From XML to XML: The why and how of making the biodiversity literature accessible to researchers

We present the ABLE document collection, which consists of a set of annotated volumes of the Bulletin of the British Museum (Natural History). These follow our work on automating the markup of scanned copies of the biodiversity literature, for the purpose of supporting working taxonomists. We consider an enhanced TEI XML markup language, which is used as an intermediate stage in translating from the initial XML obtained from Optical Character Recognition to the target taXMLit. The intermediate representation allows additional information from external sources such as a taxonomic thesaurus to be incorporated before the final translation into taXMLit

Technical Report: CSVM Ecosystem

The CSVM format is derived from CSV format and allows the storage of tabular like data with a limited but extensible amount of metadata. This approach could help computer scientists because all information needed to uses subsequently the data is included in the CSVM file and is particularly well suited for handling RAW data in a lot of scientific fields and to be used as a canonical format. The use of CSVM has shown that it greatly facilitates: the data management independently of using databases; the data exchange; the integration of RAW data in dataflows or calculation pipes; the search for best practices in RAW data management. The efficiency of this format is closely related to its plasticity: a generic frame is given for all kind of data and the CSVM parsers don't make any interpretation of data types. This task is done by the application layer, so it is possible to use same format and same parser codes for a lot of purposes. In this document some implementation of CSVM format for ten years and in different laboratories are presented. Some programming examples are also shown: a Python toolkit for using the format, manipulating and querying is available. A first specification of this format (CSVM-1) is now defined, as well as some derivatives such as CSVM dictionaries used for data interchange. CSVM is an Open Format and could be used as a support for Open Data and long term conservation of RAW or unpublished data.Comment: 31 pages including 2p of Anne

Representation and Encoding of Heterogeneous Data in a Web Based Research Environment for Manuscript and Textual Studies

This paper describes the general architecture of a digital research environment for manuscript and textual studies (particularly those pertaining to ancient Greek and Byzantine texts), and it discusses some questions of data representation and encoding in the framework of such an online research platform. The platform is being developed by the project Teuchos. Zentrum für Handschriften- und Textforschung, established in 2007 by the Institut für Griechische und Lateinische Philologie (Universität Hamburg) in cooperation with the Aristoteles-Archiv (Freie Universität Berlin). Teuchos is a long-term infrastructural project of the Universität Hamburg. It is currently in its three-year initial phase which is being co-funded by the German Research Foundation (DFG) through the "Thematic Information Networks" scheme within the "Scientific Library Services and Information Systems" programme. We introduce the main object types to be handled by our system and describe the overall functionality of the online platform. The paper focuses on the representations of two main object types: manuscripts as textual witnesses and watermarks, with an emphasis on the former. Since the adequate encoding of different layers of structure of a transmitted text is particularly relevant to optimising users' choices of navigating both digital images of the containing manuscripts and trancriptions of the text contained, this topic is discussed in some detail. We introduce the formal data model and the corresponding encoding for the object types discussed. The project encodes textual data in XML, aiming for TEI conformance where possible. Since no accepted XML model exists for the encoding of metadata within a watermark collection, we briefly explain how we chose to model the objects to accomodate the collections the project is making accessible

HUDDL for description and archive of hydrographic binary data

Many of the attempts to introduce a universal hydrographic binary data format have failed or have been only partially successful. In essence, this is because such formats either have to simplify the data to such an extent that they only support the lowest common subset of all the formats covered, or they attempt to be a superset of all formats and quickly become cumbersome. Neither choice works well in practice. This paper presents a different approach: a standardized description of (past, present, and future) data formats using the Hydrographic Universal Data Description Language (HUDDL), a descriptive language implemented using the Extensible Markup Language (XML). That is, XML is used to provide a structural and physical description of a data format, rather than the content of a particular file. Done correctly, this opens the possibility of automatically generating both multi-language data parsers and documentation for format specification based on their HUDDL descriptions, as well as providing easy version control of them. This solution also provides a powerful approach for archiving a structural description of data along with the data, so that binary data will be easy to access in the future. Intending to provide a relatively low-effort solution to index the wide range of existing formats, we suggest the creation of a catalogue of format descriptions, each of them capturing the logical and physical specifications for a given data format (with its subsequent upgrades). A C/C++ parser code generator is used as an example prototype of one of the possible advantages of the adoption of such a hydrographic data format catalogue

BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)

We describe a binding schema markup language (BSML) for describing data interchange between scientific codes. Such a facility is an important constituent of scientific problem solving environments (PSEs). BSML is designed to integrate with a PSE or application composition system that views model specification and execution as a problem of managing semistructured data. The data interchange problem is addressed by three techniques for processing semistructured data: validation, binding, and conversion. We present BSML and describe its application to a PSE for wireless communications system design

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach