Reuse Of Data Research Articles

FAIR degree assessment in agriculture datasets using the F-UJI tool

For the agricultural scientific community, data sharing is crucial both for the advancement of the discipline and ability to meet global challenges, such as the target no. 2, i.e., “Zero Hunger,” of the Sustainable Development Goals (SDG 2030). In this context, FAIR (Findable, Accessible, Interoperable and Reusable) principles play an important role, as they guarantee the findability, accessibility, interoperability, and reusability of shared data. To improve the practice of data sharing, institutions, funders, and publishers are increasingly demanding data be shared as well as be of an acceptable level of quality, including compliance with FAIR principles. Therefore, the objective of this work is twofold: first, this research aims to determine the degree of compliance with the FAIR principles exhibited by a number of datasets; and second, it aims to explore useful and valid methodologies and procedures that can be used to perform this evaluation quickly, automatically, and effectively. For this purpose, the Data Citation Index (DCI) was used to obtain many datasets in the field of agriculture, which were further grouped by repositories and evaluated using the automated assessment tool F-UJI provided by the FAIRsFAIR project. The results indicated that the principle that exhibited the highest scores was “Findable”, while “Reusable” received the lowest scores, as none of the analysed repositories achieved a 50% compliance score in this respect. The datasets published in the Zenodo and Dryad repositories exhibited better overall results in terms of the FAIR principles, and the AG Commons repository was the third best rated repository, representing only one of the first three repositories belonging to the agricultural sector. Regarding the use of F-UJI as an automated assessment tool and DCI as a source for obtaining datasets, we conclude that this methodology is useful, and that although it can be improved, it is easy to use and implement by other scientific groups and agents of interest.

Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments.

Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.

WASP—A Web Application to Support Syntactically and Semantically Correct SNOMED CT Postcoordination

Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED-CT-coded data but also for flexibly augmenting its 350,000 concepts by allowing a controlled combination of these. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides users through the creation of postcoordinated expressions (PCEs) while ensuring adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of the PCEs created with it.

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records.

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.

Hydrographic open data for society

Hydrography is defined as: “The branch of applied sciences which deals with the measurement and description of the physical features of oceans, seas, coastal areas, lakes and rivers, as well as with the prediction of their change over time, for the primary purpose of safety of navigation and in support of all other marine activities, including economic development, security and defence, scientific research, and environmental protection” (Publication S-32). Due to their broad concept hydrographic data and information provide the foundational bases for marine space management, fisheries, coastal environment, policy decisions, shipping, energy and oil industry, etc. Portuguese Hydrographic Institute (IH) manages geospatial datasets from several scientific and technical domains. Data management has been on daily agenda and always has an internal priority. Facing the digital transformation tsunami and rapid evolution of society data requirements is the main driven for developing an internal sustainable open data strategy aligned with findable, accessible, interoperable and reusable (FAIR) principles and as open as possible. The European Open Data and Open Science strategies combined with the need to fill the ocean knowledge gaps are changing the way how data producers deal with geospatial information. This article presents several IH projects to increase sharing and reuse of hydrographic data by society.

The types of research data receiving scholarly credit within and across the science, engineering, and mathematics fields

This study examined the types of data that receive formal scholarly credit within and across the science, engineering, and mathematics (SEM) fields. The topics of whether data types are used in a way that encourages data reuse has not been actively studied. This study applied an exploratory method because formal data citation is a relatively new area. The Data Citation Index (DCI) of the Web of Science (WoS) was selected because the DCI provides a single access point to 400 data repositories worldwide across multiple disciplines. Nearly all citations were of quantitative data. The types that received the most credit were, in descending order, ribonucleic acid (RNA), crystal structure, protein sequence data, crystallographic data, Sequence Read Archive (SRA), genomic, images, nucleotide sequencing information, molecular structure, and crystallographic information, though citation was diverse across the various disciplines within these fields. In particular, qualitative data received no scholarly credit. This study contributes to better understanding of data types for data reuse.

REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research

Clinical research outcomes depend on the correct definition of the research protocol, the data collection strategy, and the data management plan. Furthermore, researchers often need to work within challenging contexts, as is the case in tuberculosis services, where human and technological resources for research may be scarce. Electronic Data Capture Systems mitigate such risks and enable a reliable environment to conduct health research and promote result dissemination and data reusability. The proposed solution is based on needs pinpointed by researchers, considering the need for an accommodating solution to conduct research in low-resource environments. The REDbox framework was developed to facilitate data collection, management, sharing, and availability in tuberculosis research and improve the user experience through user-friendly, web-based tools. REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users' perceptions of the system's usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features.

Research data management services in academic libraries: a comparative study of South Asia and Southeast Asia

Purpose This study aims to understand the research data management (RDM) services offered by academic libraries in South Asian and Southeast Asian countries. This study aims to evaluate the library and information science professionals’ required RDM skills and the challenges faced with providing RDM services. Design/methodology/approach The research methodology for this study used a survey method with purposive sampling. Data were collected through online structured questionnaires, which were used to examine the current state of RDM services offered in academic libraries in South Asia and Southeast Asia. Findings South Asian and Southeast Asian region major types of RDM services provided were data repository, data management training, maintaining Web resources, data study and analysis, and promoting awareness of reusable data sources. Little attention was given to advisory services on data analysis/mining/visualization and supporting reproducibility and workflow transparency. The results indicated that most respondents agreed that metadata standards and data management planning skills were required for RDM services in South Asia and Southeast Asia. Originality/value This study is significant because it offers a comprehensive assessment of ongoing RDM services in academic libraries of South Asia and Southeast Asia. Most current literature focuses on best practices in developed nations. This study highlights the need for more competent and dedicated academic staff for effective RDM services. Library professionals can use this study to identify the gaps in RDM services and suggest formative measures to overcome such challenges.

Undetermined: A semi-academic exploration of the future of European data spaces via science fiction

This paper concerns the future — a scenario that is not so distant in time, as the future starts today. With the increasing datafication of our society and the approval of the European Union Data Governance Act that establishes conditions and safeguards to encourage the free flow and reuse of data for scientific, economic and societal progress, we are left wondering whether the existing challenges surrounding personal data management will be solved by then — or whether they will only be exacerbated. This sci-fi short story depicts a future where individuals, since a young age, question whether they still retain agency over their lives and their destinies, given that data-hungry personalized services surveil them extensively and steer their personal development. Returning to science fiction narratives to explore a societal issue at the edge of technology and law has a threefold purpose, in: (1) developing critical skills and exploring future consequences safely through the imagination; (2) fostering foresight and proactivity in policy-making; and (3) democratizing the debate about technological developments that concern us all. If this story depicts a desirable or undesirable future is left to the readers to assess.

Commitments for Ethically Responsible Sourcing, Use, and Reuse of Patient Data in the Digital Age: Cocreation Process.

Personal information, including health-related data, may be used in ways we did not intend when it was originally shared. However, the organizations that collect these data do not always have the necessary social license to use and share it. Although some technology companies have published principles on the ethical use of artificial intelligence, the foundational issue of what is and is not acceptable to do with data, not just the analytical tools to manage it, has not been fully considered. Furthermore, it is unclear whether input from the public or patients has been included. In 2017, the leadership at a web-based patient research network began to envision a new kind of community compact that laid out what the company believed, how the company should behave, and what it promised both to the individuals who engaged with them and to the community at large. While having already earned a social license from patient members as a trusted data steward with strong privacy, transparency, and openness policies, the company sought to protect and strengthen that social license by creating a socially and ethically responsible data contract. Going beyond regulatory and legislative requirements, this contract considered the ethical use of multiomics and phenotypic data in addition to patient-reported and generated data. A multistakeholder working group sought to develop easy-to-understand commitments that established expectations for data stewardship, governance, and accountability from those who seek to collect, use, and share personal data. The working group cocreated a framework that was radically patient-first in its thinking and collaborative in the process of its codevelopment; it reflected the values, ideas, opinions, and perspectives of the cocreators, inclusive of patients and the public. Leveraging the conceptual frameworks of cocreation and participatory action research, a mixed methods approach was used that included a landscape analysis, listening sessions, and a 12-question survey. The methodological approaches used by the working group were guided by the combined principles of biomedical ethics and social license and shaped through a collaborative and reflective process with similarities to reflective equilibrium, a method well known in ethics. Commitments for the Digital Age are the output of this work. The six commitments in order of priority are (1) continuous and shared learning; (2) respect and empower individual choice; (3) informed and understood consent; (4) people-first governance; (5) open communication and accountable conduct; and (6) inclusivity, diversity, and equity. These 6 commitments-and the development process itself-have broad applicability as models for (1) other organizations that rely on digitized data sources from individuals and (2) patients who seek to strengthen operational policies for the ethical and responsible collection, use, and reuse of that data.

Assessment of the Croatian Open Data Portal Using User-Oriented Metrics

Open data portals are web services that serve as a central access point for all government-published open data and can exist at local, regional, national, and international levels. They are an important element of most open data initiatives that have enabled a large amount of government data to be widely available. However, data quantity and quality are not the only aspects that should be considered when publishing data. To improve the reusability of data and to achieve greater impact and benefits from open data, it is important to consider user-oriented aspects of the portal management, discovery, and use of data (e.g., organizing the portal in a user-centric way, providing accurate metadata, using a standardized and open data format, etc.). In this paper, we adopted the metrics proposed by the European Commission to assess compliance of the Croatian Open Data Portal with 10 user-oriented principles that open data portals should implement in terms of sustainability and added value. While the results show the government’s efforts in publishing data, some aspects such as better collaboration with data providers and other data portals, offering different visualization tools, etc. need to be improved to achieve active use and impact.

FAIR principles to improve the impact on health research management outcomes

BackgroundThe FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes. ObjectiveThis paper aims to describe the analysis of the impact on health research management outcomes of the FAIR4Health solution. MethodsTo analyse the impact on health research management outcomes in terms of time and economic savings, a survey was designed and sent to experts on data management with expertise in the use of the FAIR4Health solution. Then, differences between the time and costs needed to perform the techniques with (i) standalone research, and (ii) using the proposed solution, were analyzed. ResultsIn the context of the health research management outcomes, the survey analysis concluded that 56.57% of the time and 16800 EUR per month could be saved if the FAIR4Health solution is used. ConclusionsAdopting principles in health research through the FAIR4Health solution saves time and, consequently, costs in the execution of research involving data management techniques.

Structural under-reporting of informed consent, data handling and sharing, ethical approval, and application of Open Science principles as proxies for study quality conduct in COVID-19 research: a systematic scoping review

BackgroundThe COVID-19 pandemic required science to provide answers rapidly to combat the outbreak. Hence, the reproducibility and quality of conducting research may have been threatened, particularly regarding privacy and data...

Curating and extending data for language comparison in Concepticon and NoRaRe

Language comparison requires user-friendly tools that facilitate the standardization of linguistic data. We present two resources built on the basis of a standardized cross-linguistic format and show how the data is curated and extended. The first resource, the Concepticon, is a reference catalog for standardized concepts from linguistic research. While curating the Concepticon, we found that a variety of studies in distinct research fields collected information on word properties. However, until recently, no resource existed that contained these data to enable the comparison of the different word properties across languages. This gap was filled by the Database of Norms, Ratings, and Relations (NoRaRe), which is an extension of the Concepticon. Here, we present the major release of both resources - Concepticon Version 3.0 and NoRaRe Version 1.0 - which represents an important step in our data development. We show that extending and adapting the data curation workflow in Concepticon to NoRaRe is useful for the standardization of cross-linguistic datasets. In addition, combining datasets from different research fields enables studies grounded in language comparison. Concepticon and NoRaRe include lexical data for various languages, tools for test-driven data curation, and the possibility for data reuse. The first major release of NoRaRe is also accompanied by a new web application that allows convenient access to the data.

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models.

Promoting Data Science in Disaster Risk Reduction: Glimpses from the Global South

There are coordinated attempts to use Data Science in disaster risk reduction (DRR) across the world. Different special interest groups are formed within the data science community to formulate different action plans and strategies to use data science in the DRR. Some examples of community-driven initiatives are the CODATA Task Group FAIR Data for Disaster Risk Research (FAIR-DRR), CODATA Task Group Linked Open Data for Global Disaster Risk Research (LODGD), and ISC Integrated Research on Disaster Risk (IRDR). This chapter highlights the data science approaches, models, frameworks, and stakeholders mapping while dealing with the data collection, analysis, and reporting for strengthening data-driven decision-making at different levels. The chapter also explores the FAIR Data Principles that aim at achieving the findable, accessible, interoperable, and reusable data resources at the institutional and national levels. FAIR Data ensures various data users increasingly engage with data related to disasters. We also discuss some case studies and success stories from the Global South, which may inspire other countries in data-driven decision-making while dealing with natural disasters. The much-discussed “Data, Information, Knowledge and Action” (DIKA) model is highlighted in this chapter. For example, the recently launched “Data-Knowledge-Action for Urban Systems” podcast series of CODATA and CEPT Research and Development Foundation (CRDF) in Ahmedabad, talks about how the DIKA model helps in creating intelligent systems for DRR using scientific and quantitative data. On a podcast in related Series, the DRR practitioners and data science researchers discussed the use of big data to predict natural disasters. This chapter highlights notable examples of how big data helped predict natural disasters across the world, particularly in the Global South.

A status assessment of selected data synthesis products for ocean biogeochemistry

Ocean data synthesis products for specific biogeochemical essential ocean variables have the potential to facilitate today’s biogeochemical ocean data usage and comply with the Findable Accessible Interoperable and Reusable (FAIR) data principles. The products constitute key outputs from the Global Ocean Observation System, laying the observational foundation for information and services regarding climate and environmental status of the ocean. Using the Framework of Ocean Observing (FOO) readiness level concept, we present an evaluation framework for biogeochemical data synthesis products, which enables a systematic assessment of each product’s maturity. A new criteria catalog provides the foundation for assigning scores to the nine FOO readiness levels. As an example, we apply the assessment to four existing biogeochemical essential ocean variables data products. In descending readiness level order these are: The Surface Ocean CO2 Atlas (SOCAT); the Global Ocean Data Analysis Project (GLODAP); the MarinE MethanE and NiTrous Oxide (MEMENTO) data product and the Global Ocean Oxygen Database and ATlas (GO2DAT). Recognizing that the importance of adequate and comprehensive data from the essential ocean variables will grow, we recommend using this assessment framework to guide the biogeochemical data synthesis activities in their development. Moreover, we envision an overarching cross-platform FAIR biogeochemical data management system that sustainably supports the products individually and creates an integrated biogeochemical essential ocean variables data synthesis product; in short a system that provides truly comparable and FAIR data of the entire biogeochemical essential ocean variables spectrum.

From intention to action: The factors affecting health data sharing intention and action

IntroductionEffective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action. ObjectiveThe aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action. MethodsA web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants’ characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions. ResultsOf 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86–5.75) and action (OR: 2.8, CI: 1.14–8.21). The compensation for data sharing (OR:2.8, CI:1.14–8.21) and familiarity with data (OR:3.1, CI:1.36–8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65–3.72). ConclusionDespite expressing willingness to share their health data, the participants’ intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

Predicting psychologists’ approach to academic reciprocity and data sharing with a theory of collective action

PurposeThis study aims to investigate the factors influencing the data sharing habits of psychologists with respect to academic reciprocity.Design/methodology/approachA research model was developed based on Ostrom’s (2003) theory of collective action to map psychologists’ underlying motivations for data sharing. The model was validated by data from a survey of 427 psychologists, primarily from the psychological sciences and related disciplines.FindingsThis study found that data sharing among psychologists is driven primarily by their perceptions of community benefits, academic reciprocity and the norms of data sharing. This study also found that academic reciprocity is significantly influenced by psychologists’ perceptions of community benefits, academic reputation and the norms of data sharing. Both academic reputation and academic reciprocity are affected by psychologists’ prior experiences with data reuse. Additionally, psychologists’ perceptions of community benefits and the norms of data sharing are significantly affected by the perception of their academic reputation.Research limitations/implicationsThis study suggests that Ostrom’s (2003) theory of collective action can provide a new theoretical lens in understanding psychologists’ data sharing behaviours.Practical implicationsThis study suggests several practical implications for the design and promotion of data sharing in the research community of psychology.Originality/valueTo the best of the authors’ knowledge, this is one of the initial studies that applied the theory of collective action to the mechanisms of reputation, community benefits, norms and reciprocity in psychologists’ data sharing behaviour. This research demonstrates that perceived community benefits, academic reputation and the norms of data sharing can all encourage academic reciprocity, and psychologists’ perceptions of community benefits, academic reciprocity and data sharing norms all facilitate their data sharing intentions.

Toward a common standard for data and specimen provenance in life sciences.

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.