastructure architecture is proposed that would allow data brokers to implement FAIRS. This architecture is being constructed for the Global Burden of Animal Diseases (GBADs) to facilitate the sharing of livestock data.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Aligning restricted access data with FAIR: a systematic review

Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used 'Access Control' and 'Usage and License Terms' methods, while others such as 'Embargo on Data Release' and the use of 'Synthetic Data' were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access data.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Simple Summary

Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices.

Abstract

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

Perceptions and behavior of clinical researchers and research support staff regarding data FAIRification

The FAIR Data Principles are being rapidly adopted by many research institutes and funders worldwide. This study aimed to assess the awareness and attitudes of clinical researchers and research support staff regarding data FAIRification. A questionnaire was distributed to researchers and support staff in six Dutch University Medical Centers and Electronic Data Capture platform users. 164 researchers and 21 support staff members completed the questionnaire. 62.8% of the researchers and 81.0% of the support staff are currently undertaking at least some effort to achieve any aspect of FAIR, 11.0% and 23.8%, respectively, address all aspects. Only 46.6% of the researchers add metadata to their datasets, 39.7% add metadata to data elements, and 35.9% deposit their data in a repository. 94.7% of the researchers are aware of the usefulness of their data being FAIR for others and 89.3% are, given the right resources and support, willing to FAIRify their data. Institutions and funders should, therefore, develop FAIRification training and tools and should (financially) support researchers and staff throughout the process.

Perceptions and Opinions Towards Data-Sharing: A Survey of Addiction Journal Editorial Board Members

Background

We surveyed addiction journal editorial board members to better understand their opinions towards data-sharing.

Methods

Survey items consisted of Likert-type (e.g., one to five scale), multiple-choice, and free-response questions. Journal websites were searched for names and email addresses. Emails were distributed using SurveyMonkey. Descriptive statistics were used to characterize the responses.

Results

We received 178 responses (of 1039; 17.1%). Of these, 174 individuals agreed to participate in our study (97.8%). Most respondents did not know whether their journal had a data-sharing policy. Board members "somewhat agree" that addiction journals should recommend but not require data-sharing for submitted manuscripts [M=4.09 (SD=0.06); 95% CI: 3.97-4.22]. Items with the highest perceived benefit ratings were "secondary data use (e.g., meta-analysis)" [M=3.44 (SD=0.06); 95% CI: 3.31-3.56] and "increased transparency" [M=3.29 (SD=0.07); 95% CI: 3.14-3.43]. Items perceived to be the greatest barrier to data-sharing included "lack of metadata standards" [M=3.21 (SD=0.08); 95% CI: 3.06-3.36], "no incentive" [M=3.43 (SD=0.07); 95% CI: 3.30-3.57], "inadequate resources" [M=3.53 (SD=0.05); 95% CI: 3.42-3.63], and "protection of privacy"[M=3.22 (SD=0.07); 95% CI: 3.07-3.36].

Conclusion

Our results suggest addiction journal editorial board members believe data-sharing has a level of importance within the research community. However, most board members are unaware of their journals' data-sharing policies, and most data-sharing should be recommended but not required. Future efforts aimed at better understanding common reservations and benefits towards data-sharing, as well as avenues to optimize data-sharing while minimizing potential risks, are warranted.

Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

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Research organisations are focussed on quantifying the costs and benefits of implementing FAIR.

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Criteria used for the selection of data for FAIRification can be opaque and inconsistent.

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FAIRification effort depends on individual skills, competencies, resources, and time available.

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FAIRification should satisfy reuse scenarios, and lead to scientific and economic impacts.

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Organisational challenges include providing training to individuals and developing a FAIR organisation culture.

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.

Sharing datasets of the COVID-19 epidemic in the Czech Republic

At the time of the COVID-19 pandemic, providing access to data (properly optimised regarding personal data protection) plays a crucial role in providing the general public and media with up-to-date information. Open datasets also represent one of the means for evaluation of the pandemic on a global level. The primary aim of this paper is to describe the methodological and technical framework for publishing datasets describing characteristics related to the COVID-19 epidemic in the Czech Republic (epidemiology, hospital-based care, vaccination), including the use of these datasets in practice. Practical aspects and experience with data sharing are discussed. As a reaction to the epidemic situation, a new portal COVID-19: Current Situation in the Czech Republic (https://onemocneni-aktualne.mzcr.cz/covid-19) was developed and launched in March 2020 to provide a fully-fledged and trustworthy source of information for the public and media. The portal also contains a section for the publication of (i) public open datasets available for download in CSV and JSON formats and (ii) authorised-access-only section where the authorised persons can (through an online generated token) safely visualise or download regional datasets with aggregated data at the level of the individual municipalities and regions. The data are also provided to the local open data catalogue (covering only open data on healthcare, provided by the Ministry of Health) and to the National Catalogue of Open Data (covering all open data sets, provided by various authorities/publishers, and harversting all data from local catalogues). The datasets have been published in various authentication regimes and widely used by general public, scientists, public authorities and decision-makers. The total number of API calls since its launch in March 2020 to 15 December 2020 exceeded 13 million. The datasets have been adopted as an official and guaranteed source for outputs of third parties, including public authorities, non-governmental organisations, scientists and online news portals. Datasets currently published as open data meet the 3-star open data requirements, which makes them machine-readable and facilitates their further usage without restrictions. This is essential for making the data more easily understandable and usable for data consumers. In conjunction with the strategy of the MH in the field of data opening, additional datasets meeting the already implemented standards will be also released, both on COVID-19 related and unrelated topics.

Road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands

Objective

This study investigates current standards and operational gaps in the management and sharing of next generation sequencing (NGS) data within the healthcare and research setting and according to Findable, Accessible, Interoperable and Reusable (FAIR) principles.

Methods

The analysis was performed as the basis from which to bridge identified gaps and develop widely accepted working standards that ensure optimal reusability of genomic data in healthcare and research settings in the Netherlands. This work is part of the ‘Rational Pharmacotherapy Program’ led by ZonMw, The Netherlands Organisation for Health Research and Development, which aims to promote the efficient implementation of NGS and personalised medicine within Dutch healthcare, with an initial focus on oncology and rare diseases.

Results

Based on this analysis and as part of this programme, a consortium was formed to develop an instruction manual for FAIR genomic data in clinical care and research based on an inventory of commonly used workflows and standards in the (inter)national field of genome analysis.

Conclusions

The gap analysis presented and discussed in this paper represents the starting point for this inventory and is a possible contribution from the Netherlands to the European 1+ Million Genomes Initiative. This paper addresses the topics of data generation, data quality, (meta)data standards, data storage and archiving and data integration and exchange.

What ethical approaches are used by scientists when sharing health data? An interview study

BACKGROUND

Health data-driven activities have become central in diverse fields (research, AI development, wearables, etc.), and new ethical challenges have arisen with regards to privacy, integrity, and appropriateness of use. To ensure the protection of individuals' fundamental rights and freedoms in a changing environment, including their right to the protection of personal data, we aim to identify the ethical approaches adopted by scientists during intensive data exploitation when collecting, using, or sharing peoples' health data.

METHODS

Twelve scientists who were collecting, using, or sharing health data in different contexts in Sweden, were interviewed. We used systematic expert interviews to access these scientists' specialist knowledge, and analysed the interviews with thematic analysis. Phrases, sentences, or paragraphs through which ethical values and norms were expressed, were identified and coded. Codes that reflected similar concepts were grouped, subcategories were formulated, and categories were connected to traditional ethical approaches.

RESULTS

Through several examples, the respondents expressed four different ethical approaches, which formed the main conceptual categories: consideration of consequences, respect for rights, procedural compliance, and being professional.

CONCLUSIONS

To a large extent, the scientists' ethical approaches were consistent with ethical and legal principles. Data sharing was considered important and worth pursuing, even though it is difficult. An awareness of the complex issues involved in data sharing was reflected from different perspectives, and the respondents commonly perceived a general lack of practical procedures that would by default ensure ethical and legally compliant data collection and sharing. We suggest that it is an opportune time to move on from policy discussions to practical technological ethics-by-design solutions that integrate these principles into practice.

Will the European Health Data Space change data sharing rules?

Questions of consent and public interest research loom large.

Roles of Selective Agriculture Practices in Sustainable Agricultural Performance: A Systematic Review

Feeding the growing global population while improving the Earth’s economic, environmental, and social values is a challenge recognised in both the United Nations Sustainable Development Goals and the United Nations Framework Convention on Climate Change. Sustaining global agricultural performance requires regular revision of current farming models, attitudes, and practices. In systematically reviewing the international literature through the lens of the sustainability framework, this paper specifically identifies precision conservation agriculture (PCA), digital agriculture (DA), and resilient agriculture (RA) practices as being of value in meeting future challenges. Each of these adaptations carries significantly positive relationships with sustaining agricultural performance, as well as positively mediating and/or moderating each other. While it is clear from the literature that adopting PCA, DA, and RA would substantially improve the sustainability of agricultural performance, the uptake of these adaptations generally lags. More in-depth social science research is required to understand the value propositions that would encourage uptake of these adaptations and the barriers that prevent them. Recommendations are made to explore the specific knowledge gap that needs to be understood to motivate agriculture practitioners to adopt these changes in practice.

ASINA Project: Towards a Methodological Data-Driven Sustainable and Safe-by-Design Approach for the Development of Nanomaterials

The novel chemical strategy for sustainability calls for a Sustainable and Safe-by-Design (SSbD) holistic approach to achieve protection of public health and the environment, industrial relevance, societal empowerment, and regulatory preparedness. Based on it, the ASINA project expands a data-driven Management Methodology (ASINA-SMM) capturing quality, safety, and sustainability criteria across the Nano-Enabled Products' (NEPs) life cycle. We base the development of this methodology through value chains of highly representative classes of NEPs in the market, namely, (i) self-cleaning/air-purifying/antimicrobial coatings and (ii) nano-structured capsules delivering active phases in cosmetics. These NEPs improve environmental quality and human health/wellness and have innovative competence to industrial sectors such as healthcare, textiles, cosmetics, and medical devices. The purpose of this article is to visually exhibit and explain the ASINA approach, which allows identifying, combining, and addressing the following pillars: environmental impact, techno-economic performance, functionality, and human and environmental safety when developing novel NEPs, at an early stage. A metamodel supports the above by utilizing quality data collected throughout the NEPs' life cycle, for maximization of functionality (to meet stakeholders needs) and nano-safety (regulatory obligations) and for the minimization of costs (to meet business requirements) and environmental impacts (to achieve sustainability). Furthermore, ASINA explores digitalization opportunities (digital twins) to speed the nano-industry translation into automatic progress towards economic, social, environmental, and governance sustainability.