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13,276 results for “IT management”
Survey used and data gathered for research into adoption of carbon management strategies amongst universities E Lewis-Brown et al 2022
<p>Survey used and data gathered for research into adoption of carbon management strategies amongst universities 2022, which forms part of a PhD thesis and will be submitted for publication in a journal. </p>
NOAA NCCOS Assessment: Priority Areas Recommended for Shallow Coral Reef Management in the South Florida Coast from 2021-04-26 to 2021-05-21
<p>The National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application (Buja and Christensen 2019) to identify mapping needs along the south Florida coast to support shallow coral reef management by NOAA’s Coral Reef Conservation Program (CRCP). Eighteen participants from local federal, state, academic, and other institutions entered their priorities in an online participatory Geographic Information System (pGIS). Participants used virtual coins to denote their priorities in 10.4 km<sup>2</sup> hexagonal grid cells overlaid on the study area. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important, what data types were needed, and data collection methodologies using a pre-set list of options. Results were compiled, summarized, and mapped to identify high priority areas, reasons for those priorities, and information needs. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in south Florida.</p> <p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coast of south Florida. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p> <p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the pGIS process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online pGIS, the study area was divided into 1761 hexagonal grid cells 10.4 km<sup>2</sup> in size. Existing relevant spatial datasets (<em>e.g.</em>, bathymetry, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. The pGIS was used by 18 participants to convey their recommendations. Each participant was provided with 530 virtual coins to place into grid cells that they wished to prioritize. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 53 coins could be placed into an individual grid cell by each respondent. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported what data types were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: habitat map/characterization, shoreline characterization, ground truthing (e.g. photos and videos collected using ROVs or AUVs), elevation (e.g. bathymetry and topography), backscatter and intensity (e.g. surfaces used to delineate between hard and soft substrate), 2D map product (e.g. static images used to visualize bottom type, presence/absence of taxa), georectified photomosaics (e.g. 3D products created from structure for motion), and water column (e.g. for fish biomass detection). Respondents also reported what method of data collection was desired in each priority cell. Only one response was required and were selected from the following list: satellite, lidar, multibeam echosounder, split beam echosounder, side-scan sonar, photogrammetry, drop-camera, and uncrewed systems. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 10.4 km<sup>2</sup> grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data products. Also included is a ranking of each grid cell based on the total number of coins, management uses, and agencies allocating coins in the respective cell. For a complete description of the process and analysis see: Kraus et al., 2022.</p> <p> </p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 4 - Analysis Data Sheet
<p>This dataset is extended data to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- Data Analysis Sheet and results table</p> <p>Note: The data is anonymized (i.e., all IP addresses as well as personal comments were deleted)</p> <p>The revised version was published after the peer-review process of the original article on zenodo.org</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Livestock management promotes bush encroachment in savanna systems by altering plant-herbivore feedback
<p>This repository contains all code to reproduce the analysis in Koch et al. 2022 "Livestock management promotes bush encroachment in savanna systems by altering plant-herbivore feedback".</p> <p>We use a set of coupled differential equations to describe competition between shrubs and grasses, as well as plant biomass consumption via grazing and browsing. Grazers were assumed to receive a certain level of care from farmers, so that grazer densities emerge dynamically from the combined effect of vegetation abundance and farmer<br>support. Our main goal was to understand how critical transitions from grass-dominated to shrub-dominated system states were affected by the dynamic role of grazing.</p> <p>Our results show that bistability emerges for intermediate levels of farmer support due to positive feedback that arises from competition between shrubs and grasses and from herbivory. We furthermore demonstrate that disturbances, such as drought events, trigger abrupt transitions from the grass dominated to the shrub dominated state and that the system becomes more susceptible to disturbances with increasing farmer support.</p>
Assessing the value of monitoring to biological inference and expected management performance for a European goose population
<p>1. Informed conservation and management of wildlife require sufficient monitoring to understand population dynamics and to direct conservation actions. Because resources available for monitoring are limited, conservation practitioners must strive to make monitoring as cost-effective as possible.</p> <p>2. Our focus was on assessing the value of monitoring to the adaptive harvest management (AHM) program for pink-footed geese (Anser brachyrhynchus). We conducted a retrospective analysis to assess the costs and benefits of a capture-mark-resight (CMR) program, a productivity survey, and biannual population censuses. Using all available data, we fit an integrated population model (IPM) and assumed that inference derived from it represented the benchmark against which reduced monitoring was to be judged. We then fit IPMs to reduced sets of monitoring data and compared their estimates of demographic parameters and expected management performance against the benchmark IPM.</p> <p>3. Costs and the precision and accuracy of key demographic parameters decreased with the elimination of monitoring data. Eliminating the CMR program, while maintaining other monitoring instruments, resulted in the greatest cost savings, usually with small effects on inferential reliability. Productivity surveys were also expensive and some reduction in survey effort may be warranted. The biannual censuses were inexpensive and generally increased inferential reliability.</p> <p>4. The expected performance of AHM strategies was surprisingly robust to a loss of monitoring data. We attribute this result to explicit consideration of parametric uncertainty in harvest-strategy optimization and the fact that a broad range of population sizes is acceptable to stakeholders.</p> <p>5. Synthesis and applications: Our study suggests that existing or potential monitoring instruments for wildlife populations should be scrutinized as to their cost-effectiveness for improving biological inference and management performance. Using Svalbard pink-footed geese as a case study, we show that the loss of some existing monitoring instruments may not be as adverse as commonly assumed if data are jointly analyzed in an integrated population model. Finally, regardless of the monitoring data available, we suggest that conservation strategies that explicitly account for uncertainty in demography are more likely to be successful than those that do not<span>.</span></p>
Data from "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction"
<p>Sediment data from Steart Marshes described in Mossman et al. "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction".</p> <p>Data are provided as a .xlsx file (Data package.xlsx) with four tabs. Tab 1 has column heading descriptions. Tab 2 has total carbon samples. Tab 3 has total organic carbon samples. Tab 4 has bulk density samples. Each tab is also provided as a seperate csv file.</p>
Research Data Management: Data Lifecycle
<p>This diagram has been created by the team of data steward at the University of Bologna (Alma Mater Studiorum - Università di Bologna, UniBo) in October 2022. It proposes a data lifecycle model inspired by the University of Virginia Library’s model (<a href="https://guides.lib.virginia.edu/c.php?g=515290&p=3522215">https://guides.lib.virginia.edu/c.php?g=515290&p=3522215</a>). It has been developed in parallel to the Research Data Management Decision Tree, available here: <a href="https://doi.org/10.5281/zenodo.7190004">https://doi.org/10.5281/zenodo.7190004</a></p> <p>Emphasis is put on a careful planning of data management, which should always precede data collection (and re-use of existing data). A reference to the opportunity of creating and maintaining a Data Management Plan (DMP) has been added. This is not always compulsory, but is increasingly required by funders.</p> <p>Collecting, analysing and storing data (and possibly sharing them with a group) remain at the heart of the lifecycle, constituting what we called “data handling”. Here, the process is not linear, and researchers tend to move from one stage to another in a recursive fashion. </p> <p>At any point during data handling, it is possible to deposit data: the responsibility for storing and safekeeping is passed on to the repository, the time-scale shifts from short-term to long-term, and data become citable (and possibly discoverable) by the wider scholarly community and beyond. Importantly, deposited data can always be re-used as the basis for a new round of collection/analysis/storage that will lead to a new deposit, and so on.</p>
Supplementary material 1 from: Pontoppidan M, Nachman G (2013) Spatial Amphibian Impact Assessment – a management tool for assessment of road effects on regional populations of Moor frogs (Rana arvalis). Nature Conservation 5: 29-52. https://doi.org/10.3897/natureconservation.5.4612
Full model description following the protocol suggested by Grimm et al. (2006, 2010) and model parameterisation. (doi: 10.3897/natureconservation.5.4612.app). File format: Adobe PDF document (pdf).:
Supplementary material 2 from: Bonnett G, Kushner J, Saltonstall K (2014) The reproductive biology of Saccharum spontaneum L.: implications for management of this invasive weed in Panama. NeoBiota 20: 61-79. https://doi.org/10.3897/neobiota.20.6163
Proportion of seeds that germinated each week between September and December from samples taken at 12 sites. 100 seeds were germinated from each of three replicate samples and tested for germination in laboratory conditions. Results are presented as the mean and the error bar represents the standard error of the mean.
Supplementary material 1 from: Bonnett G, Kushner J, Saltonstall K (2014) The reproductive biology of Saccharum spontaneum L.: implications for management of this invasive weed in Panama. NeoBiota 20: 61-79. https://doi.org/10.3897/neobiota.20.6163
The table gives the latitude and longitude, description and number of genotypes found among the 3 plants of Saccharum spontaneum tested from each of 22 Sites. Sites 1–12 were used to assess seed germinability through time.
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Topics, approaches and contributions of research about CRIS
<p>The size of the diamonds indicates the amount of articles belonging 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>
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>
Figure1. Three-layer image decomposition with content protection-Access Management in Medical Image Databases Based on New Format and Contents Protection with Inverse Pyramid Decomposition
<p>The image preparation for the image database with layered access is shown on Fig. 1. The<br> image is archived layer by layer and the watermarks are inserted together with the image<br> processing. The ROI (if there is one in the image) is processed in such a way, that to permit direct<br> access for authorized users (separate pyramid is developed for the ROI representation).</p>
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