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Provenance Description of Metadata Vocabularies for the Long-term Maintenance of Metadata

Information System (OAIS) and PREMIS , provenance of digital objects is a required component that has to be recorded for longevity of digital objects. As provenance of metadata is crucial for metadata longevity of such digital objects, how to formally and consistently describe the provenance of metadata over time is an important issue. Provenance of metadata schemas and provenance of metadata vocabularies, as well as provenance of metadata terms have to be consistently recorded

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Geochemical assessment of claystone deposits from the Patti Formation, Southern Bida Basin, Nigeria

.M. (1997): Trace and rare earth elemental variation in Arabian Sea sediments through a transect across the oxygen minimum zone. Geochimica et Cosmochimica Acta , 61(12), pp. 2375–2388. [5] Cullers, R.L. (2002): Implications of elemental concentrations for provenance, redox conditions and metamorphic studies of shales and limestones near Pueblo, Colorado, USA. Chemical Geology , 19(4), pp. 305–327. [6] Armstrong-Altrin, J.S., Verma, S.P., Madhavaraju, J., Lee, Y.I., Ramasamy, S. (2003): Geochemistry of Late Miocene Kudankulam Limestones, South India

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Geochemistry of Fluvial Sediments from Geregu, Southwest Nigeria

-650. [4] McLennan, S.M. (1989): Rare earth elements in sedimentary rocks: Influence of provenance and sedimentary processes. Reviews in Mineralogy, 21, pp. 169-200. [5] McLennan, S.M., Hemming, S., McDaniel, D.K., Hanson, G.N. (1993): Geochemical approaches to sedimentation, provenance and tectonics. In: Johnson, M.J., Basu, A. (Eds.). Processes controlling the composition of clastic sediments. Geological Society of America Special Paper 284, pp. 21-40. [6] Condie, K.C. (1993): Chemical composition and evolution of upper continental crust

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Contents Index to Volume 2

& Lucinda Johnston   2017,2(1),51–69 Insight into the Disciplinary Structure of Nanoscience & Nanotechnology Chunjuan Luan & Alan L. Porter   2017,2(1),70–88 Usage Count: A New Indicator to Detect Research Fronts Guoqiang Liang, Haiyan Hou, Zhigang Hu, Fu Huang, Yajie Wang & Shanshan Zhang   2017,2(1),89–104 Provenance Description of Metadata Vocabularies for the Long-term Maintenance of Metadata Chunqiu Li & Shigeo Sugimoto   2017,2(2),41–55 Enhancing Navigability: An Algorithm for Constructing Tag Trees Chong Chen & Pengcheng Luo   2017

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Data-driven Discovery: A New Era of Exploiting the Literature and Data

. Provenance has been introduced to data and workflows in scientific research to provide detailed documentation to enable scientific reproducibility. The World Wide Web Consortium has recommended a standard representation for provenance in a human readable and machine understandable way ( Groth & Moreau, 2013 ). Transparency must be considered essential and achieved through active citation and provenance to further advance transparent sciences. Connecting Intelligence Machines taking their full place at the table of data-driven discovery is a significant step; these

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Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

two concepts. 2.1 Metadata: A Value-added Language Metadata has been loosely defined and popularized as data about data, information about information. More comprehensive definitions address metadata as structured data supporting functions associated with an object , an object being any “entity, form, or mode” ( Greenberg, 2005 , 2010; Lytras, Sicilia, & Cechinel, 2013 ). Examples of metadata functions include data discovery, access, use, provenance tracking, authenticity and security verification, preservation management, and other activities throughout the

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Dpaper: An Authoring Tool for Extractable Digital Papers

Publication Package (SPP). This tool is used to package raw data, provenance products, algorithms, software, text, context, and metadata, wherein scientists are able to capture, index, store, share, exchange, reuse, compare, and integrate scientific results. SPP is a compound digital object based on a number of scientific models and represented as a Resource Description Framework (RDF) package. Relations among internal digital objects in compound objects are either explicitly defined by the inference of ontology in the stage of metadata capture, or defined by the scientist

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A Framework for the Assessment of Research and Its Impacts

studies. Finally, the “unit-free property” of data, in terms of data quality aims at reaching a kind of “objectivity,” for empirical purpose and for data reuse. The provenance initiative ( Moreau et al., 2008 ) is a clear example of describing better data for different purposes, including also the opening or sharing of data. Quality as acceptability (suitability) for application (fitness for purpose) is the overarching concept, which keeps together the building blocks of the three dimensions of our framework. It is a characteristic in all the three dimensions. The

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Smart Data for Digital Humanities

-bearing objects (textual or non-textual, digitized or non-digitized) in all kinds of formats (examples can be found in Figure 3 ). These primary data resources are held in special collections, archives, oral history files, annual reports, provenance indexes, and inventories, to name just a few. The nature of such data is quite different from, for instance, that of the data used by the “digital universe” that “is made up of images and videos on mobile phones uploaded to YouTube, digital movies populating the pixels of our high-definition TVs, banking data swiped in an ATM

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Big Data and Data Science: Opportunities and Challenges of iSchools

cases dealing with only structured data. The narrow sense of data curation is archiving data to a permanent storage for the future retrieval. In the context of big data, the scope of data curation may be expanded to include the processes of collecting, integrating, organizing, annotating, and publishing data from various sources in order to keep track of provenance of data. These activities are closely related to the maintenance of data quality and metadata management. Data quality is an important requirement for successful data science projects, and metadata

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