Adaptation of DSpace to the Specific Needs of the Agricultural Sciences and Technology Community

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PostersThe aim of this project is to explore the customization of DSpace according to the use of controlled vocabularies. The plug-in shall be easy–to–install modules available free for download on the DSpace site. The objective is the adaptation of DSpace to the specific needs of the Agricultural Sciences and Technology community in order to assure quality in metadata creation. Knowledge Organization Systems such as the AGROVOC thesaurus provide mechanisms for sharing information in a standardized manner by recommending the use of common semantics.Food and Agriculture Organization of the United Nation

Software Usability:A Comparison Between Two Tree-Structured Data Transformation Languages

This paper presents the results of a software usability study, involving both subjective and objective evaluation. It compares a popular XML data transformation language (XSLT) and a general purpose rule-based tree manipulation language which addresses some of the XML and XSLT limitations. The benefits of the evaluation study are discussed

FAIRness and Usability for Open-access Omics Data Systems

Omics data sharing is crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the FAIRness of NASAs GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. The range of overall FAIRness scores was 6-12 (out of 14), average 10.1, and standard deviation 2.4. The range of Pass ratings for the metrics was 29-79%, Partial Pass 0-21%, and Fail 7-50%. The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. Reusability of metadata, in particular, was frequently not well supported. We relate our experiences implementing semantic integration of omics data from some of the assessed systems for federated querying and retrieval functions, given their shortcomings in data interoperability. Finally, we propose two new principles that Big Data system developers, in particular, should consider for maximizing data accessibility

dLibra Digital Library Framework Overview

Pozna

Image Retrieval: Challenges and Solutions

Análisis de las características peculiares de la imagen como documento y de sus consecuencias de cara al desarrollo de sistemas automatizados de recuperación de este tipo de documentación. Tras desarrollar los conceptos de imagen y recuperación de imagen, se afronta el análisis de los retos que la imagen presenta desde el punto de vista de su recuperación, en especial el vacío semántico, y se describen las principales soluciones encontradas en la literatura sobre el tema principalmente desde 1990 hasta el presente. Se concluye que el enfoque actual (SBVIR) se caracteriza por simultanear el código visual y el código lingüístico en la representación.Analysis of the peculiar characteristics of images as documents and its consequences for the development of automated retrieval of this type of documentation. After developing the concepts of image and image retrieval, the challenges that image presents from the point of view of retrieval are analyzed, especially the semantic gap, and the main solutions found in the literature since 1990 to present are described. The current approach (SBVIR) is characterized by the simultaneous employment of the visual code and the language in representing images

Ontology Based Document Annotation: Trends and Open Research Problems

Metadata is used to describe documents and applications, improving information seeking and retrieval and its understanding and use. Metadata can be expressed in a wide variety of vocabularies and languages, and can be created and maintained with a variety of tools. Ontology based annotation refers to the process of creating metadata using ontologies as their vocabularies. We present similarities and differences with respect to other approaches for metadata creation, and describe languages and tools that can be used to implement these annotations

FAIRness and Usability for Open-Access Omics Data Systems

Omics data sharing is especially crucial to the biological research community, and the last decade or two has seen a huge rise in collaborative analysis systems, databases, and knowledge bases for omics and other systems biology data. We assessed the "FAIRness" of NASA's GeneLab Data Systems (GLDS) along with four similar kinds of systems in the research omics data domain, using 14 FAIRness metrics. 14 metrics. The range of Pass ratings was 29-79% of the 14 metrics, Partial Pass 0-21%, and Fail 7-50%. The range of overall FAIRness scores was 5-12 (out of 14). The systems we evaluated performed the best in the areas of data findability and accessibility, and worst in the area of data interoperability. We propose two new principles that Big Data systems, in particular, should consider for increasing data accessibility. We relate our experiences implementing semantic integration of omics data from several systems for the federated querying and retrieval functions of the GLDS, given the shortcomings in data interoperability of these systems

State tagging for improved Earth and environmental data quality assurance

Environmental data allows us to monitor the constantly changing environment that we live in. It allows us to study trends and helps us to develop better models to describe processes in our environment and they, in turn, can provide information to improve management practices. To ensure that the data are reliable for analysis and interpretation, they must undergo quality assurance procedures. Such procedures generally include standard operating procedures during sampling and laboratory measurement (if applicable), as well as data validation upon entry to databases. The latter usually involves compliance (i.e., format) and conformity (i.e., value) checks that are most likely to be in the form of single parameter range tests. Such tests take no consideration of the system state at which each measurement is made, and provide the user with little contextual information on the probable cause for a measurement to be flagged out of range. We propose the use of data science techniques to tag each measurement with an identified system state. The term “state” here is defined loosely and they are identified using k-means clustering, an unsupervised machine learning method. The meaning of the states is open to specialist interpretation. Once the states are identified, state-dependent prediction intervals can be calculated for each observational variable. This approach provides the user with more contextual information to resolve out-of-range flags and derive prediction intervals for observational variables that considers the changes in system states. The users can then apply further analysis and filtering as they see fit. We illustrate our approach with two well-established long-term monitoring datasets in the UK: moth and butterfly data from the UK Environmental Change Network (ECN), and the UK CEH Cumbrian Lakes monitoring scheme. Our work contributes to the ongoing development of a better data science framework that allows researchers and other stakeholders to find and use the data they need more readily