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13,276 results for “IT management”
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>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</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>
Coupling nitrogen removal and watershed management to improve global lake water quality
<p>Updated dataset and code for the article "Coupling nitrogen removal and watershed management to improve global lake water quality" after removing deep lakes.</p>
EJPSOIL CarboSeq crop and soil management database
<p><span><strong>CarboSeq crop and soil management database :</strong> </span>The database of the EJPSOIL CarboSeq WP2 with (meta)data of long term agricultural field experiments for Tier2 (SOC) and where available Tier 3 (carbon input) from Europe</p>
Research Data Management Life Cycle
<p>An overview of the research data management life cycle with proper licensing.</p> <p> </p>
Systemic Failure of European Fisheries Management
<p>Supporting data, R scripts, and resulting figures for Froese et al. (submitted): Systemic Failure of European Fisheries Management</p> <p> </p> <p>Authors: Rainer Froese 1*, Noa Steiner 2, Eva Papaioannou 1, Liam MacNeil 1, Thorsten Reusch 1, Marco Scotti 1,3 </p> <p>Affiliations: </p> <p>1 GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstraße 1-3, 24148 Kiel, Germany </p> <p>2 Institute of Agricultural Economics, University of Kiel, Olshausenstraße 40, 24118 Kiel, Germany</p> <p>3 Institute of Biosciences and Bioresources, National Research Council of Italy, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy</p> <p>*Corresponding author. Email: rfroese@geomar.de </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>
Overview of preregistrations in the management and marketing fields
<p>This dataset provides an overview of preregistrations that have been included in peer-reviewed articles (in leading journals) in the management, marketing, and information systems disciplines.</p> <p>NB:</p> <p>'Experiment Count' = Number of experiments in each case (incl. supplementary studies)</p> <p>'Preregistration Count' = Number of preregistrations in each case</p> <p>'Publicly Available' = Number of preregistrations that were retrievable at the point of publication of this analysis</p> <p>'Preregistration Ratio' = Preregistration Count / Experiment Count (indicates how many studies were preregistered)</p> <p>'Institutional Affiliation by Region' = Indicates whether an institution/region appears in the corresponding article (note that several appearances of one institution in a single article is not treated differently)</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>
A multi-species approach to manage effects of land cover and weather on upland game birds
<p class="Text">Loss and degradation of grasslands in the Great Plains region has resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify effects of land cover and weather changes on lesser and greater prairie-chickens (<i>Tympanuchus pallidicinctus </i>and <i>T. cupido</i>, respectively), northern bobwhites (<i>Colinus virginianus</i>), and ring-necked pheasants (<i>Phasianus colchicus</i>). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie-chicken and ring-necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie-chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie-chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring-necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species.</p>
FIGURE 3 in Diversity And Abundance Of Phytoseiidae (Acari: Mesostigmata) In Three Crop Management Strategies Of Citrus Orchards In Tunisia
FIGURE 3: Time variation of Phytoseiidae densities and diversity in orchard (2) (a) on citrus leaves, (b) on weeds.
FIGURE 4 in Diversity And Abundance Of Phytoseiidae (Acari: Mesostigmata) In Three Crop Management Strategies Of Citrus Orchards In Tunisia
FIGURE 4: Time variation of Phytoseiidae densities and diversity in orchard (3) (a) on citrus leaves, (b) on weeds.
FIGURE 2 in Diversity And Abundance Of Phytoseiidae (Acari: Mesostigmata) In Three Crop Management Strategies Of Citrus Orchards In Tunisia
FIGURE 2: Time variation of Phytoseiidae and Tetranychidae densities on citrus leaves in orchard (1).
FIGURE 1 in Diversity And Abundance Of Phytoseiidae (Acari: Mesostigmata) In Three Crop Management Strategies Of Citrus Orchards In Tunisia
FIGURE 1: Time variation of Phytoseiidae densities and diversity in orchard (1) (a) on citrus leaves, (b) on weeds.
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>
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.