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3,225 results for “Case of study”
Hofstede's Cultural A Case Study: Working Performance of Chinese Expatriates in Dubai
<p>The data are from the paper " Hofstede’s Cultural A Case Study: Working Performance of Chinese Expatriates in Dubai".</p>
Data for article: Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018
<p>The data consists of the indicators for spatio-temporal analysis of wood Ecosystem Service (ES) potential, supply, and demand across Europe between 2008 and 2018. This dataset was used for the analysis of temporal trends of wood ES in the study "Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018".</p> <p>The data are collected and compiled from open access statistical databases. They consist of three parts:</p> <p>1. The PDF file with a detailed description of the data and all the input sources from which it was derived.</p> <p>2. The Zip file with 3 separate Excel files containing the short description of the indicators for mapping spatio-temporal changes, namely wood ES potential, wood ES supply and wood ES demand and their values between 2008 and 2018 at three different levels: continental, national and regional. Note that for the regional level only supply and demand indicators are available.</p> <p>3. The tiff file representing the spatial resolution for visualisation and analysis of the data.</p> <p> </p> <p> </p> <p>Resolution:</p> <p>Data are available for 3 spatial levels, in the temporal dimension between 2008 and 2018 (annually). All 3 indicators are available at continental (European) and national scales. The study area covers 24 countries of the European Union (EU) and Switzerland. Supply and demand are also available at regional level. At the regional scale, we assessed supply and demand using the nomenclature of territorial units for statistics (NUTS 3; n = 1061) and local administrative units (LAU; n = 957) from the year 2016 (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.">Eurostat, 2016</a>). The visualisation of the resolution is available in the tiff file attached to the data.</p> <p>The data are visualised and analysed in the ETRS 1989 LAEA projection.</p> <p> </p> <p>For more information on the indicators used, data collection and processing, see the supplementary files of the published article.</p> <p> </p> <p> </p> <p><strong>DEFINITIONS of ES mapping indicators used in the study: </strong></p> <p><em>ES potential</em> - the hypothetical maximum yield of services potentially available for supply.</p> <p><em>ES supply</em> – the amount of the mobilized service within the ecosystem capable to provide a service at a given location in a certain time (frequently referred in literature as ES flow).</p> <p><em>ES demand </em>- the need for the ecosystem-based service by the end users. In this study demand is analysed from the perspective of service end-user.</p>
Supplementary material from: Modeling biodiesel properties by preference learning: case study of cetane number
<p><span>Data obtained from the available bibliography between the year 2002 and 2022. This document contains the FAME distribution and cetane number of 543 biodiesels.</span></p> <p><span> </span><span>The dataset is divided into three sections within the worksheet.</span></p> <ul> <li><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></li> <li><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></li> <li><span>The third and final section describes the measured property Cetane Number.</span></li> </ul>
MixME Celtic Sea gadoid case study
<p>Input data objects to a MixME simulation. Contains the stock and fleet structures required to run a proof-of-concept Management Strategy Evaluation for Celtic Sea cod, haddock and whiting using MixME.</p>
Nitrogen fertilization plot level data - a Finnish case study
<p>The data is based on the fertilization study where we evaluated the spatial evenness of nitrogen (N) fertilization (measured fertilization dose on circular plots with radius of 7,98 m) in fertilized treatments and effects of fertilization treatments (target of 0, 150 and 200 kg N /ha) on short-term volume growth responses in two ground-fertilized (2018) Scots pine and in two airborne-fertilized (2019) Norway spruce study sites on medium fertile (Myrtillus-type, MT) upland forest sites in Eastern Finland. It was also studied the relationships between measured fertilization dose, N concentrations in needles and soil organic (humus) layer, and volume growth of trees (calculated based on diameter at breast height and height of sample trees) based on block level averages (based on data measured for three circular plots in each block). In each study area, we established three one hectare replicate blocks (with three circular study plots of 200 m² in each) for fertilizer treatments with target of 0, 150 and 200 kg N /ha. Spatial evenness of the fertilization was measured with textile funnels. Height, breast height diameter and vitality (living/dead) of trees were measured annually in each circular plot. Nutrient concentrations in needles (analysed in Yara laboratory in United Kingdom) and soil organic (humus) layer (analysed at UEF laboratory) were measured once. </p>
Solvent-triggered shape change in gradient-based 4D printed bilayers: case study on semi-crystalline polymer networks
<p>This dataset comes from the following paper:</p> <p>Lorenzo Bonetti, Aron Cobianchi, Daniele Natali, Stefano Pandini, Massimo Messori, Maurizio Toselli, Giulia Scalet, Solvent-triggered shape change in gradient-based 4D printed bilayers: case study on semi-crystalline polymer networks, Soft Matter, 2024. <a href="https://doi.org/10.1039/D4SM00304G" target="_blank" rel="noopener">https://doi.org/10.1039/D4SM00304G</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>.xls files of the experimental data</li> <li>. m of the theoretical computations</li> </ul>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
Data affiliated with "Solid bitumen as an indicator of petroleum migration, thermal maturity, and contact metamorphism: A case study in the Barrandian Basin (Silurian - Devonian), Czech Republic"
<p>Fourier transform infrared (FTIR) spectra affiliated with the publication "<span>Solid bitumen as an indicator of petroleum migration, thermal maturity, and contact metamorphism: A case study in the Barrandian Basin (Silurian - Devonian), Czech Republic" in International Journal of Coal Geology (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.coal.2024.104493" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.coal.2024.104493</span></a>).<br></span></p> <p><span>The FTIR data are saved as CSVs under the numbers and names corresponding to Table in Supplement 1. Columns A corresponds to wavenumbers in cm-1, columns B and C to absorbance measured in two measurements.</span></p>
Figure 3 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study
Figure 3. Sea anemone Diadumene lineata on the mangrove trunk.
Figure 8 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study
Figure 8. Sphaeroma terebrans and its juveniles colonize in wooden holes.
Data from: Skeletal mineralogy of marine organisms shaped by seawater temperature and evolutionary history - a case study of cheilostome bryozoans
<p>The record of CaCO<sub>3</sub> biominerals serves as a valuable repository documenting Earth's evolutionary history and environmental changes. An in-depth understanding of the mineralogical diversity within calcifying organisms is essential for interpreting the evolutionary record of CaCO<sub>3</sub> and evaluating the adaptability of biomineralizers to past and future environmental change. To offer insights into the relative importance of environment vs. phylogenetic history in determining mineralogy, this study explores the modern-day global distribution of mineralogies in cheilostome bryozoans.</p> <p>Cheilostome bryozoans vary considerably in their mineral composition: in our dataset 65% of the species possess purely calcite skeletons, 15% exclusively employ aragonite, and 20% exhibit mixed (i.e., calcite and aragonite) mineralogies. Temperature is the predominant measured environmental factor influencing bryozoan skeletal mineralogy, accounting for 20% of its variability across species, when phylogenetic relatedness is unaccounted for. Bryozoans in lower latitudes, characterized by higher seawater temperatures, have higher aragonite concentrations. By accounting for phylogenetic structure using a subset of 87 species for which we have topological information, 40% of the observed mineralogical variability could be attributed to present-day temperature. In contrast, depth and salinity played minor roles, explaining less than 1% of the mineralogical variation each.</p> <p>This study emphasizes the influence of evolutionary history on the mineralogical variability of calcifying organisms, even when it can be shown that a single environmental factor (temperature) explains a substantial amount of this variability. When confronted with changing temperature, calcifiers such as bryozoans are likely to respond in diverse ways, depending on the species, given their phylogenetic relatedness and the external conditions they meet.</p>
T-Factor. WP2 Dataset: Advanced Case Study Interviews for university of Łódź, Poland
<p>This dataset presents three anonymized interviews performed on-line by a researcher from the Department of Social Pedagogy of the University of Łódź, Poland as part of the activities in Work Package 2 of the T-Factor project. The content of that package is relevant to the issue of advance cases of meanwhile uses in Lodz. The interviewees represent different institutions or organizations involved in an urban regeneration in Łódź and their stories are focused on their understandings on meanwhile uses and the ways how they contribute to the overall effort to manage this time in Lodz.</p>
Case study in Ouray (Colorado, USA)
<div>The dataset is located in Ouray, Colorade, USA. It is the point clouds collected by an Optech ILRIS-3D laser scanner at four scan positions in 2004 from Lato et al., 2013. The scanning time was about 15 min, the resolution was about 2 cm, and there was a total of 1,515,722 points. </div>
A case study of the Chesapeake Bay using the CAFE framework
<p>This file includes nutrient budget data for the Chesapeake Bay watershed, organized by county, year, and system.</p>
Modelling input data for the case studies of the paper "Strategic bidding in light-robust day-ahead electricity markets".
<p><span>This data package includes the modelling input data to replicate the results of the case studies included in the paper "Strategic bidding in light-robust day-ahead electricity markets". </span></p> <p><span>This supplementary data package includes the following files:</span></p> <p><span><span>-<span> </span></span></span><span>Meta Data – Input data: Dataset containing the input data for the strategic bidding behavior problem. It includes bids from conventional, demand and stochastic players and the scenarios for system imbalance and real-time production.</span></p> <p><span><span>-<span> </span></span></span><span>Readme.txt: Includes a detailed description of the data packages</span></p> <p><span> </span></p> <p><span>Sources of data:</span></p> <p><span>* Ordoudis, C., Pinson, P., Morales, J. M., & Zugno, M. (2016). An updated version of the IEEE RTS 24-bus system for electricity market and power system operation studies. Technical University of Denmark.</span></p> <p><span>* Silva-Rodriguez, L., Sanjab, A., Fumagalli, E., Virag, A., & Gibescu, M. (2022). A light robust optimization approach for uncertainty-based day-ahead electricity markets. Electric Power Systems Research, 212, 108281. https://doi.org/10.1016/J.EPSR.2022.108281<span> </span></span></p> <p><span>* Derived (scaled down) from Elia. (2024). Open data. Retrieved from https://www.elia.be/en/grid-data/open-data<span> </span></span></p> <p><span>* Own data<span> </span></span></p> <p><span><span> </span></span></p>
Assessing the potential of camera traps for estimating activity pattern compared to collar-mounted activity sensors: A case study on Eurasian lynx (Lynx lynx) in South-Eastern Norway
<div> <div> <div> <div> <p>The diel activity patterns of animals convey information about physiology, ecological niches and animal behaviour relevant for both applied conservation and more theoretical research. However, these patterns are challenging to study in the field. The current gold-standard approach to quantify the movements and activity patterns of medium to large wildlife species is to use Global Positioning Systems (GPS) collars equipped with activity sensors (e.g., accelerometers). A more recent approach consists of inferring activity patterns from the time-stamped pictures of wildlife obtained from the camera traps now routinely used in wildlife monitoring projects. However, few studies have attempted to validate estimates of activity patterns obtained from camera traps against those obtained from activity sensors. In this study, we compared the diel activity pattern of the Eurasian lynx Lynx lynx inferred from detections by a network of over 300 camera traps active between 2010 and 2020, to activity patterns obtained from 18 GPS-collared lynx (8 females, 10 males) equipped with 2-axis accelerometer sensors, in the same area of southern Norway. Our results suggest that camera traps can be used to estimate diel activity curves that are comparable to those obtained from accelerometers. In our study 75 detections were sufficient to approximate the diel activity pattern obtained from accelerometer. Subsampling indicated that a low number of detections results in a coarser approximation of the diel activity pattern.</p> </div> </div> </div> </div>
Datasets and source code for case study on Pomerini, Tanzania
Open the record for dataset details and reuse information.
Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2
<p>The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO<sub>2</sub> fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite.</p> <p>This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here.</p> <p>Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the <em>load</em> function or can be read into R using the <em>R.matlab</em> package.</p> <ul> <li><strong>H.tar.gz</strong>: This tar file contains the <strong>H</strong> matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The <strong>H</strong> matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the <strong>H</strong> matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li><strong>Z.mat</strong>: This file contains the synthetic OCO-2 observations used in the case study. </li> <li><strong>areas_us.mat</strong>: This file lists the area of each model grid box used in the case study in units of meters<sup>2</sup>. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO<sub>2</sub> fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li><strong>distmat.mat</strong>: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li><strong>land_mask.mat</strong>: We only estimate CO<sub>2</sub> fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li><strong>H_all_OCO2.mat</strong>: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li><strong>Xvar.tar.gz</strong>: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat Evaporation</li> <li>Xvar_msdwswrf.mat Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat Specific humidity</li> <li>Xvar_stl1.mat Soil temperature level 1</li> <li>Xvar_stl3.mat Soil temperature level 3</li> <li>Xvar_swvl1.mat Volumetric soil water layer 1</li> <li>Xvar_t2m.mat 2 metre temperature</li> <li>Xvar_tp.mat Total precipitation</li> <li>Xvar_mer.mat Mean evaporation rate</li> <li>Xvar_pev.mat Potential evaporation</li> <li>Xvar_r.mat Relative humidity</li> <li>Xvar_swvl3.mat Volumetric soil water layer 1</li> <li>Xvar_tcc.mat Total cloud cover</li> </ul> </li> </ul>
Figure 2 in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study
Figure 2. Monthly captures of B. dorsalis in methyl eugenol traps on Moorea.
Data from: Challenging trophic position assessments in complex ecosystems: calculation method, choice of baseline, trophic enrichment factors, season and feeding guild do matter. A case study from Marquesas Islands coral reefs
<p>Assessments of ecosystem functioning are a fundamental ecological challenge and an essential foundation for ecosystem-based management. An understanding of species trophic positions (TP) is essential to characterize food web architecture. However, despite the intuitive nature of the concept, empirically estimating TP is a challenging task due to the complexity of trophic interaction networks. Various alternative methods are proposed to assess TPs, including different approaches to account for the different sources of organic matter at the base of the food web (the "baseline"). However, it is often not clear which methodological approach and which baseline choices are the most reliable. Using an ecosystem-wide assessment of a tropical reef (Marquesas Islands, French Polynesia, with available data for 70 coral reef invertebrate and fish species), we tested whether different commonly used TP estimation methods yield similar results and, if not, whether it is possible to identify the most reliable method. We found significant differences in TP estimates of up to 1.7 TP for the same species, depending on the method and the baseline used. When using bulk stable isotope data, the choice of the baseline significantly impacted TP values. Indeed, while δ<sup>15</sup>N values of macroalgae led to consistent TP estimates, those using phytoplankton generated unrealistically low TP estimates. The use of a conventional enrichment factor (i.e. 3.4 ‰) or a "variable" enrichment factor (i.e. according to feeding guilds) also produced clear discrepancies between TP estimates. Regarding the use of different calculation methods, TPs obtained with δ<sup>15</sup>N values of source amino acids (compound specifics isotope analysis) were close to those assessed with macroalgae, but evidenced the opposite seasonal pattern, with significantly lower TPs in winter than in summer for the majority of assessed species, with particularly pronounced differences for lower TP species. We use the observed differences to discuss possible drivers of the diverging TP estimates and the potential ecological implications.</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.