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Nowadays, one of the greatest challenges in data meshes revolves around detecting and creating data domains and data products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly complicated issue and suggests a standardized approach that integrates the concept of data blueprints with data meshes. In essence, a novel standardization framework is proposed that creates data products using a metadata semantic enrichment mechanism, the latter also offering data domain readiness and alignment. The approach is demonstrated using real-world data produced by multiple sources in a poultry meat production factory. A set of functional attributes is used to qualitatively compare the proposed approach to existing data structures utilized in storage architectures, with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests a successful performance. [ABSTRACT FROM AUTHOR]

This paper addresses the research gap in the realm of data-driven transformation by leveraging the Resource-Based View (RBV) theory and the dynamic capabilities concept to the contours of a data-driven enterprise. It confronts the limitations of conventional digital and data transformation programs, which often prioritize technological enhancements over crucial organizational and cultural shifts. Proposing a more holistic perspective, the Data-Driven Enterprise Architecture Framework (DDA) is introduced, emphasizing the domain decomposition and productization of an architecture, distributed ownership, and federated governance, while ensuring the continuous harmonization of data, application, and business architecture. A case study featuring a leading pharmaceutical company illustrates the practical implementation of the DDA framework as a pillar of their Digital Transformation Strategy. By integrating scalable and distributed data architecture into the overarching Enterprise Architecture landscape, the company has initiated their data-driven transformation journey, showcased through their initial and very early results. This research not only offers valuable insights for pharmaceutical organizations navigating the complexities of data-driven transformations, but also addresses a research gap in the field. [ABSTRACT FROM AUTHOR]

Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of data meshes and data products in response to user requests, ensuring that stakeholders have access to specific portions of the data mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates the secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of data meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory. [ABSTRACT FROM AUTHOR]

Although innovation from analytics is surging in the manufacturing sector, the understanding of the data-driven innovation (DDI) process remains a challenge. Drawing on a systematic literature review, thematic analysis and qualitative interview findings, this study presents a seven-step process to understand DDI in the context of the UK manufacturing sector. The findings discuss the significance of critical seven-step in DDI, ranging from conceptualisation to commercialisation of innovative data products. The results reveal that the steps in DDI are sequential, but they are all interlinked. The proposed seven-step DDI process with solid evidence from the UK manufacturing and research implications based on dynamic capability theory, institutional theory and TOE framework establish the building blocks for future studies and industry practice. [ABSTRACT FROM AUTHOR]

Nowadays, one of the greatest challenges in data meshes revolves around detecting and creating data domains and data products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly complicated issue and suggests a standardized approach that integrates the concept of data blueprints with data meshes. In essence, a novel standardization framework is proposed that creates data products using a metadata semantic enrichment mechanism, the latter also offering data domain readiness and alignment. The approach is demonstrated using real-world data produced by multiple sources in a poultry meat production factory. A set of functional attributes is used to qualitatively compare the proposed approach to existing data structures utilized in storage architectures, with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests a successful performance.

Ocean salinity is an important variable that affects the ocean stratification. We compared the salinity and ocean stratification in the tropical Pacific derived from the Argo (Array for Real-time Geostrophic Oceanography data), EN4 (Ensemble 4 analysis), SODA (the Simple Ocean Data Assimilation reanalysis), IAP (Institute of Atmospheric Physics data), and ORAS4 (Ocean Reanalysis System 4) over 2005–2017. Results show that the spatial distribution of climatological mean of sea surface salinity (SSS) in all the products is consistent, and the low salinity region showed large deviation and strong dispersion. The Argo has the smallest RMSE and the highest correlation with the ensemble mean, while the IAP shows a high-salinity deviations relative to other datasets. All the products show high positive correlations between the sea surface density (SSD) and SSS with respect to the deviations of climatological mean from ensemble mean, suggesting that the SSD deviation may be mainly influenced by the SSS deviation. In the aspect of the ocean stratification, the mixed layer depth (MLD) climatological mean in the Argo shows the highest correlation with the ensemble mean, followed by EN4, IAP, ORAS4, and SODA. The Argo and EN4 show thicker barrier layer (BL) relative to the ensemble mean while the SODA displays the largest negative deviation in the tropical western Pacific. Furthermore, the EN4, ORAS4, and IAP underestimate the stability in the upper ocean at the depths of 20–140 m, while Argo overestimates ocean stability. The salinity fronts in the western-central equatorial Pacific from Argo, EN4, and ORAS4 are consistent, while those from SODA and IAP show large deviations with a westward position in amplitude of 0°–6° and 0°–10°, respectively. The SSS trend patterns from all the products are consistent in having ensemble mean with high spatial correlations of 0.95–0.97. [ABSTRACT FROM AUTHOR]

The Rover-mounted Subsurface Penetrating Radar (RoSPR) is one of the scientific payloads onboard China's first independent Mars exploration mission, Tianwen-1. The radar aims to characterize the thickness of the upper Martian soil and investigate the subsurface stratigraphy by collecting and processing the data. This article is mainly divided into two parts, the introduction of data pre-processing and analysis of pre-processed radar signals, aiming at helping scientists make more effective use of radar data. The first part describes the operating principle of the RoSPR and the procedure of radar data pre-processing at all levels. Data pre-processing is mainly designed to transfer the raw data format to a common PDS (Planetary Data System) and eliminate the influence of the instrument. In the signal analysis part, the performances of both self-check signals and echo signals of low- and high-frequency channels are analyzed, which indicate a stable radar system and are useful for background removal. Phase and time calibration is of great importance for improving data quality and making the radar data more accurate. Moreover, further processing is required to obtain clear radar images, such as filtering, background removal and gain setting. [ABSTRACT FROM AUTHOR]

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions. [ABSTRACT FROM AUTHOR]

Despite the fact that the Great Lakes contain roughly 20% of the world's surface freshwater, there is a relatively limited body of recent work in peer reviewed literature that addresses recent trends in lake levels. This work is largely coming from a handful of authors who are most well-versed in the complexities of monitoring and modeling in a basin that spans an international border and contains vast areas of surface water connected by both natural and managed connecting channel flows. At the same time, the recent dramatic changes from record low water levels in the early 2010's to record high water levels across the Great Lakes in 2019 and 2020 have brought significant attention to the hydroclimatic conditions in the basin, underscoring the need to bring new approaches and diverse perspectives (including from outside the basin) to address hydroclimate research challenges in the Great Lakes. Significant effort has led to advancements in data and model coordination among U.S. and Canadian federal agencies throughout the decades, and at the same time research from the broader community has led to higher resolution gridded data products. In this paper, we aim to present the current state of data and models for use in hydrological simulation with the objective of providing a guide to navigating the waters of Great Lakes hydroclimate data. We focus on data for use in modeling water levels, but we expect the information to be more broadly applicable to other hydroclimate research. We approach this by including perspectives from both the Great Lakes water management community and the broader earth science community.

There has been a huge increase in the use of digital technology throughout healthcare in recent years, with everything from apps to wearable tech. The mental health and wellbeing sector has been no exception. There are a wide variety of digital mental health apps available directly from app stores, making therapeutic techniques accessible for every smartphone user. The COVID-19 pandemic and ensuing quarantines and lockdowns, followed by the current economic recession, have redefined the tech world's agenda. There has been an increased emphasis on mental wellbeing. Many of the well-known tech companies, whose core business is not even related to healthcare (such as Facebook, Telefonica, or Google) have invested in mental wellbeing, either through “moonshots” or by introducing new product segments. For their critics, this is a “do-good” gesture intended to detract attention from their data extraction processes. This leads us to question, what is it that these companies want to recommend to people through the use of mental wellbeing tech? What is the new set of values that they are promoting? In this article we critically analyse digital mental health products. We discuss how they might become a political tool, speculate on their side effects, and investigate outcomes of their increasing popularity. We want to move beyond the personal data privacy debate and tackle other potential issues – what does this data sharing mean in terms of a shift in collective psychology and ideologies? What is the potential for them to become political tools? Is this a step towards human and non-human convergence?

The Tianwen-1 mission is China's first Mars exploration mission. To complete the scientific objectives of "orbiting, landing, and patrolling" in one mission, thirteen instruments for acquiring multi-disciplinary data are configured. The data products with rich ancillary data and flexible structures will facilitate the data application. To integrate the requirements of experts from disparate disciplines and leverage new information technologies, a complete redesign of data products is promoted in the Tianwen-1 mission. We discuss the whole process of data product generation from the data pipeline design to data validation. We design the various data products according to the application requirements of each data type, including radar echo data, spectrum data, image data, energy spectrum data, magnetic field data, meteorological data, and acoustic data. Since various error sources will exist in the generation chain of the data products, data validation is essential before release to the public; the validation activities are discussed at the end. We deliver these scientific products to the community in a timely manner, with ancillary information and quality information. This paper can provide practical reference for Tianwen-1 data application. [ABSTRACT FROM AUTHOR]

Abstract The Gao Fen 7 (GF‐7) satellite is China's first submeter, high‐resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF‐7 laser altimeter system is the successor of the ZiYuan3‐02 (ZY3‐02 satellite) laser altimeter. The key objective of the GF‐7 laser altimetry system is to provide high‐precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF‐7 laser altimeter system payload specifications, mission objectives, in‐orbit calibration plan, data products, and service policy. In the future, the GF‐7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection.

New coastal and ocean observing stations and instruments deployed across the globe are providing increasing amounts of meteorological, biological, and oceanographic data. While these developments are essential for the development of various data products to inform decision-making among coastal communities, more data does not automatically translate into more benefits to society. Rather, decision-makers and other potential end-users must be included in an ongoing stakeholder-driven process to determine what information to collect and how to best streamline access to information. We present a three-step approach to develop effective tailored data products: (1) tailor stakeholder engagement to identify specific user needs; (2) design and refine data products to meet specific requirements and styles of interaction; and (3) iterate engagement with users to ensure data products remain relevant. Any of the three steps could be implemented alone or with more emphasis than others, but in order to successfully address stakeholders’ needs, they should be viewed as a continuum—as steps in a process to arrive at effective translation of coastal and ocean data to those who need it. Examples from the Regional Associations of the U.S. Integrated Ocean Observing System (IOOS®), the Texas General Land Office, and the Vanuatu Meteorology and Geo-hazards Department (VMGD) are woven throughout the discussion. These vignettes illustrate the value of this stakeholder-driven approach and provide a sample of the breadth of flexibility and customizability it affords. We hope this community white paper inspires others to evaluate how they connect their stakeholders to coastal and ocean observing data and provides managers of observing systems with a guide on how to evolve in a manner that addresses societal needs.

SCATSAT-1 carries a Ku-band scatterometer with a scanning pencil beam configuration. It deploys two beams, a vertically polarized outer beam and a horizontally polarized inner beam, to cover a swath of 1800 km. The mission mainly caters to oceanographic applications and weather forecasting, with the data being extensively used for cyclogenesis predictions across the globe and specifically, the tropical region. Since the launch of SCATSAT-1 in September 2016, the satellite and payload performances as well as mission and ground segment operations have been found to be nominal and satisfactory. This article highlights various levels of operational data products as well as algorithms used for deriving radar backscatter and retrieving wind vector data from scatterometer measurements. [ABSTRACT FROM AUTHOR]

COST (European Cooperation in Science and Technology) is a funding organisation for research and innovation networks. One of the objectives of the COSTAction called "Mobilising Data, Policies and Experts in Scientific Collections" (MOBILISE) is to work on documents for expert training with broad involvement of professionals from the participating European countries. The guideline presented here in its general concept will address principles, strategies and standards for long term preservation and archiving of data constructs (data packages, data products) as addressed by and under control of the scientific collections community. The document is being developed as part of the MOBILISE Action targeted towards primarily scientific staff at natural scientific collection facilities, as well as management bodies of collections like museums, herbaria and information technology personnel less familiar with data archiving principles and routines. The challenges of big data storage and (distributed, cloud-based) storage solutions as well as that of data mirroring, backing up, synchronisation and publication in productive data environments are well addressed by documents, guidelines and online platforms, e.g., in the DISSCo knowledge base (see Hardisty et al. (2020)) and as part of concepts of the European Open Science Cloud (EOSC). Archival processes and the resulting data constructs, however, are often left outside of the considerations. This is a large gap because archival issues are not only simple technical ones as addressed by the term "bit preservation" but also envisage a number of logical, functional, normative, administrative and semantic issues as addressed by the term "functional long-term archiving". The main target digital object types addressed by this COST MOBILISE Guideline are data constructs called Digital or Digital Extended Specimens and data products with the persistent identifier assignment lying under the authority of scientific collections facilities. Such digital objects are specified according to the Digital Object Architecture (DOA, see Wittenburg et al. 2018) and similar abstract models introduced by Harjes et al. (2020) and Lannom et al. (2020). The scientific collection-specific types are defined following evolving concepts in the context of the Consortium of European Taxonomic Facilities (CETAF), the research infrastructure DiSSCo (Distributed System of Scientific Collections), and the Biodiversity Information Standards (TDWG). Archival processes are described following the OAIS (Open Archival Information System) reference model. The archived objects should be reusable in the sense of the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles. Organisations like national (digital) archives, computing or professional (domain-specific) data centers as well as libraries might offer specific archiving services and act as partner organisations of scientific collections facilities. The guideline consists of key messages that have been defined. They address the collection community, especially the staff and leadership of taxonomic facilities. Aspects of several groups of stakeholders are discussed as well as cost models. The guideline does not recommend specific solutions for archiving software and workflows. Supplementary information is delivered via a wiki-based platform for the COST MOBILISE Archiving Working Group WG4. [ABSTRACT FROM AUTHOR]

Abstract Essential Biodiversity Variables (EBV) are fundamental variables that can be used for assessing biodiversity change over time, for determining adherence to biodiversity policy, for monitoring progress towards sustainable development goals, and for tracking biodiversity responses to disturbances and management interventions. Data from observations or models that provide measured or estimated EBV values, which we refer to as EBV data products, can help to capture the above processes and trends and can serve as a coherent framework for documenting trends in biodiversity. Using primary biodiversity records and other raw data as sources to produce EBV data products depends on cooperation and interoperability among multiple stakeholders, including those collecting and mobilising data for EBVs and those producing, publishing and preserving EBV data products. Here, we encapsulate ten principles for the current best practice in EBV-focused biodiversity informatics as 'The Bari Manifesto', serving as implementation guidelines for data and research infrastructure providers to support the emerging EBV operational framework based on trans-national and cross-infrastructure scientific workflows. The principles provide guidance on how to contribute towards the production of EBV data products that are globally oriented, while remaining appropriate to the producer's own mission, vision and goals. These ten principles cover: data management planning; data structure; metadata; services; data quality; workflows; provenance; ontologies/vocabularies; data preservation; and accessibility. For each principle, desired outcomes and goals have been formulated. Some specific actions related to fulfilling the Bari Manifesto principles are highlighted in the context of each of four groups of organizations contributing to enabling data interoperability - data standards bodies, research data infrastructures, the pertinent research communities, and funders. The Bari Manifesto provides a roadmap enabling support for routine generation of EBV data products, and increases the likelihood of success for a global EBV framework. Highlights • Data products for Essential Biodiversity Variables (EBV) positively improve human ability to address biodiversity challenges • Primary observation data must be processed in a global, trans-national information technology framework • Many technical, social and legal factors influence data interoperability and interoperability across infrastructures • The 'Bari Manifesto' offers ten interoperability principles guiding production and management of EBV data products • Standards bodies, infrastructures, researchers and funders can take practical steps to improve support for EBV data products [ABSTRACT FROM AUTHOR]

Abstract Scientific research as well as management of the marine environment, and sustainable blue growth are based on the availability of quality-assured observations, reliable data and solid scientific-based information. These represent three consecutive steps of Data-Information-Knowledge-Wisdom paradigm on the pyramid of wisdom, providing different layers of information. EMODnet (European Marine Observation and Data network) is one of the key infrastructures engaged in collecting, facilitating access and promoting use and re-use of marine observation and data products for both scientific research and marine environmental management. Its Spatial Data Infrastructure (SDI) represents a powerful mechanism to support the implementation of the Marine Strategy Framework Directive Article 19.3 in accordance with the INSPIRE Directive standards and implementing rules. Standardized, harmonized and validated chemical data collections are made available for water quality evaluation at a regional scale, establishing interoperability between the data sets from the many different providers (more than 60 in EMODnet Chemistry). Concentration maps of nutrients, chlorophyll-a and dissolved oxygen are computed on a standard grid, providing information at a regular time interval, per season and over several vertical layers, including the deepest one. Dedicated Open Geospatial Consortium standard services for browsing, viewing and downloading chemistry observation data and data products for the European waters have been developed, and are actively maintained and monitored. These results can provide knowledge layers and can also answer the needs of the directive on Maritime Spatial Planning (EU, 2014), which requires the integration of multidisciplinary data and information on the state of the marine environment with maritime and human activities. Highlights • Results achieved by the EMODnet Chemistry and the viewing services developed to download and share them. • Observations and information are discussed, in relation to MSFD, INSPIRE Directive standards and Maritime Spatial Planning. • Data availability is an important component of the MSP process for environmental management of protected areas. • In the framework of EMODnet Chemistry, 160 maps were realized, covering 11 parameters, 4 seasons and aggregated for 30 depth. [ABSTRACT FROM AUTHOR]

The current policy emphasis on data-driven decision-making is creating the right incentives for government agencies around the world that have not traditionally disseminated their administrative data to do so. The literature on statistical disclosure control focuses on the technical aspects of a variety of methods designed to protect data confidentiality. There is, however, a void in the literature in regard to what other elements are necessary to create and sustain a successful initiative. This paper examines six case studies of individual-level datasets. It reviews current practice in several domains and summarizes recommendations from expert practitioners including challenges for future initiatives. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) methods provide high-resolution maps of surface deformation applicable to many scientific, engineering, and management studies. Despite its utility, the specialized skills and computer resources required for InSAR analysis remain as barriers for truly widespread use of the technique. Reduction of radar scenes to maps of temporal deformation evolution requires not only detailed metadata describing the exact radar and surface acquisition geometries, but also a software package that can combine these for the specific scenes of interest. Furthermore, the range-Doppler reference frame and radar coordinate system itself are confusing, so that many users find it hard to incorporate even useful products in their customary analyses. Finally, the sheer data volume needed for interferogram time series makes InSAR analysis challenging for many analysis systems. We show here that it is possible to deliver radar data products to users that address all of these difficulties, so that the data acquired by large, modern satellite systems are ready to use in more natural coordinates, without requiring further processing, and in as small volume as possible. [ABSTRACT FROM PUBLISHER]

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment. [ABSTRACT FROM AUTHOR]