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13,276 results for “IT management”
Effect size data for the meta-analysis article ""Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis"
<p>This Exel file includes the effect size dataset used for the statistical analysis for the paper "Effect of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis", which will be published in the Journal of Applied Ecology in 2018.</p> <p>This meta-analysis was conducted in the course of the project VineDivers (<a href="http://www.vinedivers.eu/">www.vinedivers.eu</a>) funded through the 2013-2014 BiodivERsA/FACCE-JPI joint call for research proposals, with the national funders: Austrian Science Fund (FWF), Spanish Ministry for Economy and Competitiveness (MINECO), French National Research Agency (ANR), Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) and Federal Ministry of Education and Research (BMBF/Germany). P. Batáry was supported by the German Research Foundation (DFG BA4438/2-1) and by the Economic Development and Innovation Operational Programme of Hungary (GINOP–2.3.2–15–2016–00019).</p> <p> </p>
Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)
<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>
Piece of CAKE: A Comprehensive Queue Management Solution for Home Gateways
<p>This is the evaluation dataset and online appendix for the paper "Piece of CAKE: A Comprehensive Queue Management Solution for Home Gateways".</p> <p>Also published at <a href="https://www.cs.kau.se/tohojo/cake/">https://www.cs.kau.se/tohojo/cake/</a></p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Instance of the learning ecosystem metamodel: model of the ecosystem for knowledge management in Spanish Public Administration
<p>The conceptual model definition of the learning ecosystem for knowledge management in the Spanish Public Administration has been made by defining three views or packages from the ecosystems metamodel presented above. The views correspond to the three main parts identified in the learning ecosystem metamodel (<a href="http://doi.org/10.5281/zenodo.829859">http://doi.org/10.5281/zenodo.829859</a>): software components, human elements and the relationship among each other.</p>
Instance of the learning ecosystem metamodel: model of the ecosystem for scientific knowledge management in a PhD programme
<p>The learning ecosystem modeled from the Ecosystems Metamodel is oriented to manage the scientific knowledge generated in the scope of the PhD Program on Education in the Knowledge Society at the University of Salamanca (<a href="https://knowledgesociety.usal.es">https://knowledgesociety.usal.es</a>).</p> <p>The model (M1-level) has been divided in three packages: the ecosystem tools model; the ecosystem users model; and the ecosystem services model. Furthermore, the classes from the metamodel (M2-level) are represented to indicate which classes are used to model the example.</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL managed grass
<p>This is model output from LPJmL for managed grass as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Research Data Management - Persistent identifier (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Research Data Management – Basics (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Code and data for "Optimising habitat management for amphibians: from simple models to complex decisions"
<p>The zip archive contains code and data to reproduce the analysis contained in the following manuscript:</p> <p>Scroggie, M.P., Preece, K., Nicholson, E., McCarthy, M.A., Parris, K.M. and Heard, G.W. Optimising habitat management for amphibians: from simple models to complex decisions.</p> <p>Included in the archive is the source code of two R packages (METAPOP, and METAPOPPLAN), which must first be installed, along with their various dependencies which include Rcpp, RcppArmadillo, sp, spdep and rgeos. As package METAPOPPLAN contains C++ code, installation requires the presence of the appropriate C++ compilers and other software development tools. These should be available or easily installable on Linux or other Unix based systems, but Microsoft Windows users must first install the appropriate version of Rtools, which can be downloaded from https://cran.r-project.org/bin/windows/Rtools/.</p> <p>With all appropriate packages installed, the analysis can be replicated by running the included Makefile.</p> <p>Total execution time will be quite long, due to the large number of simulations that must be run. On my Windows system, with 12 cores and 4GB of RAM, execution took approximately 10 days. The code will run much faster if the various management scenarios included in the analysis are executed in parallel. This can be done by executing make with a -j argument specifying the number of cores to utilise. For example, if your system has 12 cores, invoke *make* as follows:</p> <p>make -j 12</p> <p>Overall execution time will scale roughly with the number of available cores, up to a maximum of 24 (the total number of management scenarios).<br> </p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
Seasonal and Interannual Variability of Areal Extent of the Gulf Hypoxia from a Coupled Physical-Biogeochemical Model: A New Implication for Management Practice
<p>netcdf data and code for JGR manuscript: seasonal and interannual variability of areal extent of the Gulf Hypoxia from a coupled physical-biogeochemical model: A new implication for management practice</p>
Illustrative Darwin core archive to output data from a citizen science platform to a collection management system
<p>Illustrative DwC archive to send data back to a collection management system from a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Dataset accompanying Riesch et al. 2019. Grazing by wild red deer: management options for the conservation of semi-natural open habitats. Journal of Applied Ecology
<p>This repository contains vegetation biomass and forage quality data used by Riesch et al. in an article accepted in Journal of Applied Ecology.</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original article.</p>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
A taxonomy for improving industry-academia communication in IoT vulnerability management. Additional Material
<p>Research interview and Workshop Protocol from the paper "A taxonomy for improving industry-academia communication in IoT vulnerability management".</p>
Soil bacterial biodiversity is driven by long-term pasture management, poultry litter, and cattle manure inputs
<p>These raw data is used as the supplementary information for the manuscript "Soil bacterial biodiversity is driven by long-term pasture management, poultry litter, and cattle manure inputs". </p>
Research Data Management – Reuse of Research Data (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p> <p> </p> <p>Slides can be found here: <a href="https://doi.org/10.5281/zenodo.3269237">10.5281/zenodo.3269237</a></p>
Sustainability-Based Long-Term Management of Bridges under Multi-Hazard Exposure
<p>Corresponding data set for Tran-SET Project No. 17STOKS01. Abstract of the final report is stated below for reference:</p> <p>"Bridges are under deterioration due to various mechanical and environmental stressors. Hydraulic-related hazards (e.g., flood and scour), aggressive environmental conditions, and seismic events (e.g., earthquake) are recognized as the most significant threats to the safety of bridges. In traditional risk assessment methods for structures susceptible to damage due to floods and other natural hazards (e.g., corrosion and seismic events), future hazard predictions are conducted using historic return periods and climate records. However, recent increase in flood intensity in central-southern states indicate that future hazard occurrence rate may not necessarily follow past trends. Accordingly, current design, assessment, and management methodologies should adapt to these changes in order to ensure the satisfactory performance of bridges under the combined or cumulative action of hazards. This project addresses this need by presenting a framework for risk quantification and optimum management of bridges susceptible to damage due to floods, flood induced scour, and other gradual deterioration mechanism (e.g., corrosion and fatigue). Downscaled climate data, adopted from the global climate models, are employed to predict future flood hazard at a given location. Probabilistic simulation is used to quantify the time-dependent failure probability, which subsequently helps quantify the long-term sustainability through the systematic integration of economic, social, and environmental metrics associated with bridge failures. These profiles can be next used to obtain optimum interventions required to extend the service life while maintaining the structural performance above prescribed thresholds."</p>
Prioriteringsscore op basis van weging aantal stemmen - bijlage bij: Landelijk Coördinatiepunt Research Data Management (LCRDM) Positioning paper voor 2019 en verder
<p>Bijlage bij Positinioningpaper (juni 2019) https://doi.org/10.5281/zenodo.3266833</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.