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            "abstractNote": "The Libraries Transforming Humanities (LTH) project is focused on creating a network of resources in the humanities and building tools to facilitate contributions to research from a wide range of users. The Systematic Assertion Model (SAM) was developed as a framework to describe the provenance roles and agents essential to the identity of scientific data. SAM supports an explicit accounting of the events and roles that are essential to the creation of text-like resources, and contains classes and properties that can be used to define vocabularies to support translation, annotation and curation of textual resources. In SAM, data are symbol structures that are the expressive form of certain kinds of assertion events. In a scientific context, the attention is on assertions that are warranted by computations or observations of phenomena in the natural world. In the context of humanities research, attention is on the creation of texts or other intellectual works, as well as on assertions warranted by computations and observations of such works. The creation events for texts are a kind of indication event, where abstract structures are pointed to by some agent at a specific time and place. Since speech acts and other general kinds of indication events were not included in previous versions of SAM, applying the model to humanities research calls for an extension of the model. We present an example workflow from the LTH project demonstrating an RDF vocabulary based on this extended version of SAM. The section of text is connected to the original creative agent, while annotations of the text and a proposed translation are connected to users of the system. This use of SAM supports a unified account of the provenance roles and agents involved in textual research in the humanities.",
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            "title": "What Dataset Descriptions Actually Describe: Using the Systematic Assertion Model to Connect Theory and Practice",
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            "abstractNote": "Scientific data is encoded and described with the aim of supporting retrieval, meaningful interpretation and reuse. Encoding standards for datasets like FGDC, DwC, EML typically include tagged metadata elements along with the encoded data, suggesting that, per the Dublin Core 1:1 principle, those elements apply to one and only one entity (a specimen, observation, dataset, etc.). However, in practice vocabularies are often used to describe different dimensions of scientific data collection and communication processes. Discriminating these aspects offers a more precise account of how symbols and the propositions they express acquire the status of \"data\" and \"data content,\" respectively. In this poster we present an analysis of species occurrence records basecd on the Systematic Assertion Model (SAM).",
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            "abstractNote": "The connection between notation and the content it expresses is always contingent, and mediated through complex layers of interpretation. Some content bears directly on the encoder's intention to convey a particular meaning, while other content concerns the structures in and through which that meaning is expressed and organized. Interpretive frames are abstractions that serve as context for symbolic expressions. They form a backdrop of dependencies for data management and preservation strategies. Situation semantics offers a theoretical grounding for interpretive frames that integrates them into a general theory of communication through markup and other notational structures.",
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            "title": "Completeness, coverage & equivalence in scientific data records",
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                    "firstName": "Andrea K.",
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            "abstractNote": "Previously we asked, \"When is a record data and when is it a fish?\" (Wickett et al., 2012). In this work, we ask,\"when and in what contexts are a record and a fish equivalent?\" We describe and compare a collection of potentially equivalent records describing a Mola mola, or Ocean Sunfish, specimen. We calculate the Metadata Coverage Index (MCI) of each record and explore the use the Systematic Assertion Model (Dubin, 2010) to support investigation of the assertions contained in these data records.",
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            "title": "Identifying content and levels of representation in scientific data",
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                    "creatorType": "author",
                    "firstName": "Karen M.",
                    "lastName": "Wickett"
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                    "firstName": "Allen H.",
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            "abstractNote": "Heterogeneous digital data that has been produced by different communities with varying practices and assumptions, and that is organized according to different representation schemes, encodings, and file formats, presents substantial obstacles to efficient integration, analysis, and preservation. This is a particular impediment to data reuse and interdisciplinary science. An underlying problem is that we have no shared formal conceptual model of information representation that is both accurate and sufficiently detailed to accommodate the management and analysis of real world digital data in varying formats. Developing such a model involves confronting extremely challenging foundational problems in information science. We present two complementary conceptual models for data representation, the Basic Representation Model and the Systematic Assertion Model. We show how these models work together to provide an analytical account of digitally encoded scientific data. These models will provide a better foundation for understanding and supporting a wide range of data curation activities, including format migration, data integration, data reuse, digital preservation strategies, and assessment of identity and scientific equivalence.",
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