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1,069 results for “Data managment”
Sahana et al. Supplementary Data for Global Transboundary River Research: Databases, Case Study Analysis, and Regional Statistics for Sustainable Management
<p><span>This dataset supports our comprehensive review article on transboundary river research, exploring its implications for sustainable management worldwide. Utilizing machine learning, we analyzed 4,237 publications and conducted an in-depth desk review of 325 selected papers, examining a total of 4,713 case studies spanning 286 river basins globally. The study provides critical insights into upstream, midstream, and downstream regions, offering a complete view of challenges and opportunities in transboundary river management. Supplementary Data 1 contains the main database used in this study, sourced from Scopus, Web of Science, and Google Scholar. Additionally, Supplementary Data 2 and 3, included in the spreadsheet, offer statistics and further resources essential for understanding regional and cross-regional dynamics in river basin governance. These supplementary resources include key statistics, case study metadata, and tools, helping to facilitate a deeper exploration of basin-specific and global trends in transboundary water management. This collection of data and resources provides a valuable foundation for researchers and policymakers in advancing sustainable transboundary river management practices.</span></p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Dataset Scooping review digital platforms and data management in Education
<p>Dataset used for a scoping review about digital platforms and data management in education</p>
NFDI4BIOIMAGE data management illustrations by Henning Falk
<p>These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.<br><br><strong>Attribution</strong><br><br>Please include an attribution similar to: <br>"Data annoation matters", NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, <a href="https://doi.org/10.5281/zenodo.14186100">https://doi.org/10.5281/zenodo.14186100</a>, is used under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. Modifications to this illustration include cropping.</p> <div> </div>
Data and Software for "Probabilistic Trade-offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks"
<p><span>Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"</span></p> <p><span>This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.</span></p> <p><strong><span>Contents</span></strong></p> <ol> <li><strong><span>Data: Model Inputs</span></strong><span><br>This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.<br>The key files are:</span></li> <ul> <li><span>Input_HydrologicalData</span></li> <li><span>Input_SystemData</span></li> </ul> <li><strong><span>Results: Model Output Analysis</span></strong><span><br>This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).<br>Key files include:</span></li> <ul> <li><strong><span>SDDP Model Outputs</span></strong><span> (MATLAB format): </span></li> <ul> <li><span>EnergyPriority_Baseline.mat</span></li> <li><span>EnergyPriority_2070.mat</span></li> <li><span>EnergyPriority_2100.mat</span></li> <li><span>AgriculturePriority_Baseline.mat</span></li> <li><span>AgriculturePriority_2070.mat</span></li> <li><span>AgriculturePriority_2100.mat</span></li> </ul> <li><strong><span>Extracted Model Results</span></strong><span> (Excel format): </span></li> <ul> <li><span>Organized for each policy and climate scenario to facilitate analysis.</span></li> </ul> </ul> <li><strong><span>Software: Data Analysis and Visualization</span></strong><span><br>Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.<br>Scripts provided:</span></li> <ul> <li><span>CDF_outflow.py</span><span>: Analyzes cumulative distribution functions for river discharge.</span></li> <li><span>CDF_sectors.py</span><span>: Examines sectoral water use distributions.</span></li> <li><span>PCP_SI.py</span><span>: Generates Parallel Coordinate Plots for trade-offs analysis.</span></li> </ul> <li><strong><span>Instructions: README File</span></strong><span><br>A comprehensive README file explains:</span></li> <ul> <li><span>Details of model input data.</span></li> <li><span>Instructions to interpret the SDDP model outputs.</span></li> </ul> </ol> <p><strong><span>Instructions:</span></strong><span><br></span><span>The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.</span></p>
Research Data Management Life Cycle
<p>An overview of the research data management life cycle with proper licensing.</p> <p> </p>
Reference Manager Data Citation Analysis
<p>DESCRIPTION:</p> <p>This package contains data used to analyze citation metadata completeness and correctness for several common reference managers used in scholarly research and several common repositories in the Earth, space, and environmental sciences.</p> <p>METHODS:</p> <p>Metadata fields for import and export methods and for 8 metadata fields (authors/creators, publisher, DOI, dataset title, version, access date, publication date, and resource type) were collected from reference managers via all import methods available (app or wizard and plugin) during summer 2024 from most recent software versions of all. To encode data, citation information for each dataset as imported by Reference Manager was compared to that registered for the DOI with DataCite. Correct metadata for each of 8 fields for both import and export was encoded as 0, incorrect as 1, and missing as '' or nan. See publication and software package for more information.</p> <p>FILES:</p> <p>FOLDER 'coded-data' contains files that include information (DOIs) about the data examined in this study, preserved copies of exported data citations used in the data interpretation and processing, and the processed data itself encoded in columns.</p> <p>FOLDER 'datacite-metadata-profiles' includes the raw metadata from each dataset DOI at the time of analysis, included for reproducibility purposes. </p> <p>FOLDER 'bibtex-files' includes the downloaded .bib files, where available, for each dataset DOI examined.</p> <p>See README file for more information.</p>
Data from: Different management practices influence growth of small plants in species-rich hay meadows through shading
<p>Data from: Different management practices influence growth of small plants in species-rich hay meadows through shading, published in Applied Vegetation Science. Photosynthetically active radiation (PAR) and Dry Matter Yield (DMY)</p>
Survey data on joint cropland management among agri-food cooperatives in Mediterranean Spanish Regions
<p>This dataset presents data collected from joint cropland management practices survey in agri-food cooperatives of Mediterranean Spanish Regions. The objective was to examine to what extent cooperatives offer joint services, including joint management or integral exploitation of smallholdings, for the incorporation of new professionals. Data collection was conducted to five agri-food organizations: three agri-food cooperatives federations -Castilla-La Mancha, Comunitat Valenciana, and Murcia-, two second degree agri-food cooperatives -Anecoop and Unió Nuts-, all of them located in Mediterranean Spanish Regions. A total of 1.168 survey questionnaires were distributed between July 2020 and February 2021 across five organizations through the snowball sampling method. Data from 112 collected questionnaires were correctly answered, but 106 were selected for analysis. The dataset includes socio-economic data, productive information, and innovative characteristics from agri-food cooperatives surveyed, all in order to be able to examine the relationship between those factors and joint cropland management practices they carry on.</p>
Fire danger indices historical data from the Copernicus Emergency Management Service
<p>June, July August consolidated data.</p> <p>Generated using Copernicus Climate Change Service information 2021.</p>
Data Set of Publication - Outcome of Scleral Rupture Primary Management Without Vitrectomy Jakarta Eye Trauma Study
<p>This dataset was collected for research purposes. The dataset was about the scleral rupture we found in our teritories</p>
Big data analytics in support of the under-rail maintenance management at Vitória – Minas Railway
<p>This video describes an ongoing study using data collected by an instrumented ore car on Vitória–Minas Railway, operated by Vale in Brazil. The research uses big data analysis methods over collected data by the instrumented car during its voyages. Railway geometry issues can cause undesirable movements on the wagons that can cause discomfort for passengers or instability for the cargo. In the worst scenario, derailments can occur. Each second, several sensors installed on the instrumented car collect data about velocity, acceleration, and movements on the wagon. The volume of collected data is impressive since the railway has about 2,000 km of extension. That volume compels us to use big data analytics methods. As the result of the research, the team aims to establish some levels of operational conditions, named as severity indexes, which can indicate to the maintenance teams the necessity of intervention on the railway.</p>
Data_Sheet_2_Management of Hypercholesterolemia Through Dietary ß-glucans–Insights From a Zebrafish Model
<p>Supplementary tables for Management of Hypercholesterolemia Through Dietary ß-glucans–Insights From a Zebrafish Model</p>
Data_Sheet_1_Management of Hypercholesterolemia Through Dietary ß-glucans–Insights From a Zebrafish Model
<p>Supplementary figures for Management of Hypercholesterolemia Through Dietary ß-glucans–Insights From a Zebrafish Model</p>
Data from: Predicting habitat suitability and connectivity for management and conservation of urban wildlife: A real-time web application for grassland water voles
<ol> <li>Natural habitats in urban areas provide benefits for both humans and biodiversity. However, to achieve biodiversity gains we require new techniques to determine habitat suitability and ecological connectivity that will inform urban planning and development.</li> <li>Using an example of an urban population of water voles (<i>Arvicola amphibius</i>) we developed a habitat suitability model and a resistance-surface-based model of landscape connectivity to identify potential connectivity between areas of suitable habitat. We then updated the environmental variables according to new urban development plans and used our models to generate spatially explicit predictions of both habitat suitability and connectivity.</li> <li>To make models accessible to urban and conservation planners we developed an interactive mapping tool that provided users with a graphical user interface (GUI) to inform conservation planning for this species.</li> <li>The model found that habitat suitability for water voles was related to distance from key environmental variables, such as built-up areas and urban green spaces, while the connectivity model identified important corridors connecting areas of potential distribution for this species.</li> <li>Future development plans altered the potential spatial distribution of the water vole population, reducing the extent of suitable habitat in some core areas. The interactive mapping tool made available suitable habitat and connectivity maps for conservation managers to assess new planning applications and for the development of a conservation action plan for water voles.</li> <li>Synthesis and applications: We believe this approach provides a framework for future development of nature conservation tools that can be used by planners to inform ecological decision making, increase biodiversity and reduce human-wildlife conflict in urban environments.</li> </ol>
A formative usability study of workflow management systems in label-free digital pathology - Data and Code
<p>This repository holds the necessary data and code as well as a descriptive Readme file that was used for our publication "A formative usability study of workflow management systems in label-free digital pathology" by Markus Jelonek et al. (2022), submitted to F1000Research.</p> <p> </p> <p>Abstract:</p> <p>We present a formative usability study that investigates the usability of different<br> workflow management systems in the field of biomedical data analysis. Specifically, we study a task in the field of so-called label-free digital pathology and investigate one graphical user interface based workflow and one script-based workflow to solve the task. Our main intention is to gain first insights into the systematic study of usability in the context of biomedical image analysis, and formulate experiences and guidelines for future usability studies dealing with workflow management systems. Embedded in a specific setup dealing with label-free digital pathology, the core question behind our contribution is how usability studies for scientific workflow management can be conducted, and how they can be used systematically to improve such tools. Further, we address specific questions about the resource utilisation and management of usability studies, including the recruitment of participants as well as the design of specific workflows to be investigated.</p>
Data supporting "Crop yield after 5 decades of contrasting residue management"
<p>Data supporting the pubblication "Crop yield after 5 decades of contrasting residue management" by Piccoli et al. (2020). Nutr Cycl Agroecosyst (2020) 117:231–241. https://doi.org/10.1007/s10705-020-10067-9(0123456789().,-volV() 0123458697().,-volV)</p>
Managing for the unexpected: building resilient forest landscapes to cope with global change: Supporting data
<p><strong>Input files</strong> and <strong>installers </strong>of the versions of LANDIS-II, PnET-Succession and other extensions used in the associated paper. They can be used to to reproduce results of the study.</p> <p>The model documentation is freely available at <a href="https://www.landis-ii.org/">https://www.landis-ii.org/</a></p> <p>The LANDIS-II code is distributed under an open source license at <a href="https://github.com/LANDIS-II-Foundation">https://github.com/LANDIS-II-Foundation</a>.</p> <p>If interested in using this dataset for a research study or project, please contact <a href="https://www.marco-mina.com">Marco Mina</a></p> <p>---------------------</p> <p>Mina, M., Messier, C., Duveneck, M., Fortin, M. J., & Aquilué, N. (2022) <strong>Managing for the unexpected: building resilient forest landscapes to cope with global change</strong>. <em>Global Change Biology </em>28, 4323– 4341 <em> </em><a href="https://doi.org/10.1111/gcb.16197">https://doi.org/10.1111/gcb.16197</a></p> <p>ABSTRACT. Natural disturbances exacerbated by novel climate regimes are increasing worldwide, threatening the ability of forest ecosystems to mitigate global warming through carbon sequestration and to provide other key ecosystem services. One way to cope with unknown disturbance events is to promote the ecological resilience of the forest by increasing both functional trait and structural diversity and by fostering functional connectivity of the landscape to ensure a rapid and efficient self-reorganization of the system. We investigated how expected and unexpected variations in climate and biotic disturbances affect ecological resilience and carbon storage in a forested region in southeastern Canada. Using a process-based forest landscape model (LANDIS-II), we simulated ecosystem responses to climate change and insect outbreaks under different forest policy scenarios – including a novel approach based on functional diversification and network analysis – and tested how the potentially most damaging insect pests interact with changes in forest composition and structure due to changing climate and management. We found that climate warming, lengthening the vegetation season, will increase forest productivity and carbon storage, but unexpected impacts of drought and insect outbreaks will drastically reduce such variables. Generalist, non-native insects feeding on hardwood are the most damaging biotic agents for our region, and their monitoring and early detection should be a priority for forest authorities. Higher forest diversity driven by climate-smart management and fostered by climate change that promotes warm-adapted species, might increase disturbance severity. However, alternative forest policy scenarios led to a higher functional and structural diversity as well as functional connectivity – and thus to higher ecological resilience – than conventional management. Our results demonstrate that adopting a landscape-scale perspective by planning interventions strategically in space and adopting a functional trait approach to diversify forests is promising for enhancing ecological resilience under unexpected global change stressors.</p>
Data for: Size spectrum model reveals importance of considering species interactions in a freshwater fisheries management context
<p>Inland fisheries have significant cultural and economic value around the globe, providing dietary protein, income, and recreation. Consequently, methods for monitoring and managing these important fisheries are continually being refined. In marine systems, multi-species size spectrum models have been increasingly used to explore management scenarios of important fish stocks within an ecosystem-based fisheries management framework; however, these models have not been applied in freshwater systems. In this study, we developed a multi-species size spectrum model for the fish community of Lake Nipissing, a large, productive lake in Ontario, Canada. To the best of our knowledge, this is the first fully calibrated multi-species size spectrum model for an inland fishery. Using this model, we explored the impacts of different management scenarios on fish community dynamics while taking species interactions into account. Specifically, we examined how changes in fishing mortality affect: (1) species biomass; (2) community size structure; and (3) stock recovery times. We found that community dynamics following changes in fishing mortality were driven by complex interactions among species, including competition and predation. The greatest changes in biomass and community size structure were observed following changes in fishing mortality to top predators, with community size structure most strongly influenced by changes in mortality to the largest species in the community. Counter to predictions based on generation time, the smallest species in our model exhibited the longest time to recovery due to strong competition and predation. Our results demonstrate the importance of taking an ecosystem-based approach and considering species interactions in the management of inland fisheries and highlight the potential of size spectrum model use in freshwater systems.</p>
Future-proofing the koala: synergizing genomic and environmental data for effective species management
<p><span>Climatic and evolutionary processes are inextricably linked to conservation. Avoiding extinction in rapidly changing environments often depends upon a species' capacity to adapt in the face of extreme selective pressures. Here, we employed exon capture and high-throughput next-generation sequencing to investigate the mechanisms underlying population structure and adaptive genetic variation in the koala (<em>Phascolarctos cinereus</em>), an iconic Australian marsupial that represents a unique conservation challenge because it is not uniformly threatened across its range.</span> <span>An examination of 250 specimens representing 91 wild source locations revealed that five major genetic clusters currently exist on a continental scale. The initial divergence of these clusters appears to have been concordant with the Mid-Brunhes Transition (</span><span>∼</span><span> 430–300 kya), a major climatic reorganization that increased the amplitude of Pleistocene glacial-interglacial cycles. While signatures of polygenic selection and environmental adaptation were detected, strong evidence for repeated, climate-associated range contractions and demographic bottleneck events suggests that geographically isolated refugia may have played a more significant role in the survival of the koala through the Pleistocene glaciation than <em>in situ</em> adaptation. Consequently, the conservation of genome-wide genetic variation must be aligned with the protection of core koala habitat to increase the resilience of threatened populations to accelerating anthropogenic threats. Finally, we propose that the five major genetic clusters identified in this study should be accounted for in future koala conservation efforts (e.g. guiding translocations), as existing management divisions in the states of Queensland and New South Wales do not reflect historic or contemporary population structure.</span></p>
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.