Standard Application Programming Interface Research Articles

Plugin-based interoperability and ecosystem management for the MolSSI Driver Interface Project.

The MolSSI Driver Interface (MDI) Project is an effort to simplify and standardize the process of enabling tight interoperability between independently developed code bases and is supported by numerous software packages across the domain of chemical physics. It enables a wide variety of use cases, including quantum mechanics/molecular mechanics, advanced sampling, path integral molecular dynamics, machine learning, abinitio molecular dynamics, etc. We describe two major developments within the MDI Project that provide novel solutions to key interoperability challenges. The first of these is the development of the MDI Plugin System, which allows MDI-supporting libraries to be used as highly modular plugins, with MDI enforcing a standardized application programming interface across plugins. Codes can use these plugins without linking against them during their build process, and end-users can select which plugin(s) they wish to use at runtime. The MDI Plugin System features a sophisticated callback system that allows codes to interact with plugins on a highly granular level and represents a significant advancement toward increased modularity among scientific codes. The second major development is MDI Mechanic, an ecosystem management tool that utilizes Docker containerization to simplify the process of developing, validating, maintaining, and deploying MDI-supporting codes. Additionally, MDI Mechanic provides a framework for launching MDI simulations in which each interoperating code is executed within a separate computational environment. This eliminates the need to compile multiple production codes within a single computational environment, reducing opportunities for dependency conflicts and lowering the barrier to entry for users of MDI-enabled codes.

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

Objective. Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study. Approach. Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate and the mean arterial pressure for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control. Main results. Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems. Significance. We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Enabling massively parallel, ad hoc exploration of the design space for simulation models within a serverless environment

This paper presents a massively parallel, cloud-computing framework for the ad hoc evaluation of discrete-event simulation (DES) models to enable broad exploration of the design space for model parameters. Parallel evaluation is enabled through use of a serverless computing environment allowing thousands of simultaneous experiments, on demand, without the need to explicitly provision or manages hardware. A standard Simulation Evaluation application programming interface (API) was designed for evaluating simulation functions that enables language independence between client application and simulation model, encouraging reuse of simulation models for multiple purposes (what-if analysis, ranking and selection, sensitivity analysis, or optimization). Extensions to the Java Simulation Library (JSL)27 enable rapid deployment of models built with the JSL as parameterized serverless functions implementing the Simulation Evaluation API. New Java packages facilitate the calling of any serverless functions that implement the Simulation Evaluation API.

A national survey of digital health company experiences with electronic health record application programming interfaces.

This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs "without special effort" will be crucial to ensure future technology robustness and durability.

Electrifying the case review process for better speed, reach, and impact

Background: Prevention of healthcare-associated infections (HAIs) requires timely feedback to and input from all staff involved in patient care to best identify practice gaps and improvement targets. However, multidisciplinary review of HAI events can be challenging to promptly complete given staffing shortages and the excess administrative burden of emailed and printed forms and disjointed analyses, reporting, and visualization tools. Plagued by a lack of feedback from attending and ordering physicians, difficulty transcribing and analyzing nonstandardized data, and challenges in summarizing and distributing actionable findings, we sought to reduce turnaround time (TAT), improve data collection, and broaden communication of HAI contributing factors and proposed solutions. Methods: A secure web application for electronic data capture and reporting, Research Electronic Data Capture (REDCap), was used; the software application is free to nonprofit organizations. The review process is now initiated by an infection preventionist entering HAI information into an initial survey, which automatically cascades information into 4 subsequent surveys, distributed through automated email links, providing an opportunity for individual responses from the nursing unit, the attending provider, an infectious disease physician, and the ordering provider for the positive test that detected the HAI. Survey questions focus on evaluation of adherence to CDC and SHEA HAI prevention strategies. Reminders are automatically generated and continue to be sent to involved staff until their portion is completed. Survey responses are automatically summarized upon completion of all reviews and are shared with several stakeholders, including hospital leadership, the care team, infection prevention staff, and quality-control partners (Fig.). Discrete qualitative and quantitative data are exported in a standard application-programming interface (API) format for immediate analysis and interpretation. Results: After the review process was launched using new electronic technology, the average TAT and completion rate improved from 23 days and 40% to 7 days and 95%, respectively. Input from ordering and attending physicians, once extremely rare, became frequent. Nuanced insight into causative and preventive factors, previously unachievable, occurred during review of all 38 HAIs reported in December 2022. Reviewers believed that 48% of HAIs reviewed could have been prevented. Conclusions: Applying electronic technology to HAI case review improved completion and timeliness of reviews by both providers and nurses. By sharing data and insights with all stakeholders in real time, the new procedure permitted multidirectional communication between the care teams and increased awareness of patient harm as well as ownership of patient safety. Our process is freely and readily generalizable to any nonprofit healthcare facility.Disclosures: None

A secured big-data sharing platform for materials genome engineering: State-of-the-art, challenges and architecture

Materials are the foundation of social development. The vigorous development of big-data technology has brought new opportunities for material research and development, gradually entering the data-driven paradigm. How to safely collect, store and utilize material big-data to realize the design and prediction of advanced materials has essential research significance and value. Many material big-data platforms have been constructed to gather multi-source heterogeneous material data. However, these traditional platforms are hard to realize the safe and efficient circulation and utilization of data. Relying on the national Materials Genome Engineering (MGE) project, we built a secured big-data sharing platform and proposed corresponding data collection, storage, utilization, and security solutions. On the one hand, the blockchain framework working as a ‘middleware’ provides a standard application program interface for data interaction between participants, and participants do not need to perceive the underlying system framework; on the other hand, it provides a unified management and security mechanism for the platform. In terms of collection, the dynamic container model is used to solve the data normalization problem, thereby improving data quality. In terms of storage, data adaptors store normalized data in different databases for distributed storage and unified scheduling. The platform provides a unified service gateway to schedule all services for data utilization. The secured big-data sharing platform can improve data utilization, promote material data sharing, accelerate material discovery, and serve the data needs of high-throughput computing and the design of new materials.

Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience.

Chronic dermatologic disorders can cause significant emotional distress. Sentiment analysis of disease-related tweets helps identify patients' experiences of skin disease. To analyze the expressed sentiments in tweets related to alopecia areata (AA), hidradenitis suppurativa (HS), and psoriasis (PsO) in comparison to fibromyalgia (FM). This is a cross-sectional analysis of Twitter users' expressed sentiment on AA, HS, PsO, and FM. Tweets related to the diseases of interest were identified with keywords and hashtags for one month (April, 2022) using the Twitter standard application programming interface (API). Text, account types, and numbers of retweets and likes were collected. The sentiment analysis was performed by the R "tidytext" package using the AFINN lexicon. A total of 1,505 tweets were randomly extracted, of which 243 (16.15%) referred to AA, 186 (12.36%) to HS, 510 (33.89%) to PsO, and 566 (37.61%) to FM. The mean sentiment score was -0.239 ± 2.90. AA, HS, and PsO had similar sentiment scores (p = 0.482). Although all skin conditions were associated with a negative polarity, their average was significantly less negative than FM (p < 0.0001). Tweets from private accounts were more negative, especially for AA (p = 0.0082). Words reflecting patients' psychological states varied in different diseases. "Anxiety" was observed in posts on AA and FM but not posts on HS and PsO, while "crying" was frequently used in posts on HS. There was no definite correlation between the sentiment score and the number of retweets or likes, although negative AA tweets from public accounts received more retweets (p = 0.03511) and likes (p = 0.0228). The use of Twitter sentiment analysis is a promising method to document patients' experience of skin diseases, which may improve patient care through bridging misconceptions and knowledge gaps between patients and healthcare professionals.

Loose coupling in future distributed space safety simulations

Distributed simulations play an important role in complex space safety related examinations. Realizing an associated system-of-systems solution with as less software adaptation development effort as possible represents a key challenge in this context. The paper presents a microservice-based approach, considering possibilities of the new Simulation Interoperability Standards Organization (SISO) standard Space Reference Federate Object Model. Different High-Level Architecture (HLA) compliant solutions are conversed, ranging from usage of the HLA evolved standard's web service application programming interface (API) over the implementation of a tightly coupled bridging federate to the exploitation of the future HLA (HLA 4) version's standardized federate protocol. A human-in-the-loop space flight simulator concept use case is presented and discussed.

Analyzing sentiments and themes on cannabis in Canada using 2018 to 2020 Twitter data

IntroductionThe Canadian Cannabis Act came into effect on October 17, 2018, which allowed Canadian adults to consume cannabis for non-medical purposes (Government of Canada, Cannabis regulations (SOR/2018-144). Cannabis Act, (2018a); Parliament of Canada, C-45: an Act respecting cannabis and to amend the Controlled Drugs and Substances Act, the Criminal Code and other Acts, 2018). With this major policy change, it is unknown how the attitude of the public changed and how information on cannabis changed. Social media platforms, including Twitter, are significant venues for studying emerging patterns in social issues such as cannabis legalization. This study aimed to examine sentiments, themes and contents of cannabis-related tweets by suppliers (both licensed and unlicensed) and general tweets in Canada. To our knowledge, this is the first analysis that mixes sentiment analysis and thematic analysis of Canadians’ cannabis-related Twitter data.MethodA sample of Canadian cannabis-related tweets was collected from January 2018 to August 2020 through the Twitter Application Programming Interface (API). Using a standard access token and the Twitter Standard Search API, tweets were extracted based on Twitter handles to capture the content of both licensed and unlicensed cannabis retailers in Canada, as well as relevant cannabis-related keywords to capture public content. We conducted sentiment and positive polarity analyses, and content analysis to identify attitudes and themes around cannabis use in Canada.ResultsThis study gathered and analyzed a total of 44,970 tweets in the sentiment analysis and a total of 1035 tweets in the thematic analysis. Descriptive analysis showed that monthly tweets peaked prior to legalization in October 2018 and again during the initial wave of the COVID-19 pandemic in February and March 2020. The data showed an overall positive sentiment polarity with a high of + 0.24 in April 2019 and a low of + 0.14 in March 2020.Thematic analysis revealed the themes: (i) education/information, (ii) uses of cannabis, (iii) cannabis products including packing, quality, price, types, and sources, (iv) cannabis policies including regulations and public safety, (v) access, (vi) social issues include gender and stigma, and (vii) COVID-19 impact.ConclusionThis study combined the power of big data collection and analysis with manual coding and analysis methods to extract rich content from large data using social media communications on issues related to cannabis in Canada. The findings of this study may inform policies on advertising cannabis products and highlighted some patterns related to education, access, and safety that deserve further investigation.

Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment.

The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI.

Systems Modeling Language (SysML v2) Support for Digital Engineering

ABSTRACTThe OMG Systems Modeling Language™ (SysML®) was adopted in 2006 and has been used by many organizations to support their efforts to transition to a model‐based systems engineering (MBSE) approach. SysML v2 is the next generation Systems Modeling Language and is intended to address many of the limitations of SysML v1. This paper highlights how the SysML v2 language and the new standard Application Programming Interface (API) can enable MBSE and digital engineering.SysML v2 is being developed by the SysML v2 Submission Team (SST) in response to requirements issued by the Object Management Group (OMG). The final submission to the OMG is planned for 2022. The draft SysML v2 specifications and the open‐source SysML v2 pilot implementation can be found at https://github.com/Systems-Modeling.

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Application Programming Interface (API) call feature analysis is the prominent method for dynamic android malware detection. Standard benchmark android malware API dataset includes features with high dimensionality. Not all features of the data are relevant, filtering unwanted features improves efficiency. This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance. In the first phase fuzzy benchmarking is used to select the top best features, and in the second phase meta-heuristic optimization algorithms viz., Moth Flame Optimization (MFO), Multi-Verse Optimization (MVO) & Whale Optimization (WO) are run with Machine Learning (ML) wrappers to select the best from the rest. Five ML methods viz., Decision Tree (DT), Random Forest (RF),K-Nearest Neighbors (KNN), Naïve Bayes (NB) & Nearest Centroid (NC) are compared as wrappers. Several experiments are conducted and among them, the best post reduction accuracy of 98.34% is recorded with 95% elimination of features. The proposed novel method outperformed among the existing works on the same dataset.

Difficulty Regulating Social Media Content of Age-Restricted Products: Comparing JUUL's Official Twitter Timeline and Social Media Content About JUUL.

In 2018, JUUL Labs Inc, a popular e-cigarette manufacturer, announced it would substantially limit its social media presence in compliance with the Food and Drug Administration's (FDA) call to curb underage e-cigarette use. However, shortly after the announcement, a series of JUUL-related hashtags emerged on various social media platforms, calling the effectiveness of the FDA's regulations into question. The purpose of this study is to determine whether hashtags remain a common venue to market age-restricted products on social media. We used Twitter's standard application programming interface to download the 3200 most-recent tweets originating from JUUL Labs Inc's official Twitter Account (@JUULVapor), and a series of tweets (n=28,989) from other Twitter users containing either #JUUL or mentioned JUUL in the tweet text. We ran exploratory (10×10) and iterative Latent Dirichlet Allocation (LDA) topic models to compare @JUULVapor's content versus our hashtag corpus. We qualitatively deliberated topic meanings and substantiated our interpretations with tweets from either corpus. The topic models generated for @JUULVapor's timeline seemingly alluded to compliance with the FDA's call to prohibit marketing of age-restricted products on social media. However, the topic models generated for the hashtag corpus of tweets from other Twitter users contained several references to flavors, vaping paraphernalia, and illicit drugs, which may be appealing to younger audiences. Our findings underscore the complicated nature of social media regulation. Although JUUL Labs Inc seemingly complied with the FDA to limit its social media presence, JUUL and other e-cigarette manufacturers are still discussed openly in social media spaces. Much discourse about JUUL and e-cigarettes is spread via hashtags, which allow messages to reach a wide audience quickly. This suggests that social media regulations on manufacturers cannot prevent e-cigarette users, influencers, or marketers from spreading information about e-cigarette attributes that appeal to the youth, such as flavors. Stricter protocols are needed to regulate discourse about age-restricted products on social media.

User Response to Extended Reality Sponsorship Activations on Social Media: Exploring Impressions of GoPro’s Use of 360° Video in Marketing

This research explores user impressions of 360° video as a platform for sponsorship activation, seeking to examine consumers’ responses to 360° virtual environments as a form of extended reality (XR) marketing as well as the structural and experiential antecedents to XR’s potential as a vehicle for brand marketing and sponsorship activation. GoPro was selected as a case study for analysis; 25 brand posts on GoPro’s YouTube channel were identified featuring 360° video integration as sponsorship communications, for which all video metadata and user comments were collected using programming software R to access YouTube’s standard application programming interface (API). Coding of the user commentary data subsequently revealed four primary themes, indicative of users’ impressions of 360° video technology. Notably, the study’s findings suggest that effective 360° integrations—alongside positive user experiences and access to the technology—pose tangible benefits to consumers’ attitudes and experiences, and present sponsors with a viable media through which to activate partnerships.

Controller-Free Hand Tracking for Grab-and-Place Tasks in Immersive Virtual Reality: Design Elements and Their Empirical Study

Hand tracking enables controller-free interaction with virtual environments, which can, compared to traditional handheld controllers, make virtual reality (VR) experiences more natural and immersive. As naturalness hinges on both technological and user-based features, fine-tuning the former while assessing the latter can be used to increase usability. For a grab-and-place use case in immersive VR, we compared a prototype of a camera-based hand tracking interface (Leap Motion) with customized design elements to the standard Leap Motion application programming interface (API) and a traditional controller solution (Oculus Touch). Usability was tested in 32 young healthy participants, whose performance was analyzed in terms of accuracy, speed and errors as well as subjective experience. We found higher performance and overall usability as well as overall preference for the handheld controller compared to both controller-free solutions. While most measures did not differ between the two controller-free solutions, the modifications made to the Leap API to form our prototype led to a significant decrease in accidental drops. Our results do not support the assumption of higher naturalness for hand tracking but suggest design elements to improve the robustness of controller-free object interaction in a grab-and-place scenario.

The BLAS API of BLASFEO

Basic Linear Algebra Subroutines For Embedded Optimization (BLASFEO) is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization and other applications targeting relatively small matrices. BLASFEO defines an application programming interface (API) which uses a packed matrix format as its native format. This format is analogous to the internal memory buffers of optimized BLAS, but it is exposed to the user and it removes the packing cost from the routine call. For matrices fitting in cache, BLASFEO outperforms optimized BLAS implementations, both open source and proprietary. This article investigates the addition of a standard BLAS API to the BLASFEO framework, and proposes an implementation switching between two or more algorithms optimized for different matrix sizes. Thanks to the modular assembly framework in BLASFEO, tailored linear algebra kernels with mixed column- and panel-major arguments are easily developed. This BLAS API has lower performance than the BLASFEO API, but it nonetheless outperforms optimized BLAS and especially LAPACK libraries for matrices fitting in cache. Therefore, it can boost a wide range of applications, where standard BLAS and LAPACK libraries are employed and the matrix size is moderate. In particular, this article investigates the benefits in scientific programming languages such as Octave, SciPy, and Julia.

A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

Smart grids with interoperability improve grid reliability by collecting system information and transferring it to an energy management system and associated applications through a seamless end-to-end connection. To achieve interoperability, it is required to exchange the semantic information within the different domains. The international electrotechnical commission has established the Common Information Model (CIM) tool, which is a standard application programming interface for the exchange of semantic information in power systems. CIM provides a robust framework for accurate data sharing, merging, and transformation into reusable information. However, as CIM provides a basic framework for information exchange, various practical issues arise in establishing an energy management system capable of exchanging information using CIM. This paper aims to offer a comprehensive understanding by summarizing and categorizing the research on the practical use of CIM for interoperability in smart grids. Many papers are analyzed and the issues are classified into CIM extension, harmonization, and validation to address the issues that arise when establishing an integrated information exchange system.

Compatible byte-addressable direct I/O for peripheral memory devices in Linux

Memory devices can be used as storage systems to provide a lower latency that can be achieved by disk and flash storage. However, traditional buffered input/output (I/O) and direct I/O are not optimized for memory-based storages. Traditional buffered I/O includes a redundant memory copy with a disk cache. Traditional direct I/O does not support byte addressing. Memory-mapped direct I/O, which optimizes file operations for byte-addressable persistent memory and appears to the CPU as a main memory. However, it has an interface that is not always compatible with existing applications. In addition, it cannot be used for peripheral memory devices (e.g., networked memory devices and hardware RAM drives) that are not interfaced with the memory bus. This paper presents a new Linux I/O layer, byte direct I/O (BDIO), that can process byte-addressable direct I/O using the standard application programming interface. It requires no modification of existing application programs and can be used not only for the memory but also for the peripheral memory devices that are not addressable by a memory management unit. The proposed BDIO layer allows file systems and device drivers to easily support BDIO. The new I/O achieved 18% to 102% performance improvements in the evaluation experiments conducted with online transaction processing, file server, and desktop virtualization storage.

Lightweight IoT middleware for rapid application development

Sensors connected to the cloud services equipped with data analytics has created a plethora of new type of applications ranging from personal to an industrial level forming to what is known today as Internet of Things (IoT). IoT-based system follows a pattern of data collection, data analytics, automation, and system improvement recommendations. However, most applications would have its own unique requirements in terms of the type of the smart devices, communication technologies as well as its application provisioning service. In order to enable an IoT-based system, various services are commercially available that provide services such as backend-as-a-service (BaaS) and software-as-a-service (SaaS) hosted in the cloud. This, in turn, raises the issues of security and privacy. However there is no plug-and-play IoT middleware framework that could be deployed out of the box for on-premise server. This paper aims at providing a lightweight IoT middleware that can be used to enable IoT applications owned by the individuals or organizations that effectively securing the data on-premise or in remote server. Specifically, the middleware with a standardized application programming interface (API) that could adapt to the application requirements through high level abstraction and interacts with the application service provider is proposed. Each API endpoint would be secured using Access Control List (ACL) and easily integratable with any other modules to ensure the scalability of the system as well as easing system deployment. In addition, this middleware could be deployed in a distributed manner and coordinate among themselves to fulfil the application requirements. A middleware is presented in this paper with GET and POST requests that are lightweight in size with a footprint of less than 1 KB and a round trip time of less than 1 second to facilitate rapid application development by individuals or organizations for securing IoT resources.

The SPASE Data Model: A Metadata Standard for Registering, Finding, Accessing, and Using Heliophysics Data Obtained From Observations and Modeling

AbstractThe Space Physics Archive Search and Extract Consortium has developed and implemented the SPASE Data Model that provides a common language for registering a wide range of Heliophysics data and other products. The Data Model enables discovery and access tools such that any researcher can obtain data easily, thereby facilitating research, including on space weather. The Data Model includes descriptions of Simulation Models and Numerical Output, pioneered by the Integrated Medium for Planetary Exploration (IMPEx) group in Europe, and subsequently adopted by the Community Coordinated Modeling Center (CCMC). The SPASE group intends to register all relevant Heliophysics data resources, including space‐, ground‐, and model‐based. Substantial progress has been made, especially for space‐based observational data and associated observatories, instruments, and display data. Legacy product registrations and access go back more than 50 years. Real‐time data will be included. The National Aeronautics and Space Administration (NASA) portion of the SPASE group has funding that assures continuity in the upkeep of the Data Model and aids with adding new products. Tools are being developed for making and editing data descriptions. Digital Object Identifiers (DOIs) for Data Products can now be included in the descriptions. The data access that SPASE facilitates is becoming more uniform, and work is progressing on Web Service access via a standard Application Programming Interface. The SPASE Data Model is stable; changes over the past 9 years were additions of terms and capabilities that are backward compatible. This paper provides a summary of the history, structure, use, and future of the SPASE Data Model.