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Dataset results
213 results for “Research management”
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics covered in the analyzed articles
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Process of systematic mapping
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications per country
<p>Publications per country. Map based on longitude and latitude. The circle’s colors show each country, while their size indicates the number of articles.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Publications by year and research areas
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Contributions of the studies
<p>The size of the rectangles shows the amount of articles in each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Research approaches
<p>The size of the diamonds shows the number of articles that belong to each category.</p> <p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Figure 4 in New directions in weed management and research using 3D imaging
Figure 4. Three-dimensional point cloud reconstructions of soybean (A, top view; B, front view) and cereal rye (Secale cereale L.) (C, top view; D, front view). Note the voids in the soybean point cloud (B) caused by dense canopy cover. Such voids are largely absent in cereal rye (D) due to a more even canopy with greater light penetration.
Figure 3 in New directions in weed management and research using 3D imaging
Figure 3. Data pipeline for calculating canopy height and estimating biomass in the field using red, green, and blue (RGB) images and depth data.
Figure 2 in New directions in weed management and research using 3D imaging
Figure 2. Red,green,and blue (RGB) image of soybeans and weeds (A) and corresponding 3D point cloud reconstruction (B). Lower panels show point cloud reconstructions from different angles,including a top view (C), top view offset 45° from vertical (D), front view (E), under canopy and offset 45° (F), directly under canopy (G), facing canopy from behind (H), facing canopy offset 45° right (I), side view (J), and facing canopy offset 45° left (K).
Figure 1 in New directions in weed management and research using 3D imaging
Figure 1. Use of images taken from different angles to create a 3D reconstruction in structure-from-motion (SfM; top) vs. stereo-vision photogrammetry (bottom).
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>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania
<p>This data set presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community.</p> <p>The data set was used to support both the mini-dissertation as well as a paper published in the Data Science Journal. The journal paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country, academic institution. The paper also mentions the requirements for the sustainability of these initiatives.</p>
Figure 2 in Tench (Tinca tinca) in Sicily: current knowledge and research needs for conservation and management
Figure 2. – Recent distribution of tench in Sicily (dots: streams and rivers; squares: natural lakes and ponds; triangles: artificial habitats. Black symbols: tench found; open symbols: tench not found).
Figure 1 in Tench (Tinca tinca) in Sicily: current knowledge and research needs for conservation and management
Figure 1. – Past distribution of tench in Sicily (dots: streams and rivers; squares: natural lakes and ponds).
Fig. 2 in Trapping soybean looper (Lepidoptera: Noctuidae) in the southeastern USA and implications for pheromone-based research and management
Fig. 2. Mean number of Ctenoplusia oxygramma male moths captured at each of 3 trial locations where they were recorded as present. Note: Bio Pseudoplusia lures were not used at the LA-Crowley location, were installed at the LA-Ben Hur location on 2 Aug 2019, and were installed at the FL-Jay location for the entire trial period.
Fig. 1 in Trapping soybean looper (Lepidoptera: Noctuidae) in the southeastern USA and implications for pheromone-based research and management
Fig. 1. Mean number of Chrysodeixis includens male moths captured at each of 5 trial locations. Note: Bio Pseudoplusia lures were not used at the LA-Crowley or MS-Kiln locations, were installed at the LA-Ben Hur location on 2 Aug 2019 and at the MS-Starkville location on 31 Jul 2019, and were installed at the FL-Jay location for the entire trial period.
BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
<p><span><span>The demand for agent-based models to explore the effects of environmental change on pollinator population dynamics is growing. However, models need a simple yet flexible interface to enable adoption by a wide range of stakeholders. </span></span><span><span>We introduce BEE-STEWARD: a research and decision-support software tool, enabling researchers, policy-makers, land management advisors, and practitioners to predict and compare the effects of bee-friendly management interventions on bumblebee populations over several years. </span></span><span><span>BEE-STEWARD integrates the BEESCOUT and <i>Bumble</i>-BEEHAVE agent-based models of bumblebee behaviour, colony growth and landscape exploration into a user-friendly interface, with reconstructed code, and expanded functionality. Bespoke automatic reports can be created to illustrate how different land management interventions can affect the densities of bumblebees and their colonies over time. </span></span><span><span>BEE-STEWARD could be an important virtual test-bed for scientists exploring the impacts of different stressors on bumblebees and used by those with little or no modelling experience, enabling a shared methodology between research, policy, and practice.</span></span></p>
Guideline for a FAIR Cultural Studies Research Data Management
<p>Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“.</p> <p>Die <strong>Online-Handreichung</strong> ist verfügbar unter <a href="https://nfdi4culture.de/go/E3625">https://nfdi4culture.de/go/E3625</a>.</p> <p>Als <strong>PDF</strong> ist diese Handreichung verfügbar unter <a href="https://doi.org/10.5281/zenodo.7716941">https://doi.org/10.5281/zenodo.7716941</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.