Content Metadata Research Articles

Blue-cloud DAB: developing a platform to harmonize, assess and disseminate marine metadata collections

AbstractThe integration and harmonization of marine data from diverse sources are vital for advancing global oceanographic research and ensuring seamless discovery and access of critical datasets. This paper presents a comprehensive analysis of the metadata harmonization efforts within the Blue-cloud 2026 project, which brokers data from numerous Blue Data Infrastructures (BDIs), leveraging the Discovery and Access Broker technology. The platform enables discovery and analysis of marine data collections while facilitating interoperability with other components of the marine digital ecosystem, such as virtual laboratories and the Semantic Analyzer. It also supports the flow of Blue-cloud information to other initiatives like the Global Earth Observations System of Systems. For data managers, the findings emphasize the importance of enhancing metadata quality, revealing discrepancies in core metadata elements, and the need for more consistent use of controlled vocabularies. For cyberinfrastructure developers, the study details the challenges of accommodating a wide array of interfaces from different data systems, highlighting the adoption of an extensible brokering architecture that harmonizes metadata models and protocols. The study also emphasizes the importance of metadata analysis in ensuring effective searches for end users, highlighting challenges in aggregating diverse sources, where data providers may have structured the content with different objectives compared to those of the system of systems. End users will gain insights into the current metadata content of Blue-cloud, enabling them to search and access data from multiple BDIs with an understanding of the technical complexities behind the scenes.

《数据论文出版元数据》国家标准研制与实践

In August 2023, the national standard for GB/T 42813-2023 Data paper publication metadata was officially released. This standard specifies the content and framework of metadata required for data paper publication, including dataset metadata, data paper metadata, as well as their description methods, extension principles, and methods. It is applicable to the description, preservation, online publishing, and sharing services associated with data papers and their corresponding datasets. The release of this standard marks the first comprehensive agreement at the national level regarding the new mode of data paper publishing, namely the open sharing of scientific data, which has addressed the prior absence of standardized guidance in the field of data publishing. It facilitates data publishing institutions in promptly releasing data papers and their associated data, providing support for the rights and protection of scientific data. The standard holds significant importance in promoting the establishment of an open and shared ecosystem for scientific data. Since its publication, certain data journals or scientific data publishing platforms have begun adopting the elements and definitions specified in the standard, thus engaging in a more extensive practice.

What do you do or with whom? Understanding happiness with the tourism experience: an AI approach applied to Instagram

More and more tourists are sharing their experiences on their social media through a combination of photos, texts, and hashtags. But there is a scarcity of studies in literature on analyzing tourists’ visual content in relation to tourism destinations. To address this gap in literature, this study explores how and with whom users express the greatest happiness in holiday destinations, and how they share it with their community, through a mixed methods approach composed of analysis of images, text, and metadata. This approach allows us to address the objective of this research, which is the prediction of the happiness felt by tourists during their experience, using innovative techniques that allow the independent variables to be obtained. To predict tourist satisfaction, two sources of data, photos and texts, are analyzed: a novel approach and little explored in the literature, but necessary due to the interaction of both variables. This study applies various artificial intelligence analyses on visual content (deep learning), and textual and metadata content (machine learning) to 39,235 Instagram posts shared by tourists since the emergence of Instagram thirteen years ago, at a cultural and gastronomic tourist destination. The findings of the visual content analysis showed that socialization and company, that is, traveling and interacting with people, was a key aspect of a positive tourism experience. In addition, the gender of the people with whom they traveled, and the tourist’s narcissism were also key aspects in the generation and expression of positive emotions. Regarding the analysis of the textual content, the results showed that when tourists enjoyed a positive experience, they became more involved in the generation of content, that is, they showed their happiness through positive words.

Impact Of Emotional Factor Segmentation Generated By AI On Digital Marketing Platform

This paper explores using AI to perceive emotional motivators, the underlying factors that drive human emotions. The paper discusses the capacity of AI to revolutionize the way we interact with machines, mentioning examples of its use in Netflix's recommendation gadget, Spotify's personalized playlists, and customer support chatbots. It also examines AI techniques used for segmentation, including natural language processing (NLP), facial recognition and picture analysis, voice analytics, behavioral pattern evaluation, and social media sentiment evaluation. Finally, the paper discusses the forms of information used to pick out emotional motivators, together with user behavior facts, consumer demographics facts, person comments information, content metadata, and social media records.

Immersive Technologies for Digital Libraries

The purpose of the study is to analyse modern immersive technologies and the possibility of their application in the development of digital libraries in the context of the introduction of virtual, augmented and mixed reality technologies, as well as the use of artificial intelligence technologies. Research methods. To achieve the research goal, the methods of analysis and synthesis, generalisation of theoretical data, and a systematic approach were used. This made it possible to analyse the peculiarities of the use of immersive technologies in digital libraries and to determine the benefits of introducing such technologies in the creation of a modern information space of libraries. The scientific novelty lies in the analysis of immersive technologies in the context of their implementation in modern digital libraries. An analysis of the role of artificial intelligence, and augmented and virtual reality technologies in creating a modern information space in libraries has shown both the advantages and disadvantages of digitalisation in libraries. The ethical issues related to inclusiveness in the use of information resources and the protection and preservation of users’ data are analysed. Recommendations and approaches for the successful implementation of these technologies in digital libraries are proposed. Conclusions. Immersive technologies are becoming more widespread and their use in libraries is expanding. Ethical and legal issues in the use of virtual reality and augmented reality will inevitably arise in the future. Today, the Western world is making greater use of both technologies, although they are free and, in some cases less expensive than before. The article examines the transformative intersection of new technologies and digital libraries, highlighting the path to an enriched and accessible knowledge landscape. Focusing on artificial intelligence (AI), machine learning (ML), natural language processing (NLP), augmented reality (AR) and virtual reality (VR), it explores how these technologies are redefining the work of digital libraries. Artificial intelligence and machine learning algorithms enable intuitive content management and recommendations, changing the way users interact with digital resources. NLP bridges the gap between human language and digital systems by enhancing search functions. AR overlays digital information on the physical world, expanding the possibilities of interactive learning, while VR immerses users in virtual realms, revolutionising educational paradigms. This article discusses the integration of these technologies into digital libraries not only to preserve huge amounts of knowledge but also to present information in attractive and accessible formats. Through the creation of artificial intelligence-based metadata and tagging of content, digital libraries are systematically organized and enriched, improving search accuracy. These innovations not only preserve the past, but also illuminate a future where knowledge is publicly available, fostering curiosity, learning, and research. This article explores the potential of these technologies and describes the expectations of library users to ensure a user-centred approach in shaping the digital libraries of the future. The use of immersive technologies is making a significant contribution to the evolution of digital libraries, paving the way for inclusive and engaging knowledge experiences for diverse users around the world.

Factoring textual reviews into user preferences in multi-criteria based content boosted hybrid filtering (MCCBHF) recommendation system

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users’ individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users’ satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user’s interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user’s preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms.

Deep Neural Networks-based Classification Methodologies of Speech, Audio and Music, and its Integration for Audio Metadata Tagging

Videos contain visual and auditory information. Visual information in a video can include images of people, objects, and the landscape, whereas auditory information includes voices, sound effects, background music, and the soundscape. The audio content can provide detailed information on the story by conducting a voice and atmosphere analysis of the sound effects and soundscape. Metadata tags represent the results of a media analysis as text. The tags can classify video content on social networking services, like YouTube. This paper presents the methodologies of speech, audio, and music processing. Also, we propose integrating these audio tagging methods and applying them in an audio metadata generation system for video storytelling. The proposed system automatically creates metadata tags based on speech, sound effects, and background music information from the audio input. The proposed system comprises five subsystems: (1) automatic speech recognition, which generates text from the linguistic sounds in the audio, (2) audio event classification for the type of sound effect, (3) audio scene classification for the type of place from the soundscape, (4) music detection for the background music, and (5) keyword extraction from the automatic speech recognition results. First, the audio signal is converted into a suitable form, which is subsequently combined from each subsystem to create metadata for the audio content. We evaluated the proposed system using video logs (vlogs) on YouTube. The proposed system exhibits a similar accuracy to handcrafted metadata for the audio content, and for a total of 104 YouTube vlogs, achieves an accuracy of 65.83%.

FAIR Data Point: A FAIR-Oriented Approach for Metadata Publication

ABSTRACTMetadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles. We present the core components and features of the FDP, its approach to metadata provision, the criteria to evaluate whether an application adheres to the FDP specifications and the service to register, index and allow users to search for metadata content of available FDPs.

Temporal pattern mining from user-generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available; the link is given in the dataset section.

Linked Metadata for FAIR Digital Objects Carrying Computable Knowledge

Linked Metadata for FAIR Digital Objects Carrying Computable Knowledge

“Might relapse today” - The categorization of discussions in the r/benzorecovery subreddit

IntroductionThe social media platform Reddit is a contemporary context where we have an opportunity to identify problems experienced by people regarding different aspects of life. The platform is virtually anonymous which might make users discuss their problems more freely. Reddit is divided in subreddits where different subjects are discussed and the discussions are controlled by creators and moderators. I have identified a quite active subreddit targeted towards recovering addicts of benzodiazepines; r/benzorecovery.Objectives* To analyze strategies of recovery in user narrative * To identify techniques commonly used and the how they are described * To construct metadata in order to assess how frequent the discussion of a different techniques areMethodsTechnically, what is done in this study, is adding mark-up metadata to different discussion. A rudimentary form of analysis suitable with a larger digital corpus where content metadata is added (Gilliland Swetland 2000). The metadata is constructed through a hermeneutical method in which the researcher analyses the subreddit.ResultsAnswering question like: Example: DIY-tapering; different ways to limit drug use by using less. 1) how common are discussion of taperings in relation to other subjects? 2) Is tapering commonly discussed together with other subjects and techniques?ConclusionsUsing a method of categorization and metadata mark-up we could gain a good understanding of the problems among recovering benzodiazepine addicts. We will also have the possibility to identify concepts that addicts themselves discuss and relate these to professional concepts thus creating better possibilities of communication between professionals and clients.DisclosureNo significant relationships.

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach.

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view.

Biomedical Scholarly Article Editing and Sharing using Holistic Semantic Uplifting Approach

Efficient practices to provide access to biomedical publications facilitate the timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. At present, the portable document format (PDF) is one of the dominating formats to share scientific knowledge offline. Additionally, some HTML-based formats have been introduced to share scientific content online. Online Search engines, e.g., GoogleScholar, require machine-interpretable metadata to correctly index items in a context-aware manner for accurate biomedical literature searches. We have developed a lightweight technical infrastructure (goSemantically) and miniaturized that as Google Docs add-ons that helps authors to add machine-interpretable metadata at the content and structural levels while authoring biomedical content. The infrastructure uses the NCBO Bioportal resources to annotate the biomedical content with appropriate semantic vocabularies. It further utilizes the Schema.org meta tags and provides an intuitive interface for users to associate the semantics tags at the document level. Additionally, our infrastructure supports users in exporting their content in various online interoperable formats preserving the embedded semantics. As a result, the biomedical metadata content would easily be indexed by search engines, making them more favorable for semantic intelligence searches.

9. Adherence to Public Health Recommendations in TikTok Content During the Early Days of the SARS-CoV-2 Pandemic

At the beginning of the SARS-CoV-2 pandemic, the Centers for Disease Control and Prevention (CDC) recommended widespread shutdowns of schools, businesses, and other activities. Internet use increased dramatically, with the short-form video social media platform TikTok gaining popularity among adolescents and young adults (AYA). Displays of health behaviors on social media have been shown to impact actual health behaviors in AYA, yet the scope of prevention behaviors related to the current global pandemic is unprecedented. Therefore, the objective of this study was to evaluate adherence to CDC infection prevention guidelines and information about COVID-19 as represented by TikTok content creators, or influencers, at the beginning of the pandemic in the United States. The study sample included content posted by the 150 most-followed influencers on TikTok between March 1, 2020 and April 27, 2020. Study team members identified and extracted all posts within the specified dates that referenced COVID-19 and/or pandemic precautions (e.g., social distancing, school closures). A codebook was developed based on the CDC Guidelines for COVID-19 prevention as of March 1, 2020 and included displays of behavior that either followed (e.g., staying home, washing hands) or did not follow (e.g., wearing a mask incorrectly) the guidelines at the time. Posts were also coded for the presence or absence of TikTok’s “learn the facts about COVID-19” banner, which had been implemented by the platform to combat misinformation. Rounds of 20-40 posts were used to train coders against a master coder, and coders began reviewing posts independently once agreement was over 80%. We performed descriptive statistics on content metadata and code frequency. Of 11,063 posts made during the study dates, 1,305 (11.7%) posts were COVID-related, with an average of 9.2 COVID-related posts per influencer. Each influencer’s combined COVID-related content had an average of 44.4 million views and over 8 million likes. Most (81.5%, n=1,064) posts contained behaviors that could be coded for CDC guidelines. Of these posts, 79.4% solely demonstrated behavior following CDC guidelines, with the most frequent being staying at home (n=805) and handwashing (n=50). 7.1% of posts solely demonstrated behavior that did not follow CDC guidelines, with the most frequent being not wearing face cover in public (n=41) or having contact with others outside the household (n=33). Posts that contained a combination of behaviors that followed or did not follow the guidelines comprised 10.3% of the sample. The COVID facts banner was only present in 3% of COVID-related posts. At the beginning of the pandemic, AYA were felt to be at low risk for severe COVID-19 disease, but were encouraged to follow infection control measures to protect at-risk populations like the elderly. We found that the most popular US TikTok influencers created COVID-related content that reached millions of users on a platform mainly used by AYA. This content usually demonstrated adherence to public health guidance at the time, suggesting positive implications for future health messaging on social media platforms.

The PCDDB (Protein Circular Dichroism Data Bank): A Bioinformatics Resource for Protein Characterisations and Methods Development

The Protein Circular Dichroism Data Bank (PCDDB) [https://pcddb.cryst.bbk.ac.uk] is an established resource for the biological, biophysical, chemical, bioinformatics, and molecular biology communities. It is a freely-accessible repository of validated protein circular dichroism (CD) spectra and associated sample and metadata, with entries having links to other bioinformatics resources including, amongst others, structure (PDB), AlphaFold, and sequence (UniProt) databases, as well as to published papers which produced the data and cite the database entries. It includes primary (unprocessed) and final (processed) spectral data, which are available in both text and pictorial formats, as well as detailed sample and validation information produced for each of the entries. Recently the metadata content associated with each of the entries, as well as the number and structural breadth of the protein components included, have been expanded. The PCDDB includes data on both wild-type and mutant proteins, and because CD studies primarily examine proteins in solution, it also contains examples of the effects of different environments on their structures, plus thermal unfolding/folding series. Methods for both sequence and spectral comparisons are included.The data included in the PCDDB complement results from crystal, cryo-electron microscopy, NMR spectroscopy, bioinformatics characterisations and classifications, and other structural information available for the proteins via links to other databases. The entries in the PCDDB have been used for the development of new analytical methodologies, for interpreting spectral and other biophysical data, and for providing insight into structures and functions of individual soluble and membrane proteins and protein complexes.

New Approaches Towards the Delivery of Service Information Using Semantic Correlation Rules

Knowledge about products and their applications, as well as how to use them, is often distributed in different places within companies. In the past, this has been an internally and externally accepted situation leading to time-consuming searches for required data. Nowadays, expectations of in-time delivery of information and the general view of knowledge processes as well as on associated data are changing radically. Traditional methods and approaches for storing, distributing and accessing information are therefore in many cases inappropriate and are questioned on industry-side. For example, in the domain of customer service of B2B products in machinery industry, it was common to communicate by phone with experts and help-desk-organizations, who provided the required information by experience or by informal information exchange networks. Under the pressure of digitalization and the constant increase in efficiency, as well as the demand of customers to obtain requested information more quickly, new ways of providing and connecting information are becoming increasingly relevant. It is still unrealistic to assume that all relevant information can be prepared specific to each target group and to each application within a short period of time. Rather, the challenge is to create and store information in the future in a structured way with meaningful semantic and consistent metadata and, on the other hand, to relate this new information to existing information in such a way that the user can be helped quickly and easily. Several preconditions are crucial to build up a new information infrastructure: First, management has to be convinced of the benefits of storage and intelligent search environments and to get support from relevant stakeholders. Subsequently, new information architectures require smart approaches for content authoring and metadata enrichment supporting information delivery via different media channels for relevant customer groups. Time and effort for setting up this new architectures has to be argued and justified..

Automation of quality control of digital topographic maps at the scale 1:50 000 of the Main State Topographic Map in Ukraine

Abstract. This research examines the problem of automation of quality control of digital topographic maps at the scale 1:50 000 of the Main State Topographic Map in Ukraine for the creation and maintaining the seamless topographic database for national needs, which is located on the Geoportal to ensure the development of the National Spatial Data Infrastructure (NSDI) in Ukraine. The authors determined stages of quality control of digital topographic maps at the scale of 1:50 000 for the automation one according to ISO 19157:2013 Geographic Information – Data quality. The realized automated quality control of geodatabases of updated digital topographic maps at a scale of 1:50 000 provides high speed and quality of validation: validation of quality data elements; validation of quality metadata; validation of positional accuracy. The software package “Validate” was developed for automation quality control geodatabases of digital topographic maps at the scale of 1:50 000 using the Python programming language to verify logical consistency, compliance with the rules of topological relationships between features on the map, availability and content of metadata. The “Validate” can be used for automation quality control geodatabase for any map scale, performing setting according to the requirements of the geospatial models.

BioSamples database: FAIRer samples metadata to accelerate research data management.

The BioSamples database at EMBL-EBI is the central institutional repository for sample metadata storage and connection to EMBL-EBI archives and other resources. The technical improvements to our infrastructure described in our last update have enabled us to scale and accommodate an increasing number of communities, resulting in a higher number of submissions and more heterogeneous data. The BioSamples database now has a valuable set of features and processes to improve data quality in BioSamples, and in particular enriching metadata content and following FAIR principles. In this manuscript, we describe how BioSamples in 2021 handles requirements from our community of users through exemplar use cases: increased findability of samples and improved data management practices support the goals of the ReSOLUTE project, how the plant community benefits from being able to link genotypic to phenotypic information, and we highlight how cumulatively those improvements contribute to more complex multi-omics data integration supporting COVID-19 research. Finally, we present underlying technical features used as pillars throughout those use cases and how they are reused for expanded engagement with communities such as FAIRplus and the Global Alliance for Genomics and Health. Availability: The BioSamples database is freely available at http://www.ebi.ac.uk/biosamples. Content is distributed under the EMBL-EBI Terms of Use available at https://www.ebi.ac.uk/about/terms-of-use. The BioSamples code is available at https://github.com/EBIBioSamples/biosamples-v4 and distributed under the Apache 2.0 license.

Governance and stewardship for research data and information sharing: Issues and prospective solutions in the transdisciplinary plant phenotyping and imaging research center network

Societal Impact StatementHigh‐throughput plant phenotyping is a transdisciplinary field of research that provides a systematic approach to assessing and understanding the life cycle of plants and aims to integrate plant genotype with plant ecophysiology and agronomy. Sharing data and information (D&I) is key to accelerating new scientific breakthroughs and innovations in plant phenotyping. The development of a governance and stewardship framework can overcome institutional, legal, and technical barriers that limit D&I sharing. This framework governs how decisions are made about D&I, how researchers engage with each other to manage D&I, and improves the content, discoverability, accessibility, and usability of D&I from different sources.Summary Despite the widely acknowledged value to be added by sharing research data and information (D&I), significant institutional, legal, and technical barriers remain to be addressed, particularly in managing transdisciplinary research. The Plant Phenotyping and Imaging Research Centre (P2IRC) offers an illuminating case study in how various researchers are exchanging their D&I to accommodate the requirements of transdisciplinary research. Using social network analysis to explore the current state of D&I sharing in P2IRC, we found that D&I sharing was modest, with most exchanges taking place between academic researchers and predominantly within the same discipline. Lacking benefits arising from broader dissemination, scholars are not optimally sharing their D&I. Many researchers identified a range of barriers that limited sharing. These barriers are mainly rooted in the absence of D&I governance and stewardship. To overcome barriers, we offer recommendations and solutions for developing a governance and stewardship framework that is tailored to the P2IRC project, but relevant to the wider research world. This includes how to assist adoption of a governance model, accountability and oversight mechanisms, and an operational framework for effective data collection, analysis, possession, and dissemination. We provide recommendations that enable data stewards to preserve and improve the content, discoverability accessibility, and usability of data and metadata by making data interoperable from semantic, syntactic/technical, and legal perspectives. A governance framework would help open all aspects of research outputs, while achieving the access and benefit‐sharing (ABS) goal and contributing to the preservation and sustainable use of D&I.

On the Calculation and Usage of HDR Static Content Metadata

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HDR10 is a common platform-agnostic video format that is widely used for ultrahigh-definition (UHD-1)/4K content distribution. HDR10 includes 10-bit video data formatted for the perceptual quantization (PQ) electro-optical transfer function (EOTF) using color encoding primaries corresponding to International Telecommunication Union - Radiocommunication (ITU-R) BT.2020 (and BT.2100) and includes optional “static metadata.” HDR10 static metadata typically comprises two different types of metadata, mastering display metadata and content metadata. This paper discusses the meaningful calculation and usage of static content metadata for the display and processing of HDR10 content. Outlier values in the high-dynamic-range (HDR) pixel values can exist in content being mastered today and a simple statistical approach is presented to reduce the outlier’s influence on the static content metadata values. Additionally, this paper describes how a display or process can utilize good and reliable maximum content light level (MaxCLL) values to improve the reproduction of creative intent</i> .