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10 results for “virtual research environments”
Virtual Research Environments Ethnography: a Preliminary Study
<p>Datasets accompanying the paper “Virtual Research Environments Ethnography: a Preliminary Study”, a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories.</p> <p>While for legal reasons we can not share the original datasets obtained by querying the databases, since they include copyrighted data, we can share the two datasets derived from the query results and the two topic modelling datasets.</p> <p>The dataset “<strong>main_dataset.csv</strong>” consists of the merged query results from ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink databases. It is structured into six columns: (i) doi; (ii) title; (iii) content_type; (iv) publication year; (v) keyword_search; (vi) DB.</p> <p>The ‘<strong>doi</strong>’, ‘<strong>title</strong>’, and ‘<strong>publication_year</strong>’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively.</p> <p>The ‘<strong>content_type</strong>’ label refers to the different and normalised typologies of resources: (a) Article; (b) Book, (c) Book Chapter; (d) Chapter; (e) Chapter ReferenceWorkEntry; (f) Conference Paper; (g) Conference Review; (h) Early Access Articles; (i) Editorial; (j) Erratum; (k) Letter; (l) Magazines; (m) Masters Thesis; (n) Note; (o) Ph.D. Thesis; (p) Retracted; (q) Review; (r) Short Survey; (s) Standards. (c) and (d) refer to the same type of entry (they are used in different databases), while in the case of (e) we observed that it is used in the Springer database to refer mainly to encyclopaedic entries.</p> <p>The ‘<strong>keyword_search</strong>’ label is used for identifying the keyword group used for formulating the query: (a) science gateway | scientific gateway; (b) virtual laboratory | Vlab; or (c) virtual research environment.</p> <p>The ‘<strong>DB</strong>’ label indicates the provenance of the entries from one of the five databases we selected for our study: (a) ACM; (b) IEEE; (c) ScienceDirect; (d) scopus; and (e) Springer, identifying the ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink respectively.</p> <p>The dataset “<strong>filtered_dataset.csv</strong>” consists of the deduplicated and filtered entries (journal articles and conference papers from 2010 onward, with a DOI assigned) from the “main_dataset.csv” we used as the final dataset for answering our research questions. It is structured into ten columns: (i) doi; (ii) title; (iii) venue; (iv) publication_year; (v) content_type; (vi) abstract; (vii) keywords; (viii) science gateway | scientific gateway; (ix) virtual laboratory | Vlab; and (x) virtual research environment.</p> <p>As for the previous dataset, the ‘<strong>doi</strong>’, ‘<strong>title</strong>’, and ‘<strong>publication_year</strong>’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively.</p> <p>The ‘<strong>venue</strong>’ label is used for indicating the conference or the journal the entries refer to. The values derive from the original query results.</p> <p>The ‘<strong>abstract</strong>’ and ‘<strong>keyword</strong>’ labels are used for the abstracts and the keywords associated with the entries. The values are mainly derived from the original query results, as we integrated the missing ones by querying OpenAIRE.</p> <p>The ‘<strong>science gateway | scientific gateway</strong>’, ‘<strong>virtual laboratory | Vlab</strong>’ and ‘<strong>virtual research environment</strong>’ labels indicate the connection between the entries and the keyword group used for denoting them. The values are binary (1 if the keywords belong to the group, 0 if they do not).</p> <p>The datasets “<strong>sg_vlab_vre_topics_datasets.csv</strong>” and “<strong>sgvlabvre_topics_dataset.csv</strong>” consist of the three datasets and of the unique dataset resulting from topic modelling, the first (corpus divided into three datasets) and the second analysis (corpus as a whole) respectively. They share the same structure: (i) Topic; (ii) #studies; (iii) Representative word; (iv) Representative word weight.</p> <p>The ‘<strong>Topic</strong>’ label is used for the topic denomination and the values consist of an alphanumeric string indicating the dataset and the progressive topic number: (a) SG, for the scientific gateway dataset; (b) VRE, for the virtual research environment dataset; (c) VLAB, for the virtual laboratory dataset; and (d) A, for the corpus as a whole.</p> <p>The ‘<strong>#studies</strong>’ label indicates the number of studies contributing to each topic.</p> <p>The ‘<strong>Representative word</strong>’ and ‘<strong>Representative word weight</strong>’ labels are used for denoting the keywords describing each topic and their weights respectively.</p>
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Database of movement behavior and EEG in virtual audiovisual everyday-life environments for hearing aid research
<p>This database contains movement behavior (head, eye, torso) and EEG signals of 21 young normal-hearing (11 male, 11 female, mean age 25 +/- 3.6 years) and 19 elderly normal-hearing subjects (9 male, 12 female, mean age 69 +/- 5.4 years) measured in virtual audiovisual listening environments in the laboratory. The virtual audiovisual environments that were used are: a living room, a lecture hall, a cafeteria, a street and a train station. The video and audio material for the environments is also available (see Related identifiers). The methods and an analysis of the movement behavior are described in Hendrikse et al. (2019). The supplementary materials to this paper that are published here include plots of the gaze trajectories of the subjects in all environments, plotted separately for the young and elderly subjects so that they can be compared, and histograms of the head-, eye- and torso-rotation for the environments that were not included in the paper.</p>
Biographische Informationssysteme (DPBs, Digital Knowledge Databases, Virtual Research Environments)
<p>The table is an overview of database and online systems to manage/publish prosopographical and biographical data. </p>
SIMCor - R-Statistical Environment embedded in the SIMCor Virtual Research Environment
<p>Using RStudio, the capabilities of running statistical evaluations in a standardised manner directly on the SIMCor Virtual Research Environment (VRE) has been realised. Via a web frontend, scripts written in the R programming language can be directly evaluated on the data, such as the virtual cohorts, available at the VRE.</p>
Fact Sheet: Introduction Virtual Research Environments and Overview
<p><strong>English:</strong></p> <p>Virtual Research Environments (VREs) are digital working platforms which enable scienctists to work cooperatively in a time- and location-independent way. Due to the increase of digital research data and the international collaboration of researchers, VREs gain in importance. Numerous VREs have been developed in different projects to support the work of researchers in a variety of disciplines. However, the thematic focus and functionality differ between available VREs.</p> <p>The new fact sheet summarizes important information about and requirements for VREs. In addition to the fact sheet we developed a summary of currently existing VREs for different disciplines which gives an overview over their scope and functionality.</p> <p>-------------------------------------------</p> <p><strong>German:</strong></p> <p>Virtuelle Forschungsumgebungen (VFUs) sind digitale Arbeitsplattformen, die es Wissenschaftlern ermöglichen zeit- und ortsunabhängig vernetzt zusammenzuarbeiten. Durch die Zunahme an digitalen Forschungsdaten und die stärkere internationale Zusammenarbeit von Wissenschaftlern gewinnen diese Anwendungen an Bedeutung. In den letzten Jahren wurden in zahlreichen Projekten neue VFUs für verschiedene Fachdisziplinen entwickelt. Dabei sind der thematische Schwerpunkt und der Funktionsumfang der verschiedenen VFUs sehr unterschiedlich.</p> <p>Mit der neuen Handreichung möchten wir allen Interessierten wichtige Informationen rund um das Thema VFU zur Verfügung stellen und einen Überblick über momentan verfügbare Systeme geben. Zusätzlich zu der Handreichung haben wir eine Zusammenfassung mit Informationen zu Virtuellen Forschungsumgebungen aus verschiedenen Fachbereichen zusammengestellt.</p> <p><strong>Daten in der Tabelle:</strong></p> <ul> <li>Name der Virtuellen Forschungsumgebung</li> <li>Fachgebiet</li> <li>Offenheit</li> <li>Zugänglichkeit</li> <li>Verfügbarkeit von Informationen zum Betriebs-/Nutzungsmodell</li> <li>Read-Only</li> <li>Themengebunden</li> <li>Möglichkeit der Verwaltung eigener Daten</li> <li>Eigene Daten zugriffsbeschränkt</li> <li>Link</li> </ul>
Figure 1 from: Smith V, Rycroft S, Brake I, Scott B, Baker E, Livermore L, Blagoderov V, Roberts D (2011) Scratchpads 2.0: a Virtual Research Environment supporting scholarly collaboration, communication and data publication in biodiversity science. ZooKeys 150: 53-70. https://doi.org/10.3897/zookeys.150.2193
Figure 1 - Scratchpad usage statistics from February 2007 to September 2011. The black dashed line represents the number of Scratchpad community sites (in hundreds) and the blue solid line represents the number of registered users (in thousands). As of September 2011 we have switched to recording the number of active users (currently 4424) since this figure provides a more accurate guide to usage.
Figure 2 from: Smith V, Rycroft S, Brake I, Scott B, Baker E, Livermore L, Blagoderov V, Roberts D (2011) Scratchpads 2.0: a Virtual Research Environment supporting scholarly collaboration, communication and data publication in biodiversity science. ZooKeys 150: 53-70. https://doi.org/10.3897/zookeys.150.2193
Figure 2 - Screenshots of the Scratchpad 2 publication module showing an example workflow. Top, the section writing tool showing material and methods section; middle, the relationship selector that allows a taxon and additional materials to be associated with a section of the publication; and bottom, supplementary files such as illustrations, photos or graphs can be added to complete the publication.
SIMCor - Demo of the Virtual Research Environment (VRE)
<p>This video demonstrates the usability of the Virtual Cohort Generators that were embedded into the SIMCor Virtual Research Environment. Via a REST API, a virtual cohort can be generated by specifying the output directory, the number of samples to be included in the cohort as well as specific model parameters. A unique virtual cohort ID is then generated together with the relevant virtual patient information, including the virtual surface geometries. In this example virtual patients for TAVI in-silico assessment of TAVI implantation were generated.</p>
Virtual audiovisual everyday-life environments for hearing aid research
<p>The virtual audiovisual environments in this database are published in conjunction with the paper Hendrikse et al. (2019). These virtual audiovisual environments were designed based on a number of situations with high importance and/or occurrence in everyday-life of young and elderly normal-hearing and hearing-impaired persons. They were designed to have a big range of different target sources (one or multiple live speakers or loudspeakers, close or further away) and distractors (concurrent speakers or other sounds, close or further away, moving or stationary). Their purpose was to enable the measurement of realistic head-, eye- and torso-movement and EEG during listening tasks in everyday-life situations. A database of the measured movement behavior and EEG of 21 young normal-hearing and 19 elderly normal-hearing subjects is also available (see Related identifiers). The methods and an analysis of the subjective experience of these environments are published in Hendrikse et al. (2019). The environments include: a living room (watching the news on TV), a lecture hall (listening to a lecture), a cafeteria (listening to a 4-person conversation), a train station (listening to the announcements) and a street (listening to a 4-person conversation at a bus stop or waiting for someone at a cross-section). The environments contain speech material in German. 3D videos of the environments can also be found on Youtube: <a href="https://www.youtube.com/playlist?list=PL-v3EIoK6ZarlOvYqCu7LpDext3Iafwtr">https://www.youtube.com/playlist?list=PL-v3EIoK6ZarlOvYqCu7LpDext3Iafwtr</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.