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The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.

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Open data is a crucial prerequisite for inventing and disseminating the innovative practices needed for agricultural development. To be usable, data must not just be open in principle—i.e., covered by licenses that allow re-use. Data must also be published in a technical form that allows it to be integrated into a wide range of applications. The webinar will be of interest to any institution seeking ways to publish and curate data in the Linked Data cloud. This webinar describes the technical solutions adopted by a widely diverse global network of agricultural research institutes for publishing research results. The talk focuses on AGRIS, a central and widely-used resource linking agricultural datasets for easy consumption, and AgriDrupal, an adaptation of the popular, open-source content management system Drupal optimized for producing and consuming linked datasets. Agricultural research institutes in developing countries share many of the constraints faced by libraries and other documentation centers, and not just in developing countries: institutions are expected to expose their information on the Web in a re-usable form with shoestring budgets and with technical staff working in local languages and continually lured by higher-paying work in the private sector. Technical solutions must be easy to adopt and freely available.

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About the Webinar In May 2012, the Library of Congress announced a new modeling initiative focused on reflecting the MARC 21 library standard as a Linked Data model for the Web, with an initial model to be proposed by the consulting company Zepheira. The goal of the initiative is to translate the MARC 21 format to a Linked Data model while retaining the richness and benefits of existing data in the historical format. In this webinar, Eric Miller of Zepheira will report on progress towards this important goal, starting with an analysis of the translation problem and concluding with potential migration scenarios for a broad-based transition from MARC to a new bibliographic framework.

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Libraries around the world have a long tradition of maintaining authority files to assure the consistent presentation and indexing of names. As library authority files have become available online, the authority data has become accessible -- and many have been published as Linked Open Data (LOD) -- but names in one library authority file typically had no link to corresponding records for persons and organizations in other library authority files. After a successful experiment in matching the Library of Congress/NACO authority file with the German National Library's authority file, an online system called the Virtual International Authority File was developed to facilitate sharing by ingesting, matching, and displaying the relations between records in multiple authority files. The Virtual International Authority File (VIAF) has grown from three source files in 2007 to more than two dozen files today. The system harvests authority records, enhances them with bibliographic information and brings them together into clusters when it is confident the records describe the same identity. Although the most visible part of VIAF is a HTML interface, the API beneath it supports a linked data view of VIAF with URIs representing the identities themselves, not just URIs for the clusters. It supports names for person, corporations, geographic entities, works, and expressions. With English, French, German, Spanish interfaces (and a Japanese in process), the system is used around the world, with over a million queries per day. Speaker Thomas Hickey is Chief Scientist at OCLC where he helped found OCLC Research. Current interests include metadata creation and editing systems, authority control, parallel systems for bibliographic processing, and information retrieval and display. In addition to implementing VIAF, his group looks into exploring Web access to metadata, identification of FRBR works and expressions in WorldCat, the algorithmic creation of authorities, and the characterization of collections. He has an undergraduate degree in Physics and a Ph.D. in Library and Information Science.

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In certain types of _slow burn_ emergencies, careful accumulation and evaluation of information can offer a crucial advantage. The SARS outbreak in the first decade of the 21st century was such an event, and ad hoc journal clubs played a critical role in assisting scientific and technical responders in identifying and developing various strategies for halting what could have become a dangerous pandemic. This paper describes a process for leveraging emerging semantic web and digital library architectures and standards to (1) create a focused collection of bibliographic metadata, (2) extract semantic information, (3) convert it to the Resource Description Framework /Extensible Markup Language (RDF/XML), and (4) integrate it so that scientific and technical responders can share and explore critical information in the collections. systems.

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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