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1,069 results for “Data managment”
Data literacy and research data management survey
<p>Data from Czech version of Data literacy and research data management multinational study.</p>
Data Management Plan (final): SPPELME
Open the record for dataset details and reuse information.
Data: The effects of glucose addition and water table manipulation on peat quality of drained peatland forests with different management practices
<p>The file contain data on peat chemical quality and peat decomposition. We studied how glucose addition, water table and forest harvesting affect the chemical composition of peat and decomposition rate (carbon dioxide fluxes) and soil water quality. Experiments and results are presented in:</p> <p>Aaltonen H., Zhu X., Khatun R., Laurén A., Palviainen M., Könönen M., Peltomaa E., Berninger F., Köster K., Ojala A., Pumpanen J. 2022. The effects of glucose addition and water table manipulation on peat quality of drained peatland forests with different management practices. Soil Science Society of America Journal 86:1625–1638. <a href="https://doi.org/10.1002/saj2.20419">https://doi.org/10.1002/saj2.20419</a></p> <p> </p>
Appendix 5: Supplementary data for Chapter 4 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development'
<p>Appendix 5: Supplementary data for Chapter 4 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Appendix 4: Supplementary data for Chapter 3 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for the management of biodiversity alongside human development'
<p>Appendix 4: Supplementary data for Chapter 3 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Appendix 6: Supplementary data for Chapter 5 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development'
<p>Appendix 6: Supplementary data for Chapter 5 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Data from: Pathogen-mediated selection and management implications for white-tailed deer exposed to chronic wasting disease
<p>Pathogens can cause host extinction, affect population dynamics, and influence natural selection. Host susceptibility to pathogens can vary by species, demographics, and genetics which affect epizootic dynamics; ultimately determining population trends and evolution. Chronic wasting disease (CWD), a fatal neuro-degenerative prion disease of cervids, has varying host susceptibility conferred by polymorphisms of the prion protein gene (PRNP) at the 96 codon for white-tailed deer (<em>Odocoileus virginianus</em>). Deer with the homozygous Glycine allele (96GG) are most susceptible and a single Serine allele (96GS/96SS) reduces the risk of infection and mortality. We developed epizootiological models that demonstrate CWD infection and disease-associated mortality were higher for the more susceptible (96GG) genotype; and, infection was higher for males than females. We used population models to evaluate future shifts in genotype frequencies under alternative harvest and infection rate scenarios. Genetic shifts towards less susceptible genotypes were predicted as CWD prevalence increased during the course of an outbreak. This further increased CWD prevalence, and likely environmental contamination from prion shedding, due to longer incubation periods. Alternative harvest management strategies directly influenced CWD prevalence and spread, the rate of genetic selection, and deer population growth. </p> <p><em>Synthesis and applications</em>: We show that CWD transmission varied by sex, age classes, and genotypes, and that CWD disease-associated mortality varied by genotype. Together, these forces lead to CWD-mediated genetic selection for a white-tailed deer population. We predict that genetic selection pressure increases when hunter harvest pressure is lowered, and conversely, increasing hunter harvest can reduce genetic selection rates of antlered deer. Our results support the control of CWD prevalence by aggressive harvest of adult males because they have the highest infection rates. Our results have strong implications for evolution, disease ecology, geographic spread, disease mitigation, and cervid population management.</p>
Data from: Grassland management regimes alter the coordination of plant functional traits and nutrient resorption in semiarid grasslands
<p>Grazing and enclosure (grazing exclusion) are the main grassland management regimes that affect nutrient cycling and ecosystem function by altering plant traits. However, the coordination among plant functional traits and nutrient resorption under different grassland management regimes remains unclear. We examined the coordination of eight root and four leaf traits along with the nitrogen (N) and phosphorus (P) resorption efficiencies under four grazing intensities and two enclosure chronosequences in a semiarid steppe ecosystem in China. The principal components analysis (PCA) of root traits and multiple factors analysis of root and leaf traits showed two-dimensional economic spaces at the individual level. Increasing grazing intensity shifts species and community trait composition from conservative to acquisitive with increases in specific root length, specific root area, root alkaline phosphatase activity, and specific leaf area, accelerating nutrient cycling and coordination among traits. Increasing grazing intensity promoted nutrient resorption efficiency. Such adaptations optimize plant nutrient acquisition and nutrient conservation under nutrient-poor conditions. Conversely, long-term enclosure increases plant nutrient and light acquisition by promoting root tissue density, mycorrhizal colonization, and specific leaf area. The conservative (e.g., <em>Stipa grandis</em>) and acquisitive species (e.g., <em>Carex korshinskyi</em>) dominated under grazing, whereas the mid-acquisitive species, including <em>Leymus chinensis</em> and <em>Agropyron cristatum</em>,<em> </em>dominated under enclosure. The observed N and P resorption efficiencies decrease with increasing PC1 score (increase in nutrient acquisition by itself) in root PCA, indicating the trade-off between nutrient-conservative and nutrient-acquisitive strategies. Community-weighted mean traits were primarily driven by intraspecific trait variation, enhancing the adaptability of plants and communities to environmental changes and external stressors. Our study highlights the coordination among above- and below-ground traits, as well as the trade-off between root nutrient acquisition and leaf nutrient resorption under different grassland management regimes in semi-arid grassland ecosystems. From these findings, we conclude that enhancing soil nutrient availability is the most effective approach to the solution to grazing-induced grassland degradation. This knowledge is crucial for devising more effective strategies for sustainable land use and biodiversity conservation in grassland ecosystems.</p>
Technology Acquisition Plans to Foster Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses: Raw Data and TAP Template
<p>This is a data set and TAP template to accompany the paper</p> <p>"Technology Acquisition Plans to Foster<span> </span>Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses" by Tenbergen and Mead published at the 36th International Conference on Software Engineering Education and Training.</p>
Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste [Data]
<p>Data of article <em>Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste</em></p>
Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste
<p>Data of article <em>Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste</em></p>
Data and code for "Tree species abundance changes at the edges of their climatic distribution: an interplay between climate change, plant traits, and forest management"
<p>## Secondary data and code to accompany the research entitled "Tree species abundance changes at the edges of their climatic distribution: an interplay between climate change, plant traits, and forest management" by Padullés Cubino et al. (2024).</p> <p># There are four folders with (1) "raw data", (2) "processed data", (3) "results", and (4) "scripts".</p> <p># The raw and processed data folders contain the CSV and XLSX files with all the data used for analysis and produced from them</p> <p># The "results" folder contains the figures and table presented in the manuscript.</p> <p># The "scripts" folder contains four scripts for the analyses described in the manuscript:</p> <p> 01_preparation_ClimEdge.R -> data cleaning and processing</p> <p> 02_script_Fig1.R -> code to produce Fig1</p> <p> 03_script_Fig2.R -> code to produce Fig2</p> <p> 04_script_Table1_Fig3.R -> code to produce Table 1 and Fig3</p> <p># If anything is unclear, please contact the corresponding author for clarification (padullesj@gmail.com).</p>
Data and RCode used in Andraczek et al. 2024: "Weak reciprocal relationships between productivity and plant biodiversity in managed grasslands"
<p>To study the causal effects between biodiversity and productivity we collected seasonal data (in spring and summer 2020 and 2021) of both biodiversity and productivity and potential confounding variables (environmental and land-use related variables) across 150 grassland sites all being part of the Biodiversity Exploratories project (https://www.biodiversity-exploratories.de/en/), specifically, within three regions in Germany (i.e. Schorfheide-Chorin, Swabian Alb and Hainich-Dün). This dataset comprises variables collected by the BEF-Loops project (funded by DFG: Nr. 433266560, Exploratories project phase Nr. PL 891/3-1) and other projects within the Biodiversity Exploratories.</p> <p>This upload contains the following files:</p> <ol> <li>ShortTerm.seasonal_20.10.23.csv: processed data containing temporal data on biodiversity, productivity and additional abiotic and biotic covariates</li> <li>Meta data.xlsx: Meta data file for processed data</li> <li>Andraczek et al. - Processing of seasonal data.Rmd: RCode used to create processed data</li> <li>Andraczek et al. - Calculation of community weighted means of specific leaf area.Rmd: RCode used to calculate community weighted means of specific leaf area (included in processed data)</li> <li>Andraczek et al. - Main analysis.Rmd: RCode use to produce figures and results presented in main publication (including supplementary information)</li> <li>Sensitivity analysis testing the robustness of estimated effects against bias of unobserved confounding variables (using sensemakr in R)</li> </ol>
Supplemental data for the paper "Managing CO2 under global and country-specific net-zero emissions targets in Europe"
<p>This repository includes results discussed in the paper "Managing CO2 under global and country-specific net-zero emissions targets in Europe".</p>
Statistical data - Waste management sector transformation and its impact towards JGT in Montenegro
<p>The dataset represents statistical data collected and interpreted by the authors in the framework of the research titled "<span>Waste Management Sector Transformation </span><span>and its Impact towards JGT in Montenegro"</span></p> <p>The dataset contains tables and graphs that are employed within the research and may be accessed by the general public in compliance with the Creative Commons Attribution license. The datasheets include:</p> <ol> <li>Typical of RDF recovered from municipal solid wastes generated in industrialized nations</li> <li>Waste types are used as fuels in EU-27 cement kilns</li> <li>GHG performance of typical fuels</li> <li>Amount of waste generated and managed per inhabitant in Montenegro</li> <li>Population and waste in targeted municipalities</li> <li>Amount of waste generated and collected in the study area</li> <li>Number of Inhabitants in targeted municipalities</li> <li>Waste quantities (in tons) in (a) Old Royal Capital Cetinje; (b) Danilovgrad and (c) Podgorica</li> </ol>
Supplementary material 7 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Sharing and publishing your data" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 6 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Analyzing your data and handling the outputs" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 4 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Saving and backing up your data" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 3 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Organizing your data" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
Supplementary material 5 from: Borghi J, Abrams S, Lowenberg D, Simms S, Chodacki J (2018) Support Your Data: A Research Data Management Guide for Researchers. Research Ideas and Outcomes 4: e26439. https://doi.org/10.3897/rio.4.e26439
A draft guide that corresponds with the "Getting your data ready for analysis" row of the RDM rubric. Suggested points of customization are highlighted in yellow (discipline-specific) and red (institution-specific).
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.