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1,069 results for “Data managment”
Data Management Plan (DMP)
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Supplementary material 1 from: Borisenko A, Young R, Hanner R (2024) A lab-centric, workflow-based data management system for environmental DNA research. Research Ideas and Outcomes 10: e120483. https://doi.org/10.3897/rio.10.e120483
eDNA Laboratory Database Schema Outline
Data set for "Overstory dynamics regulate the spatial variability in forest-floor CO2 fluxes across a managed boreal forest landscape"
<p>This data set is a compilation of forest stand characteristics, forest-floor environmental conditions, soil properties, ecosystem carbon stocks, and annual forest-floor CO<sub>2</sub> fluxes. The data set is composed by 3-year mean annual values obtained from biometric- and chamber-based flux measurements conducted during the period 2016–2018. Negative values of forest-floor CO<sub>2</sub> fluxes indicate carbon uptake and positive values indicate carbon release. Data were collected in 50 forest stands within the Krycklan Catchment Study (<a href="https://www.slu.se/Krycklan">https://www.slu.se/Krycklan</a>), a multi-scale long-term monitored boreal catchment spanning 68 km<sup>2</sup> in northern Sweden. Selected forest stands encompassed different soil types (sediment vs. till), dominant tree species (pine vs. spruce), and age classes (from initiation to old-growth stands).</p> <p>Variables, units, and definitions are found in the 1_metadata_Martínez-García_et_al._forest-floor_CO2_fluxes.xlsx file</p> <p>More details can be found in Martínez-García et al. (2022) Overstory dynamics regulate the spatial variability in forest-floor CO<sub>2</sub> fluxes across a managed boreal forest landscape. Agricultural and Forest Meteorology. 318: 108916. <a href="https://doi.org/10.1016/j.agrformet.2022.108916">https://doi.org/10.1016/j.agrformet.2022.108916</a></p> <p>Contact information:</p> <p>Ph.D. Eduardo Martínez García (<a href="mailto:eduardo.martinez@slu.se">eduardo.martinez@slu.se</a>, <a href="mailto:edu.martinez.garcia@gmail.com">edu.martinez.garcia@gmail.com</a>)</p> <p>Professor Matthias Peichl (<a href="mailto:matthias.peichl@slu.se">matthias.peichl@slu.se</a>)</p> <p>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgränd 17, SE-901 83, Umeå, Sweden</p>
Data from: Inter-specific variation in the potential for upland rush management advocated by agri-environment schemes to increase breeding wader densities
<p>Encroachment of rush Juncus spp. in the United Kingdom uplands poses a threat to declining wader populations due to taller, denser swards that can limit foraging and breeding habitat quality for some species. Rush management via cutting, implemented through agri-environment schemes (AESs), could thus increase wader abundance, but there is insufficient assessment and understanding of how rush management influences upland waders. Across two upland regions of England [South West Peak (SWP) and Geltsdale nature reserve, Cumbria], we surveyed waders over four visits in fields where rush was managed according to AES prescriptions (treatment; n = 21) and fields without rush management that were otherwise ecologically similar (control; n = 22) to assess how the densities of breeding wader pairs respond to rush management in the short-term. We find evidence for regional variation in the response of waders to rush management, with densities of Common Snipe Gallinago gallinago significantly higher in treatment than control fields in the SWP, but not Geltsdale. There were no statistically significant responses to treatment on densities of Eurasian Curlew Numenius arquata or Northern Lapwing Vanellus vanellus. The 95% confidence intervals for the treatment parameter estimates suggest that this may be due to limited statistical power in the case of Lapwing. For Curlew, however, any potential increases in densities are negligible. There was no evidence that variation in rush cover, which ranged from 10 to 70%, influenced densities of any of our three focal species. Our results suggest that rush management through AES prescriptions delivered in isolation of other interventions may not lead to general increases in breeding wader densities in the short-term, but benefits may arise in some situations due to regional and inter-specific variation in effectiveness. Rush management supported with interventions that improve soil conditions and thus food availability, or reduce predation pressure, may enable AES rush management to generate benefits. Additional research is required to maximise the potential benefits of rush management for each species through the development of prescriptions that tailor to individual species' optimum sward structure.</p>
Data for: Local contributions to beta-diversity in urban pond networks: implications for biodiversity conservation and management
<p><b>Aim:</b> An understanding of how biotic communities are spatially organised is necessary to identify and prioritize habitats within landscape-scale biodiversity conservation. Local Contribution to Beta diversity (LCBD) identifies individual habitats that make a significant contribution to beta-diversity and may have important practical implications, particularly for conservation of habitat networks. In this study, we develop and apply a conservation prioritisation approach based on LCBD in aquatic invertebrate communities from 132 ponds.</p> <p><b>Location:</b> Five urban settlements in the UK: Halton, Loughborough, Stockport, Birmingham, Huddersfield.</p> <p><b>Methods:</b> We partition LCBD into richness difference (nestedness: <sub>RichDiff</sub>LCBD) and species replacement (turnover: <sub>Repl</sub>LCBD) and identify key environmental variables driving LCBD. We examine LCBD at two scales relevant to conservation planning: within urban settlements and nationally across the UK.</p> <p><b>Results:</b> Significant differences in LCBD values were recorded among the five settlements. In four of the five urban settlements studied, pond sites with the greatest LCBD values typically showed high replacement values. Significant LCBD sites and sites with high taxonomic diversity together supported more of the regional species pool (70%-97%) than sites with high taxonomic diversity alone (54% to 94%) or what could be protected by the random selection of sites. LCBD was significantly associated with vegetation shading, surface area, altitude and macrophyte cover.</p> <p><b>Main conclusions:</b> Conservation prioritisation that incorporates LCBD and sites with high taxonomic diversity improves the effectiveness of conservation actions within pond habitat networks, ensures site supporting high biodiversity are protected, and provides a method to define a spatial network of protected sites. Identifying new, effective conservation approaches, particularly in urban areas where resources may be scarce and conflicts regarding land use exist, is essential to ensure biodiversity is fully supported and detrimental anthropogenic effects are reduced.</p>
Data from: Efficacy of spotlights and thermal cameras to detect lions, Panthera leo, and spotted hyenas, Crocuta crocuta, depends on species and management regime
<p class="MsoNormal">Accurate abundance estimates can contribute to effective management of large carnivore populations. Lion, <em>Panthera leo</em>, and spotted hyena, <em>Crocuta crocuta</em>, populations are frequently estimated at night by eliciting their approach using broadcasted vocalizations. Spotlights are typically used to observe these species on approach but can disturb animals and adversely affect counts. We compared the efficacy of spotlight with red filters and forward looking infrared (FLIR) thermal monocular to enumerate lions and spotted hyenas in Serengeti National Park (SNP; non-hunted area) and Maswa Game Reserve (MGR; hunted area), Tanzania, during 2015−2017. We established 119 call-in sites in SNP and 20 in MGR and conducted repeated call-ins at 1–2 week intervals. During call-ins we conducted systematic paired counts using both devices. We assessed the influence of device order, species, hunting regime, and land cover on species counts. We found that FLIR was more efficacious for counting hyenas in MGR and spotlight for counting lions in SNP. We found evidence for temporary artificial light disturbance in MGR, as counts were higher when FLIR was used as the second device. Habitat type within 200 m of call-in sites did not influence device performances. Greater spotlight efficacy in SNP is a likely consequence of lower perceived risk and less anthropogenic disturbance compared to MGR. To improve accuracy of counts and subsequent population estimates for lions and spotted hyenas, we recommend consideration of variation in device efficacy, based on species surveyed and management regime.</p>
data for "Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms"
<p>These data were used in article "Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms" (https://doi.org/10.3390/agronomy12010183)</p>
Reproducibility data for tables in "A machine learning approach to portfolio pricing and risk management for high dimensional problems"
<p>This dataset contains all the information necessary for reproducing the tables in the paper "A machine learning approach to portfolio pricing and risk management for high dimensional problems".</p> <p>The raw benchmark data can be found in the Zenodo dataset "Benchmark and training data for replicating financial and insurance examples" (https://zenodo.org/record/3837381). To the extend necessary, only summary data from that dataset is used in this dataset.</p> <p>The dataset includes a jupyter notebook file that explains what the different files contain, and provides sample code to analyze the information and reproduce the tables.</p>
Data from "Cooperative Management of Ecosystem Services: Coalition Formation, Landscape Structure and Policies"
<p>Simulated data generated to perform the simulations of the paper "Bareille, F., Zavalloni, M., Raggi, M., & Viaggi, D. (2021). Cooperative management of ecosystem services: coalition formation, landscape structure and policies. Environmental and Resource Economics, 79(2), 323-356."</p>
Supplementary material 1 from: Woolfrey L (2017) Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa. Research Ideas and Outcomes 3: e14837. https://doi.org/10.3897/rio.3.e14837
Data quality issues: Accuracy - issues around data collection.
Figure 1 from: Woolfrey L (2017) Data Management Plan: Opening access to economic data to prevent tobacco related diseases in Africa. Research Ideas and Outcomes 3: e14837. https://doi.org/10.3897/rio.3.e14837
Figure 1 - Tobacco Data in Africa Project Data Inventory 2016. Original data available as Suppl. material 1.
Figure 2 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e14673. https://doi.org/10.3897/rio.3.e14673
Figure 2 - The core issues and principles that a Research Data Management policy should address, adapted from Hodson and Molloy 2015
Figure 1 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e14673. https://doi.org/10.3897/rio.3.e14673
Figure 1 - Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used.
Figure 2 from: Neylon C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773. https://doi.org/10.3897/rio.3.e21773
Figure 2 - The Cultural Science model of Hartley and Potts (2014).The co-creation of culture and group in the context of an external environment.
Figure 2 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e21705. https://doi.org/10.3897/rio.3.e21705
Figure 2 - The core issues and principles that a Research Data Management policy should address, adapted from Hodson and Molloy (2015)
Figure 1 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e21705. https://doi.org/10.3897/rio.3.e21705
Figure 1 - Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used.
FAIRmat Tutorial 6: Experimental data management in NOMAD
<p>Experiments are a key ingredient of materials science. Instruments are built, data are collected on a routine basis in every laboratory in universities, research institutes, and industrial sites, but often data is only used for answering a specific question in the hands of a local investigator. In the past, the full potential of data has not been explored, because the heterogeneity of research impeded meaningful comparisons beyond dedicated project teams. For the scientific community, this means that the same experiment has to be performed repeatedly, and there is no way to form a big data infrastructure enabling modern interoperable solutions, like artificial intelligence assisted research.</p> <p>FAIRmat, as one of the flagship projects of the <a href="https://www.nfdi.de/" target="_blank" rel="noopener">German National Research Data Infrastructure</a>, is working on providing experimental data management solutions from the lab scale to the national or international level. We are presenting a hands-on tutorial on how we support the data lifecycle from planning and running an experiment to collecting and annotating data according to international community standards for searchability, and reuse. For this purpose, we bring a very simple experiment of a temperature dependent I-V measurement in as an example and let every participant follow us and connect to our central data management services, to view, explore and work with the datasets collected on-the-fly.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
FAIRmat Tutorial 2: Electronic lab notebooks and FAIR data management
<p>This tutorial is reviewing the need of using Electronic Lab Notebooks (ELNs) in materials science research labs, synthesis, and experimental characterization facilities. A special emphasis is put on efficiently collecting all metadata and perform a FAIR data management which not only facilitates organizing the work better and making the processes in the lab more performant, but also guarantees the possibility of data reuse via offering machine readability and machine interpretability.</p> <p>The tutorial is also addressing the questions: Which kind of choices are available and how to set up such ELN system for a laboratory? A demonstration of such a lab setup will also be provided on the first day.</p> <p>On the second day, three more presentations will guide you through the details of implementing such a setup for your own lab. For this purpose, we have chosen relevant, but simple use cases which can help you in mapping our strategies and solutions onto your institute.</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Supplementary material 1 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 formatted version of the Support Your Data RDM rubric.
Experimental data: Adaptive Production Rescheduling System for Managing Unforeseen Disruptions
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ScienceDex guides
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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.