Reuse Of Data Research Articles

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

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

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

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

FAIR principles to improve the impact on health research management outcomes

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

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

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

Curating and extending data for language comparison in Concepticon and NoRaRe

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

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

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

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

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

A status assessment of selected data synthesis products for ocean biogeochemistry

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

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

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

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

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

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

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

Overcoming Challenges to Open Research Practices – A Perspective From the Global South: A Commentary on “(Why) Are Open Research Practices the Future for the Study of Language Learning?”

Overcoming Challenges to Open Research Practices – A Perspective From the Global South: A Commentary on “(Why) Are Open Research Practices the Future for the Study of Language Learning?”

Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools

Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identifier), each offering advantages specific to various use-cases. Representation of the multi-component structures of nanomaterials (NMs), though, remains out of scope for all the currently available standards, as the nature of NMs sets new challenges on formalizing the encoding of their structure, interactions and environmental parameters. In this work we identify a set of principles that a NM representation should adhere to in order to provide “machine-friendly” encodings of NMs, i.e. encodings that facilitate machine processing and cooperation with nanoinformatics tools. We illustrate our principles by showing how the recently introduced InChI-based NM representation, might be augmented, in principle, to also encode morphology and mixture properties, distributions of properties, and also to capture auxiliary information and allow data reuse.

Functional Synthetic Biology.

Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability and reductions in technical risk and cost.

Expanding the boundaries of previously obtained informed consent in research: Views from participants in the Personalised Risk-based Mammascreening study.

Understanding participants' concerns and information needs regarding broadened consent is crucial to ensure transparency and participant autonomy. Our study qualitatively examined these issues in women participating in the Personalized RISk-based MAmmascreening study (PRISMA). The original PRISMA informed consent was project-specific (i.e., breast cancer research), limiting the scope of secondary research. We explored participants' needs for broadened consent to preserve informed decision-making while maximising the potential re-use of data. Focus groups (FGs) were performed following a semistructured discussion guide. Two independent researchers analysed the data thematically using an inductive approach. Twenty-three asymptomatic women and 13 women diagnosed with breast cancer were randomly divided into six FGs. Four superordinate themes were identified: (1) Normalization, (2) Attitude towards the pharmaceutical industry, (3) Privacy and (4) Knowledge. Our participants viewed data sharing as an important conduit for advancing medical science. Perceived integrity was more often attributed to noncommercial than commercial parties, with a marked mistrust towards the pharmaceutical industry. Most requested information needs related to data protection. Participants' ideal consent process would confer a range of options; for example, they would be able to choose with whom data can be shared, whether data will be de-identified or anonymous, the expiration date of their consent and how, if requested, general and personal study results would be disclosed. Our participants expressed clear information needs and a strong desire to be actively engaged in future data sharing decisions. Given that many researchers collaborate with commercial parties, building public confidence in these institutions would be beneficial. Illustrative examples addressing privacy concerns and clarifying difficult terms would aid consent decision-making. Although our participants displayed great altruism in sharing their data and accepted that broad consent would ultimately facilitate future research, broad consent did not reflect their ideal situation. Dynamic consent may be an option but warrants further feasibility research. Women were recruited from the general breast cancer screening population. Their perceptions and information needs, as reported in this study, will not only inform broadened consent for PRISMA but ideally guide other consent templates and decisions regarding consent processes.

Abstract 719: Advocacy organizations as critical players in the research ecosystem: A case study in the value and use of real world evidence

Abstract Background: Patient-reported outcomes (PROs) are defined as critical data points in the overall assessment of healthcare quality. Coupled with advances in treatment and the evolution of patient-provider relationships, there is an increased emphasis on evaluating patient satisfaction and wellness, often most accurately captured using PROs to describe aspects of a patient’s health status, as directly reported by patients. The importance of direct patient reporting has been highlighted by both FDA and the European Medicines Agency (EMA). The Colorectal Cancer Alliance’s Never Too Young (N2Y) Survey provides accurate, accessible, and reusable data to show trends in young-onset colorectal cancer (YO-CRC) incidence rates, patient care, quality of life outcomes and research. Methods: To support the YO-CRC community and understand unmet needs, the Colorectal Cancer Alliance (Alliance) launched an annual N2Y survey in 2015. Survivors and caregivers played an indispensable role in the development of this survey. In 2022, the Alliance engaged in a comprehensive literature review to analyze how N2Y survey data and publications have been cited and utilized in research, showcasing survey data's value and global impact. Results: Data from our surveys produced 22 abstracts cited by more than 150 publications. The data provides healthcare providers and researchers with valuable information and metrics that did not previously exist from a YO-CRC patient and caregiver perspective. These reports are particularly important to patients with CRC due to the adverse effects of colorectal cancer treatments. Many citations reference the reports to emphasize the need to focus on YO-CRC for a national screening program and the importance of provider vigilance towards symptoms regardless of age., Additionally, our reports were used to elucidate the need for YO centers that comprehensively address the unique needs of patients with specific services selected based on PRO data from the Colorectal Cancer Alliance. Conclusion: Innovative advocacy efforts like PRO collection from organizations like the Colorectal Cancer Alliance using validated and systematic methodology can facilitate efficient, life-saving decision-making globally. With careful consideration and acknowledgment of PROs, positive care experiences can develop to better clinical outcomes overall. PROs indicate the importance of teams who actively communicate well with each other and with the patient to achieve positive outcomes through personalized, precision medicine and better position future research with implications for education and practice. As we advance, the patient and patient advocacy community should work collaboratively with researchers, industry, clinicians and investigators. Citation Format: Kimberly Lynn Newcomer, Andrea Goodman. Advocacy organizations as critical players in the research ecosystem: A case study in the value and use of real world evidence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 719.

5610939 RARE ANAEMIA DISORDERS EUROPEAN EPIDEMIOLOGICAL PLATFORM (RADEEP)

Background: Rare Anaemia Disorders (RAD) include a highly heterogeneous group of rare and ultra-rare haematological conditions. The numbers of RAD patients included in clinical research are rarely adequate in one centre or even country. However, the lack of uniform standards for data collection has led to the implementation of patients’ registries through different approaches, gathering fragmented data and hampering collaborative projects and generation of evidence for translation into clinical practice. Aims: To promote basic and clinical research on RAD at the EU level by setting up a European epidemiological platform for the secure exchange and integration of RAD data, while securing patient rights in agreement with EU General Data Protection Regulation (GDPR). Method: The Rare Anaemia Disorders European Epidemiological Platform (RADeep) is an initiative endorsed by the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet) under the frame of the European Blood Disorders Platform (ENROL), the ERN-EuroBloodNet umbrella for European patients’ registries on rare haematological diseases. RADeep is built in line with the ENROL and EU Rare Disease (RD) Platform. RADeep contributes to ENROL by sharing pseudonymised patient data. RADeep scope includes any type of RAD (ORPHA108997), including among others Sickle Cell Disease (SCD), Thalassaemia (THAL), enzymopathies and membranopathies. RAD patient data are collected from existing registries, hospital records or databases in the EU, with the aim to assess the prevalence, incidence and survival of RAD patients stratified by demographics and severity. Secondary objectives include the assessment of the main clinical manifestations and treatments of RAD subgroups. Results: RADeep legal frame of has been established in line with GDPR, enabling both entering medical data from available sources and re-use of data with third parties. RADeep solution is implemented ensuring interoperability with existing platforms based on the FAIRification of data through international ontologies (eg. ORPHA, HPO, ICD) and the implementation of SPIDER as the pseudonymisation tool provided by the EU-RD Platform. RADeep common data set (CDS) integrates 122 mandatory elements (including the 16 CDS for RD registration from EU-RD-Platform) and 38 optional, covering demographic and epidemiological data, clinical manifestations, laboratory exams and treatments. The modular structure of RADeep allows the expansion of the CDS with specific modules for research and clinical studies. The data collection is centralized through national contacts or scientific societies. Study sample size for SCD and THAL has been estimated on a mapping exercise, with a recruitment target of 20.627 SCD and 8.409 THAL patients in 15 EU countries. As of today, 10 ongoing collaborations represent the first pilots for data transfer to RADeep: Belgium, Cyprus, Denmark, France, Germany, Greece, Italy, Spain, Portugal, and Sweden, covering 89,6% and 95,4% of the estimated targets. Conclusion: RADeep offers a GDPR-compliant platform as the EU approach for the standardised collection of RAD patients data, key for the development of projects as the collaborations ongoing with GenoMed4ALL on artificial intelligence for personalized medicine, or INHERENT Network. RADeep will also provide the evidence to enhance clinical trials, guidelines and health policies that will allow better provision of healthcare to RAD patients accross EU.

GRIP: A Graph Neural Network Accelerator Architecture

We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. Accelerating GNNs is challenging because they combine two distinct types of computation: arithmetic-intensive <i>vertex-centric</i> operations and memory-intensive <i>edge-centric</i> operations. GRIP splits GNN inference into a three edge- and vertex-centric execution phases that can be implemented in hardware. GRIP specializes each unit for the unique computational structure found in each phase. For vertex-centric phases, GRIP uses a high performance matrix multiply engine coupled with a dedicated memory subsystem for weights to improve reuse. For edge-centric phases, GRIP use multiple parallel prefetch and reduction engines to alleviate the irregularity in memory accesses. Finally, GRIP supports several GNN optimizations, including an optimization called vertex-tiling that increases the reuse of weight data. We evaluate GRIP by performing synthesis and place and route for a <inline-formula><tex-math notation="LaTeX">$28 \;\mathrm{n}\mathrm{m}$</tex-math></inline-formula> implementation capable of executing inference for several widely-used GNN models (GCN, GraphSAGE, G-GCN, and GIN). Across several benchmark graphs, it reduces 99th percentile latency by a geometric mean of <inline-formula><tex-math notation="LaTeX">$17\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$23\times$</tex-math></inline-formula> compared to a CPU and GPU baseline, respectively, while drawing only <inline-formula><tex-math notation="LaTeX">$5 \;\mathrm{W}$</tex-math></inline-formula> .

A systematic map of cassava farming practices and their agricultural and environmental impacts using new ontologies: Agri‐ontologies 1.0

Abstract Cassava is consumed by 800 million people and is a staple crop in Africa. Its production may increase under climate change due to its high drought tolerance. We produced a systematic map of scientific studies about cassava farming practices, with the aim of identifying knowledge gaps and clusters. Our secondary aim was to develop a classification system for [1] farming interventions and [2] agricultural, economic and environmental outcomes. Standardised classification systems facilitate data reuse, including for evidence synthesis, and promote research efficiency. Following our published protocol, we searched eight publication databases using the search string ‘cassava OR mandioca OR manihot OR manioc OR yuca’ in December 2017. We screened 36,580 records and included publications that measured the impact of cassava farming practices on agricultural, economic or environmental outcomes, including yield, soil, water, wildlife and labour. We classified the resultant 1599 publications by interventions, outcomes, location, study year and study design. We assessed coding consistency using Kappa scores. We found regional knowledge clusters (Nigeria, Columbia and Brazil accounted for 45.5% of country occurrences) and gaps (e.g. the Democratic Republic of Congo). There were knowledge clusters for interventions testing cultivar type, fertiliser use and diversifying crop rotations and outcomes related to crop production (e.g. yield/biomass). We found knowledge gaps for environmental interventions and outcomes (e.g. 5% of studies measured pollutants or wildlife). In terms of study design, reporting standards were poor (e.g. 24% of studies did not report start dates), average study duration was 2 years, and average publication delays were 4 years. The Kappa scores indicated that we successfully developed consistent ontologies (named Agri‐ontologies 1.0). The map and ontologies are available online: https://www.metadataset.com/. This systematic map of cassava farming practices can direct researchers and funders to knowledge gaps that need addressing, and reviewers to knowledge clusters for synthesis. Better research practices should be promoted within cassava research, as poor reporting standards, short study durations and long publication delays result in an ineffective research environment. This systematic map provides an evidence base for cassava production and the ontologies (Agri‐ontologies 1.0) can be applied to other systems to facilitate more efficient and effective synthesis.

An Energy-Efficient Accelerator for Medical Image Reconstruction From Implicit Neural Representation

This work presents an energy-efficient accelerator for medical image reconstruction from implicit neural representation (INR). The accelerator implements an INR-based algorithm to deliver high-quality medical image reconstruction with arbitrary resolution from a compact implicit format. In particular, we propose a dedicated hardware architecture based on an optimized computation flow for the INR-based reconstruction algorithm, which co-designs data reuse and computation load. The proposed architecture takes in the coordinate of the intersection of three scans and outputs all the voxel intensities, minimizing the data movement between on-chip and off-chip. To validate the proposed accelerator, we build a proof-of-concept prototype demonstration system using field programmable gate array (FPGA). We also map our design to 40nm CMOS technology to measure the performance of the proposed accelerator. The implementation results show that, running at 400MHz, the proposed accelerator is capable of processing medical images with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> resolution in real-time at 26.3 frames per second (FPS), with a power consumption of only 795 mW. Comparison results show that the performance, as well as the energy efficiency of the proposed accelerator, outperforms the central processing unit (CPU)-based and graphic processing unit (GPU)-based implementations.