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A reliable data analysis center plays a crucial role in the successful execution of a space mission. The Science Operation and Data analysis Center (SODC) of Advanced Space-based Solar Observatory (ASO-S) is a bridge between the science team of ASO-S and data users (Huang et al. 2019). ASO-S plays a crucial role in understanding the solar eruptions (such as flares and coronal mass ejections) and the magnetism behind them. In this article, we outline the current status of ASO-S, as well as its data products and analysis software. These resources aid in the enhanced understanding of solar magnetism and its associated energetic eruptions. [ABSTRACT FROM AUTHOR]

At present, data trading venues have been built all over the country, and listed data products have hit new highs, but onsite data trading is still not active.After comprehensive analysis of data products listed in the current major data trading venues, it was proposed that the lack of asset characteristics of data products is the main reason for the current ＆quot;more listed data goods but less trading＆quot;.Then, three requirements of asset characteristics of data products were discussed, and a basic framework for data product assetization was proposed, including the path of realizing value, the listing principle of＆quot;assets are listed＆quot;, the asset verification framework of data product, the infrastructure of development-utilization-tradingcirculation, operational trading logic of ＆quot;listing is assets＆quot; and the regular mechanism of evaluation, review and warning.This provides a reference for data product transactions and boom the construction of data market.

At present, data trading venues have been built all over the country, and listed data products have hit new highs, but onsite data trading is still not active. After comprehensive analysis of data products listed in the current major data trading venues, it was proposed that the lack of asset characteristics of data products is the main reason for the current "more listed data goods but less trading". Then, three requirements of asset characteristics of data products were discussed, and a basic framework for data product assetization was proposed, including the path of realizing value, the listing principle of "assets are listed", the asset verification framework of data product, the infrastructure of development-utilization-trading-circulation, operational trading logic of "listing is assets" and the regular mechanism of evaluation, review and warning. This provides a reference for data product transactions and boom the construction of data market. [ABSTRACT FROM AUTHOR]

Data products play an important role in driving the digital transformation of enterprises, establishing the market of data elements, and fostering the sustainable development of the economy. Based on the research of data products in Chinese academic circles, this paper summarizes and defines the concept of data products, systematically collects Chinese policies related to data elements, and forms a literature review of data products. With the CiteSpace bibliometric analysis software, it conducts an analysis of authoritative literature on data products both domestically and internationally up to 2023 by using the relevant research literature from China National Knowledge Infrastructure and Web of Science databases as data sources. It delves into the author institution, topic evolution and research trend within the realm of data product research. The results demonstrate that data products are multi-dimensional and complex, with their evolution significantly influenced by policy and legal frameworks. Since 2010, both domestic and international research on data products has seen rapid growth. The Chinese academia has gradually expanded from focusing on data market expansion and service system to meteorology and earth sciences, while the international research has involved the application of data products in soil moisture, UV scattering, precipitation monitoring, remote sensing technology, geoclimate and other fields. The research recommends strengthening interdisciplinary, cross-field, cross-team and industry-academia-research-application cooperation and international exchanges. With the development of new technologies such as artificial intelligence, it should pay more attention to the quality and performance evaluation of data products, data security and privacy protection, and the value co-creation and income distribution of data products. This paper is aimed to promote the application of data products in social management and business fields, and maximizing the value and potential of data products.

Object is distributed data mesh. The article reviews main types of data product consumers. There are a lot of them depending on technology stack, architecture, components, purpose, etc. At the same time, it’s critical to keep inventory of all consumers of data products in distributed data mesh platform as it allows to notify them about changes in data products, collect and keep requirements for the data product during its lifecycle. The aim is to collect list of data products consumers automatically. It allows to reduce manual effort which will decrease operational cost of data mesh support. To achieve the goal a couple of automated methods for detecting data product consumers were analyzed, in particular, API-based method, event-based method, metadata-based method. After deep investigation of mentioned methods, it was discovered that API-based and event-based methods require significant changes of data mesh platform architecture by adding new software components. At the same time, metadata-based method doesn’t have such limitations and provide acceptable results. A couple of approaches of adopting metadata-based method of data product consumers identification were proposed for different technologies and components. Ref. 7, pic. 4

With the rapid development of global navigation satellite system (GNSS), GNSS data products have been widely used for high-precision positioning and navigation applications. They are typically downloaded from the international GNSS service (IGS) analysis centers and continuously operating reference stations (CORS). However, the conventional GNSS data download method is cumbersome, repetitive, and time-consuming, and it is challenging to meet the demands for rapid acquisition of multi-source data products. Therefore, we have developed a GNSS data download software with Python, which provides an interactive interface for the Windows or Linux operating system to realize the efficient and stable download for a large amount of GNSS data. The software includes five main function modules: Global IGS Data, Post-Processing Product, Regional CORS Data, Custom Download, and Data Decompression. It has the characteristics of diverse data products, map interaction support, and station information retrieval, which can meet the needs of different users. [ABSTRACT FROM AUTHOR]

This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider's incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm's decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider's effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm's data collection strategy. In particular, the firm can boost its profit by increasing the product's capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm's profit may actually decrease (i.e., more data are not necessarily better). This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2022.4333. [ABSTRACT FROM AUTHOR]

Same as those usual product markets, data products circulating in a data market can be categorized into standard products and non-standard products.Currently, standardized data products such as music, images and video clips are effectively circulating in the market, while large-scale big data product in a broad sense is facing numerous circulation obstacles.One such obstacle is the measurement and valuation of data products, which requires a measurable standard data product form to solve.On the basis of the data box model, BoxedData was proposed, as a standard form of data products.A data product that adopts the form of BoxedData consists of two parts, which are inbox data and packing materials.Inbox data refers to the cubic data structure with three dimensions of time, space and content, which generally includes images, shapes, videos, sound, text, structured data and other types of data.Packing materials includes product registration certificate, product instructions, product quality certificate, product compliance certificate and other documents.BoxedData aims to provide data factor market with a standard data product form which is measurable and evaluable.

Developing the markets of data as a factor of production is the key in the efficient allocation of data factor.However, the early practices of data markets in China have revealed a series of problems, which urgently calls for a systematic review and analysis on the data market theoretical mechanisms.The circulation process of data products was analyzed from different perspectives, such as transaction cost theory, electronic market framework, and electronic trading mode.And it was further proposed that the effects of the data computability were two-fold.On the one hand, the computability enabled data to be analyzed so as to fit in the specific demand in certain industries.On the other hand, the computability was also likely to remove the data transaction process from the market, also known as platform disintermediation.Based on the classical theoretical framework of electronic market, the offerings of data products were divided into four quadrants and analysis was conducted correspondingly.Finally, suggestions for data product suppliers and data transaction platform providers were put forward.

For a long time, the purpose of data quality research is to fulfill requirements of the normal operation of the organization’s own information system.With the construction and development of data market, the requirements on data quality have changed from “self-use” to “other use” and “need for supervision”.The data products quality in the data market is the focus of data users (buyers) and market regulators.The demands of users and regulators for data product quality were analyzed, and a framework of data product quality was proposed innovatively.On this basis, taking BoxedData as an example, the corresponding quality dimensions, quality indicators and quality assessment models were construct from three aspects of time, space and content integrity.The quality framework was suitable for detecting and assessing resource data products, and could provide effective detection methods and standards for data product buyers and market regulators.

The long-term ecosystem observation can provide data of water, soil, meteorology and biology to support ecosystem evaluation and management.At present, there is a lack of long-term observation data product system of ecosystem in China, so no effective guidance can be provided for the production of data products.Firstly the data product system of international eco-environmental research networks was analyzed.Then a long-term observation data product system of ecosystem based on the data product classification, data product gradation two dimensions was proposed, combining the existing long-term observation protocols and data resources of ecosystem in China.It could not only express the content of long-term observation data products, but also the process of data product production and processing, and had been applied to design the long-term observation data product system of forest ecosystem of Chinese ecosystem research network (CERN).This data product system has good practical value and will play an important role in improving the value of long-term observation data.

Data markets are platforms that provide the necessary infrastructure and services to facilitate the exchange of data products between data providers and data consumers from different environments. Over the last decade, many data markets have sprung up, capitalising on the increased appreciation of the value of data and catering to different domains. In this work, we analyse the existing body of scientific literature on data markets to provide the first comprehensive overview of research into the design of data markets, regardless of scientific background or application domain. In doing so, we contribute to the field in several ways: 1) We present an overview of the state of the art in academic research on data markets and compare this with existing market trends to identify potential gaps. 2) We identify important application domains and contexts where data markets are being put into practice. 3) Finally, we provide taxonomies of both design problems for data markets and the solutions that are being investigated to address them. We conclude our work by identifying common types of data markets and corresponding best practices for designing them. The outcome of this work is intended to serve as a starting point for software architects and engineers looking to design data markets.

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

Data are invaluable. How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work. [ABSTRACT FROM AUTHOR]

Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks. [ABSTRACT FROM AUTHOR]

Abstract Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.

Same as those usual product markets, data products circulating in a data market can be categorized into standard products and non-standard products. Currently, standardized data products such as music, images and video clips are effectively circulating in the market, while large-scale big data product in a broad sense is facing numerous circulation obstacles. One such obstacle is the measurement and valuation of data products, which requires a measurable standard data product form to solve. On the basis of the data box model, Boxed Data was proposed, as a standard form of data products. A data product that adopts the form of Boxed Data consists of two parts, which are inbox data and packing materials. Inbox data refers to the cubic data structure with three dimensions of time, space and content, which generally includes images, shapes, videos, sound, text, structured data and other types of data. Packing materials includes product registration certificate, product instructions, product quality certificate, product compliance certificate and other documents. Boxed Data aims to provide data factor market with a standard data product form which is measurable and evaluable. [ABSTRACT FROM AUTHOR]

Developing the markets of data as a factor of production is the key in the efficient allocation of data factor. However, the early practices of data markets in China have revealed a series of problems, which urgently calls for a systematic review and analysis on the data market theoretical mechanisms. The circulation process of data products was analyzed from different perspectives, such as transaction cost theory, electronic market framework, and electronic trading mode. And it was further proposed that the effects of the data computability were two-fold. On the one hand, the computability enabled data to be analyzed so as to fit in the specific demand in certain industries. On the other hand, the computability was also likely to remove the data transaction process from the market, also known as platform disintermediation. Based on the classical theoretical framework of electronic market, the offerings of data products were divided into four quadrants and analysis was conducted correspondingly. Finally, suggestions for data product suppliers and data transaction platform providers were put forward. [ABSTRACT FROM AUTHOR]

For a long time, the purpose of data quality research is to fulfill requirements of the normal operation of the organization’s own information system. With the construction and development of data market, the requirements on data quality have changed from “self-use” to “other use” and “need for supervision”. The data products quality in the data market is the focus of data users (buyers) and market regulators. The demands of users and regulators for data product quality were analyzed, and a framework of data product quality was proposed innovatively. On this basis, taking BoxedData as an example, the corresponding quality dimensions, quality indicators and quality assessment models were construct from three aspects of time, space and content integrity. The quality framework was suitable for detecting and assessing resource data products, and could provide effective detection methods and standards for data product buyers and market regulators. [ABSTRACT FROM AUTHOR]

The Universal Hydrographic Data Model is a new generation of marine geographic information data model, referred to as S-100 standard. In addition to supporting Electronic Navigational Charts (ENC) production, S-100 can be used as a Geographic Information System standard to model all maritime objects. Therefore, it is necessary to carry out a comprehensive study on S-100. In this review, we begin with a succinct overview of the history and development of S-100. Then we discussed the problems of S-100 and the corresponding solutions. Finally, some research directions of S-100 are proposed to provide reference for future research and application. [ABSTRACT FROM AUTHOR]

The availability and accessibility of oceanographic data is critical to the sustainability of our oceans into the future. Ocean temperature climatology data products utilizing long time series provide context to ocean warming and allow the identification of anomalous environmental conditions. Here we describe a new methodology to create a daily subsurface temperature climatology using data from three different sources with varying spatial and temporal coverage. The Port Hacking National Reference Station off South East Australia is the site of bottle data collected typically every 1 to 4 weeks at discrete depths between 1953 and 2010, and since 2009 near-monthly vertical profiling CTD profiles and 5 min moored data at various depths. Calculating an unbiased climatology using temperature data sets obtained via different methods, with varying resolution and uncertainty, is challenging. To account for days with limited bottle data, and thus limit the bias from more recent higher temporal resolution data, a time-centered moving window of ±2 days was used to incorporate data collected on neighboring days. To account for different data sources measured on the same date, a date-averaging method was used. As moored data between 2009 and 2019 represented 70% of data for a given day of the year but approximately 1/7 of the 66 year temperature record, a novel data source ratio was implemented to avoid bias toward warmer recent years. Data were organized into their corresponding observed years, and a ratio of 6:1 between bottle and mooring observation years was enforced. To assess the methodology, the steps provided here were tested using synthetically-created temperature data with similar properties to the real observations. The lowest root mean square errors calculated between the known synthetic climatology statistics and the different solution-dependent synthetic climatology statistics confirmed the methodology. The resulting daily temperature climatology shows the seasonal cycle as a function of depth, related to changes in stratification and vertical mixing, and allows for the identification of temperature anomalies. The methodology presented in this paper is readily applicable to other sites across Australia and worldwide where long records exist consisting of multiple data sets with varying sampling characteristics.

As a network of connected sensors to transform data into knowledge, Urban Platforms have been rooted in several smart city projects. However, this has often resulted in them being no more than IoT dashboards. More recently, there has been an increased interest in supporting the data governance and distributed architecture of Urban Platforms in order to adjust these with the administrative structure in a specific city. In addition, Urban Platforms also deal with data roaming between different stakeholders including other cities, different government levels, companies and citizens. Nevertheless, the first deployments have led to an inflexible “smart cities in a box” approach that does not help with building digital skills and causes vendor lock-in to products that do not scale. There is a need to start with simple and widespread urban services through a collaborative joint cross-border, hands-on effort. In order to meet the level of interoperability, international standards should be adopted. The aim of an Urban Open Platform (UOP), introduced in this paper, is to support not only data acquisition but also various types of data processing: data is aggregated, processed, manipulated and extended within the city context. Conceptually, special attention has been put on scalability, roaming and reliability in urban environments. This article introduces the UOP uniquely in the cross-border data exchange context of two European capital cities, Helsinki and Tallinn, and validates it with 10 real-life urban use cases.

Earth observation community has entered into the era of big data. Family of Landsat sensors have collected massive medium resolution satellite images, which are valuable for long-term land surface monitoring. In order to significantly reduce the magnitude of data processing for remote sensing data users, Landsat-based Ready to Use (RTU) products have been produced. Main RTU products, including orthorectified products, land surface reflectance, land surface temperature, large-area mosaic image, and standard image map products, are described. The resulting Landsat RTU products are hosted on the RSGS earth observation data sharing web site for free download (http://ids.ceode.ac.cn/rtu/). These new products will provide consistent, standardized, multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences. In the coming years, CASEarth DataBank system will be constructed, which is an intelligent data service platform for providing not only the RTU products from multi-source satellite data, but also big earth data analysis methods.

Abstract We provide an overview of data products from observations by the Japanese Venus Climate Orbiter, Akatsuki, and describe the definition and content of each data-processing level. Levels 1 and 2 consist of non-calibrated and calibrated radiance (or brightness temperature), respectively, as well as geometry information (e.g., illumination angles). Level 3 data are global-grid data in the regular longitude–latitude coordinate system, produced from the contents of Level 2. Non-negligible errors in navigational data and instrumental alignment can result in serious errors in the geometry calculations. Such errors cause mismapping of the data and lead to inconsistencies between radiances and illumination angles, along with errors in cloud-motion vectors. Thus, we carefully correct the boresight pointing of each camera by fitting an ellipse to the observed Venusian limb to provide improved longitude–latitude maps for Level 3 products, if possible. The accuracy of the pointing correction is also estimated statistically by simulating observed limb distributions. The results show that our algorithm successfully corrects instrumental pointing and will enable a variety of studies on the Venusian atmosphere using Akatsuki data.

Instrumented moorings (hereafter referred to as moorings), which are anchored buoys or an anchored configuration of instruments suspended in the water column, are highly valued for their ability to host a variety of interchangeable oceanographic and meteorological sensors. This flexibility makes them a useful technology for meeting end user and science-driven requirements. Overall, societal needs related to human health, safety, national security, and economic prosperity in coastal areas are met through the availability of continuous data from coastal moorings and other complementary observing platforms within the Earth-observing system. These data streams strengthen the quality and accuracy of data products that inform the marine transportation industry, the tourism industry, fisheries, the military, public health officials, coastal and emergency managers, educators, and research scientists, among many others. Therefore, it is critical to sustain existing observing system networks, especially during this time of extreme environmental variability and change. Existing fiscal and operational challenges affecting the sustainability of observing networks will likely continue into the next decade, threatening the quality of downstream data and information products – especially those used for long-term monitoring, planning, and decision-making. This paper describes the utility of coastal moorings as part of an integrated coastal observing system, with an emphasis on stakeholder engagement to inform observing requirements and to ensure data products are tailored to user needs. We provide 10 recommendations for optimizing moorings networks, and thus downstream data products, to guide regional planners, and network operators: 1.Develop strategies to increase investment in coastal mooring networks2.Collect stakeholder priorities through targeted and continuous stakeholder engagements3.Include complementary systems and emerging technologies in implementation planning activities4.Expand and sustain water column ecosystem moorings in coastal locations5.Coordinate with operators and data managers across geographic scales6.Standardize and integrate data management best practices7.Provide open access to data8.Promote environmental health and operational safety stewardship and regulatory compliance9.Develop coastal mooring observing network performance metrics10.Routinely monitor and assess the design of coastal mooring networks

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

We provide an overview of data products from observations by the Japanese Venus Climate Orbiter, Akatsuki, and describe the definition and content of each data-processing level. Levels 1 and 2 consist of non-calibrated and calibrated radiance (or brightness temperature), respectively, as well as geometry information (e.g., illumination angles). Level 3 data are global-grid data in the regular longitude-latitude coordinate system, produced from the contents of Level 2. Non-negligible errors in navigational data and instrumental alignment can result in serious errors in the geometry calculations. Such errors cause mismapping of the data and lead to inconsistencies between radiances and illumination angles, along with errors in cloud-motion vectors. Thus, we carefully correct the boresight pointing of each camera by fitting an ellipse to the observed Venusian limb to provide improved longitude-latitude maps for Level 3 products, if possible. The accuracy of the pointing correction is also estimated statistically by simulating observed limb distributions. The results show that our algorithm successfully corrects instrumental pointing and will enable a variety of studies on the Venusian atmosphere using Akatsuki data. [ABSTRACT FROM AUTHOR]