Qualitative Virtual Team Research as Training Method in a Postgraduate Program in Administration

This study is a retrospective review of methodological strategies employed during a virtual team-based training qualitative study about the emergent process of adapting to remote education among students and professors from a Master Management Program. The aim of this study was to test the technique of collaborative research as an educational and training strategy for Ph.D. students of management who are inexperienced in qualitative inductive research carried out in a virtual environment. A professor and eight Ph.D. students formed the research team and applied a qualitative inductive approach. As a result, 18 methodological steps emerged, which required just over one hundred hours of work. We describe advantages and challenges faced during the process, including greater credibility and validity for the results, technical and interactional difficulties of the virtual research environment, and difficulty reaching consensus in the data analysis stage. The findings also highlight the importance of coordination, active participation, and continuous assessment as Ph.D. educational and teaching strategies. Qualitative Virtual Team Research has proved to be a potential training tool for beginning researchers. We also contribute to the body of research on Ph.D. education and teaching by detailing the procedures used to coordinate the project and clarifying details regarding the strategies used to reach consensus in data analysis development.

VirES for Swarm & Virtual Research Environment: Software, guides & infrastructure to boost accessibility of Swarm

<p>ESA's Swarm mission continues to deliver excellent data providing insight into a wide range of geophysical phenomena. The mission is an important asset whose data are used within a number of critical resources, from geomagnetic field models to space weather services. As the product portfolio grows to better deliver on the mission's scientific goals, we face increasing complexity in accessing, processing, and visualising the data and models. ESA provides “VirES for Swarm” [1] (developed by EOX IT Services) to help solve this problem. VirES is a web-based data retrieval and visualisation tool where the majority of Swarm products are available. VirES has a graphical interface but also a machine-to-machine interface (API) for programmable use (a Python client is provided). The VirES API also provides access to geomagnetic ground observatory data, as well as forwards evaluation of geomagnetic field models to give data-model residuals. The "Virtual Research Environment" (VRE) adds utility to VirES with a free cloud-based JupyterLab interface allowing scientists to immediately program their own analysis of Swarm products using the Python ecosystem. We are augmenting this with a suite of Jupyter notebooks and dashboards, each targeting a specific use case, and seek community involvement to grow this resource.</p><p>[1] https://vires.services</p>

VirES for Aeolus - Virtual Research Environment (VRE)

<p><span>The VirES for Aeolus service (https://aeolus.services) has been successfully running </span><span>by EOX </span><span>since August 2018. The service </span><span>provides</span><span> easy access </span><span>and</span><span> analysis functions for the entire data archive of ESA's Aeolus Earth Explorer mission </span><span>through a web browser</span><span>.</span></p><p><span>This </span>free and open service <span>is being extended with a Virtual Research Environment (VRE). </span><span>The VRE </span><span>builds on the available data access capabilities of the service and provides </span><span>a </span><span>data access Application Programming Interface (API) a</span><span>s part of a </span><span>developing environment </span><span>i</span><span>n the cloud </span><span>using </span><span>JupyterHub and </span><span>JupyterLab</span><span> for processing and exploitation of the Aeolus data. </span>In collaboration with Aeolus DISC user requirements are being collected, implemented and validated.</p><p>Jupyter Notebook templates, an extensive set of tutorials, and documentation are being made available to enable a quick start on how to use VRE in projects. <span>The VRE is intended to support and simplify </span><span>the </span><span>work of (citizen-) scientists </span><span>interested in</span><span> Aeolus data by being able to </span><span>quickly develop processes or algorithms that can be </span><span>shar</span><span>ed or used to create </span><span>visualizations</span><span> for publications. Having a unified constant platform could potentially also be very helpful for calibration and validation activities </span><span>by </span><span>allowing easier result comparisons. </span></p>

V-FOR-WaTer: A Virtual Research Environment for Environmental Research

<p>The virtual research environment V-FOR-WaTer aims at simplifying data access for environmental sciences, fostering data publications and facilitating data analyses. By giving scientists from universities, research facilities and state offices easy access to data, appropriate pre-processing and analysis tools and workflows, we want to accelerate scientific work and facilitate the reproducibility of analyses.</p><p>The prototype of the virtual research environment consists of a database with a detailed metadata scheme that is adapted to water and terrestrial environmental data. Present datasets in the web portal originate from university projects and state offices. We are also finalising the connection of V-FOR-WaTer to GFZ Data Services, an established repository for geoscientific data. This will ease publication of data from the portal and in turn give access to datasets stored in this repository. Key to being compatible with GFZ Data Services and other systems is the compliance of the metadata scheme with international standards (INSPIRE, ISO19115).</p><p>The web portal is designed to facilitate typical workflows in environmental sciences. Map operations and filter options ensure easy selection of the data, while the workspace area provides tools for data pre-processing, scaling, and common hydrological applications. The toolbox also contains more specific tools, e.g. for geostatistics and soon for evapotranspiration. It is easily extendable and will ultimately also include user-developed tools, reflecting the current research topics and methodologies in the hydrology community. Tools are accessed through Web Processing Services (WPS) and can be joined, saved and shared as workflows, enabling more complex analyses and ensuring reproducibility of the results.</p>

Integration of evapotranspiration estimates from scaled sap flow values and eddy covariance measurements in the BRIDGET toolbox

<p>Comparing estimates of evapotranspiration (ET) from different in-situ measurements – or between in-situ measurements and remote sensing products or modelling outputs – always entails the challenge of different scales and method-specific uncertainties. Especially when the estimates originate in different research disciplines, addressing and quantifying the various sources of uncertainty of the scaled ET values becomes a difficult task for individual researchers who are not familiar with all the methodological details.</p><p>The BRIDGET toolbox – developed within the Digital Earth project – wants to support the integration and scaling of diverse in-situ ET measurements by providing tools for storage, merging and visualisation of multi-scale and multi-sensor ET data. This requires an appropriate metadata description for the various measurements as well as an assessment of method-specific uncertainties which need to be supported by domain experts. We combine these tools in a standalone python package and also implement them in an existing virtual research environment (V-FOR-WaTer).</p><p>Our first use case defines and quantifies the various sources of uncertainty when scaling sap flow values from individual sensor measurements in a tree up to the transpiration estimate of a stand. Comparison estimates come from eddy covariance measurements, lysimeters and remote sensing products.</p>

LA RECOLECCIÓN DE DATOS COMO LABORATORIO EPISTEMOLÓGICO. ALGUNAS REFLEXIONES ACERCA DEL ENTORNO VIRTUAL DE INVESTIGACIÓN REVISTAS CULTURALES 2.0

Resumen: Sobre la base de las experiencias ganadas con el entorno virtual de investigación Revistas Culturales 2.0 queremos reflexionar sobre los retos epistemológicos que supone la recolección de datos. Mientras que los “datos” de la literatura solían considerarse en la investigación filológica como algo previo al proceso epistemológico, en las Humanidades Digitales forman parte integral e incluso pueden ser el resultado de la investigación. Es necesario, pues, empezar a valorar esta importancia epistémica tanto para una mayor aceptación de los métodos de las Humanidades Digitales en las disciplinas filológicas como para mejorar la colaboración entre archivos y bibliotecas, y la investigación en el futuro.Abstract: Based on our experiences gained with the virtual research environment Revistas culturales 2.0, we want to reflect on the epistemological challenges of data collection. While in traditional literary criticism “data” used to be considered as something prior to the epistemological process, in Digital Humanities they are an integral part and may even be the result of research. It is necessary to begin to value this epistemic importance both for greater acceptance of the methods of the Digital Humanities in the philological disciplines and to improve collaboration between archives and libraries and research in the future.

Reconstructions of droughts in Germany since 1500 – combining hermeneutic information and instrumental records in historical and modern perspectives

The present article deals with the reconstruction of drought time series in Germany since 1500. The reconstructions are based on historical records from the virtual research environment Tambora (tambora.org, 2018) and official instrumental records. The historical records and recent data were related to each other through modern index calculations, drought categories and their historical equivalents. Historical and modern written documents are also taken into account to analyze the climatic effects and consequences on the environment and society. These pathways of effects are derived and combined with different drought categories. The derived historical precipitation index (HPI) is correlated with the standardized precipitation index (SPI). Finally, a historical drought index (HDI) and a historical wet index (HWI) are derived from the basic monthly precipitation index (PI) from 1500 onward. Both are combined for the historical humidity index (HHI). On this basis, the long-term development of dryness and drought in Germany since 1500, as well as medium-term deviations of drier and wetter periods and individual extreme events, is presented and discussed.

A conceptual virtual research environment model for the management of research data, a South African perspective

PurposeThe purpose of this article is to give an overview of the development of a Virtual Research Environment (VRE) conceptual model for the management of research data at a South African university.Design/methodology/approachThe research design of this article consists of empirical and non-empirical research. The non-empirical part consists of a critical literature review to synthesise the strengths, weaknesses (limitations) and omissions of identified VRE models as found in literature to develop a conceptual VRE model. As part of the critical literature review concepts were clarified and possible applications of VREs in research lifecycles and research data lifecycles were explored. The empirical part focused on the practical application of this model. This part of the article follows an interpretivist paradigm, and a qualitative research approach, using case studies as inquiry method. Case studies with a positivist perspective were selected through purposive sampling, and inferences were drawn from the sample to design and test a conceptual VRE model, and to investigate the management of research data through a VRE. Investigation was done through a process of participatory action research (PAR) and included semi-structured interviews and participant observation data collection techniques. Evaluation of findings was done through formative and summative evaluation.FindingsThe article presents a VRE conceptual model, with identified generic component layers and components that could potentially be applied and used in different research settings/disciplines. The article also reveals the role that VREs play in the successful management of research data throughout the research lifecycle. Guidelines for setting up a conceptual VRE model are offered.Practical implicationsThis article assisted in clarifying and validating the various components of a conceptual VRE model that could be used in different research settings and disciplines for research data management.Originality/valueThis article confirms/validates generic layers and components that would be needed in a VRE by synthesising these in a conceptual model in the context of a research lifecycle and presents guidelines for setting up a conceptual VRE model.

A Web-Based Virtual Research Environment for Marine Data

<p>Like most areas of research, the marine sciences are undergoing an increased use of observational data from a multitude of sensors. As it is cumbersome to download, combine and process the increasing volume of data on the individual researcher's desktop computer, many areas of research turn to web- and cloud-based platforms. In the scope of the SeaDataCloud project, such a platform is being developed together with the EUDAT consortium.</p><p>The SeaDataCloud Virtual Research Environment (VRE) is designed to give researchers access to popular processing and visualization tools and to commonly used marine datasets of the SeaDataNet community. Some key aspects such as user authentication, hosting input and output data, are based on EUDAT services, with the perspective of integration into EOSC at a later stage.</p><p>The technical infrastructure is provided by five large EUDAT computing centres across Europe, where operational environments are heterogeneous and spatially far apart. The processing tools (pre-existing as desktop versions) are developed by various institutions of the SeaDataNet community. While some of the services interact with users via command line and can comfortably be exposed as JupyterNotebooks, many of them are very visual (e.g. user interaction with a map) and rely heavily on graphical user interfaces.</p><p>In this presentation, we will address some of the issues we encountered while building an integrated service out of the individual applications, and present our approaches to deal with them.</p><p>Heterogeneity in operational environments and dependencies is easily overcome by using Docker containers. Leveraging processing resources all across Europe is the most challenging part as yet. Containers are easily deployed anywhere in Europe, but the heavy dependence on (potentially shared) input data, and the possibility that the same data may be used by various services at the same time or in quick succession means that data synchronization across Europe has to take place at some point of the process. Designing a synchronization mechanism that does this without conflicts or inconsistencies, or coming up with a distribution scheme that minimizes the synchronization problem is not trivial.</p><p>Further issues came up during the adaptation of existing applications for server-based operation. This includes topics such as containerization, user authentication and authorization and other security measures, but also the locking of files, permissions on shared file systems and exploitation of increased hardware resources.</p>