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13,276 results for “IT management”
Fig. 2 in Saproxylic beetle (Coleoptera) communities and forest management practices in coniferous stands in southwest Nova Scotia, Canada
Fig. 2. Overstory composition for dominant tree species based on importance value (Importance Value = Relative Density + Relative Dominance + Relative Frequency). Site descriptions: 1 & 2 – 40-80 yr, CT; 3 & 4 – 40-80 yr, none; 5 – 80-120 yr, US; 6 & 7 – 80-120, none; 8 – 120+ yr, S; 9 – 120+ yr, S/SH; 10 & 11 – 120+ yr, none. CT = Commercial thinning; US = Uniform selection harvest; SH = Shelterwood harvest; S = Selection harvest.
Fig. 7 in Saproxylic beetle (Coleoptera) communities and forest management practices in coniferous stands in southwest Nova Scotia, Canada
Fig. 7. Mean species richness of beetles in different forest stand age classes, including standard deviation from both the present study and Bishop (1998).
Fig. 5 in Saproxylic beetle (Coleoptera) communities and forest management practices in coniferous stands in southwest Nova Scotia, Canada
Fig. 5. Rarefaction curve demonstrating projected species richness for number of individuals based on Bishop (1998), and the present study (Dollin et al.) beetle collections.
Fig. 8 in Saproxylic beetle (Coleoptera) communities and forest management practices in coniferous stands in southwest Nova Scotia, Canada
Fig. 8. Mean species richness across harvest treatment, including standard deviation, for 11 stands in southwestern Nova Scotia.
Fig. 3 in Saproxylic beetle (Coleoptera) communities and forest management practices in coniferous stands in southwest Nova Scotia, Canada
Fig. 3. Volume of coarse woody debris (CWD) by decay class for 11 stands in southwestern Nova Scotia as measured by Thompson (2004). Decay classes are summarized as follows: "1" is freshly dead, little to no rot; in "2", the bole is mostly sound; "3" has well-established rot and significant bark loss; "4" is advanced decay; and "5" is rotted through but still of wood character. Site descriptions: 1 & 2 – 40-80 yr, CT; 3 & 4 – 40-80 yr, none; 5 – 80-120 yr, US; 6 & 7 – 80-120, none; 8 – 120+ yr, S; 9 – 120+ yr, S/SH; 10 & 11 – 120+ yr, none. CT = Commercial thinning; US = Uniform selection harvest; SH = Shelterwood harvest; S = Selection harvest.
Figure 1 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation
Figure 1. Terrestrial biodiversity in the Middle East is strongly influenced by seasonality: Desert area in northern Saudi Arabia after the winter rainfall (photo F. Krupp).
The relationship between the leadership of business organizations to manage conflicts within their organizations and with other entities
<p>Descripition of the dataset:</p> <p>The questionnaire was made to the leaders of the companies was made in July 2019, consisting of 10 questions where the subject under study is addressed. <br> The questionnaire was personally conducted to 390 leaders of Madrid, consisting of 10 questions ranging from demographic variables, leadership style in conflict management and methods used for conflict resolution.<br> The database has been subsequently anonymised, so that each respondent cannot be identified.</p> <p>Dataset relations:</p> <p>Paper: What are the determinants in conflict management and leadership employed? An analysis of the determinants for the Spanish case</p> <p>Abstract:The purpose of this study is to examine the relationship between the leadership of business organizations to manage conflicts within their organizations and with other entities. <br> For this reason, an exhaustive review of the literature on leadership decision-making and conflict management has been carried out, focusing on sociodemographic variables that have the greatest influence. <br> In addition, a study of conflict management has been carried out in the community of Madrid. The purpose of the study was to identify the most decisive sociodemographic variables that explain how leaders manage conflicts in the most important business nucleus in Spain. <br> The statistical study was based on information collected on demographic variables and leadership styles in conflict management through a survey carried out in July 2019 of 394 leaders of Madrid organizations.</p>
Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH): outcome data
<p>The datasets reports baseline and outcome data from the 'Geriatric CO-mAnagement for Cardiology patients in the Hospital (G-COACH)' experimental study. The study evaluated the effectiveness of a geriatric co-management programme on the cardiac care units of the University Hospitals Leuven. Sample included patients aged 75 years or older. Measurements included: demographic, functional status, cognitive status, depressive symptoms, anxiety symptoms, quality of life, physical performance, readmission rates, survival.</p> <p>Please see Word document for more information.</p> <p>Please see protocols for more information:</p> <p><a href="https://clinicaltrials.gov/ct2/show/NCT02890927">https://clinicaltrials.gov/ct2/show/NCT02890927</a></p> <p><a href="https://bmjopen.bmj.com/content/8/10/e023593">https://bmjopen.bmj.com/content/8/10/e023593</a></p> <p>The evaluation study is available at https://agsjournals.onlinelibrary.wiley.com/doi/full/10.1111/jgs.17093 </p>
Dataset and Simulation Files for article "Alumina coating for dispersion management in ultra-high Q microresonators"
<p>Link to arXiv submission: <a href="https://arxiv.org/abs/2009.07826">https://arxiv.org/abs/2009.07826</a></p> <ul> <li>Figure 1 <ul> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> <li>Data, Jupyter scripts, and simulations files for parts (f,g,h)</li> </ul> </li> <li>Figure 2 <ul> <li>Raw AFM data for use in Gwydeon software</li> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> </ul> </li> <li>Figure 3 <ul> <li>Raw data and Jupyter processing scripts for transmission spectrum calibration (parts - b)</li> <li>Frequency calibrated transmission spectrum and GVD/ Quality factor Jupyter scripts (parts d-g)</li> </ul> </li> <li>Figure 4 <ul> <li>Experimental data for parts a-g</li> </ul> </li> <li>Supplementary data <ul> <li>Raw ellipsometer data</li> <li>1D COMSOL FEM solver and analytical validation (including Julia Jupyter script)</li> <li>Quality factor estimation based on scattering and water adsorption</li> </ul> </li> </ul>
Data from: Deer-mediated ecosystem service vs. disservice depends on forest management intensity
MANUSCRIPT ABSTRACT <p>As global terrestrial biodiversity declines via land-use intensification, society has placed increasing value on non-commercial species as providers of ecosystem services. Yet, many deer species and non-crop plants are perceived negatively when they decrease crop productivity, leading to reduced economic gains and human-wildlife conflict. We hypothesized that deer provide an ecosystem service in forest plantations by controlling competition and promoting crop-tree growth, although the effects of herbivory may depend on forest management intensity. If management negatively affects foraging habitat at local and landscape scales, then we expected browsing to shift to less-palatable crop trees. To test these hypotheses, we established a 5-year experiment that manipulated early forest management intensity via herbicide treatments and access of two deer species to vegetation via exclosures. Contrary to our hypothesis, deer provided an ecosystem service at high management intensities and a disservice occurred with low-intensity management. Crop-tree growth and survival was greatest when herbivory and herbicides suppressed broadleaf regeneration. In contrast, crop-tree growth was lowest when broadleaf vegetation was retained and crop-trees were subject to both browse damage and competition. We found a positive, yet variable, association between deer detections and stand- and landscape-scale broadleaf habitat, and despite initial reductions in forage, herbivory pressure was similar among management intensities. When broadleaf vegetation was suppressed by herbicides and herbivory, selection of herbaceous forage by deer intensified, likely aiding in the service. Overall, our findings indicate that the effects of vegetation management for promoting timber production are highly dependent on the presence of large herbivores.</p> <p>Synthesis and applications: Although deer are thought to reduce crop productivity in many systems, we found that herbivory switched from reducing crop tree growth where non-crop vegetation was retained, to promoting crop tree growth when both herbivory and herbicides suppressed competing vegetation. However, the provision of this ecosystem service is likely contingent on the amount of forage available in the landscape and subsequent foraging pressure. We conclude that nature's capacity to provide ecosystem services depends on the intensity of management at local and landscape scales.</p>
Self-contained 4-BSS's dataset of spectrum management in WLANs
<p>This folder contains the self-contained dataset of 4 BSS's analyzed in the thesis by <em>Sergio Barrachina-Muñoz, "Responsive Spectrum Management for Wireless Local Area Networks: from Heuristic-based Policies to Model-Free Reinforcement Learning", 2020</em>.</p> <p>-------------------------------------------------------------<br> <strong>*** General info ***</strong></p> <p>The dataset has been generated simulating all the spectrum management configurations (including primary channel and maximum bandwidth) in a 4-BSS's deploymment. Simulations have been performed with the Komondor wireless network simulator (<a href="https://github.com/wn-upf/Komondor">https://github.com/wn-upf/Komondor</a>).</p> <p><strong>*** Dataset structure ***</strong></p> <p>The dataset is composed of 1 file, dataset.csv, containing all the combinations of spectrum management configurations.</p> <p><strong>*** File format ***</strong></p> <p>The dataset.csv file is composed of 53 columns and 1679616 rows. <br> - Each column is a parameter or performance metric of the global spectrum management configuration, i.e., the configuration of all the BSS's.<br> - Each row is a realization of the global configuration.<br> - Column sim_code refers to the simulation code.</p> <p>The colums for each BSS are (only showing for BSS A):<br> - bss_A_code: code of the BSS<br> - action_ix_A: action (or BSS configuration) index<br> - status_ix_A: status index (combination of action and traffic load)<br> - primary_A: primary channel of the BSS<br> - max_bw_ix_A: index of the maximum allowed bandwidth of the BSS<br> - load_ix_A: traffic load index of the BSS<br> - load_A: traffic load [pkt/s] of the BSS<br> - thr_A: throughput [Mbps] of the BSS<br> - d_A: packet delay [ms] of the BSS<br> - rts_lost_A: number of RTS lost by BSS A<br> - rts_sent_A: number of RTS sent by BSS A<br> - frames_lost_A: number of frames lost by BSS A<br> - frames_sent_A: number of frames sent by BSS A</p>
The Collection Management System Collection - Crowd-sourcing a list of digital repository options
<p><strong>The Collection Management System Collection - Crowd-sourcing a list of digital repository options</strong></p> <p>This dataset contains a list of digital repository options for collection management systems. It has been started and complited by Ashley Blewer.<br> The data set contains:</p> <ul> <li>a PDF capture of the blog describing motivation and background, columns of the spreadsheet and further resources; originally published at https://bits.ashleyblewer.com/blog/2017/08/09/collection-management-system-collection/</li> <li>The dataset / spreadsheet of The Collection Management System Collection, originally published at https://docs.google.com/spreadsheets/d/1cXOug3qM0pNNeD_wssiVEv9c0W1Y5I1VDTnSPTk7fb4/<br> The data was exported from the google spreadsheet on November 14th 2020 into the following formats: <ul> <li>PDF</li> <li>XLSX</li> <li>CSV</li> <li>TSV</li> </ul> </li> </ul> <p>The list contains basic information, administration considerations, interface considerations, technical considerations and social considerations for 70 different repository systems.</p>
Data management planning - Training for trainers, part I-III: answers to per-assignments
<p>The data have been collected as part of data management planning training for trainers. Data consists participants answers to pre-assignments.</p> <p>Consent for data sharing</p> <ul> <li>First session: Consent for datat sharing was asked afterwards by email</li> <li>Second and third session: Consent was asked when collecting answers on the e-form.</li> </ul> <p>The slides of the DMP training fro trainers is available on SlideShare:</p> <ul> <li>Session I: <a href="https://www2.slideshare.net/MariKuusniemi/part-i-data-management-planning-training-for-trainers">https://www2.slideshare.net/MariKuusniemi/part-i-data-management-planning-training-for-trainers</a></li> <li>Session II: <a href="https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-ii">https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-ii</a></li> <li>Session III: <a href="https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-iii">https://www2.slideshare.net/MariKuusniemi/data-management-planning-training-for-trainers-part-iii</a></li> </ul> <p>The training was organised by Tuuli Office.</p>
Farmer adaptive behavior and risk management in EU agriculture
<p>Risk and risk management are essential elements of agriculture and affect the wellbeing of farm households. Farmers react to production, market and institutional risks and challenges by taking measures on or off the farm. Such risk management measures are often costly and have implications for up- and downstream industries as well as the environment. The risk exposure of European farms is increasing. For example, climate change will increase the frequency and magnitude of extreme weather events like droughts, heatwaves and heavy rainfalls that potentially have detrimental effects on agricultural production. Thus, the adaptive capacity and risk management options in European agriculture need to be improved. Policy shall support this process. Policies are needed to support a diversity of risk management solutions and not only focus on a few solutions. Strategies to cope with risk often go beyond the level of the individual farm. Cooperation, learning and sharing of risks play a vital role in European agriculture and shall be strengthened. Thus, coordinated policies targeting beyond the individual farm and considering all the stakeholders involved in the risk management strategies are needed to ensure their effective implementation. Moreover, policies need to facilitate to take full advantage of the rapid technological progress and improved data availability (e.g. based on satellite imagery) to develop a wider set of risk management strategies.</p>
Improved risk management towards more resilient EU faming systems
<p>EU farming systems are facing increasing economic, social, environmental and institutional challenges. Finding the opportunities to improve risk management contributes to enhancing the farming systems’ resilience. Based on the participation of a wide variety of stakeholders across European agricultural sectors, four main avenues to improve risk management are proposed: 1) Useful, accessible and well-structured information; 2) Professional, adapted and widespread training and advice and boosted knowledge transfer; 3) Developing and spreading new forms of cooperation among farming system actors; and 4) New/ improved products and services adapted to current and future needs of the farming systems. Not only farmers, farmers’ households and associations but also value chain actors, financial institutions, NGOs and public administration are encouraged to be part of the opportunities to improve risk management towards resilient farming systems.</p>
Next Steps: How the FDNext Project is Tackling Research Data Management … and Farewell to Emma
<p>In this episode we talk to Kerstin Helbig about the research data management (RDM)project FDNext, which is also where our co-host Emma Harris' new role will be based. We discussed what the approach of FDNext is, the challenges of implementing effective RDM, and how it fits into the wider framework of Open and FAIR Data initiatives. </p> <p><strong>Episode Links</strong></p> <p><a href="https://www.forschungsdaten.org/index.php/FDNext">FDNext (German language)</a></p> <p><a href="https://zenodo.org/record/4071471#.X791NmhKhPY">FDMentor RDM Train-the-Trainer Concept</a></p> <p><a href="https://www.researchgate.net/profile/Kerstin_Helbig">Kerstin Helbig</a></p> <p><a href="https://www.linkedin.com/in/emma-a-harris-6bb865123/">Emma Harris</a></p>
Figure 3 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
Figure 3 The taxon richness, Shannon index, Simpson index, and Evenness index (mean ± SD) of soil Acari at different treatment sites at the Safari Zoological Center, Israel, December 2013. OE = open places under enclosure, OT = open places under trampling; EE = E. camaldulensis canopy habitat under enclosure, ET =E. camaldulensis canopy habitat under trampling, TE =T. aphylla canopy habitat under enclosure, TT =T. aphylla canopy habitat under trampling, CE =C. sempervirens canopy habitat under enclosure, CT =C. sempervirens canopy habitat under trampling. Different letters represent significance at p<0.05.
Figure 2 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
Figure 2 The abundance (individuals per 10 g dry soil substrate; mean ± SD) of soil microarthropod taxa extracted from core samples at different treatment sites at the Safari Zoological Center, Israel, December 2013. OE = open places under enclosure, OT = open places under trampling; EE =E. camaldulensis canopy habitat under enclosure, ET =E. camaldulensis canopy habitat under trampling, TE = T. aphylla canopy habitat under enclosure, TT =T. aphylla canopy habitat under trampling, CE = C. sempervirens canopy habitat under enclosure, CT =C. sempervirens canopy habitat under trampling. Different letters within the same group represent significance at p<0.05.
Figure 1 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
Figure 1 Location of study sites at the Safari Zoological Center, Israel. OE = open places under enclosure, OT = open places under trampling; EE =E. camaldulensis canopy habitat under enclosure, ET = E. camaldulensis canopy habitat under trampling, TE =T. aphylla canopy habitat under enclosure, TT = T. aphylla canopy habitat under trampling, CE =C. sempervirens canopy habitat under enclosure, CT = C. sempervirens canopy habitat under trampling.
Dataset used for making conclusions in article Gesture-controlled image management for operating room: A randomized crossover study.
<p>Dataset used for article titled:</p> <p>Gesture-controlled image management for operating room: A randomized crossover study.</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.