SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE

Discipline Formation in Information Management: Case Study of Scientific and Technological Information Services

Discipline formation in information management is investigated through a case study of the origi-nation and development of information services for scientific and technical information in Australia. Particular reference is made to a case of AESIS, a national geoscience, minerals and petroleum reference database coordinated by the Australian Mineral Foundation. This study pro-vided a model for consideration of similar services and their contribution to the discipline. The perspective adopted is to consider information management at operational, analytical and strate-gic levels. Political and financial influences are considered along with analysis of scope, perform-ance and quality control. Factors that influenced the creation, transitions, and abeyance of the service are examined, and some conclusions are drawn about an information management disci-pline being exemplified by such services

Versioning data is about more than revisions : A conceptual framework and proposed principles

A dataset, small or big, is often changed to correct errors, apply new algorithms, or add new data (e.g., as part of a time series), etc. In addition, datasets might be bundled into collections, distributed in different encodings or mirrored onto different platforms. All these differences between versions of datasets need to be understood by researchers who want to cite the exact version of the dataset that was used to underpin their research. Failing to do so reduces the reproducibility of research results. Ambiguous identification of datasets also impacts researchers and data centres who are unable to gain recognition and credit for their contributions to the collection, creation, curation and publication of individual datasets. Although the means to identify datasets using persistent identifiers have been in place for more than a decade, systematic data versioning practices are currently not available. In this work, we analysed 39 use cases and current practices of data versioning across 33 organisations. We noticed that the term ‘version’ was used in a very general sense, extending beyond the more common understanding of ‘version’ to refer primarily to revisions and replacements. Using concepts developed in software versioning and the Functional Requirements for Bibliographic Records (FRBR) as a conceptual framework, we developed six foundational principles for versioning of datasets: Revision, Release, Granularity, Manifestation, Provenance and Citation. These six principles provide a high-level framework for guiding the consistent practice of data versioning and can also serve as guidance for data centres or data providers when setting up their own data revision and version protocols and procedures.Peer reviewe

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Comprehensive compendium of Arabidopsis RNA-seq data, A

2020 Spring.Includes bibliographical references.In the last fifteen years, the amount of publicly available genomic sequencing data has doubled every few months. Analyzing large collections of RNA-seq datasets can provide insights that are not available when analyzing data from single experiments. There are barriers towards such analyses: combining processed data is challenging because varying methods for processing data make it difficult to compare data across studies; combining data in raw form is challenging because of the resources needed to process the data. Multiple RNA-seq compendiums, which are curated sets of RNA-seq data that have been pre-processed in a uniform fashion, exist; however, there is no such resource in plants. We created a comprehensive compendium for Arabidopsis thaliana using a pipeline based on Snakemake. We downloaded over 80 Arabidopsis studies from the Sequence Read Archive. Through a strict set of criteria, we chose 35 studies containing a total of 700 biological replicates, with a focus on the response of different Arabidopsis tissues to a variety of stresses. In order to make the studies comparable, we hand-curated the metadata, pre-processed and analyzed each sample using our pipeline. We performed exploratory analysis on the samples in our compendium for quality control, and to identify biologically distinct subgroups, using PCA and t-SNE. We discuss the differences between these two methods and show that the data separates primarily by tissue type, and to a lesser extent, by the type of stress. We identified treatment conditions for each study and generated three lists: differentially expressed genes, differentially expressed introns, and genes that were differentially expressed under multiple conditions. We then visually analyzed these groups, looking for overarching patterns within the data, finding around a thousand genes that participate in stress response across tissues and stresses

Biodiversity Data: Refinement of Technology and Implementation Methods

Biodiversity data consists of taxonomic specimens and information that inform our interpretations of ecosystems and life on Earth. Museum projects, exhibitions, and research utilize biodiversity data to construct answers and educational programming for staff and visitors. Cleaning and maintaining biodiversity data, however, is a difficult challenge that involves moderation and refinement of data entry, inventory, workflows, and protocols. Creating an ideal framework that involves the utilization of technology and the management practices of data standards will help in developing baseline recommendations for institutions struggling to maintain their biodiversity collections. Surveys were sent to listservs and museum professionals to acquire interpretation and data surrounding biodiversity data practices. From survey results, three interviews/case studies were performed with one staff member, respectively, from the University of Wyoming Museum of Vertebrates, Bernice Pauahi Bishop Museum, and the Smithsonian National Museum of Natural History. These interviews and surveys, in conjunction with a literature review, were conducted to explore processes and strategies currently being utilized to develop biodiversity data frameworks. Results indicate a strong desire for customizable and malleable databases that integrate institutional-level decision-making and preventative error protocols. In addition, thorough documentation and active engagement with staff and volunteers contribute to long-term benefits to data management standards

The EPOS Research Infrastructure: a federated approach to integrate solid Earth science data and services

The European Plate Observing System (EPOS) is a Research Infrastructure (RI) committed to enabling excellent science through the integration, accessibility, use and re-use of solid Earth science data, research products and services, as well as by promoting physical access to research facilities. This article presents and describes the EPOS RI and introduces the contents of its Delivery Framework. In November 2018, EPOS ERIC (European Research Infrastructure Consortium) has been granted by the European Commission and was established to design and implement a long-term plan for the integration of research infrastructures for solid Earth science in Europe. Specifically, the EPOS mission is to create and operate a highly distributed and sustainable research infrastructure to provide coordinated access to harmonized, interoperable and quality-controlled data from diverse solid Earth science disciplines, together with tools for their use in analysis and modelling. EPOS relies on leading-edge e-science solutions and is committed to open access, thus enabling a step towards the change in multidisciplinary and cross-disciplinary scientific research in Earth science. The EPOS architecture and its Delivery Framework are discussed in this article to present the contributions to open science and FAIR (Findable, Accessible, Interoperable, and Reusable) data management, as well as to emphasize the community building process that supported the design, implementation and construction of the EPOS RI.publishedVersio

An Integrated Object Model and Method Framework for Subject-Centric e-Research Applications

A framework that integrates an object model, research methods (workflows), the capture of experimental data sets and the provenance of those data sets for subject-centric research is presented. The design of the Framework object model draws on and extends pre-existing object models in the public domain. In particular the Framework tracks the state and life cycle of a subject during an experimental method, provides for reusable subjects, primary, derived and recursive data sets of arbitrary content types, and defines a user-friendly and practical scheme for citably identifying information in a distributed environment. The Framework is currently used to manage neuroscience Magnetic Resonance and microscopy imaging data sets in both clinical and basic neuroscience research environments. The Framework facilitates multi-disciplinary and collaborative subject-based research, and extends earlier object models used in the research imaging domain. Whilst the Framework has been explicitly validated for neuroimaging research applications, it has broader application to other fields of subject-centric research