Consciousness is based on the association of notions or a neural network. Similarly, the creation of the next generation Internet (semantic web) is impossible without attributes that allow the semantic association of documents and their integration into an information context. To achieve these goals, the Universal Metadata Standard (UMS) may serve as a basis for documentography and is functionally required for interpretation of documents by automatic operating systems.

Metadata management is crucial for efficient data governance, resource discovery, and decision-making in today's data-driven landscape. Traditional metadata approaches often face challenges such as manual processes, siloed systems, and scalability issues. The integration of artificial intelligence (AI) technologies, including natural language processing (NLP), machine learning (ML), and generative AI, offers transformative solutions to these challenges. AI-driven metadata management automates tasks like metadata generation, enhances data discoverability, and ensures compliance with governance standards. This paper explores the evolution (...) of metadata management, examines the impact of AI integration, and presents a framework for intelligent automation in metadata stewardship. (shrink)

As organizations increasingly rely on vast volumes of structured and unstructured data, effective data governance becomes a top priority. Metadata—the data about data—plays a foundational role in ensuring transparency, compliance, accessibility, and quality within data ecosystems. However, traditional metadata management approaches often fall short in scalability, accuracy, and automation. This paper explores the transformative role of artificial intelligence (AI) in metadata management, focusing on how AI can automate metadata creation, enforce governance policies, and support regulatory compliance. (...) By analyzing current tools, frameworks, and implementation strategies, we present a forward-looking vision of AI-driven metadata management as a cornerstone of modern data governance practices. (shrink)

In the contemporary data-driven landscape, effective metadata management is pivotal for informed decision-making. Traditional methods often fall short in handling the complexities and scale of modern data environments. Artificial Intelligence (AI) introduces transformative capabilities to metadata management, enhancing automation, accuracy, and accessibility. This paper explores the integration of AI into metadata management, focusing on its impact on data governance, resource discovery, and decision-making processes. By examining current challenges and opportunities, the study aims to provide a comprehensive understanding (...) of how AI can revolutionize metadata management for smarter decisions. (shrink)

In discussions of state surveillance, the values of privacy and security are often set against one another, and people often ask whether privacy is more important than national security.2 I will argue that in one sense privacy is more important than national security. Just what more important means is its own question, though, so I will be more precise. I will argue that national security rationales cannot by themselves justify some kinds of encroachments on individual privacy (including some kinds that (...) the United States has conducted). Specifically, I turn my attention to a recent, well publicized, and recently amended statute (section 215 of the USA Patriot Act3), a surveillance program based on that statute (the National Security Agency’s bulk metadata collection program), and a recent change to that statute that addresses some of the public controversy surrounding the surveillance program (the USA Freedom Act).4 That process (a statute enabling surveillance, a program abiding by that statute, a public controversy, and a change in the law) looks like a paradigm case of law working as it should; but I am not so sure. While the program was plausibly legal, I will argue that it was morally and legally unjustifiable. Specifically, I will argue that the interpretations of section 215 that supported the program violate what Jeremy Waldron calls “legal archetypes,”5 and that changes to the law illustrate one of the central features of legal archetypes and violation of legal archetypes. The paper proceeds as follows: I begin in Part 1 by setting out what I call the “basic argument” in favor of surveillance programs. This is strictly a moral argument about the conditions under which surveillance in the service of national security can be justified. In Part 2, I turn to section 215 and the bulk metadata surveillance program based on that section. I will argue that the program was plausibly legal, though based on an aggressive, envelope-pushing interpretation of the statute. I conclude Part 2 by describing the USA Freedom Act, which amends section 215 in important ways. In Part 3, I change tack. Rather than offering an argument for the conditions under which surveillance is justified (as in Part 1), I use the discussion of the legal interpretations underlying the metadata program to describe a key ambiguity in the basic argument, and to explain a distinct concern in the program. Specifically that it undermines a legal archetype. Moreover, while the USA Freedom Act does not violate legal archetypes, and hence meets a condition for justifiability, it helps illustrate why the bulk metadata program did violate archetypes. (shrink)

Data lineage and metadata tracking are crucial components of modern data governance frameworks, enabling transparency, regulatory compliance, and operational efficiency across data pipelines. This paper explores the implementation of a robust metadata management and lineage tracking system using OpenLineage and Amundsen. We examine the current challenges in metadata visibility and data observability in enterprise environments and propose an integrated solution that captures, stores, and visualizes end-to-end lineage data. The paper outlines architecture, methodology, and key benefits of combining (...) Open Lineage’s open standard lineage metadata with Amundsen’s user centric data catalog. (shrink)

Disputes at the intersection of national security, surveillance, civil liberties, and transparency are nothing new, but they have become a particularly prominent part of public discourse in the years since the attacks on the World Trade Center in September 2001. This is in part due to the dramatic nature of those attacks, in part based on significant legal developments after the attacks (classifying persons as “enemy combatants” outside the scope of traditional Geneva protections, legal memos by White House counsel providing (...) rationale for torture, the USA Patriot Act), and in part because of the rapid development of communications and computing technologies that enable both greater connectivity among people and the greater ability to collect information about those connections. -/- One important way in which these questions intersect is in the controversy surrounding bulk collection of telephone metadata by the U.S. National Security Agency. The bulk metadata program (the “metadata program” or “program”) involved court orders under section 215 of the USA Patriot Act requiring telecommunications companies to provide records about all calls the companies handled and the creation of database that the NSA could search. The program was revealed to the general public in June 2013 as part of the large document leak by Edward Snowden, a former contractor for the NSA. -/- A fair amount has been written about section 215 and the bulk metadata program. Much of the commentary has focused on three discrete issues. First is whether the program is legal; that is, does the program comport with the language of the statute and is it consistent with Fourth Amendment protections against unreasonable searches and seizures? Second is whether the program infringes privacy rights; that is, does bulk metadata collection diminish individual privacy in a way that rises to the level that it infringes persons’ rights to privacy? Third is whether the secrecy of the program is inconsistent with democratic accountability. After all, people in the general public only became aware of the metadata program via the Snowden leaks; absent those leaks, there would have not likely been the sort of political backlash and investigation necessary to provide some kind of accountability. -/- In this chapter I argue that we need to look at these not as discrete questions, but as intersecting ones. The metadata program is not simply a legal problem (though it is one); it is not simply a privacy problem (though it is one); and it is not simply a secrecy problem (though it is one). Instead, the importance of the metadata program is the way in which these problems intersect and reinforce one another. Specifically, I will argue that the intersection of the questions undermines the value of rights, and that this is a deeper and more far-reaching moral problem than each of the component questions. (shrink)

As data volumes grow exponentially, metadata has become essential for data organization, retrieval, and governance. Artificial Intelligence (AI) is revolutionizing metadata management by automating processes, enhancing data quality, and enabling real-time updates. This paper explores the evolution of metadata practices through AI, highlighting key trends, tools, and transformative impacts. We discuss popular AI models, assess their implementation in metadata generation, and analyze the benefits and challenges of AI integration. Our study concludes that AI-powered metadata systems (...) are more scalable, accurate, and adaptive, making them crucial in modern data ecosystems. (shrink)

Enterprise metadata management (EMM) has emerged as a critical capability for organizations seeking to manage complex, distributed, and growing data environments. However, traditional metadata systems often struggle with scale, accuracy, and adaptability. This paper investigates the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in modernizing enterprise metadata strategies. AI and ML not only automate metadata generation but also improve metadata quality, integration, and real-time responsiveness. Through literature insights, comparative tools analysis, and workflow (...) modeling, this study demonstrates how intelligent metadata systems act as the "smart backbone" of enterprise data ecosystems, enabling smarter governance, search, and analytics. (shrink)

The rapid expansion of digital data has made metadata crucial in organizing, managing, and retrieving information effectively. Machine learning (ML) offers powerful tools to automate and enhance metadata processing, leading to improved accuracy, scalability, and efficiency. This paper explores how ML algorithms are applied to metadata extraction, classification, annotation, and enrichment. We review current research, examine the methodologies employed, and present a comparative analysis of techniques. Our findings suggest that supervised learning models, especially deep learning architectures, outperform (...) traditional rule-based systems in most scenarios. However, challenges remain in terms of interpretability, bias, and data quality. (shrink)

Enterprise metadata management (EMM) has emerged as a critical capability for organizations seeking to manage complex, distributed, and growing data environments. However, traditional metadata systems often struggle with scale, accuracy, and adaptability. This paper investigates the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in modernizing enterprise metadata strategies. AI and ML not only automate metadata generation but also improve metadata quality, integration, and real-time responsiveness. Through literature insights, comparative tools analysis, and workflow (...) modeling, this study demonstrates how intelligent metadata systems act as the "smart backbone" of enterprise data ecosystems, enabling smarter governance, search, and analytics. (shrink)

In today’s digital landscape, the exponential growth of unstructured and semi-structured data has led to increasing challenges in data management and retrieval. Metadata—descriptive data that provides context to content— plays a crucial role in mitigating this complexity. However, traditional metadata systems are often fragmented, manually curated, and inconsistent, leading to inefficiencies across data workflows. Artificial Intelligence (AI) introduces a transformative approach to metadata optimization by automating generation, improving accuracy, and enabling intelligent context understanding. This paper explores how (...) AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision are reengineering metadata systems across industries. We present a review of current literature, a comparative table of traditional versus AI-powered methods, a research methodology for implementation, and a conceptual model of AI in the metadata lifecycle. The study demonstrates that AI not only enhances metadata quality and usability but also empowers organizations to derive deeper insights and ensure data governance at scale. (shrink)

In an increasingly data-driven world, metadata has become the silent workhorse enabling efficient data discovery, integration, and governance. However, traditional metadata strategies often fall short in managing the scale, speed, and complexity of modern data environments. This paper explores how Artificial Intelligence (AI), particularly Machine Learning (ML) and Natural Language Processing (NLP), is reshaping metadata strategies by automating creation, enrichment, validation, and maintenance processes. By reviewing recent advancements and real-world applications, this study identifies AI-powered metadata as (...) a strategic enabler for unlocking data potential in domains ranging from enterprise data lakes to digital libraries. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

This paper covers the home-made bombing used at the 2013 Boston Marathon annual races even though we knew before the trial began that the verdict will have to be 'guilty' because the people of Boston demanded that verdict and received the first lap of the verdict on 8 April 2015. Neither beautiful technical rulings nor breaches of prosecution disclosure rules nor metadata queries nor tampered evidence would have held sway at this trial.

Successful replication is a hallmark of scientific truth. Discordant evidence refers to the situation where findings from different studies of the same phenomenon do not agree. Although evidential discordance can spur scientific discovery, it also gives scientists a reason to rationally disagree and thereby compromises the formation of scientific consensus. Discordance indicates that facts about the phenomenon of interest remain unsettled and that a finding may not be reliably replicable. We single out persistent evidential discordance as a particularly difficult problem (...) for the epistemology of science, and distinguish between different causes of evidential discordance – non-systematic error, noise, and bias. Unlike discordance brought about by non-systematic error or noise, persistent discordance often cannot be rationally resolved by temporarily suspending judgment and collecting more data within existing lines of inquiry. We suggest that the analysis of enriched lines of evidence (Boyd 2018) provides a useful approach to diagnosing and evaluating episodes of persistent evidential discordance. Attention to the line of evidence, which extends from raw data to an evidential claim supporting or disconfirming a hypothesis, can help researchers to locate the source of discordance between inconsistent findings. We argue that reference to metadata, information about how the data were generated and processed, can be a key step in the process of resolving normative questions of correctness, i.e., whether a line of evidence provides a legitimate answer to a particular research question. We illustrate our argument with two cases: the alleged discovery of gravitational waves in the late 1960s, and the social priming controversy in experimental psychology. (shrink)

This chapter addresses the morality of two types of national security electronic surveillance (SIGINT) programs: the analysis of communication “metadata” and dragnet searches for keywords in electronic communication. The chapter develops a standard for assessing coercive government action based on respect for the autonomy of inhabitants of liberal states and argues that both types of SIGINT can potentially meet this standard. That said, the collection of metadata creates opportunities for abuse of power, and so judgments about the trustworthiness (...) and competence of the government engaging in the collection must be made in order to decide whether metadata collection is justified in a particular state. Further, the moral standard proposed has a reflexive element justifying adversary states’ intelligence collection against one another. Therefore, high-tech forms of SIGINT can only be justified at the cost of justifying cruder versions of signals intelligence collection practiced by a state’s less-advanced adversaries. (shrink)

The trial took place at Bristol Crown Court, England, United Kingdom for the murder of Joanna Yeates, and Dr Vincent Tabak was the Defendant. The author attended at court for this trial and this paper notes many of the obvious and unsatisfactory legal and procedural points in this trial. Dr Vincent Tabak was convicted of the murder at this trial. Of course the jury were not to know the finer points of law as the lower court judge did not advise (...) the jury of any such points before they adjourned to make their decision. All but one concluded that Dr Tabak was guilty as charged, although even the charge was vague and all-encompassing as he was charged with ‘murder between Friday 17 December 2010 and Sunday 19 December 2010’. Defence counsel, very eminent barrister-at-law, appeared to be cowed by the other side and let them walk all over this case procedurally. For example, prosecution counsel handed him 1200 pages of evidence on the first day of the trial, as if to FULFIL disclosure obligations. E was stunned and asked the court for time off. But this was never again discussed. -/- The lower court judge appeared as if he had already decided on the case. When Defence Counsel asked him if he had before him a copy of a certain document, he flippantly say he had it but had left it on his desk in his room. When Defence Counsel, of international renown, was ready to present his opening speech and cross-examination of his client, Dr Vincent Tabak, this lower court judge dismissed the jury to their lunch break knowing that counsel was going to speak. Court had to be adjourned and the members of the jury quickly assembles by the court porters as they were leaving the court, which must have hampered their interest in what counsel’s statement said. A photograph of a dead body by a roadside, allegedly, Miss Yeates, was shown by projector slide a dozen irrelevant times during the trial period, thus embedding that photograph in the jury’s mind. The computer evidence leaves much to be desired and could, some suggested, easily have been concocted by the prosecution team, abusing the law of metadata evidence. Te lady who presented it did not give the court her qualifications and her expertise if any- and much more that beggars belief and loses trust in the British Criminal Justice System, such as it is, following ‘form over substance’. -/- . (shrink)

Основой сознания является ассоциативная связь понятий, согласованная работа элементов нейрональной сети. Аналогично этому, создание интернета нового поколения (semantic web) невозможно без атрибутов, позволяющих осуществлять семантическую связь документов и интеграцию их в информационный контекст. Для реализации этих целей предлагается ввести Универсальный Стандарт Метаданных (universal metadata standard, ums), который мог бы служить основой документографии (documentography), функционально необходимой для интерпретации документов в автоматических операционных системах.

The words we call slurs are just plain vanilla descriptions like ‘cowboy’ and ‘coat hanger’. They don't semantically convey any disparagement of their referents, whether as content, conventional implicature, presupposition, “coloring” or mode of presentation. What distinguishes 'kraut' and 'German' is metadata rather than meaning: the former is the conventional description for Germans among Germanophobes when they are speaking in that capacity, in the same way 'mad' is the conventional expression that some teenagers use as an intensifier when they’re (...) emphasizing that social identity. That is, racists don’t use slurs because they’re derogatory; slurs are derogatory because they’re the words that racists use. To use a slur is to exploit the Maxim of Manner (or Levinson’s M-Principle) to signal one’s affiliation with a group that has a disparaging attitude towards the slur’s referent. This account is sufficient to explain all the familiar properties of slurs, such as their speaker orientation and “nondetachability,” with no need of additional linguistic mechanisms. It also explains some features of slurs that are rarely if ever explored; for example the variation in tone and strength among the different slurs for a particular group, the existence of words we count as slurs, such as 'redskin', which almost all of their users consider to be respectful, and the curious absence in Standard English of any commonly used slurs—by which I mean words used to express a negative attitude toward an entire group—for Muslims and women. (shrink)

Environmental activism is crucial for raising public awareness and support toward addressing the climate crisis. However, using climate change mitigation as the cause for blockage, vandalism, and harassment activities might be counterproductive and risk causing negative repercussions and declining public support. The paper describes a dataset of metadata of 89 blockage, vandalism, and harassment events happening in recent years. The dataset comprises three main categories: 1) Events, 2) Activists, and 3) Consequences. For researchers interested in environmental activism, climate change, (...) and sustainability, the dataset is helpful in studying the effectiveness and appropriateness of strategies to raise public awareness and support. For researchers in the field of security studies and green criminology, the dataset offers resources to study features and impacts of blockage, vandalism, and harassment events. The Bayesian Mindsponge Framework (BMF) analytics was employed to validate the dataset. Consequently, the estimated result aligns with the Mindsponge Theory’s theoretical reasoning. (shrink)

We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of the US Army intelligence community within the framework of the Distributed Common Ground System (DCGS-A). IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and (...) class='Hi'>metadata registries, thereby enhancing the degree to which the content formulated with their aid will be available to computational reasoning. (shrink)

Background Retraction is a mechanism for alerting readers to unreliable material and other problems in the published scientific and scholarly record. Retracted publications generally remain visible and searchable, but the intention of retraction is to mark them as “removed” from the citable record of scholarship. However, in practice, some retracted articles continue to be treated by researchers and the public as valid content as they are often unaware of the retraction. Research over the past decade has identified a number of (...) factors contributing to the unintentional spread of retracted research. The goal of the Reducing the Inadvertent Spread of Retracted Science: Shaping a Research and Implementation Agenda (RISRS) project was to develop an actionable agenda for reducing the inadvertent spread of retracted science. This included identifying how retraction status could be more thoroughly disseminated, and determining what actions are feasible and relevant for particular stakeholders who play a role in the distribution of knowledge. -/- Methods These recommendations were developed as part of a year-long process that included a scoping review of empirical literature and successive rounds of stakeholder consultation, culminating in a three-part online workshop that brought together a diverse body of 65 stakeholders in October–November 2020 to engage in collaborative problem solving and dialogue. Stakeholders held roles such as publishers, editors, researchers, librarians, standards developers, funding program officers, and technologists and worked for institutions such as universities, governmental agencies, funding organizations, publishing houses, libraries, standards organizations, and technology providers. Workshop discussions were seeded by materials derived from stakeholder interviews (N = 47) and short original discussion pieces contributed by stakeholders. The online workshop resulted in a set of recommendations to address the complexities of retracted research throughout the scholarly communications ecosystem. -/- Results The RISRS recommendations are: (1) Develop a systematic cross-industry approach to ensure the public availability of consistent, standardized, interoperable, and timely information about retractions; (2) Recommend a taxonomy of retraction categories/classifications and corresponding retraction metadata that can be adopted by all stakeholders; (3) Develop best practices for coordinating the retraction process to enable timely, fair, unbiased outcomes; and (4) Educate stakeholders about pre- and post-publication stewardship, including retraction and correction of the scholarly record. -/- Conclusions Our stakeholder engagement study led to 4 recommendations to address inadvertent citation of retracted research, and formation of a working group to develop the Communication of Retractions, Removals, and Expressions of Concern (CORREC) Recommended Practice. Further work will be needed to determine how well retractions are currently documented, how retraction of code and datasets impacts related publications, and to identify if retraction metadata (fails to) propagate. Outcomes of all this work should lead to ensuring retracted papers are never cited without awareness of the retraction, and that, in public fora outside of science, retracted papers are not treated as valid scientific outputs. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the (...) development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

Climate change, pollution, and deforestation have a negative impact on global mental health. There is an environmental justice dimension to this challenge as wealthy people and high-income countries are major contributors to climate change and pollution, while poor people and low-income countries are heavily affected by the consequences. Using state-of-the art data mining, we analyzed and visualized the global research landscape on mental health, climate change, pollution and deforestation over a 15-year period. Metadata of papers were exported from PubMed®, (...) and both relevance and relatedness of terms in different time frames were computed using VOSviewer. Co-occurrence graphs were used to visualize results. The development of exemplary terms over time was plotted separately. The number of research papers on mental health and environmental challenges is growing in a linear fashion. Major topics are climate change, chemical pollution, including psychiatric medication in wastewater, and neurobiological effects. Research on specific psychiatric syndromes and diseases, particularly on their ethical and social aspects is less prominent. There is a growing body of research literature on links between mental health, climate change, pollution, and deforestation. This research provides a graphic overview to mental healthcare professionals and political stakeholders. Social and ethical aspects of the climate change-mental health link have been neglected, and more research is needed. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to provide granular, context-aware insights. This (...) research introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the (...) OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR. Competing Interest StatementThe authors have declared no competing interest. (shrink)

The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. Such (...) annotation enables several objectives, including searching, browsing and cross-database data integration. A key benefit conferred by realismbased ontology is that suitably annotated research results are able to be aggregated and compared in a fashion that is based on the underlying reality that the science is studying. This has the potential of increasing the power of statistical analysis and meta-analysis in data-driven science. This aspect has been fruitfully exploited in the investigation of the functions of genes in molecular biology. Cognitive neuroscience uses functional neuroimaging to investigate the brain correlates of areas of mental functioning such as memory, planning and emotion. The use of functional neuroimaging to study affective phenomena such as the emotions is called ‘affective neuroscience’. BrainMap is the largest curated database of coordinates and metadata for studies in cognitive neuroscience, including affective neuroscience (Laird et al., 2005). BrainMap data is already classified and indexed using a terminology for classification, called the ‘Cognitive Paradigm Ontology’ (CogPO), that has been developed to facilitate searching and browsing. However, CogPO has been developed specifically for the BrainMap database, and the data are thus far not annotated to a realism-based ontology which would allow the discovery of interrelationships between research results across different databases on the basis of what the research is about. In this contribution, we describe ongoing work that aims to annotate affective neuroscience data, starting with the BrainMap database, using the Emotion Ontology. We describe our objectives and technical approach to the annotation, and mention some of the challenges. (shrink)

The history of twentieth-century American psychology is often depicted as a history of the rise and fall of behaviorism. Although historians disagree about the theoretical and social factors that have contributed to the development of experimental psychology, there is widespread consensus about the growing and declining influence of behaviorism between approximately 1920 and 1970. Since such wide-scope claims about the development of American psychology are typically based on small and unrepresentative samples of historical data, however, the question rises to what (...) extent the received view is justified. This paper aims to answer this question in two ways. First, we use advanced scientometric tools to quantitatively analyze the metadata of 119.278 papers published in American journals between 1920 and 1970. We reconstruct the development and structure of American psychology using co-citation and co-occurrence networks and argue that the standard story needs reappraising. Second, we argue that the question whether behaviorism was the ‘dominant’ school of American psychology is historically misleading to begin with. Using the results of our bibliometric analyses, we argue that questions about the development of American psychology deserve more fine-grained answers. (shrink)

Standard citation-based bibliometric tools have severe limitations when they are applied to periods in the history of science and the humanities before the advent of now-current citation practices. This paper presents an alternative method involving the extracting and analysis of mentions to map and analyze links between scholars and texts in periods that fall outside the scope of citation-based studies. Focusing on one specific discipline in one particular period and language area—Anglophone philosophy between 1890 and 1979—we describe a procedure to (...) create a mention index by identifying, extracting, and disambiguating mentions in academic publications. Our mention index includes 1,095,765 mention links, extracted from 22,977 articles published in 12 journals. We successfully link 93% of these mentions to specific philosophers, with an estimated precision of 82% to 91%. Moreover, we integrate the mention index into a database named EDHIPHY, which includes data and metadata from multiple sources and enables multidimensional mention analyses. In the final part of the paper, we present four case studies conducted by domain experts, demonstrating the use and the potential of both EDHIPHY and mention analyses more generally. (shrink)

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the (...) Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers. (shrink)

Purpose: This article aims to analyze and summarize the practical experience of deploying and integrating platforms for open journals, conferences, and repositories with support for the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). It highlights the errors and challenges that journal and repository managers and administrators, as well as conference organizers, may encounter and offers recommendations to prevent and avoid these issues. Design / Method / Approach: The research is based on the analysis of real cases of deploying (...) platforms for open science, including the analysis of errors and difficulties encountered during this process. The methodology includes the analysis of real-world cases, applying methods such as testing, validating the OAI-PMH protocol, observation, and studying regulatory and technical documents. Findings: Key errors and difficulties that organizers may face when creating open journals, conferences, and repositories with OAI-PMH support have been identified. Specific solutions are proposed to ensure the reliable operation of the metadata harvesting protocol (PMH), initial setup, and further integration of these platforms. Theoretical Implications: The study extends the understanding of the problems and challenges in the field of open science, particularly concerning the organizational and technical aspects of platforms with OAI-PMH support. Practical Implications: Practical recommendations are provided for administrators and managers of open journals, conferences, and repositories to avoid common mistakes and effectively use OAI-PMH to ensure data interoperability and accessibility. Originality / Value: The article presents an original analysis of real cases of implementing and integrating platforms with OAI-PMH support, making it a valuable resource for practitioners in the context of deploying platforms for open science. Research Limitations / Future Research: The study is limited to the analysis of specific cases and does not cover all possible aspects of deploying and integrating web applications with OAI-PMH support. Future research may focus on the quantitative analysis of the effectiveness of implementing these platforms and their long-term impact on open science. Paper Type: Case Study / Practitioner Paper. (shrink)

As statistics is the backbone of the digital financial system dependence on centralized cloud storage structures makes users prone to troubles concerning statistics breaches operational price and lack of control this paper examines the deployment of a decentralized cloud storage DCS framework with the use of interplanetary file system IPFS and Ethereum blockchain clever contracts to triumph over those drawbacks the gadget proposed here improves protection and information availability by incorporating aes-256 encryption sharding of records and decentralized metadata control (...) by the introduction of a working prototype based on react.js, Ethereum wallet, ether.js and solidity this mission illustrates the viability of a decentralized statistics garage whilst resolving troubles with latency user adoption and value effectiveness experimental consequences affirm enhancements in safety and availability establishing a strong platform for additional research on decentralized storage architectures. (shrink)

In the pharmaceutical and healthcare industries, the secure handling of sensitive drug component data is crucial. This project presents a comprehensive system for the secure storage, encryption, and controlled access of drug datasets using modern encryption techniques and cloud-based storage. Users can upload drug-related CSV files through a web interface, which are then processed and encrypted selectively before being uploaded to Amazon S3. Each file is associated with a unique encryption key, which is securely stored and can only be retrieved (...) by authorized users via password-based authentication. The backend is built with Spring Boot, implementing robust encryption logic, metadata storage, and integration with AWS S3. The frontend (developed using Next.js) enables users to upload, view, and manage their encrypted files in an intuitive interface. Key features of the system include role-based access control, data confidentiality, and secure key retrieval mechanisms, ensuring that only legitimate users can decrypt and access sensitive data. This project demonstrates a scalable and secure solution for privacy preserving storage of drug components, making it highly relevant in scenarios where data confidentiality and regulatory compliance are critical. It also lays the groundwork for future integration of machine learning or data mining pipelines once decryption is authorized, ensuring both security and analytical capabilities. (shrink)

Representing the kinetic state of a patient (posture, motion, and activity) during vital sign measurement is an important part of continuous monitoring applications, especially remote monitoring applications. In contextualized vital sign representation, the measurement result is presented in conjunction with salient measurement context metadata. We present an automated annotation system for vital sign measurements that uses ontologies from the Open Biomedical Ontology Foundry (OBO Foundry) to represent the patient’s kinetic state at the time of measurement. The annotation system is (...) applied to data generated by a wearable personal status monitoring (PSM) device. We demonstrate how annotated PSM data can be queried for contextualized vital signs as well as sensor algorithm configuration parameters. (shrink)

Phishing attacks are major threats to email security and pose challenges, while cyber attackers utilize increasingly sophisticated means to deceive the user and steal away important information. Well-established ways of detecting phishing attacks, such as rule-based systems or simple machine-learning models, usually cannot deal efficiently with such advanced threats. This research proposes an approach to detect phishing attacks on email systems, which deploys natural language processing and deep learning technologies. The method proposes to improve the detection accuracies and efficiencies of (...) phishing emails, which consequently enhances the protection of emails against popular cyberattack attempts and aids in securing users from such attacks. The research involves designing an active model that utilizes the strength of NLP-based text analysis for DL-oriented pattern identification. The NLP techniques used in this study include tokenization, stop-word removal, and context- based analysis to extract significant features from the email messages. Such context information greatly helps this model differentiate between actual emails and phishing ones. Deep learning algorithms exploited here are based on CNN and LSTM networks, offering sinusoidally varying parameters for optimum recognition of patterns from different perspectives in the email data's spatial and temporal domains. The experiment results validated that the hybrid model was far superior to the conventional methods in robbing phish attack detection. The model stood at an accuracy of 97.5%, much ahead of the baseline models purely having rule-based systems or traditional machine learning algorithms. The model also performed well under real-time detection conditions, with low latency and high throughput support for deployment in an active email environment. Evidence of the model's capability to sense new variants of future phishing threats also came in the automatic updating and continuous learning, thus making it applicable to newly emerging threats. The study's practical implications are numerous in providing advances in email protection. Companies can use the model to ward off phishing attacks with reduced chances of data breaches and financial loss. Due to its low cost and scalable features, this solution can be used in organizations from small- and medium-sized to large enterprise levels. The improved detection of phishing attacks brings organizations closer to compliance with data protection and cybersecurity regulations, thus minimizing their chances of noncompliance and the fines that come with it. Future research paths include exploring even newer NLP methods consisting of transformer-based models for feature extraction about contextual understanding. Exploring hybrid approaches that can merge DL with other techniques, such as reinforcement learning in ML, will create a more robust and adaptive phishing filter. Future fields for research are multimodal data, that is, a mixture of email metadata and behavior. Merely considering hybrid techniques merging artificial intelligence with machine learning approaches such as reinforcement learning can generate an even more robust and adaptive phishing filter. Coupling all these technical solutions with user awareness and education programs would greatly enhance overall security. (shrink)

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management : second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli, Efstathiou and Hislop in our analysis of the use of (...) KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge. (shrink)

When a widely reused ontology appears in a new version which is not compatible with older versions, the ontologies reusing it need to be updated accordingly. Ontobull has been developed to automatically update ontologies with new term IRI(s) and associated metadata to take account of such version changes. To use the Ontobull web interface a user is required to (i) upload one or more ontology OWL source files; (ii) input an ontology term IRI mapping; and (where needed) (iii) provide (...) update settings for ontology headers and XML namespace IDs. Using this information, the backend Ontobull Java program automatically updates the OWL ontology files with desired term IRIs and ontology metadata. The Ontobull subprogram BFOConvert supports the conversion of an ontology that imports a previous version of BFO. A use case is provided to demonstrate the features of Ontobull and BFOConvert. (shrink)

This paper presents an NLP-driven approach to identifying fake statements made by public figures. The system employs natural language processing (NLP) techniques to analyze textual data and classify statements as true or false. Given the rise of digital media, misinformation has become a global challenge, influencing public opinion and political discourse. Fake news spreads rapidly through social and mainstream media, making fact-checking increasingly difficult. The proposed system processes statements independently, without relying on metadata, using techniques such as tokenization, stemming, (...) and stop-word removal. This method enhances the accuracy of classification by focusing on the linguistic features of statements. Future improvements may incorporate additional contextual data to refine detection accuracy further. Developed using Python and MySQL, this system provides a scalable and efficient solution to combat misinformation, contributing to more reliable information dissemination in the digital age. (shrink)

The aim of this work is to understand the retraction phenomenon in the arts and humanities domain through an analysis of the retraction notices – formal documents stating and describing the retraction of a particular publication. The retractions and the corresponding notices are identified using the data provided by Retraction Watch. Our methodology for the analysis combines a metadata analysis and a content analysis (mainly performed using a topic modeling process) of the retraction notices. Considering 343 cases of retraction, (...) we found that many retraction notices are neither identifiable nor findable. In addition, these were not always separated from the original papers, introducing ambiguity in understanding how these notices were perceived by the community (i.e., cited). Also, we noticed that there is no systematic way to write a retraction notice. Indeed, some retraction notices presented a complete discussion of the reasons for retraction, while others tended to be more direct and succinct. We have also reported many notices having similar text while addressing different retractions. We think a further study with a larger collection should be done using the same methodology to confirm and investigate our findings further. (shrink)

This study examines the concept of “description” and its theoretical foundations. The literature about it is surprisingly limited, and its usage is vague, sometimes even conflicting. Description should be considered in relation to other processes, such as representation, data capturing, and categorizing, which raises the question about what it means to describe something. Description is often used for any type of predication but may better be limited to predications based on observations. Research aims to establish criteria for making optimal descriptions; (...) however, the problems involved in describing something have seldom been addressed. Specific ideals are often followed without examine their fruitfulness. This study provides evidence that description cannot be a neutral, objective activity; rather, it is a theory-laden and interest-based activity. In information science, description occurs in processes such as document description, descriptive metadata assignment, and information resource description. In this field, description has equally been used in conflicting ways that mostly do not evince a recognition of the value- and theory-laden nature of descriptions. It is argued that descriptive activities in information science should always be based on consciously explicit considerations of the goals that descriptions are meant to serve. (shrink)

Sau 14 tháng bền bỉ, phương pháp đổi mới việc rà soát tổng quan lý thuyết xã hội của ISR, dựa trên dữ liệu metadata có cấu trúc, đã được xuất bản bởi tạp chí MethodsX (Elsevier Q2 SCImago | ISI Web of Science). Bài có tiêu đề: “Making social sciences more scientific” [1].

Here we present an experimental model to be applied to the storage and retrieval of information based on an associative information system’s sensory and motor state change data, aiming to represent the dynamics of a dynamic perceptual system. The model and database implementation use a universal information storage structure holding both data and metadata within the same structure. This model is characterized by the emphasis on associative information about the represented system derived from raw data, which are in their (...) turn produced by the associative system’s interactions with the environment. Instead of defining objects using descriptive relations, this model stores relations between occurents where the represented system is not replicated in its various components, but defined by its relations when they occur. This model therefore represents the dynamics and interaction of systems such as human perception, rather than imposing artificial boundaries and qualities. In essence, the model is an alternative to perceptual knowledge accumulation, which, as we show, can be applied to a database design. (shrink)

El propósito de este trabajo es analizar los objetos digitales que pueblan nuestras pantallas y redes: vídeos de YouTube, perfiles de Facebook, imágenes de Flickr, archivos de Dropbox, correos electrónicos, podcasts, blogs, y demás. Estos objetos están codificados en binario (0/1) y, en su forma más elemental, son bits y bytes. Para desentrañar estas entidades intangibles, que son realidades insertas en el tejido de nuestra existencia cotidiana, se examinan los supuestos ontológicos y epistemológicos que las rodean. Desde esta perspectiva, se (...) analizan diferentes marcos conceptuales y definiciones, así como los enfoques filosóficos más representativos. También se explora el papel central de los nuevos paradigmas computacionales, la infraestructura TIC y las tecnologías emergentes. Se introduce un modelo de realismo integrativo que encapsula las tesis recogidas a lo largo del documento. El texto concluye con unas breves reflexiones sobre los hallazgos. (shrink)

Digital Rights Management (DRM) covers the description, identification, trading, protection, monitoring and tracking of all forms of rights over both tangible and intangible assets. The Digital Object Identifier (DOI) system provides a framework for the persistent identification of entities involved in this domain. Although the system has been very well designed to manage object identifiers, some important questions relating to the creation and assignment of identifiers are left open. The paradigm of a Referent Tracking System (RTS) recently advanced in the (...) healthcare and life sciences environment is able to fill these gaps. This is demonstrated by pointing out inconsistencies in the existing DOI models and by showing how they can be corrected using an RTS. (shrink)