Data Access Research Articles

Cloud computing and spatial hydrology for monitoring the Buyo and Kossou reservoirs in Côte d'Ivoire

The Buyo and Kossou reservoirs are crucial for water supply, agricultural irrigation, and hydroelectric power generation in Côte d'Ivoire. However, climate change threatens the stability and availability of these water resources by increasing rainfall variability, extending drought periods, and intensifying extreme weather events. These challenges underscore the need for precise and continuous monitoring of water levels and surface areas to ensure sustainable management. Due to the scarcity of gauging stations, the objective of this study is to leverage cloud computing technologies along with altimetric and satellite data, for effective reservoir monitoring. Tools like the EO-Africa program's Innovation Lab and Google Earth Engine (GEE), along with advanced image processing software such as PyGEE-SWToolbox and AlTis, were used to process large datasets from the Sentinel-1, Sentinel-2, and Sentinel-3 satellites. These satellites delivered extensive, high-resolution imagery and altimetric data, crucial for monitoring changes in the reservoirs. The processed data were validated with in-situ measurements, yielding a Root Mean Square Error (RMSE) of less than 0.4 m and a correlation coefficient exceeding 0.90. The results highlighted water surface and level changes from 2016 to 2022, with downward trends and seasonal variations closely aligning with in-situ measurements. The study also revealed that the relationship between water levels and surface areas is influenced by both precipitation and the hydrological regimes of the Bandama and Sassandra rivers, demonstrating the complexity of water dynamics in these reservoirs. This research emphasizes the effectiveness of integrating spatial hydrology with cloud computing tools for fast and accurate monitoring of large reservoir. The use of these advanced technologies provides near real-time, reliable, and easily accessible data, offering a significant advantage for water resource management in Côte d'Ivoire.

What drives the corporate payoffs of using generative artificial intelligence?

Artificial Intelligence, a set of technologies that aim to replicate human cognitive functions, has seen remarkable improvements over the last decade. In particular, generative AI (GenAI), a subset of AI able to generate content tasks based on Large Language Models (LLM), has recently gained momentum. Based on an extensive analysis of generative AI use cases in large enterprises, we find that Gen AI shows strong labor productivity improvements across metrics such as throughput time, unit cost, and task effectiveness. However, the distribution of gains is asymmetric in favor of a few companies. While the current distribution of gains does not provide evidence of a power law effect, the current asymmetry reflects differences in AI resources/capabilities across companies - mainly data access, AI talent, or AI governance.

A technical–scientific content management system on water reuse as an environmental education tool: the experience of a Portugal/Brazil partnership

ABSTRACT This study presents the Instituto Reúso de Água (IRdA) as a high-quality environmental education tool. The results measured the reach of IRdA's direct (webinars, learning series, and mentoring) and indirect (technical–scientific repository and the production of more easily assimilated data) educational actions since its conception (Period 01), operation of the platform (Period 02), and operation of IRdA (Period 03), through its digital platform and social media. The direct actions proved to be effective in disseminating knowledge, highlighting a preference for the topic ‘regulation’ and adherence to content produced in different video formats. On the other hand, the indirect actions showed that the scientific approach is less accessed due to its complex language. However, when presented into more accessible data, scientific content gains confidence and can be of great relevance for the transformation of society. The results corroborate the current trend of greater educational reach in a free and digital format to build a society truly committed to water reuse and its more efficient management.

Genomic characterization of an uncommon Delftia acidovorans isolate obtained from aBulgarian immunocompetent outpatient diagnosed with bronchitis.

Delftia acidovorans is an aerobic, non-fermenting Gram-negative bacterium (NFGNB), found in soil, water and hospital environments. It is rarely clinically significant, most commonly affecting hospitalized or immunocompromised patients. The present study aimed to explore the genomic characteristics of a Bulgarian clinical D. acidovorans isolate (designated Dac759) in comparison to all strains of this species with available genomes in the NCBI Genome database (n = 34). Dac759 was obtained in 2021 from the sputum of a 65-year-old female immunocompetent outpatient with bronchitis. Species identification using MALDI-TOF mass spectrometry, antimicrobial susceptibility testing, whole-genome sequencing (WGS), and phylogenomic analysis were performed. The isolate demonstrated high-level resistance to colistin (16 mg L-1); resistance to gentamicin; reduced susceptibility to piperacillin, piperacillin-tazobactam, ceftazidime, cefepime, ciprofloxacin, and levofloxacin; and susceptibility to imipenem, meropenem, amikacin, and tobramycin. The observed genome size (6.43 Mb) and GC content (66.76%) were comparable with the accessible data from sequenced D. acidovorans genomes. A limited number of resistance determinants were identified in the assembled genome as follows: blaOXA-459, emrE, oqxB, and mexCD-oprJ. The phylogenomic analysis indicated a high heterogenicity of the included D. acidovorans genomes. In conclusion, to the best of our knowledge, this is the first documented case of a clinically relevant D. acidovorans isolate in Bulgaria. Unlike the majority of reports in the literature, Dac759 affected a patient with no malignancies or other preexisting comorbidities. With this in mind, its genome sequence is a valuable resource for the fundamental study of uncommon bacterial pathogens of public health importance.

Implementing national performance indicator ontology: A case study of Botswana's development agendas

This paper presents the National Performance Indicator Ontology (NPIOnto), a knowledge model designed for the semantic representation of national performance indicators (NPIs) and related performance variables to enhance the semantic capabilities of open data. The research addresses the lack of semantic representation for NPIs, particularly within Botswana's development agendas, which impedes the effective use, accessibility, and integration of performance data essential for monitoring and achieving sustainable development goals. NPIOnto was developed through five steps: discovering the ontology purpose, studying the development agenda knowledge domain, developing the conceptual knowledge model, formalizing the conceptual model, and evaluating the formal model's effectiveness in enhancing the semantic capabilities of open data resources. The evaluation, based on practical application in Botswana, demonstrated that NPIOnto significantly improves the accessibility, understandability, interoperability, integrability, and intelligibility of open data. Insights from the Botswana case study indicate that while NPIOnto is tailored to meet Botswana's development agenda needs, it can be adapted for use in other developing countries.

Measuring A Composite Financial Inclusion Index in Lower-Middle Income Countries

This study suggests a composite index of financial inclusion (FI) for financial development, based on three key dimensions: penetration, availability, and usage in lower-middle income countries. The methodology uses principal component analysis PCA to exploit a composite index of financial inclusion and test its impact of new components on financial development. On the secondary data of financial access from the sources of FAS-IMF and WB-Findex, the research result is found that there are two new components of overall financial inclusion: the traditional financial inclusive index (TFI) includes three traditional dimensions: traditional penetration (TP), traditional availability (TA), and traditional usage (TU); and the digital financial inclusive index (DFI) includes three digital dimensions: digital penetration (DP), digital availability (DA), and digital usage (DU). The research findings give the empirical evidence that the digitalized level of financial inclusion is quite weak in lower-middle income countries. The research contributes to the methodology of building a composite index of financial inclusion with optimal weights of dimensions and traditional-digital financial inclusive indexes.

Efficient provable dual receiver hybrid and light weight public key schemes based on the discrete logarithm problem without pairings

AbstractDual‐receiver proxy re‐encryption is a cryptographic technique that enables secure data sharing among multiple authorized users or entities. It has gained significant attention for its ability to manage access permissions, data confidentiality, and streamline communication channels. These schemes have been widely used in various applications, including healthcare systems, cloud computing, Internet of Things (IoT) systems, collaborative environments, and secure communication channels. This paper aims to propose two proxy re‐encryption schemes for dual receivers without pairings. The first is dual receiver lightweight proxy re‐encryption without pairings (DR‐LWPRE‐WP), which uses a public key scheme to reduce computational complexity. The second is dual receiver hybrid proxy re‐encryption without pairings (DR‐HPRE‐WP), which incorporates public key and symmetric key schemes. Both schemes offer protection against selected plaintext attacks based on the decisional Diffie‐Hellman principle. The DR‐LWPRE‐WP scheme reduces computation by approximately 53% compared to pairing‐based schemes, making it suitable for lightweight applications like the Internet of Things. The computational efficacy of these schemes offers significant benefits for resource‐constrained environments and practical implementations.

EXPRESS: Generative AI in Marketing: Promises, Perils, and Public Policy Implications

By evaluating the use pattern of GAI by businesses in marketing, this study aims to understand the subsequent impact on society and develop policy implications that promote its beneficial use. To this end, we develop an organizing framework that contends that the usage of GAI models by businesses for marketing purposes creates promises and perils for society through a specific business process. This business process is represented by the action → capabilities → transformation → impact link in the proposed framework. Additionally, we find that the level of technology infrastructure, skilled personnel, and data access moderates the influence of GAI on businesses’ ability to develop technology-driven capabilities. Furthermore, adaptive leadership and management strategies moderate the impact of these capabilities on technology-enabled business transformations. In doing so, this is the first study to critically evaluate the use of GAI in marketing from a public policy perspective. The study concludes with an agenda for future research.

PatCen: A blockchain-based patient-centric mechanism for the granular access control of infectious disease-related test records.

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape.

Data access arrangements in genomic research consortia

One of the most common terms that is used to describe entities responsible for sharing genomic data for research purposes is ‘genomic research consortium’. However, there is a lack of clarity around the language used by consortia to describe their data sharing arrangements. Calls have been made for more uniform terminology. This article reports on a review of the genomic research consortium literature illustrating a wide diversity in the language that has been used over time to describe the access arrangements of these entities. The second component of this research involved an examination of publicly available information from a dataset of 98 consortia. This analysis further illustrates the wide diversity in the access arrangements adopted by genomic research consortia. A total of 12 different access arrangements were identified, including four simple forms (open, consortium, managed and registered access) and eight more complex tiered forms (for example, a combination of consortium, managed and open access). The majority of consortia utilised some form of tiered access, often following the policy requirements of funders like the US National Institutes of Health and the UK Wellcome Trust. It was not always easy to precisely identify the access arrangements of individual consortia. Greater consistency, clarity and transparency is likely to be of benefit to donors, depositors and accessors alike. More work needs to be done to achieve this end.

The Pico Balloon Archive: Establishing the First Super Pressure Balloon Satellite Network for Atmospheric Research

Abstract We present the Pico Balloon Archive (PBA), a specialized platform for archiving, visualizing, and verifying data from pico balloons— lightweight, super pressure balloons capable of operating in the upper troposphere and lower stratosphere for durations ranging from weeks to years. With a latitudinal range from 88.77°S to 89.60°N and data collection ongoing since 2021, the PBA stands as the most spatially extensive repository for super pressure balloons to date. It offers a centralized, user-friendly online portal (http://picoballoonarchive.org) for data access, featuring a summary of flight details, downloadable raw data, and individual flight visualizations. In this study, we validate the PBA’s wind speed and direction calculations against the Integrated Radiosonde Archive (IGRA), showing strong correlations (r2 = 0.66 and 0.78 for wind speed and direction, respectively). We also highlight the PBA’s effectiveness in charting global atmospheric circulation and the capacity of certain balloons to traverse hemispheres, a feat made possible by unique stratospheric dynamics. Furthermore, we demonstrate the PBA’s effectiveness in validating Numerical Weather Prediction (NWP) Lagrangian trajectories, quantifying errors based on model initialization latitude. This continuously updated super pressure balloon network is poised to significantly aid the atmospheric science community, facilitating a deeper understanding of global atmospheric processes.

Big data classification using SpinalNet-Fuzzy-ResNeXt based on spark architecture with data mining approach

In the modern networking topology, big data is highly essential for several domains like e-commerce, healthcare, and finance. Big data classification has offered effectual performance in several applications. Still, big data classification is highly difficult and the recognized classification approaches require a longer duration and numerous resources for executing the accessible data. For resolving such issues, the spark-based classification approach is required. In this work, the hybrid SpinalNet-Fuzzy-ResNeXt model called SFResNeXt is implemented to classify the big data. Here, the SpinalNet and ResNeXt are merged, where the layers are fused with the fuzzy concept. The initial process is the outlier detection. The Holoentrophy method is used to detect the outlier data, and it is removed. Moreover, duplicate detection is performed by fingerprinting approach to detect the repeated data. The, Association Rule Mining (ARM) method is employed for feature selection. The big data is classified by the SFResNeXt. Furthermore, the SFResNeXt-based big data classification offered the accuracy, sensitivity, and specificity of 0.905, 0.914, and 0.922 using the heart disease dataset.

Fulfilling data access obligations: How could (and should) platforms facilitate data donation studies?

Research into digital platforms has become increasingly difficult. One way to overcome these difficulties is to build on data access rights in EU data protection law, which requires platforms to offer users a copy of their data. In data donation studies, researchers ask study participants to exercise this right and donate their data to science. However, there is increasing evidence that platforms do not comply with designated laws. We first discuss the obligations of data access from a legal perspective (with accessible, transparent, and complete data as key requirements). Next, we compile experiences from social scientists engaging in data donation projects as well as a study on data request/access. We identify 14 key challenges, most of which are a consequence of non-compliance by platforms. They include platforms’ insufficient adherence to (a) providing data in a concise and easily accessible form (e.g. the lack of information on when and how subjects can access their data); (b) being transparent about the content of their data (e.g. the lack of information on measures); and (c) providing complete data (e.g. the lack of all available information platforms process related to platform users). Finally, we formulate four central recommendations for improving the right to access.

Leveraging Oracle HCM for Enhanced Employee Engagement

As the current corporate environment continues to evolve, employee engagement has emerged as a critical component in determining the success of an organisation. Oracle Human Capital Management (HCM) systems provide a revolutionary chance to improve employee engagement by providing a full suite of tools that are geared to handle different areas of workforce management. This opportunity presents itself as a result of the availability of these systems. In order to provide a more unified and responsive approach to employee engagement, Oracle Human Capital Management (HCM) offers an integrated platform that incorporates basic HR tasks, talent management, workforce management, and analytics. In this article, we investigate how the capabilities of Oracle Human Capital Management (HCM) may be strategically used to cultivate a staff that is more engaged and motivated. The cloud-based design of the system guarantees accessibility and real-time data processing, both of which are crucial for interactions with workers that are timely and relevant. A variety of features that may be adapted to assist engagement activities are provided by the modules that make up Oracle Human Capital Management (HCM). These modules include Employee Central, Talent Management, and Learning Management. For example, the Talent Management module helps to connect employee goals with organisational objectives, which in turn improves both work happiness and performance. While this is going on, the Learning Management module offers individualised training and development possibilities, according to the workers' requirements for professional advancement and their goals for their careers.

Application of 5G network technology in agricultural production and information management

Establishing a management information system that adapts to the continuous development of agricultural production management today, adopting advanced 5G technology and means, and comprehensively managing the entire agricultural production is an inevitable trend in the development of agricultural production management. In order to improve the effect of agricultural production informatization management, this paper combines 5G (fifth-generation) network technology to construct agricultural production and informatization management models. In view of the massive dynamic agricultural production management data, this paper studies the efficient multi-copy management strategy, which can consider the two aspects of load balancing and energy efficiency at the same time, so as to achieve efficient data access and energy efficiency. Moreover, this paper introduces a time series-based file access heat calculation model. In terms of determining the number of copies of files, this paper proposes a copy factor allocation algorithm centered on the ranking of file access popularity, and gives a characteristic function. Through cluster analysis, it can be seen that the agricultural production informatization management model based on 5G technology proposed in this paper can effectively improve the efficiency of agricultural information management.

Neighborhood Food Access in Early Life and Trajectories of Child Body Mass Index and Obesity

Limited access to healthy foods, resulting from residence in neighborhoods with low food access, is a public health concern. The contribution of this exposure in early life to child obesity remains uncertain. To examine associations of neighborhood food access during pregnancy or early childhood with child body mass index (BMI) and obesity risk. Data from cohorts participating in the US nationwide Environmental Influences on Child Health Outcomes consortium between January 1, 1994, and March 31, 2023, were used. Participant inclusion required a geocoded residential address in pregnancy (mean 32.4 gestational weeks) or early childhood (mean 4.3 years) and information on child BMI. Residence in low-income, low-food access neighborhoods, defined as low-income neighborhoods where the nearest supermarket is more than 0.5 miles for urban areas or more than 10 miles for rural areas. BMI z score, obesity (age- and sex-specific BMI ≥95th percentile), and severe obesity (age- and sex-specific BMI ≥120% of the 95th percentile) from age 0 to 15 years. Of 28 359 children (55 cohorts; 14 657 [51.7%] male and 13 702 [48.3%] female; 590 [2.2%] American Indian, Alaska Native, Native Hawaiian, or Other Pacific Islander; 1430 [5.4%] Asian; 4034 [15.3%] Black; 17 730 [67.2%] White; and 2592 [9.8%] other [unspecified] or more than 1 race; 5754 [20.9%] Hispanic and 21 838 [79.1%] non-Hispanic) with neighborhood food access data, 23.2% resided in low-income, low-food access neighborhoods in pregnancy and 24.4% in early childhood. After adjusting for individual sociodemographic characteristics, residence in low-income, low-food access (vs non-low-income, low-food access) neighborhoods in pregnancy was associated with higher BMI z scores at ages 5 years (β, 0.07; 95% CI, 0.03-0.11), 10 years (β, 0.11; 95% CI, 0.06-0.17), and 15 years (β, 0.16; 95% CI, 0.07-0.24); higher obesity risk at 5 years (risk ratio [RR], 1.37; 95% CI, 1.21-1.55), 10 years (RR, 1.71; 95% CI, 1.37-2.12), and 15 years (RR, 2.08; 95% CI, 1.53-2.83); and higher severe obesity risk at 5 years (RR, 1.21; 95% CI, 0.95-1.53), 10 years (RR, 1.54; 95% CI, 1.20-1.99), and 15 years (RR, 1.92; 95% CI, 1.32-2.80). Findings were similar for residence in low-income, low-food access neighborhoods in early childhood. These associations were robust to alternative definitions of low income and low food access and additional adjustment for prenatal characteristics associated with child obesity. Residence in low-income, low-food access neighborhoods in early life was associated with higher subsequent child BMI and higher risk of obesity and severe obesity. We encourage future studies to examine whether investments in neighborhood resources to improve food access in early life would prevent child obesity.

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite's individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model's predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.

Caught in the Crossfire: Ecuador’s Safety Crisis Impact on Teen and Young Adult Mental Health

Introduction: During the past decade, Ecuador has become one of the most dangerous countries in Latin America, with a significant rise in violence impacting mental health. This study investigates hospital admissions for mood disorders among adolescents and young adults in Ecuador from 2021 to 2022. Methods: We used public access data from the National Institute of Census and Statistics (INEC) to analyze hospital admissions for mood disorders in individuals aged 12 to 21, categorized using the International Classification of Diseases (ICD-10) codes F30 to F39. Results: Results show a female predominance in admissions, with a female-to-male ratio of 3:1 by 2022. Admissions for depressive episodes accounted for 66.5% of cases, peaking in 2022. The regions with the highest admission rates were the Galapagos Islands and Pichincha. A notable reduction in admissions was observed in 2020, likely due to the COVID-19 pandemic´s impact on healthcare access. Discussion: The study's findings highlight significant gender disparities, diagnostic trends and regional differences in mood disorder admissions. The increasing trend, particularly for depressive episodes, underscores the need for enhanced mental health resources. Conclusion: The study concludes that the escalation of violence in Ecuador significantly impacts mental health, emphasizing the urgency of addressing social determinants of health and focussing efforts on regions most affected by conflict.

Longitudinal Multi-omic Immune Profiling Reveals Age-Related Immune Cell Dynamics in Healthy Adults.

The generation and maintenance of protective immunity is a dynamic interplay between host and environment that is impacted by age. Understanding fundamental changes in the healthy immune system that occur over a lifespan is critical in developing interventions for age-related susceptibility to infections and diseases. Here, we use multi-omic profiling (scRNA-seq, proteomics, flow cytometry) to examined human peripheral immunity in over 300 healthy adults, with 96 young and older adults followed over two years with yearly vaccination. The resulting resource includes scRNA-seq datasets of >16 million PBMCs, interrogating 71 immune cell subsets from our new Immune Health Atlas. This study allows unique insights into the composition and transcriptional state of immune cells at homeostasis, with vaccine perturbation, and across age. We find that T cells specifically accumulate age-related transcriptional changes more than other immune cells, independent from inflammation and chronic perturbation. Moreover, impaired memory B cell responses to vaccination are linked to a Th2-like state shift in older adults' memory CD4 T cells, revealing possible mechanisms of immune dysregulation during healthy human aging. This extensive resource is provided with a suite of exploration tools at https://apps.allenimmunology.org/aifi/insights/dynamics-imm-health-age/ to enhance data accessibility and further the understanding of immune health across age.

Data sharing policies across health research globally: Cross-sectional meta-research study.

Data sharing improves the value, synthesis, and integrity of research, but rates are low. Data sharing might be improved if data sharing policies were prominent and actionable at every stage of research. We aimed to systematically describe the epidemiology of data sharing policies across the health research lifecycle. This was a cross-sectional analysis of the data sharing policies of the largest health research funders, all national ethics committees, all clinical trial registries, the highest-impact medical journals, and all medical research data repositories. Stakeholders' official websites, online reports, and other records were reviewed up to May 2022. The strength and characteristics of their data sharing policies were assessed, including their policies on data sharing intention statements (a.k.a. data accessibility statements) and on data sharing specifically for coronavirus disease studies. Data were manually extracted in duplicate, and policies were descriptively analysed by their stakeholder and characteristics. Nine hundred and thirty-five eligible stakeholders were identified: 110 funders, 124 ethics committees, 18 trial registries, 273 journals, and 410 data repositories. Data sharing was required by 41% (45/110) of funders, no ethics committees or trial registries, 19% (52/273) of journals and 6% (24/410) of data repositories. Among funder types, a higher proportion of private (63%, 35/55) and philanthropic (67%, 4/6) funders required data sharing than public funders (12%, 6/49). Data sharing requirements, and even recommendations, were insufficient across health research. Where data sharing was required or recommended, there was limited guidance on implementation. We describe multiple pathways to improve the implementation of data sharing. Public funders and ethics committees are two stakeholders with particularly important untapped opportunities.