Sample data processing in an additive and reproducible taxonomic workflow by using character data persistently linked to preserved individual specimens

We present the model and implementation of a workflow that blazes a trail in systematic biology for the re-usability of character data (data on any kind of characters of pheno- and genotypes of organisms) and their additivity from specimen to taxon level. We take into account that any taxon characterization is based on a limited set of sampled individuals and characters, and that consequently any new individual and any new character may affect the recognition of biological entities and/or the subsequent delimitation and characterization of a taxon. Taxon concepts thus frequently change during the knowledge generation process in systematic biology. Structured character data are therefore not only needed for the knowledge generation process but also for easily adapting characterizations of taxa. We aim to facilitate the construction and reproducibility of taxon characterizations from structured character data of changing sample sets by establishing a stable and unambiguous association between each sampled individual and the data processed from it. Our workflow implementation uses the European Distributed Institute of Taxonomy Platform, a comprehensive taxonomic data management and publication environment to: (i) establish a reproducible connection between sampled individuals and all samples derived from them; (ii) stably link sample-based character data with the metadata of the respective samples; (iii) record and store structured specimen-based character data in formats allowing data exchange; (iv) reversibly assign sample metadata and character datasets to taxa in an editable classification and display them and (v) organize data exchange via standard exchange formats and enable the link between the character datasets and samples in research collections, ensuring high visibility and instant re-usability of the data. The workflow implemented will contribute to organizing the interface between phylogenetic analysis and revisionary taxonomic or monographic work

2011 Strategic roadmap for Australian research infrastructure

The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups   It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation. The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure. NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure

Unifying European Biodiversity Informatics (BioUnify)

In order to preserve the variety of life on Earth, we must understand it better. Biodiversity research is at a pivotal point with research projects generating data at an ever increasing rate. Structuring, aggregating, linking and processing these data in a meaningful way is a major challenge. The systematic application of information management and engineering technologies in the study of biodiversity (biodiversity informatics) help transform data to knowledge. However, concerted action is required to be taken by existing e-infrastructures to develop and adopt common standards, provisions for interoperability and avoid overlapping in functionality. This would result in the unification of the currently fragmented landscape that restricts European biodiversity research from reaching its full potential. The overarching goal of this COST Action is to coordinate existing research and capacity building efforts, through a bottom-up trans-disciplinary approach, by unifying biodiversity informatics communities across Europe in order to support the long-term vision of modelling biodiversity on earth. BioUnify will: 1. specify technical requirements, evaluate and improve models for efficient data and workflow storage, sharing and re-use, within and between different biodiversity communities; 2. mobilise taxonomic, ecological, genomic and biomonitoring data generated and curated by natural history collections, research networks and remote sensing sources in Europe; 3. leverage results of ongoing biodiversity informatics projects by identifying and developing functional synergies on individual, group and project level; 4. raise technical awareness and transfer skills between biodiversity researchers and information technologists; 5. formulate a viable roadmap for achieving the long-term goals for European biodiversity informatics, which ensures alignment with global activities and translates into efficient biodiversity policy

Game of Tenure: the role of “hidden” citations on researchers’ ranking in Ecology

Field ecologists and macroecologists often compete for the same grants and academic positions, with the former producing primary data that the latter generally use for model parameterization. Primary data are usually cited only in the supplementary materials, thereby not counting formally as citations, creating a system where field ecologists are routinely under-acknowledged and possibly disadvantaged in the race for funding and positions. Here, we explored how the performance of authors producing novel ecological data would change if all the citations to their work would be accounted for by bibliometric indicators. We collected the track record of >2300 authors from Google Scholar and citation data from 600 papers published in 40 ecology journals, including field-based, conservation, general ecology, and macroecology studies. Then we parameterized a simulation that mimics the current publishing system for ecologists and assessed author rankings based on number of citations, H-Index, Impact Factor, and number of publications under a scenario where supplementary citations count. We found weak evidence for field ecologists being lower ranked than macroecologists or general ecologists, with publication rate being the main predictor of author performance. Current ranking dynamics were largely unaffected by supplementary citations as they are 10 times less than the number of main text citations. This is further exacerbated by the common practice of citing datasets assembled by previous research or data papers instead of the original articles. While accounting for supplementary citations does not appear to offer a solution, researcher performance evaluations should include criteria that better capture authors’ contribution of new, publicly available data. This could encourage field ecologists to collect and store new data in a systematic manner, thereby mitigating the data patchiness and bias in macroecology studies, and further accelerating the advancement of ecology and related areas of biogeography

A framework to support the annotation, discovery and evaluation of data in ecology, for a better visibility and reuse of data and an increased societal value gained from environmental projects

Die vorliegende Dissertationsschrift beschäftigt sich im Kern mit der Verwendung von Metadaten in alltäglichen, datenbezogenen Arbeitsabläufen von Ökologen. Die vorgelegte Arbeit befasst sich dabei mit der Erstellung eines Rahmenwerkes zur Unterstützung der Annotation ökologischer Daten, der effizienten Suche nach ökologischen Daten in Datenbanken und der Einbindung von Metadaten während der Datenanalyse. Weiterhin behandelt die Arbeit die Dokumentation von Analysen sowie die Auswertung von Metadaten zur Entwicklung von Werkzeugen für eine Aufbereitung von Informationen über ökologische Projekte. Diese Informationen können zur Evaluation und Maximierung des aus den Projekten gezogenen gesellschaftlichen Mehrwerts eingesetzt werden. Die vorliegende Arbeit ist als kumulative Dissertation in englischer Sprache abgefasst. Sie basiert auf zwei Veröffentlichungen als Erstautor und einem zur Einreichung vorbereiteten Manuskript

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

RESEARCH DATA MANAGEMENT (RDM) SERVICES IN LIBRARIES: LESSONS FOR ACADEMIC LIBRARIES IN NIGERIA

Research funding organizations understand the importance of infrastructure and services to organize and preserve research data. Academic research libraries have been identified as locations in which to base these research data management services. Research data management services include data management planning, digital curation (selection, preservation, maintenance, and archiving), and metadata creation and conversion. However, some libraries are beginning to provide structure for research data management services. These services are starting to record some degree of success as local data policies are being formulated. The aim of this paper, therefore, is to discuss the importance of research data management in the academic libraries in Nigeria. The article summarized the research data management life cycle to include: data creation; data collection and description, data storage; data archiving and preservation; data access; data discovery and analysis, and data reuse and transformation. The paper further identified research data management tools and applications, which include DMPonline, Data Asset framework, Collaborative Assessment of Research Data Infrastructure and Objectives (CARDIO), and Curation cost exchange. Specifically, the paper examines some skills requirements for research data management in academic libraries. Some of the challenges facing effective research data management services identified by this paper include technology obsolescence, technology fragility; Lack of guidelines on good practice; Inadequate financial and human resources to manage data well, and Lack of evidence about best infrastructures