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Data democratization (DD) is a new concept rapidly becoming a game-changer, enabling companies to innovate and maintain a competitive edge in a data-driven world. This paper explores the evolution of data accessibility, from the early days of manual record-keeping to the sophisticated data management systems of today. The evolution from transactional databases to data warehouses marked a shift toward centralized data management and specialized teams, supporting the standard principles of DD contexts such as data governance (DG), privacy, management, usability, accessibility, and literacy. This paper provides an overview of the evolution of data access, from manual record-keeping to the modern data management systems of today, focusing on the challenges related to data privacy and security, integration of legacy systems, and the cultural shift required to embrace a data-driven mindset. This paper also explores both universal and holistic approaches to DD, assessing the challenges, benefits, and possibilities of their applications. An overview of industry-specific cases is included in the paper to provide practical insights that would contribute to understanding the most effective approach to data democratization. [ABSTRACT FROM AUTHOR]

We aim to illustrate the role of complete and transparent reporting coupled with access to data sourced from published systematic reviews, especially assisting in the identification of evidence for subgroups within the context of a rare disease. To accomplish this principle, we provide a real-world example encountered during the revision of the Dutch clinical practice guideline for hepatocellular carcinoma. Specifically, we retrieved insights from two Cochrane reviews to identify direct evidence concerning the diagnostic test accuracy of computed tomography and magnetic resonance imaging for detecting hepatocellular carcinomas in suspected patients without liver cirrhosis. Through reusing the Cochrane review authors' efforts already undertaken in their exhaustive literature search and selection, we successfully identified relevant direct evidence for this subgroup of suspected patients without cirrhosis and performed an evidence synthesis within the constraints of limited resources for the guideline revision. This approach holds the potential for replication in other subgroups in the context of rare diseases, contingent on the transparent and complete reporting of systematic reviews, as well as the availability and accessibility of their extracted data. Consequently, we underscore the importance of adhering to established reporting guidelines for systematic reviews, while simultaneously advocating for increased availability and accessibility to data. Such practices would not only increase the transparency and reproducibility of systematic reviews but could also increase reusability of their data. In turn, the increased reusability could result in reduced resource utilization in other sectors such as the guideline developing community as we show in our example. [ABSTRACT FROM AUTHOR]

This study explores the utilization of Binary Embedded Zero Tree Wavelet Algorithms (BEZW) to compress hyperspectral images. The primary goal is to enhance the representation of spectral data while minimizing storage and transmission requirements. The BEZW algorithm employs wavelet transformations to leverage both spectral and spatial redundancies found in hyperspectral data cubes. Its embedded zero tree structure ensures efficient encoding of small coefficients with minimal overhead. The study evaluates the performance of the BEZW method utilizing various hyper-spectral datasets and compression settings, comparing it to other compression techniques. The results illustrate that the BEZW algorithm offers a promising approach to hyperspectral image compression, achieving competitive compression ratios while preserving spectral accuracy. This makes it a valuable option for applications where efficient hyperspectral data storage and transmission are crucial. The research contributes to the field of hyperspectral imaging by introducing an effective compression method that enhances data accessibility, simplifying the utilization of hyperspectral data in resource-constrained environments. [ABSTRACT FROM AUTHOR]

This study explored the use of hospital information systems (HIS) to improve care for underprivileged groups, especially women and people with impairments. It thoroughly investigated HIS dynamics and issues in healthcare through focus groups, questionnaires and interviews. The study was carried out at Comprehensive Community Based Rehabilitation in Tanzania (CCBRT), a Tanzanian healthcare institution focused on enhancing the health of mothers and newborns and providing care for those with impairments. The study shows significant advancements in HIS deployment and enhanced data accessibility. Still, it also points out challenges like a lack of ICT skills, excessive reliance on technology, data problems, inadequate connectivity, and financial limitations. Optimizing the use of HIS in healthcare requires addressing these issues. [ABSTRACT FROM AUTHOR]

Purpose: Limited availability of life cycle assessment (LCA) data poses a significant challenge to its mainstream adoption, rendering it a central issue within the LCA community. The Global LCA Data Access (GLAD) network aims to increase the accessibility and interoperability of LCA data and offers benefits for different use cases. GLAD is an intergovernmental collaboration involving different stakeholders organized into working groups. The GLAD Nomenclature Working Group (NWG) developed a procedure and a set of criteria to map elementary flows among major nomenclature systems and reviewed bidirectional mappings. This paper provides an overview of the methodological approach followed by the NWG to achieve the resulting mapping files. Methods: The mapping procedure involves several steps of flow and compartment matches and bilateral review. The procedure is supported by an ad hoc software tool called the "GLAD Mapper Tool" developed with the NWG and which is made available for free by the European Commission. The input files for the procedure are the properly formatted source and target flow lists and a file containing the mapping criteria. The four nomenclature systems mapped are those used in ecoinvent, Environmental Footprint, IDEA, and the U.S. Federal LCA Commons. The procedure included representatives from each of these nomenclature systems to ensure a multilateral agreement on the approach to verifying and assessing the quality of the results. The iterative mapping process included different stages of bidirectional reviews to achieve a balance between mapping coverage (i.e., percentage of source flows covered by the target list) and accuracy. Results and discussion: The mapping procedure proved to be an efficient approach for LCA practitioners in mappings between different nomenclature systems. After a relatively low number of iterations, mapping coverages higher than 90% were achieved, which is driven by the availability of unique substances (flow names) and the granularity of environmental compartments. Overall, none of the four flow lists achieved full coverage and the use of approximated matches (proxy matches) for environmental compartments and/or substances was necessary when a perfect matches between flows were not possible. Conclusions: The NWG's mapping activities may serve as a starting point towards defining a central hub for mapping impact assessment methods and datasets, improving data accessibility and interoperability for the LCA community as a step towards defining a unified nomenclature system. The GLAD mapping approach is open and transparent. The approach fosters traceability in the mapping process and offers the potential for greater interoperability across the LCA community, underlining the commitment to openness and collaboration. [ABSTRACT FROM AUTHOR]

Public health officials are currently tasked with the role of regulating medicines, both during the approval process and post-market surveillance. While several successes of pharmacovigilance systems exist, pharmacovigilance systems in place are inadequate for protecting the public, as they are slow to show causation. We argue that while pharmacovigilance system were instrumental in the recall of AstraZeneca and Moderna mRNA Covid vaccines for young people during the Covid-19 pandemic, they were inadequate in identifying several clear safety signals which should have led to their withdrawal from the market. Pharmacovigilance systems have much room for improvement, both in terms of data management, accessibility, and use. We propose several guidelines for pharmacovigilance systems to take to improve their efficacy and their ability to protect the public.

High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We sought to determine the utility of "synthetic data" in training ML algorithms for the detection of tuberculosis (TB) from inflammatory biomarker profiles. A retrospective dataset (A) comprised of 278 patients was used to generate synthetic datasets (B, C, and D) for training models prior to secondary validation on a generalization dataset. ML models trained and validated on the Dataset A (real) demonstrated an accuracy of 90%, a sensitivity of 89% (95% CI, 83-94%), and a specificity of 100% (95% CI, 81-100%). Models trained using the optimal synthetic dataset B showed an accuracy of 91%, a sensitivity of 93% (95% CI, 87-96%), and a specificity of 77% (95% CI, 50-93%). Synthetic datasets C and D displayed diminished performance measures (respective accuracies of 71% and 54%). This pilot study highlights the promise of synthetic data as an expedited means for ML algorithm development.

The importance of data and digitization in healthcare cannot be overstated. These advancements have the potential to revolutionize the way healthcare is delivered, leading to improved patient outcomes, enhanced efficiency, and better decision-making by healthcare providers. Blockchain technology can play a significant role in addressing the main challenges like data accessibility, privacy, interoperability and patient empowerment, ultimately transforming the healthcare ecosystem into a data-driven economy

Copyright of Journal of Caribbean Ornithology is the property of Society for the Conservation & Study of Caribbean Birds and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Endogenous viral elements (EVEs) are remnants of viral genetic material endogenized into the host genome. They have, in the last decades, attracted attention for their role as potential contributors to pathogenesis, drivers of selective advantage for the host, and genomic remnants of ancient viruses. EVEs have a nuanced and complex influence on both host health and evolution, and can offer insights on the deep evolutionary history of viruses. As an emerging field of research, several factors limit a comprehensive understanding of EVEs: they are currently underestimated and periodically overlooked in studies of the host genome, transcriptome, and virome. The absence of standardized guidelines for ensuring EVE-related data availability and accessibility following the FAIR ('findable, accessible, interoperable, and reusable') principles obstructs our ability to gather and connect information. Here, we discuss challenges to the availability and accessibility of EVE-related data and propose potential solutions. We identified the biological and research focus imbalance between different types of EVEs, and their overall biological complexity as genomic loci with viral ancestry, as potential challenges that can be addressed with the development of a user-oriented identification tool. In addition, reports of EVE identification are scattered between different subfields under different keywords, and EVE sequences and associated data are not properly gathered in databases. While developing an open and dedicated database might be ideal, targeted improvements of generalist databases might provide a pragmatic solution to EVE data and metadata accessibility. The implementation of these solutions, as well as the collective effort by the EVE scientific community in discussing and setting guidelines, is now drastically needed to lead the development of EVE research and offer insights into host–virus interactions and their evolutionary history. [ABSTRACT FROM AUTHOR]

The article discusses the development and usage of RocHealthData.org, a web-based tool that provides access to publicly available health-related datasets. The tool allows users to access thousands of geographically displayed datasets, including social determinants of health (SDoH) data. The site has been successful in providing valuable data for understanding population health and has been used by a diverse range of users, including community members, educational institutions, and the host university. The article emphasizes the importance of making SDoH data easily accessible and highlights the role of Geographic Information Systems (GIS) in understanding the impact of SDoH on population health. The article discusses the findings of a study on the usage and impact of RocHealthData.org, a website that provides health data for the upstate New York community. The study gathered qualitative feedback from community organizations and analyzed data from Google Analytics to assess user engagement with the website. The feedback and data showed that the website was positively received and used by various organizations for specific projects. The study also highlighted the importance of timely and local data in driving user interest. The article concludes by discussing the limitations of the study and the future plans for the website. The text discusses the RocHealthData website, which is a publicly accessible local data resource created by the University of Rochester. It is a unique resource that translates social determinants of health (SDoH) into specific geographies to provide relevant and useful data. The authors express their willingness to collaborate with others to improve the translation of [Extracted from the article]

The Mediterranean Sea is an almost completely closed basin connecting several countries. Its configuration leads to its peculiarity and richness, but the intensive activities within the basin and along the coast aggravate the ecological conditions. The existing regulatory European Framework for environmental protection has already been in place through a series of legal instruments for almost 20 years. In this context, open science could play a fundamental role. The existing data must become findable, accessible, interoperable, and reusable (FAIR) to provide stakeholders and decision-makers with the instruments to understand how to improve the available information and support decisions based on the best set of existing information. Since 2009, the European Marine Observation and Data Network, EMODnet, has provided access to high-quality marine information supporting research and stakeholders' mission and objectives. Data related to pollution are collated, validated, and published using standard protocols, formats, and vocabularies, thus becoming FAIR. For marine litter, a detailed and qualified data management system for macro- and microlitter in diverse compartments was structured. Some of these data and metadata were already used to calculate the first coastline litter baselines based on harmonized and FAIR datasets (2012–2016). The availability of these data related to the Mediterranean area is relevant, but additional work is required. [ABSTRACT FROM AUTHOR]

Context: An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers. Objectives: The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers. Methods: We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management. Results: Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management. Conclusions: Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management. [ABSTRACT FROM AUTHOR]

Access to local, population specific, and timely data is vital in understanding factors that impact population health. The impact of place (neighborhood, census tract, and city) is particularly important in understanding the Social Determinants of Health. The University of Rochester Medical Center’s Clinical and Translational Science Institute created the web-based tool RocHealthData.org to provide access to thousands of geographically displayed publicly available health-related datasets. The site has also hosted a variety of locally curated datasets (eg., COVID-19 vaccination rates and community-derived health indicators), helping set community priorities and impacting outcomes. Usage statistics (available through Google Analytics) show returning visitors with a lower bounce rate (leaving a site after a single page access) and spent longer at the site than new visitors. Of the currently registered 1033 users, 51.7% were from within our host university, 20.1% were from another educational institution, and 28.2% identified as community members. Our assessments indicate that these data are useful and valued across a variety of domains. Continuing site improvement depends on new sources of locally relevant data, as well as increased usage of data beyond our local region.

Purpose: Advances in information and communication technologies have facilitated the research and production of large volumes of data, and this has led to a new paradigm in scientific research called the fourth paradigm of science. The purpose of this study was to evaluate the status of research data sharing among researchers at Kerman University of Medical Sciences. Methodology: This study is a descriptive survey research. The study population consisted of all faculty members of Kerman University of Medical Sciences with 521 members. The sample size was estimated 220 using Cochran formula. Data collection tool was a researcher-built questionnaire. Cronbach's alpha coefficient was used to calculate the reliability of different parts of questionnaire. Data were analyzed using descriptive statistics including frequency, percentage, mean and standard deviation. Findings: The status of research data sharing in terms of human factors with an average of 3.67 is in relatively desirable condition. Among the human factor variables, the factor of understanding the importance and necessity of data sharing with an average of 4.21 is in a better position than other items. Also, the status of research data sharing of technical factors with an average of 2.58 and in terms of organizational factors with an average of 2.35 is in a relatively desirable condition. Among the items of organizational factors, respectively, holding training courses, support of senior managers of the organization, standards and regulations, the existence of a special program for data sharing and communication channels between researchers are in an unfavorable condition. Also, the sharing of research data in terms of legal factors with an average of 2.36 is relatively desirable. Legal frameworks, control of data access, and the organization's obligation to share data in research projects are in poor condition. Cultural factors related to data sharing, with an average of 3.95 are in good condition. Among the items of this dimension, only two items of incentive and motivation mechanisms and reducing unsound competition are in a relatively desirable condition. Results: The results indicated that Data management programs will enable communication with the areas of production, organization, management and preparation of specialized medical data, providing a sharing process for future exploitation. Encouraging the culture of data sharing, holding training courses, designing data sharing agreements, protecting privacy and data confidentiality, financial support, and strengthening the technology infrastructure are among the suggestions of this research.

Objectives: The development of information and communication technologies has made it possible to reuse the research data of researchers in conducting new studies. Data sharing occurs when scientists make their data available for use by other researchers to conduct research or other research activities. The current research aimed to investigate the status of research data sharing among researchers in Iran in the technical and engineering fields from the individual, organizational and technical dimensions.Methods: This research was applied in terms of purpose and descriptive survey in terms of the data collection method. The research population consisted of Iranian researchers in the field of engineering in Iran who had scientific production in the WOS database in 2021. Due to the lack of email registration of all the authors, it was not possible to accurately estimate the statistical population. Therefore, to determine the statistical sample using Cochran's formula, 411 people were randomly selected from the population. The data collection tool was a questionnaire designed electronically and sent to the statistical sample. SPSS software was used to analyze the findings.Results: The results showed that organizational and technical factors have a significant effect on the individual's behavior in sharing research data. Technical factors had a greater impact on individual behavior than organizational factors. Individual factors had no significant effect on data-sharing behavior. Also, the relationship between hidden technical and organizational factors was positive and significant. In addition, the findings showed that gender has a significant effect on individual factors and researchers' job has a significant effect on organizational factors. There was no significant difference between demographic variables including age, work experience, academic rank and degree, and technical, organizational, and individual factors. In addition, the research results showed that many researchers consider the concern about the time-consuming nature of data, misusing, and misinterpreting shared data as reasons for not wanting to share research data. Despite the requirement for the researchers in this field to deliver the research data and the relative provision of hardware and infrastructure facilities to implement this, the appropriate training courses for training management and data sharing are not conducted by the organizations.There was no adequate financial or intellectual support, and no proper infrastructure for sharing data in publications. Technical factors affecting the sharing of research data, such as the familiarity and use of metadata standards for data sharing and accessibility, as well as the familiarity and use of thematic, public, and organizational repositories were in an unfavorable situation.Conclusions: Considering the research results and the research data sharing and related factors among the researchers in the field of engineering and technology have been neglected, it is necessary for the research stakeholders in the field of technology and engineering to receive appropriate organizational, technical and financial support in order to increase knowledge and willingness of researchers to share data. Furthermore, creating culture-building and understanding the capabilities of research data is also required to provide the necessary training to researchers in the field of engineering.

The emerging field of environmental DNA (eDNA) research lacks universal guidelines for ensuring data produced are FAIR–findable, accessible, interoperable, and reusable– despite growing awareness of the importance of such practices. In order to better understand these data usability challenges, we systematically reviewed 60 peer reviewed articles conducting a specific subset of eDNA research: metabarcoding studies in marine environments. For each article, we characterized approximately 90 features across several categories: general article attributes and topics, methodological choices, types of metadata included, and availability and storage of sequence data. Analyzing these characteristics, we identified several barriers to data accessibility, including a lack of common context and vocabulary across the articles, missing metadata, supplementary information limitations, and a concentration of both sample collection and analysis in the United States. While some of these barriers require significant effort to address, we also found many instances where small choices made by authors and journals could have an outsized influence on the discoverability and reusability of data. Promisingly, articles also showed consistency and creativity in data storage choices as well as a strong trend toward open access publishing. Our analysis underscores the need to think critically about data accessibility and usability as marine eDNA metabarcoding studies, and eDNA projects more broadly, continue to proliferate. [ABSTRACT FROM AUTHOR]

Abstract Background A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on. Methods We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented. Results We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research. Conclusions We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research.

Abstract Background Health-related data are collected from a variety of sources for different purposes, including secondary use for population health monitoring (HM) and health system performance assessment (HSPA). Most of these data sources are not included in databases of international organizations (e.g., WHO, OECD, Eurostat), limiting their use for research activities and policy making. This study aims at identifying and describing collection methods, quality assessment procedures, availability and accessibility of health data across EU Member States (MS) for HM and HSPA. Methods A structured questionnaire was developed and administered through an online platform to partners of the InfAct consortium form EU MS to investigate data collections applied in HM and HSPA projects, as well as their methods and procedures. A descriptive analysis of the questionnaire results was performed. Results Information on 91 projects from 18 EU MS was collected. In these projects, data were mainly collected through administrative sources, population health interview or health examination surveys and from electronic medical records. Tools and methods used for data collection were mostly mandatory reports, self-administered questionnaires, or record linkage of various data sources. One-third of the projects shared data with EU research networks and less than one-third performed quality assessment of their data collection procedures using international standardized criteria. Macrodata were accessible via open access and reusable in 22 projects. Microdata were accessible upon specific request and reusable in 15 projects based on data usage licenses. Metadata was available for the majority of the projects, but followed reporting standards only in 29 projects. Overall, compliance to FAIR Data principles (Findable, Accessible, Interoperable, and Reusable) was not optimal across the EU projects. Conclusions Data collection and exchange procedures differ across EU MS and research data are not always available, accessible, comparable or reusable for further research and evidence-based policy making. There is a need for an EU-level health information infrastructure and governance to promote and facilitate sharing and dissemination of standardized and comparable health data, following FAIR Data principles, across the EU.

Abstract Background Research networks offer multidisciplinary expertise and promote information exchange between researchers across Europe. They are essential for the European Union’s (EU) health information system as providers of health information and data. The aim of this mapping exercise was to identify and analyze EU research networks in terms of health data collection methods, quality assessment, availability and accessibility procedures. Methods A web-based search was performed to identify EU research networks that are not part of international organizations (e.g., WHO-Europe, OECD) and are involved in collection of data for health monitoring or health system performance assessment. General characteristics of the research networks (e.g., data sources, representativeness), quality assessment procedures, availability and accessibility of health data were collected through an ad hoc extraction form. Results Fifty-seven research networks, representative at national, international or regional level, were identified. In these networks, data are mainly collected through administrative sources, health surveys and cohort studies. Over 70% of networks provide information on quality assessment of their data collection procedures. Most networks share macrodata through articles and reports, while microdata are available from ten networks. A request for data access is required by 14 networks, of which three apply a financial charge. Few networks share data with other research networks (8/49) or specify the metadata-reporting standards used for data description (9/49). Conclusions Improving health information and availability of high quality data is a priority in Europe. Research networks could play a major role in tackling health data and information inequalities by enhancing quality, availability, and accessibility of health data and data sharing across European networks.

Value is routinely concentrated at the final links of food production chains as a consequence of market failures or asymmetries that distribute wealth unevenly in agri-food supply systems. Otherwise, for products with a geographic origin, the share can be more equitably distributed by adding environmental, sociocultural or technological values to products associated with sustainable models. Protecting a geographic indication (GI) of value-added products requires complex collective rules of sui generis systems, certified trademarks or business practices, including the approval of protocols or even unfair protection suitlaws. These rules are created on multilevel legislations gathering intrinsic cultural, historical and economic features. As a result, GI schemes are typically costly thus preventing access of general smallholders in product-valued chains. Digital technologies like mobile applications have a promising role in minimizing these limitations along food production chains, from pre-production to production and post-production. The pervasive spread of mobile devices with useful built-in sensors can be therefore a major consequence of the digital transformation in agriculture by means of intuitive applications combined with high-level technologies such as cloud/edge computing and Application Programming Interfaces (APIs). In this article, we present a new perspective on the digital transformation of the agri-food sector that may fasten smallholders' inclusion and access in market ecosystems of value-added products with GI. Such perspective demands the understanding of stakeholders networks for customizing mobile applications for digital authentication of product GI. The approach can foster new compliance schemes as those embedded in Environmental, Social and Governance (ESG) market initiatives.

Objectives: The development of information and communication technologies has made it possible to reuse the research data of researchers in conducting new studies. Data sharing occurs when scientists make their data available for use by other researchers to conduct research or other research activities. The current research aimed to investigate the status of research data sharing among researchers in Iran in the technical and engineering fields from the individual, organizational and technical dimensions. Methods: This research was applied in terms of purpose and descriptive survey in terms of the data collection method. The research population consisted of Iranian researchers in the field of engineering in Iran who had scientific production in the WOS database in 2021. Due to the lack of email registration of all the authors, it was not possible to accurately estimate the statistical population. Therefore, to determine the statistical sample using Cochran's formula, 411 people were randomly selected from the population. The data collection tool was a questionnaire designed electronically and sent to the statistical sample. SPSS software was used to analyze the findings. Results: The results showed that organizational and technical factors have a significant effect on the individual's behavior in sharing research data. Technical factors had a greater impact on individual behavior than organizational factors. Individual factors had no significant effect on data-sharing behavior. Also, the relationship between hidden technical and organizational factors was positive and significant. In addition, the findings showed that gender has a significant effect on individual factors and researchers' job has a significant effect on organizational factors. There was no significant difference between demographic variables including age, work experience, academic rank and degree, and technical, organizational, and individual factors. In addition, the research results showed that many researchers consider the concern about the time-consuming nature of data, misusing, and misinterpreting shared data as reasons for not wanting to share research data. Despite the requirement for the researchers in this field to deliver the research data and the relative provision of hardware and infrastructure facilities to implement this, the appropriate training courses for training management and data sharing are not conducted by the organizations. There was no adequate financial or intellectual support, and no proper infrastructure for sharing data in publications. Technical factors affecting the sharing of research data, such as the familiarity and use of metadata standards for data sharing and accessibility, as well as the familiarity and use of thematic, public, and organizational repositories were in an unfavorable situation. Conclusions: Considering the research results and the research data sharing and related factors among the researchers in the field of engineering and technology have been neglected, it is necessary for the research stakeholders in the field of technology and engineering to receive appropriate organizational, technical and financial support in order to increase knowledge and willingness of researchers to share data. Furthermore, creating culture-building and understanding the capabilities of research data is also required to provide the necessary training to researchers in the field of engineering. [ABSTRACT FROM AUTHOR]

Advances in information and communication technologies have facilitated the research and production of large volumes of data, and this has led to a new paradigm in scientific research called the fourth paradigm of science. The purpose of this study was to evaluate the status of research data sharing among researchers at Kerman University of Medical Sciences. This study is a descriptive survey research. The study population consisted of all faculty members of Kerman University of Medical Sciences with 521 members. The sample size was estimated 220 using Cochran formula. Data collection tool was a researcher-built questionnaire. Cronbach’s alpha coefficient was used to calculate the reliability of different parts of questionnaire. Data were analyzed using descriptive statistics including frequency, percentage, mean and standard deviation. Findings show that the status of research data sharing in terms of human factors with an average of 3.67 is in relatively desirable condition. Among the human factor variables, the factor of understanding the importance and necessity of data sharing with an average of 4.21 is in a better position than other items. Also, the status of research data sharing of technical factors with an average of 2.58, and in terms of organizational factors with an average of 2.35 is in a relatively desirable condition. Among the items of organizational factors, respectively holding training courses, support of senior managers of the organization, standards and regulations, the existence of a special program for data sharing and communication channels between researchers are in an unfavorable condition. Also, the sharing of research data in terms of legal factors with an average of 2.36 is relatively desirable. Legal frameworks, control of data access, and the organization›s obligation to share data in research projects are in poor condition. Cultural factors related to data sharing, with an average of 3.95 are in good condition. Among the items of this dimension, only two items of incentive and motivation mechanisms and reducing unsound competition are in a relatively desirable condition. The results indicated that data management programs will enable communication with the areas of production, organization, management and preparation of specialized medical data, providing a sharing process for future exploitation. Encouraging the culture of data sharing, holding training courses, designing data sharing agreements, protecting privacy and data confidentiality, financial support, and strengthening the technology infrastructure are among the suggestions of this research. [ABSTRACT FROM AUTHOR]

Background: A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on.Methods: We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We model the joint covariate distribution of the original data using covariate specific sequential conditional regression models, then fit a complex flexible parametric survival model from which to generate survival times conditional on individual covariate patterns. We recreate the administrative censoring mechanism using the last observed follow-up date information from the initial dataset. Metrics for evaluating the accuracy of the synthetic data, and the non-identifiability of individuals from the original dataset, are presented.Results: We successfully create a synthetic version of an example colon cancer dataset consisting of 9064 patients which aims to show good similarity to both covariate distributions and survival times from the original data, without containing any exact information from the original data, therefore allowing them to be published openly alongside research.Conclusions: We evaluate the effectiveness of the methods for constructing synthetic data, as well as providing evidence that there is minimal risk that a given patient from the original data could be identified from their individual unique patient information. Synthetic datasets using this methodology could be made available alongside published research without breaching data privacy protocols, and allow for data and code to be made available alongside methodological or applied manuscripts to greatly improve the transparency and accessibility of medical research. [ABSTRACT FROM AUTHOR]

Abstract Background Routinely collected electronic health records (EHRs) have the potential to enhance randomised controlled trials (RCTs) by facilitating recruitment and follow-up. Despite this, current EHR use is minimal in UK RCTs, in part due to ongoing concerns about the utility (reliability, completeness, accuracy) and accessibility of the data. The aim of this manuscript is to document the process, timelines and challenges of the application process to help improve the service both for the applicants and data holders. Methods This is a qualitative paper providing a descriptive narrative from one UK clinical trials unit (MRC CTU at UCL) on the experience of two trial teams’ application process to access data from three large English national datasets: National Cancer Registration and Analysis Service (NCRAS), National Institute for Cardiovascular Outcomes Research (NICOR) and NHS Digital to establish themes for discussion. The underpinning reason for applying for the data was to compare EHRs with data collected through case report forms in two RCTs, Add-Aspirin (ISRCTN 74358648) and PATCH (ISRCTN 70406718). Results The Add-Aspirin trial, which had a pre-planned embedded sub-study to assess EHR, received data from NCRAS 13 months after the first application. In the PATCH trial, the decision to request data was made whilst the trial was recruiting. The study received data after 8 months from NICOR and 15 months for NHS Digital following final application submission. This concluded in May 2020. Prior to application submission, significant time and effort was needed particularly in relation to the PATCH trial where negotiations over consent and data linkage took many years. Conclusions Our experience demonstrates that data access can be a prolonged and complex process. This is compounded if multiple data sources are required for the same project. This needs to be factored in when planning to use EHR within RCTs and is best considered prior to conception of the trial. Data holders and researchers are endeavouring to simplify and streamline the application process so that the potential of EHR can be realised for clinical trials.

Educators and researchers are reliant upon access to data to drive teaching methods, curricular improvements, and progress in medical education research. However, data are not always accessible, due to resource constraints, institutional policies, and privacy concerns. Researchers have attempted to access novel data sources through surveys, semistructured interviews, and databases; however, these methodologies are limited. To improve access to data, Freedom of Information (FOI) Acts grant researchers the ability to formally request data that any public institute holds. Researchers have been reluctant to use this tool due to negative perceptions, despite its unique benefits. To increase awareness of this underutilized methodology, we summarize the process of FOI Act requests, its strengths and weaknesses, and the ways in which health professions education can leverage FOI requests within research. We provide examples of the use of FOI requests as a research method within adjacent fields and nascent use within the field of health professions research. In doing so, we hope to highlight how FOI requests can be a useful tool in health professions education researchers and its potential to increase access to unique data sources., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine.)

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders., Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones., Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines., Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms., Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general., (© Allison Grothman, William J Ma, Kendra G Tickner, Elliot A Martin, Danielle A Southern, Hude Quan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

An understanding of indoor environmental quality (IEQ) and its effects on occupant well-being can inform building system design and operation. The use of wearables in field studies to collect subjective and objective health performance indicators (HPIs) from a large number of occupants could deliver important improvements in IEQ. To facilitate the use of wearables in IEQ studies, there is a need to identify which HPIs should be collected and to evaluate data accessibility from these devices. To address this issue, a literature review of previous IEQ studies was conducted to identify relationships between different IEQ factors and HPIs, with a focus on HPIs that were collected using wearables. A preliminary assessment of data accessibility from a selected wearable device (Fitbit Versa 2) was performed and documented. The review suggested the need to further investigate and collect sleep quality parameters, heart rate, stress response, as well as subjective ratings of comfort using wearables. The data accessibility assessment revealed issues related to missing data points and data resolution from the examined device. A set of recommendations is outlined to inform future studies.

High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We sought to determine the utility of “synthetic data” in training ML algorithms for the detection of tuberculosis (TB) from inflammatory biomarker profiles. A retrospective dataset (A) comprised of 278 patients was used to generate synthetic datasets (B, C, and D) for training models prior to secondary validation on a generalization dataset. ML models trained and validated on the Dataset A (real) demonstrated an accuracy of 90%, a sensitivity of 89% (95% CI, 83–94%), and a specificity of 100% (95% CI, 81–100%). Models trained using the optimal synthetic dataset B showed an accuracy of 91%, a sensitivity of 93% (95% CI, 87–96%), and a specificity of 77% (95% CI, 50–93%). Synthetic datasets C and D displayed diminished performance measures (respective accuracies of 71% and 54%). This pilot study highlights the promise of synthetic data as an expedited means for ML algorithm development.

Background: Health-related data are collected from a variety of sources for different purposes, including secondary use for population health monitoring (HM) and health system performance assessment (HSPA). Most of these data sources are not included in databases of international organizations (e.g., WHO, OECD, Eurostat), limiting their use for research activities and policy making. This study aims at identifying and describing collection methods, quality assessment procedures, availability and accessibility of health data across EU Member States (MS) for HM and HSPA. Methods: A structured questionnaire was developed and administered through an online platform to partners of the InfAct consortium form EU MS to investigate data collections applied in HM and HSPA projects, as well as their methods and procedures. A descriptive analysis of the questionnaire results was performed. Results: Information on 91 projects from 18 EU MS was collected. In these projects, data were mainly collected through administrative sources, population health interview or health examination surveys and from electronic medical records. Tools and methods used for data collection were mostly mandatory reports, self-administered questionnaires, or record linkage of various data sources. One-third of the projects shared data with EU research networks and less than one-third performed quality assessment of their data collection procedures using international standardized criteria. Macrodata were accessible via open access and reusable in 22 projects. Microdata were accessible upon specific request and reusable in 15 projects based on data usage licenses. Metadata was available for the majority of the projects, but followed reporting standards only in 29 projects. Overall, compliance to FAIR Data principles (Findable, Accessible, Interoperable, and Reusable) was not optimal across the EU projects. Conclusions: Data collection and exchange procedures differ across EU MS and research data are not always available, accessible, comparable or reusable for further research and evidence-based policy making. There is a need for an EU-level health information infrastructure and governance to promote and facilitate sharing and dissemination of standardized and comparable health data, following FAIR Data principles, across the EU. [ABSTRACT FROM AUTHOR]

Background: Research networks offer multidisciplinary expertise and promote information exchange between researchers across Europe. They are essential for the European Union's (EU) health information system as providers of health information and data. The aim of this mapping exercise was to identify and analyze EU research networks in terms of health data collection methods, quality assessment, availability and accessibility procedures. Methods: A web-based search was performed to identify EU research networks that are not part of international organizations (e.g., WHO-Europe, OECD) and are involved in collection of data for health monitoring or health system performance assessment. General characteristics of the research networks (e.g., data sources, representativeness), quality assessment procedures, availability and accessibility of health data were collected through an ad hoc extraction form. Results: Fifty-seven research networks, representative at national, international or regional level, were identified. In these networks, data are mainly collected through administrative sources, health surveys and cohort studies. Over 70% of networks provide information on quality assessment of their data collection procedures. Most networks share macrodata through articles and reports, while microdata are available from ten networks. A request for data access is required by 14 networks, of which three apply a financial charge. Few networks share data with other research networks (8/49) or specify the metadata-reporting standards used for data description (9/49). Conclusions: Improving health information and availability of high quality data is a priority in Europe. Research networks could play a major role in tackling health data and information inequalities by enhancing quality, availability, and accessibility of health data and data sharing across European networks. [ABSTRACT FROM AUTHOR]

Kees Mandemakers has enriched historical databases in the Netherlands and internationally through the development of the Historical Sample of the Netherlands, the Intermediate Data Structure, a practical implementation of rule-based record linking (LINKS) and personal encouragement of high quality longitudinal data in a number of countries.

Background: Routinely collected electronic health records (EHRs) have the potential to enhance randomised controlled trials (RCTs) by facilitating recruitment and follow-up. Despite this, current EHR use is minimal in UK RCTs, in part due to ongoing concerns about the utility (reliability, completeness, accuracy) and accessibility of the data. The aim of this manuscript is to document the process, timelines and challenges of the application process to help improve the service both for the applicants and data holders.Methods: This is a qualitative paper providing a descriptive narrative from one UK clinical trials unit (MRC CTU at UCL) on the experience of two trial teams' application process to access data from three large English national datasets: National Cancer Registration and Analysis Service (NCRAS), National Institute for Cardiovascular Outcomes Research (NICOR) and NHS Digital to establish themes for discussion. The underpinning reason for applying for the data was to compare EHRs with data collected through case report forms in two RCTs, Add-Aspirin (ISRCTN 74358648) and PATCH (ISRCTN 70406718).Results: The Add-Aspirin trial, which had a pre-planned embedded sub-study to assess EHR, received data from NCRAS 13 months after the first application. In the PATCH trial, the decision to request data was made whilst the trial was recruiting. The study received data after 8 months from NICOR and 15 months for NHS Digital following final application submission. This concluded in May 2020. Prior to application submission, significant time and effort was needed particularly in relation to the PATCH trial where negotiations over consent and data linkage took many years.Conclusions: Our experience demonstrates that data access can be a prolonged and complex process. This is compounded if multiple data sources are required for the same project. This needs to be factored in when planning to use EHR within RCTs and is best considered prior to conception of the trial. Data holders and researchers are endeavouring to simplify and streamline the application process so that the potential of EHR can be realised for clinical trials. [ABSTRACT FROM AUTHOR]

High quality and well-managed climate data are the cornerstone of all climate services. Consistently assessing how well the data are managed is one way to establish or demonstrate the trustworthiness of the data. This paper presents the World Meteorological Organization’s (WMO) Stewardship Maturity Matrix for Climate Data (SMM-CD) and the subsidiary SMM-CD for National and Regional Purposes (SMM-CD_NRP). Both these matrices have been developed with the support of the WMO and its High-Quality Global Data Management Framework for Climate (HQ-GDMFC). These self-assessment tools enable data managers to discover WMO recommended data stewardship practices, determine a roadmap for future development and improvement, as well as compare their process against other data providers. Datasets which have been maturity assessed are included in the WMO Climate Data Catalogue, where users can include the results of these maturity assessments into their decision-making process. The SMM-CD contains four categories (data access, usability and usage, quality management, and data management) each of which has a number of aspects, with scores assigned to one of five levels. A smaller number of categories in the SMM-CD_NRP are assigned to four levels appropriate for operationally produced datasets which are national or regional in scope. We explore a number of case studies where these matrices have been applied, as well as supply links to where the Guidance Documents and Assessment Templates (which may be updated) can be found.

COVID-19 has joined the long list of sexually dimorphic human disorders. Higher lethality in men, evident in the first reports from China, was confirmed in the subsequent Italian outbreak. Newspapers and scientific journals commented on this finding and the preexisting conditions, biological processes, and behavioral differences that may underlie it. However, little appeared to be released about sex differences in severity of disease, comorbidities, rate of recovery, length of hospital stay, or number of tests performed. Systematic analysis of official websites for 20 countries and 6 US states revealed a wide disparity in sex-disaggregated data made available to the public and scholars. Only a handful reported cases by sex. None of the other characteristics, including deaths, were stratified by sex at the time. Beyond suboptimal sex disaggregation, we found a paucity of usable raw data sets and a generalized lack of standardization of captured data, making comparisons difficult. A second round of data capture in April found more complete, but even more disparate, information. Our analysis revealed a wide range of sex ratios among confirmed cases. In countries where a male bias was initially reported, the proportion of women dramatically increased in 3 weeks. Analysis also revealed a complex pattern of sex ratio variation with age. Accurate, peer-reviewed, analysis of harmonized, sex-disaggregated data for characteristics of epidemics, such as availability of testing, suspected source of infection, or comorbidities, will be critical to understand where the observed disparities come from and to generate evidence-based recommendations for decision-making by governments. [ABSTRACT FROM AUTHOR]

With the era of big data coming, whether in computer operating system or mobile operating system, the shortcomings of traditional way of file management become increasingly obvious. For example, data access and data retrieval across devices are very inconvenient. At the same time, storage space in disks and cloud storage services is underutilized. To address these issues, we propose Rio, a personal storage system in multi-device and cloud storage. Cloud storage services, treated as one storage device in Rio, together with shared devices form a storage pool. In Rio, users can store, retrieve and access data through RESTful URI, no matter in which device or cloud services data are stored. Rio also sets up redundant storage in idle space to improve disk storage utilization. In the experiment, we compared Rio with U disk, FTP, Dropbox and other common data sharing methods. Then we tested Rio's data access performance and system overhead. The results show that Rio increases the accessibility of data greatly and only costs small system overhead. [ABSTRACT FROM AUTHOR]