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Data and list of commands required to run the Mefite d'Ansanto gas dispersion case with VIGIL v1.3.7
<p>This dataset include input file to run a probabilistic hazard assessment of CO2 dispersion in the Mefite d'Ansanto area, Southern Italy. </p> <p>Alongside the input file, pre-retrieved meteorological data for the 1000 simulations run in this study are available in the folder "simulations". These can be used to reproduce the results presented in the manuscript [EDIT] by means of the VIGIL commands listed in the file "commands.txt". In this case, after the simulations.tar.gz is decompressed into the folder "simulations", the user can avoid running weather.py (see the first command in commands.txt) and should not modify nor cancel the file days_list.txt. A completely new run is instead performed if all the VIGIL scripts are executed. </p> <p>The archive "selected_outputs.tar" contains all the statistical outputs (ECDF and persistence) of the Mefite hazard study in .grd format and the related plots as produced by VIGIL. Part of these outputs have been used to produce the figures of the manuscript. </p>
Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis
<div>This dataset contains key analysis and plotting scripts, data, and sample images.</div> <div> </div> <div>Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis</div> <div> </div> <div>Magnetic Resonance in Medicine Journal | DOI: 10.1002/mrm.30206</div> <div> </div> <div>§ Swetha Pala(1), § Antti Paajanen(1), Aapo Ristaniemi(1), Ervin Nippolainen(1), Isaac O. Afara(1), Olli Nykänen (1), Mikko J. Nissi (1*)</div> <div> </div> <div>1Department of Technical Physics, University of Eastern Finland</div> <div>§Shared authorship </div> <div> </div> <div> </div> <div> </div> <div>*Corresponding author</div> <div>Mikko J. Nissi</div> <div>Department of Technical Physics</div> <div>University of Eastern Finland, Kuopio Finland</div> <div>POB 1627</div> <div>70211 Kuopio</div> <div>mikko.nissi@uef.fi</div> <div>+358-50-5955517</div> <div> </div> <div> </div> <div>Keywords: Quantitative MRI, T1ρ relaxation, T1ρ dispersion, Compressed-sensing, radial acquisition.</div> <div> </div> <div>Included folders and files are:</div> <div>- Article_figures: all figures published in the manuscript (.eps format)</div> <div>- Data: MRI data files from 27 human cadaver samples with subfolders and files:</div> <div>- Human samples data: raw data files, along with generic analysis ROIs, zone divison inside specific samples folder, and within the parameter related data folder there are smaple specific analysis ROIs, computed profiles per spin lock amplitude. </div> <div>- CS reconstructed data files: </div> <div>- DataTables_used_for_analysis: Contains data tables per AF and reference data used for data analysis </div> <div>- Scripts: Matlab functions used for data processing and T1ρ computation, aedes plugins, and data analysis with subfolders and files:</div> <div> - Aedes_plugins: Plugins for aedes (http://aedes.uef.fi) for calculation of profiles from ROI. </div> <div> - Data analysis: Key scripts used for analysis and plotting.</div> <div> - Common functions: Some common functions that are required by the scripts/plugins. </div> <div> </div> <div>- README.txt: this file describing the contents of the dataset.</div> <div> </div> <div> </div> <div>See more info in separate readme files included in sub-folders.</div> <div> </div> <div> </div> <div>(Swetha Pala, 02 July 2024)</div> <p> </p>
Dispersing low carbon technologies through trade; Have trade agreements helped?
<p>This dataset was used in a research paper which examines the impact of trade agreements on low carbon technologies trade.</p>
Figure 4 in First data on population estimates and dispersal of Montenegrina subcristata - a field study at Virpazar, Montenegro
Figure 4. Sum of individuals counted at each observation date at site A (above) and site B (below).
Fig. 3 in Evaluation of field dispersal and survival capacity of the genetic sexing strain Tapachula-7 of Anastrepha ludens (Diptera: Tephritidae)
Fig. 3. Survival of SMR and Tap-7 strains of Anastrepha ludens afer aerial release.
Figure 2. Energy dispersive X in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 2. Energy dispersive X-ray spectroscopy of α - and γ-Al2O3NPs.
Partitioning Seed Dispersal Rate Amongst Vertebrates Vs Invertebrates Along a Land-Use Gradient
<b>Description: </b><p>Seed perdation and dispersal experiments</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/72"><b>Partitioning Seed Dispersal Rate Amongst Vertebrates Vs Invertebrates Along a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=77">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Seed removal experiments</b> (Worksheet Data)</p><p>Dimensions: 904 rows by 13 columns</p><p>Description: Experimental seed removal trials. Each trial consisted of 20 pumpkin seeds being placed on a plate, with seed fates ascertained the following day.</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sample site (Field type: Location)</li><li><b>Point </b>: SAFE project sample site (Field type: ID)</li><li><b>Date</b>: Date seeds were placed in field (Field type: Date)</li><li><b>Treatment</b>: Experimental treatment (Field type: Categorical)</li><li><b>NPlacement</b>: Unknown variable (Field type: ID)</li><li><b>RemainUneat</b>: How many seeds remained on the plate and had no evidence of having been eaten? (Field type: Numeric)</li><li><b>RemovUneat</b>: How many seeds were removed from the plate and had no evidence of having been eaten? (Field type: Numeric)</li><li><b>RemainEat</b>: How many seeds remained on the plate but had evidence of having been eaten? (Field type: Numeric)</li><li><b>RemovEat</b>: How many seeds were removed from the plate and also had evidence of being eaten? (Field type: Numeric)</li><li><b>RemovUnknown</b>: How many seeds were removed from the plate and had an unknown fate? (Field type: Numeric)</li><li><b>Rain</b>: How heavily did it rain last night? 0 being no rain and 5 being torrential rain (Field type: Numeric)</li><li><b>TreatmentSuccessFail</b>: Was the treatment successful? (Field type: Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2013-05-07 to 2013-07-27</p><p><b>Latitudinal extent: </b>4.6350 to 4.7523</p><p><b>Longitudinal extent: </b>116.9632 to 117.5934</p>
Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite"
<p>Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite", reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100013). See preprint and supplementary materials.</p>
X-ray absorption data and microscopic images of "Atomically dispersed iron(3+) sites catalyze efficient CO2 electroreduction to CO"
<p>XANES and EXAFS data (Figure 1F-H, Figure 3A-B, Figure S2F-H, Figure S3H-I, Figure S10A-B, Figure S11A-B,E-F, Figure S12A, Figure S14D-E)</p> <p>Microscopic images (Figure 1A-D, Figure S2B,D, Figure S4A-B,D-E, Figure S9A-C,E Figure S13A-D)</p> <p>of the research paper 'Atomically dispersed iron(3+) sites catalyze efficient CO2 electroreduction to CO'.</p>
The Small World of Global Marine Fisheries: The Cross-Boundary Consequences of Larval Dispersal
<p>This dataset contains the key intermediate inputs and tabular results generated as part of the paper "The Small World of Global Marine Fisheries". The main directories are as follows:</p> <p> - transitions: Matrices describing the probability of particle transitions.</p> <p> - spawn: Collated information on spawning locations and larval dynamics.</p> <p> - spawn-transits: Species-level transition probabilities.</p> <p> - weights: Collated information on EEZs and on sovereign regions.</p> <p> - atrisk: Import and export flows, and corresponding risk factors.</p> <p> - economics: Data files to support economic calculations</p> <p> - saudata: Data from Sea Around Us.</p> <p> - shapefiles: Geospatial information.</p>
Modeling the dispersion of dissolved natural gas condensates from the Sanchi incident
<p>This is the data for submission '<strong>Modeling the dispersion of dissolved natural gas condensates from the Sanchi incident</strong>'. This zip file contains 6 subfolders. The folder named "initialdataofmodel" contains the daily oceanic velocity field for the model derived from Global Ocean Sea Physical Analysis and Forecasting Products distributed by CMEMS, which can be available from <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024">http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024</a> . Other folders contain the data for different figures (experiments) based on the model we used in the paper. The model output data (lon,lat,depth) is written by Fortran as a format of 'format(8e14.6)' .</p>
lingpy/sign-language-evolution-paper: Evolutionary Dynamics in the Dispersal of Sign Languages
<p>Supplement for study on Manual Alphabet evolution.</p>
Fig. 1 in Chemical control of leaf-cutting ants: how do workers disperse toxic bait fragments onto fungus garden?
Fig. 1. Variograms columns: control, sulfluramid and indoxacarb.
Applicability of the inverse dispersion method to measure emissions from animal housings - data set & R scripts
<h2>Data availability</h2> <p>Provided are:<br>- raw data of the instruments<br>- R Scripts to reproduce the findings in the publication<br>- R outputs</p> <h2>Scripts</h2> <p>In total, there are 10 scripts provided, of which most of them are needed to reproduce the data in the publication.</p> <p>Below, a brief explanation of the content of the different scripts.</p> <ul> <li>01_Datatreatment_01_Weatherstation.r ## This script reads in the weather station data and makes it ready for further use.</li> <li>01_Datatreatment_02_Sonics.r ## This script reads in the 3D ultrasonic data and makes it ready for further use.</li> <li>01_Datatreatment_03_GasFinder.r ## This script reads in the GasFinder data and makes it ready for further use.</li> <li>01_Datatreatment_04_MFC_Pressuresensor.r ## This script reads in the mass flow controller (MFC) and pressure sensor data and makes it ready for further use.</li> <li>02_Calculation_01_bLS.r ## This script is made to run the bLSmodelR and tailored to the number cruncher of the University of Applied Sciences BFH. The code should also work on your computer but you have to adopt the number of cores.</li> <li>02_Calculation_02_Concentration.r ## This script treats the unprocessed concentration data. It removes false concentrations, applies an intercalibration, and makes the data ready for further use.</li> <li>02_Calculation_03_Emissions.r ## This script calculates emissions and makes it ready for further use.</li> <li>02_Calculation_04_contourXYZ_Plume.r ## This script calculates the plume contours in the XY and XZ plane. This script is not necessary to reproduce the findings of the publication.</li> <li>03_Apply_filter.r ## This script applies the quality filtering and makes the data ready for further use.</li> <li>04_Plots_Tables.r ## With this script one can recreate all the plots and values in the tables of the publication, the supplement, and the initial submission.</li> </ul> <p>Note, for the geometry, there is no script provided. The coordinates of the different sensors and the source are solely provided as R output.</p> <h3>Naming of instruments</h3> <p>The instruments in the publication have different names than in the scripts. In some scripts the final names are also provided but throughout the evaluation the original device names are used. Only in the script 04_Plots_Tables.r are the final names introduced. Below is an overview of what original name corresponds to the final name of the devices:</p> <h4><strong>GasFinder instruments called 'OP' in the publication</strong></h4> <ul> <li>OP-UW = GF26</li> <li>OP-2.0h = GF17</li> <li>OP-5.3h = GF18</li> <li>OP-6.8h = GF16</li> <li>OP-12h = GF25</li> </ul> <p><strong>3D ultrasonic anemometer instruments called 'UA' in the publication</strong></p> <ul> <li>UA-UW = SonicC</li> <li>UA-2.0h = SonicA</li> <li>UA-5.3h = Sonic2</li> <li>UA-6.8h = SonicB</li> </ul> <p><strong>Source</strong><br>In some of the scripts, the source might be called 'Schopf' which is a local term for 'shed'.</p> <h2>Note</h2> <p>This code was written by Marcel Bühler (minor code chunks were originally written by Christoph Häni) and is intended to reproduce the findings of the linked publication. Please feel free to use and modify it (e.g., use it to run different dispersion models), but attribution is appreciated.</p> <h2>Disclaimer</h2> <p>I do not guarantee that everything works. It might be that not all variables were changed to English for better understanding correctly. Unfortunately, it is not possible to provide all the catalogs of the bLS run, as the total size is several 100s of GB. In case you run the bLS model on your own, the result will have a minimal difference, as no bLS run produces the same result twice. This should, however, not alter the findings.</p> <h2>Contact</h2> <p>In case you have questions, please contact Marcel Bühler (mb@bce.au.dk). In case this does not work, Christoph Häni might also be able to help (christoph.haeni@bfh.ch).</p>
Fish dispersal from a sabotage-mediated massive escape event
<p>This repository is for the data and scripts used in the analysis of the article published in Aquaclture Enviroment Interaction entitled "Fish dispersal from a sabotage-mediated massive escape event" by Javier Atalah, Pablo Sánchez-Jerez, David Izquierdo, Damián Fernandez-Jover, Pablo Arechavala-Lopez, Carlos Valle, Aitor Forcada, and Kilian Toledo-Guedes.</p> <p>ABSTRACT: Farm sabotage can cause massive fish escape events with significant ecological and socio-economic risks. This study examined the fate of Mediterranean seabass Dicentrarchus labrax escapees following a large-scale escape event caused by sabotage in the Western Mediterranean Sea. We monitored the escapee density and size structure over 3 mo after the escape at increasing distances from the escape point, up to 45 km away. Fish density adjacent to the escape location 5 d after the sabotage was, on average, 114 ± 44.7 (SE) fish per 100 m2. Our analyses showed that fish density decreased by 17% for every km away from the location, dropping to 2 and 1% after 1 and 2 mo, respectively, following the escape event. As escapee density declined throughout time and space, the size distribution of seabass shifted towards larger sizes. The rapid decrease in fish densities highlights the need for contingency plans focusing on fishing efforts in the coastal areas near the escape location (<20 km) within the first 24 h. These results are paramount to mitigating the risks associated with escape events cost-effectively. We emphasise the importance of sabotage prevention measures, such as security systems that can quickly detect intruders and trigger an immediate response to deter them. Additionally, enforcing appropriate sanctions based on the severity of the damage caused could help to discourage future sabotage attempts. KEY WORDS: Aquaculture · Mediterranean seabass · Dicentrarchus labrax · Coastal zone management · Marine policy</p> <p> </p>
Blowin' in the wind: Mapping the dispersion of Metal(loid)s from Atacama Mining
<p><strong>Raw data from elemental and mineralogical analyses of surface sediments from Alto El Loa, Antofagasta Region, Chile.</strong></p> <p><strong>Blowin' in the wind: Mapping the dispersion of Metal(loid)s from Atacama Mining</strong></p> <p><strong>Authors: Nicolás C. Zanetta-Colombo<sup>1,2</sup> *, Carlos A. Manzano<sup>3,4 </sup>, Dagmar Brombierstäudl<sup>1</sup>, Zoë L. Fleming<sup>5,6</sup>, Eugenia M. Gayo<sup>6,7</sup>, David A. Rubinos<sup>8</sup>, Óscar Jerez<sup>9</sup>, Jorge Valdés<sup>10</sup>, Manuel Prieto<sup>11,12</sup>, Marcus Nüsser<sup>1,2</sup></strong></p> <p><strong><sup>1</sup></strong><sup> </sup>Department of Geography, South Asia Institute, Heidelberg University, Heidelberg, Germany.</p> <p><strong><sup>2</sup></strong><sup> </sup>Heidelberg Center for the Environment (HCE), Heidelberg University, Heidelberg, Germany.</p> <p><strong><sup>3</sup></strong><sup> </sup>Departamento de Química, Facultad de Ciencias, Universidad de Chile, Santiago, Chile.</p> <p><strong><sup>4</sup></strong><sup> </sup>School of Public Health, San Diego State University, San Diego, CA, USA</p> <p><strong><sup>5</sup></strong><sup> </sup>Centro de Investigación en Tecnologías para la Sociedad, Universidad Del Desarrollo, Santiago, Chile.</p> <p><strong><sup>6</sup></strong><sup> </sup>Center for Climate and Resilience Research (CR)2, Chile.</p> <p><strong><sup>7</sup></strong><sup> </sup>Departamento de Geografía, Universidad de Chile, Santiago, Chile.</p> <p><strong><sup>8</sup></strong><sup> </sup>Sustainable Minerals Institute–International Centre of Excellence Chile (SMI-ICE-Chile), The University of Queensland, Australia, Las Condes, Santiago, Chile.</p> <p><strong><sup>9</sup></strong><sup> </sup>Instituto de Geología Económica Aplicada (GEA), University of Concepción, Chile. Barrio Universitario S/N, Concepción, Chile.</p> <p><strong><sup>10</sup></strong><sup> </sup>Laboratorio de Sedimentología y Paleoambientes (LASPAL), Instituto de Ciencias Naturales Alexander von Humboldt, Facultad de Ciencias del Mar y de Recursos Biológicos, Universidad de Antofagasta, Antofagasta, Chile.</p> <p><strong><sup>11 </sup></strong>Millenium Nucleus in Andean Peatlands (AndesPeat), Chile</p> <p><strong><sup>12 </sup></strong>Departamento de Ciencias Históricas y Geográficas, Universidad de Tarapacá, 18 de Septiembre 2222, Arica, Chile</p> <p> </p> <p><strong><span>Data Set 1 (ds1): </span></strong><span>Elemental concentrations (mg/kg) of metal(loid)s in surface sediment samples. The dataset also includes geographic coordinates (LONG, LAT), distance to the nearest mines (Dist_mines), distance to tailings (Dist_tailing), and buffer zone classifications for tailings (Buffer_T) and mines (Buffer_M).</span></p> <p><strong>Data set 2 (ds2)</strong>: Mineralogical composition of selected surface sediment samples. </p>
Minimum Differential Dispersion Problem
<h1><span>Greedy randomized adaptive search procedure with exterior path relinking for differential dispersion minimization</span></h1> <p><span>Abraham Duarte, Jesús Sánchez-Oro, Mauricio G.C. Resende, Fred Glover, Rafael Martí,<br>Greedy randomized adaptive search procedure with exterior path relinking for differential dispersion minimization,<br>Information Sciences,<br>Volume 296,<br>2015,<br>Pages 46-60,<br>ISSN 0020-0255,<br>https://doi.org/10.1016/j.ins.2014.10.010.<br>(https://www.sciencedirect.com/science/article/pii/S0020025514009906)</span></p> <p><span><br>Abstract: We propose several new hybrid heuristics for the differential dispersion problem, the best of which consists of a GRASP with sampled greedy construction with variable neighborhood search for local improvement. The heuristic maintains an elite set of high-quality solutions throughout the search. After a fixed number of GRASP iterations, exterior path relinking is applied between all pairs of elite set solutions and the best solution found is returned. Exterior path relinking, or path separation, a variant of the more common interior path relinking, is first applied in this paper. In interior path relinking, paths in the neighborhood solution space connecting good solutions are explored between these solutions in the search for improvements. Exterior path relinking, as opposed to exploring paths between pairs of solutions, explores paths beyond those solutions. This is accomplished by considering an initiating solution and a guiding solution and introducing in the initiating solution attributes not present in the guiding solution. To complete the process, the roles of initiating and guiding solutions are exchanged. Extensive computational experiments on 190 instances from the literature demonstrate the competitiveness of this algorithm.<br>Keywords: Dispersion problems; GRASP; Variable neighborhood search; Path relinking; Path separation<br></span></p>
Table 5 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 5.</i> Model selection of apparent survival probability (Phi) for model set 3: morphology and sex.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc ΔQAICc weights likelihood parameters Deviance</td></tr><tr><th><b>{</b><i>Φ</i> <b>(<i>g</i> + length)} 219.90</b></th><td><b>0.000</b></td><td><b>0.191</b></td><td><b>1.000</b></td><td><b>4</b></td><td><b>211.810</b></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + mass)}</th><td>220.44</td><td>0.544</td><td>0.145</td><td>0.762</td><td>4</td><td>212.354</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + sex + length)}</th><td>220.46</td><td>0.567</td><td>0.144</td><td>0.753</td><td>5</td><td>210.334</td></tr><tr><th>{<i>Φ</i> (<i>g</i>)}</th><td>220.57</td><td>0.672</td><td>0.136</td><td>0.715</td><td>3</td><td>214.517</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + sex)}</th><td>220.83</td><td>0.935</td><td>0.120</td><td>0.627</td><td>4</td><td>212.745</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + sex + mass)}</th><td>221.16</td><td>1.267</td><td>0.101</td><td>0.531</td><td>5</td><td>211.034</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + bd)}</th><td>222.23</td><td>2.330</td><td>0.060</td><td>0.312</td><td>4</td><td>214.140</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + cond)}</th><td>222.45</td><td>2.557</td><td>0.053</td><td>0.278</td><td>4</td><td>214.368</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + girth)}</th><td>222.56</td><td>2.662</td><td>0.050</td><td>0.264</td><td>4</td><td>214.472</td></tr></tbody></table><p><i>Note: g</i> = group (SF, TB, and TMMC), bd = blubber depth, cond = the body condition index. <i>c ͡</i> adjustment = 1.15. Highest ranked model highlighted in bold.</p>
Table 3 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 3.</i> Model selection of apparent survival probability (Phi) for model set 1: group and time.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th><b>{<i>Φ</i> (<i>g</i>)}</b></th><td><b>222.45</b></td><td><b>0.000</b></td><td><b>0.538</b></td><td><b>1.000</b></td><td><b>3</b></td><td><b>49.658</b></td></tr><tr><th>{<i>Φ</i> (SF and</th><td>222.95</td><td>0.501</td><td>0.419</td><td>0.779</td><td>2</td><td>52.184</td></tr><tr><th>TMMC v TB)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (SF and</th><td>227.55</td><td>5.102</td><td>0.042</td><td>0.078</td><td>2</td><td>56.786</td></tr><tr><th>TB v TMMC)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (.)}</th><td>233.43</td><td>10.983</td><td>0.002</td><td>0.004</td><td>1</td><td>64.684</td></tr><tr><th>{<i>Φ</i> (<i>t</i>)}</th><td>254.10</td><td>31.655</td><td>0.000</td><td>0.000</td><td>22</td><td>41.095</td></tr><tr><th>{<i>Φ</i> (<i>g × t</i>)}</th><td>320.74</td><td>98.291</td><td>0.000</td><td>0.000</td><td>66</td><td>0.000</td></tr><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{<i>Φ</i> (mass)}</th><td>101.79</td><td>0.000</td><td>0.220</td><td>1.000</td><td>2</td><td>97.704</td></tr><tr><th>{<i>Φ</i> (T4)}</th><td>102.08</td><td>0.287</td><td>0.190</td><td>0.866</td><td>2</td><td>97.991</td></tr><tr><th>{<i>Φ</i> (.)}</th><td>102.42</td><td>0.625</td><td>0.161</td><td>0.732</td><td>1</td><td>100.387</td></tr><tr><th>{<i>Φ</i> (change in</th><td>102.80</td><td>1.013</td><td>0.133</td><td>0.603</td><td>2</td><td>98.717</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (days in rehab)}</th><td>103.47</td><td>1.679</td><td>0.095</td><td>0.432</td><td>2</td><td>99.383</td></tr><tr><th>{<i>Φ</i> (admit date)}</th><td>103.73</td><td>1.942</td><td>0.083</td><td>0.379</td><td>2</td><td>99.646</td></tr><tr><th>{<i>Φ</i> (logOH)}</th><td>104.38</td><td>2.593</td><td>0.060</td><td>0.273</td><td>2</td><td>100.297</td></tr><tr><th>{<i>Φ</i> (mass per day)}</th><td>104.46</td><td>2.666</td><td>0.058</td><td>0.264</td><td>2</td><td>100.370</td></tr></tbody></table><p><i>Note: t</i> = time, <i>g</i> = group (SF = San Francisco, TB = Tomales Bay, and TMMC = The Marine Mammal Center). <i>c ͡</i> adjustment = 1.15. Highest ranked model highlighted in bold.</p><p><i>Note</i>: T4 = total thryroxine, adjustment = 1.15.</p>
Table 7 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival
<p><i>Table 7.</i> Model selection of apparent survival probability (Phi) for model set 5 incorporating top covariates from previous models.</p><table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4)}</th><td>248.8571</td><td>0</td><td>0.37137</td><td>1</td><td>4</td><td>240.7709</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 +</th><td>250.1334</td><td>1.2763</td><td>0.19618</td><td>0.5283</td><td>5</td><td>240.0038</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 +</th><td>250.298</td><td>1.4409</td><td>0.18068</td><td>0.4865</td><td>5</td><td>240.1684</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + T4 +</th><td>251.8598</td><td>3.0027</td><td>0.08275</td><td>0.2228</td><td>6</td><td>239.678</td></tr><tr><th>mass + logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + mass)}</th><td>252.2936</td><td>3.4365</td><td>0.06662</td><td>0.1794</td><td>4</td><td>244.2074</td></tr><tr><th>{<i>Φ</i> (<i>g</i>)}</th><td>252.7458</td><td>3.8887</td><td>0.05314</td><td>0.1431</td><td>3</td><td>246.6942</td></tr><tr><th>{<i>Φ</i> (<i>g</i> + mass +</th><td>254.2608</td><td>5.4037</td><td>0.02491</td><td>0.0671</td><td>5</td><td>244.1312</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{<i>Φ</i> (<i>g</i> + logOH)}</th><td>254.3069</td><td>5.4498</td><td>0.02434</td><td>0.0655</td><td>4</td><td>246.2207</td></tr></tbody></table><p><i>Note: g</i> = group (SF, TB, and TMMC), T4 = total thyroxine, OH = PCB+DDT+ PBDE+HCH+CHLD, Phi = survival.</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.