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3,225 results for “Case of study”
FIG. 3 in Horses in the suburbs of a medieval city - a case study from Brno (Czech Republic)
FIG. 3. — Trnitá Street, Brno: excavated plot with horse burials. Gray, excavated plot; red, trenches with horse burials. Courtesy of Archaia Brno.
TABLE 4 in Horses in the suburbs of a medieval city - a case study from Brno (Czech Republic)
<p>TABLE 4. — Sites used in the comparison of withers height (all sites) and robusticity (excluding Vyšehrad).Abbreviation: <b>NMLB</b>, number of measured long bones included in this study.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th><b>Source of</b></th></tr><tr><th><b>Site</b></th><th></th><th></th><th></th><th><b>osteometric</b></th></tr><tr><th><b>Number Site</b></th><th><b>Period NMLB data</b></th></tr></tbody><tbody><tr><th>1</th><td>Brno-</td><td>Early Modern</td><td>23</td><td>Present study</td></tr><tr><th></th><td>Křídlovická</td><td></td><td></td><td></td></tr><tr><th>2</th><td>Brno-Trnitá</td><td>Late Medieval –</td><td>55</td><td>Present study</td></tr><tr><th></th><td></td><td>Early Modern</td><td></td><td></td></tr><tr><th>3</th><td>Brno-Modřice Late Medieval –</td><td>14</td><td>Present study</td></tr><tr><th></th><td></td><td>Early Modern</td><td></td><td></td></tr><tr><th>4</th><td>Mikulčice</td><td>Early Medieval</td><td>316</td><td>Chrzanowska &</td></tr><tr><th></th><td></td><td></td><td></td><td>Krupska 2003</td></tr><tr><th>5</th><td>Nemilany</td><td>Early Medieval</td><td>20</td><td>Dreslerová 2014</td></tr><tr><th>6</th><td>Pohansko</td><td>Early Medieval</td><td>27</td><td>Kratochvíl 1969;</td></tr><tr><th></th><td></td><td></td><td></td><td>Chrzanowska &</td></tr><tr><th></th><td></td><td></td><td></td><td>Krupska 2003;</td></tr><tr><th></th><td></td><td></td><td></td><td>Dreslerová 2018</td></tr><tr><th>7</th><td>Stará Boleslav Early Medieval</td><td>5</td><td>Kyselý 2003</td></tr><tr><th>8</th><td>Vyšehrad</td><td>Early Medieval</td><td>1</td><td>Kyselý 2004</td></tr><tr><th>9</th><td>Podolí</td><td>Migration</td><td>29</td><td>Ambros & Müller</td></tr><tr><th></th><td></td><td></td><td></td><td>1980</td></tr><tr><th>10</th><td>Záluží</td><td>Migration</td><td>6</td><td>Ambros & Müller</td></tr><tr><th></th><td></td><td></td><td></td><td>1980</td></tr><tr><th>11</th><td>Zdice</td><td>Modern</td><td>25</td><td>Diedrich 2017</td></tr></tbody></table>
Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys
<p>Replication datasets for the <a href="https://retroharmonize.dataobservatory.eu/articles/arabbarometer.html">retroharmonize Case Study: Working With Arab Barometer Surveys</a></p>
Spectrum-Based Feature Localization: A Case Study using ArgoUML
<p>ArgoUML dataset used in the paper: "Spectrum-Based Feature Localization: A Case Study using ArgoUML".</p> <p>This enables reproducibility, evaluation, and comparison of our study.</p> <p>In the folder ArgoUML you will find: </p> <ul> <li><em>ArgoUML_Tests_Annotated</em>. This folder contains the variants used for running test cases and obtaining execution traces from the test scenarios.</li> <li><em>BenchmarkTracesComparison</em>. This folder contains the results metrics with the granularity of the ArgoUML benchmark (see Martinez et al., 2018. https://doi.org/10.1145/3233027.3236402) and the execution traces from manual and test cases for each feature from previous work (see Michelon et al., 2021. https://doi.org/10.1145/3442391.3442403).</li> <li><em>VariantsSourceCodeComparison. </em>This folder contains the results metrics with the granularity at the method- and line-level and execution traces from manual and test cases for each feature from previous work (see Michelon et al., 2021. https://doi.org/10.1145/3442391.3442403).</li> </ul>
Data from: Dispersal in a house sparrow metapopulation: an integrative case study of genetic assignment calibrated with ecological data and pedigree information
<p class="western">Dispersal has a crucial role determining eco-evolutionary dynamics through both gene flow and population size regulation. However, to study dispersal and its consequences, one must distinguish immigrants from residents. Dispersers can be identified using telemetry, capture-mark-recapture (CMR) methods, or genetic assignment methods. All of these methods have disadvantages, such as, high costs and substantial field efforts needed for telemetry and CMR surveys, and adequate genetic distance required in genetic assignment. In this study, we used genome-wide 200K Single Nucleotide Polymorphism data and two different genetic assignment approaches (GSI_SIM, Bayesian framework; BONE, network-based estimation) to identify the dispersers in a house sparrow (<i>Passer domesticus</i>) metapopulation sampled over 16 years. Our results showed higher assignment accuracy with BONE. Hence, we proceeded to diagnose potential sources of errors in the assignment results from the BONE method due to variation in levels of inter-population genetic differentiation, intra-population genetic variation and sample size. We show that assignment accuracy is high even at low levels of genetic differentiation and that it increases with the proportion of a population that has been sampled. Finally, we highlight that dispersal studies integrating both ecological and genetic data provide robust assessments of the dispersal patterns in natural populations.</p>
Dataset of sampled and/or logged Chlorophyll, Total Suspended Matter, Turbidity and Water temperature in Dutch Case study areas
<p>Dataset of sampled and/or logged Chlorophyll, Total Suspended Matter, Turbidity and Water temperature in Dutch Case study areas at multiple stations in 2018, 2019 and 2020.</p>
A case study: evaluation of a single column model with advection to simulate fog/stratus during C-FOG experiment
<p>Those datasets are observed by C-FOG (<em>Toward Improving Coastal Fog Prediction</em>) campaign, which was organized as a field experiment combined with modelling and theoretical initiatives. The objective of C-FOG was to advance our understanding and ability to observe, simulate, and predict fog, with a particular focus on warm fog formation, development and dissipation over coastal environments.</p> <p>The uploaded observation data contains liquid water content, droplet number concentration, temperature, SST, wind, visibility, backscatter collected by ceilometer, and atmospheric profile. The details can be found in the dataset.</p> <p>Thanks for the intense observation by the C-FOG project, which collected valuable data for detailed fog research.</p>
Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
<p>Biological processes exhibit complex temporal dependencies due to the sequential nature of allocation decisions in organisms' life-cycles, feedback loops, and two-way causality. Consequently, longitudinal data often contain cross-lags: the predictor variable depends on the response variable of the previous time-step. Although statisticians have warned that regression models that ignore such covariate endogeneity in time series are likely to be inappropriate, this has received relatively little attention in biology. Furthermore, the resulting degree of estimation bias remains largely unexplored.</p> <p>We use a graphical model and numerical simulations to understand why and how regression models that ignore cross-lags can be biased, and how this bias depends on the length and number of time series. Ecological and evolutionary examples are provided to illustrate that cross-lags may be more common than is typically appreciated and that they occur in functionally different ways.</p> <p>We show that routinely used regression models that ignore cross-lags are asymptotically unbiased. However, this offers little relief, as for most realistically feasible lengths of time series conventional methods are biased. Furthermore, collecting time series on multiple subjects–such as populations, groups or individuals—does not help to overcome this bias when the analysis focusses on within-subject patterns (often the pattern of interest). Simulations (R tutorial 1 & 2), a literature search and a real-world empirical example on fairy wrens (data archived here with analyses presented in R-tutorial 3) together suggest that approaches that ignore cross-lags are likely biased in the direction opposite to the sign of the cross-lag (e.g. towards detecting density-dependence of vital rates and against detecting life history trade-offs and benefits of group living). Next, we show that multivariate (e.g. structural equation) models can dynamically account for cross-lags, and simultaneously address additional bias induced by measurement error, but only if the analysis considers multiple time series.</p> <p>We provide guidance on how to identify a cross-lag and subsequently specify it in a multivariate model, which can be far from trivial. Our tutorials with data and R code of the worked examples provide step‐by‐step instructions on how to perform such analyses.</p> <p>Our study offers insights into situations in which cross-lags can bias analysis of ecological and evolutionary time series and suggests that adopting dynamical models can be important, as this directly affects our understanding of population regulation, the evolution of life histories and cooperation, and possibly many other topics. Determining how strong estimation bias due to ignoring covariate endogeneity has been in the ecological literature requires further study, also because it may interact with other sources of bias.</p>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data2
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and houses' data</li> <li>Houses_Input_Output_JSONs_and_Statistics - Zip containing the input and output files from the proposed system, as well as their corresponding schedule statistics</li> </ul>
Load Shifting Optimization with Genetic Algorithms for Energy Cost Minimization in Households - Case Study Data
<p>The case study of this dataset uses real household data, representing five days from 0h00 to 23h59. This dataset uses a period of 15 minutes for all loads execution time and energy data. The case study considers twenty unique houses that can have up to five different shiftable appliances, each executing three process cycles.<br> <br> File Description:</p> <ul> <li>Case_Studies_Data-BAU_and_Load_Shifting - Excel containing appliances energy profile, load execution preferences, BAU consumption, and other house data</li> <li>Houses_Input_JSONs - Zip containing the input files, from each house, for the proposed system</li> </ul>
Fig. 5. — Modified from VAN KORES 1995 in Are plant adaptations to growing on serpentine soil rare or common? A few case studies from New Caledonia
Fig. 5. — Modified from VAN KORES 1995, showing soil preferences as in Fig. 1. A, Acianthus sect. Macropetalus; B, Acianthus sect. Univiscidiatus.
Fig. 2 in Are plant adaptations to growing on serpentine soil rare or common? A few case studies from New Caledonia
Fig. 2. — Cladogram of Morinda (modified from JOHANSSON 1994), showing soil preferences as in Fig. 1. A, version with ability to grow on serpentine soil as plesiomorphic; B, version with ability to grow on serpentine soils apomorphic.
FIG. 11 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 11. – Distal measurements of right cattle metatarsals (following von den Driesch 1976). Elements from immature individuals (un- fused) are here included with those from mature individuals (fused, with fusion taking place between 2-2.5 years of age [Grigson 1982: 22]). The division in size between older, smaller individuals and younger, larger ones may also represent a break between older females and younger males (figure created by Cerisa R. Reynolds).
FIG. 10 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 10. – Each cattle skull from within Plum Grove's Bone Feature exhibits impact fractures indicative of blunt force trauma, indicating that each individual was killed in the same manner. These impact fractures are clearly visible in this photograph of five of the Fea- ture's cattle skulls (photograph courtesy of William E. Whittaker).
FIG. 3 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 3. – Project location in relation the to surrounding topography, highlighting Plum Grove's location within Iowa City as well as Iowa City's location within the state of Iowa. Base map is aerial photo- graph from ISUGISSRF (2011); figure created by Bryan Kendall.
FIG. 1 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 1. – Map of the contiguous U.S., with the main course of the Mississippi River highlighted in a thicker grey line. As the site this paper focuses on is located in Iowa City, Iowa, this city is also identified here as a grey circle (map from United-States-Map.com; modified by Cerisa R. Reynolds).
FIG. 2. – A in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 2. – A map of railways across the contiguous United States in 1870, with Iowa City, Iowa, and Chicago, Illinois, highlighted (map from http://www.latinamericanstudies.org/19-century/railroads-1870.jpg; adapted by Cerisa R. Reynolds).
FIG. 6. – A in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 6. – A map of the Plum Grove Historic Farm house and the Bone Feature,identifying where unit excavations have been carried out and where testing indicates the extent of the Bone Feature. As is visible here, the Bone Feature is parallel to and runs to the east of the Plum Grove house (figure created by Bryan Kendall).
FIG. 5 in Nineteenth-century butchery and transport for a market economy: Plum Grove as a case study for commercial transactions in the Midwestern USA
FIG. 5. – The northern face of the Plum Grove house, taken in 2010. The Bone Feature discussed throughout this paper was identified roughly 4-5m east (here, to the left) of the house (photography courtesy of William E. Whittaker). Additional photographs of the site as well as digital reconstructions of the property as it would have looked at various times throughout history can be seen at: http://www.uiowa.edu/~plumgrov/index.html.
Figure 4 in Deforestation affects biogeographical regionalization: a case study contrasting potential and extant distributions of Mexican terrestrial mammals
Figure 4. Map of Mexican provinces based on models of species' distributions in t1: baj, Baja California Chiapas; ist, Isthmus of Tehuantepec; mgu, Mexican Gulf; mpa, Mexican Pacific Coast; mpl, Mexican Sierra Madre Occidental; sms, Sierra Madre del Sur; son, Sonora; tam, Tamaulipas; vol, Transmexican
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.