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6,334 results for “Directivity”
Fig. 1 in Constant fluctuating asymmetry but not directional asymmetry along the geographic distribution of Drosophila antonietae (Diptera, Drosophilidae)
Fig. 1. Locations of the sampled populations of Drosophila antonietae. Serrana (21◦ 14Ɩ S, 47◦ 34Ɩ W), Itirapina (22◦16Ɩ S, 47◦48Ɩ W), Guarapuava (25◦17Ɩ S, 51◦53Ɩ W), Cantagalo (25◦25Ɩ S, 52◦04Ɩ W), Santiago (29◦ 23Ɩ S, 54◦44Ɩ W).
The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"
<p>These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ". </p> <p>(1) AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC. </p> <p>(2) BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22.</p> <p>(3) BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs.</p> <p>(4) BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies.</p> <p>(5) BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies.</p> <p>All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here.</p> <p><strong>Note:</strong> AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019).</p> <p><strong>References</strong></p> <p>d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. <em>Journal of Applied Meteorology and Climatology</em>,<em> 22</em>(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. <em>Atmospheric Chemistry and Physics</em>,<em> 19</em>(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. <em>Atmospheric Chemistry and Physics</em>,<em> 17</em>(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017</p> <p>Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287</p>
Directed sampling datasets
<pre>Datasets:</pre> <pre>easom: optimizer-directed: N16_tol2e6_max1e6_1000SS [strict] N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e6_max1e6_1000SS [strict] 500_tol2e4_max1e4_1000SS [loose] rosen: optimizer-directed: N16_tol2e6_max1e6_1000SS2 [strict] N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e6_max1e6_1000SS [strict] 500_tol2e4_max1e4_1000SS [loose] rast: N16_tol2e6_max1e6_1000SS [strict] N16_tol2e4_max1e4_1000SS2 [loose] random: 500_tol2e6_max1e6_1000SS [strict] 500_tol2e4_max1e4_1000SS [loose] michal: N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e4_max1e4_1000SS [loose] hartmann6: N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e4_max1e4_1000SS [loose] rosen8: N16_tol2e4_max1e4_1000SS [loose] random: 500_tol2e4_max1e4_1000SS [loose] </pre>
Dataset: "On the influence of AVAS directivity on electric vehicle speed perception"
<p>This repository contains experiment results and calibrated stimuli recordings accompanying the publication: </p> <blockquote> <p>Leon Müller and Wolfgang Kropp, <em>On the influence of AVAS directivity on electric vehicle speed</em><br><em>perception</em>, submitted for publication in Inter-Noise 2024 proceedings</p> </blockquote> <p>The stimuli recordings were obtained by placing a calibrated artificial head (<em>HEAD Acoustics HMS-V</em>) at the participant listening position in the anechoic chamber.</p> <p>The AVAS sounds were generated using the Electric Vehicle Auralization Toolbox presented in:</p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The .wav files contain 32-bit float values that correspond to pressure in Pa and are named according to the following table.</p> <table> <tbody> <tr> <td><strong>AVAS Signal</strong></td> <td><strong>Directivity</strong></td> <td><strong>Vehicle Speed</strong></td> </tr> <tr> <td>T: Tonal (VW ID.3)</td> <td>B: BEM</td> <td>10: 10 km/h</td> </tr> <tr> <td>N: Noise (Tesla Model Y)</td> <td>C: Cardioid</td> <td>20: 20 km/h</td> </tr> <tr> <td> </td> <td>S: Star</td> <td> </td> </tr> <tr> <td> </td> <td>O: Omnidirectional</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
F I G U R E 3 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 3 Comparison of trait-based metrics expressed in relative number of individuals computed from eDNA () and traditional electro-fishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: ns, not significant (P> 0.05)
F I G U R E 4 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 4 Boxplots (, median value;, interquartile range;, full range;, outliers) showing the variability in the eDNAadapted fish index (six metrics) computed at three sites (Brangues, Rhins and Usses) where 10 eDNA water samples were collected once. At each site, the eDNA-based six-metric fish index was computed for each of the 45 possible pairs of samples
F I G U R E 2 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 2 Comparison of trait-based metrics expressed in number of species computed from eDNA () and traditional electrofishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: P <0.05; P <0.01; ns, not significant (P> 0.05)
F I G U R E 1 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 1 Sampling locations along river stretches (RS) A to E () of the main channel of the Rhône River, France, using both traditional electro-fishing (TEF) and eDNA., Sites sampled every 2 months (September 2015– August 2016);, sites where ten eDNA water samples (filtration capsules) were collected once;, sites located on the tributaries or the Rhône River itself sampled once for eDNA. The 10 metric fish index and the adapted six-metric fish index were computed at all sites with a black filled symbol within natural water bodies
Direct Measurement of Learning Outcomes in Engineering Programs: A Proposal for Nine Standardized Scales
<p>This database presents the results of nine different scales aimed at directly evaluating learning outcomes as generic attributes in engineering programs, as defined by the Washington Accord and the International Alliance of Engineering. Data was collected at a higher education institution focused on engineering and technology as part of quality assurance processes. Each scale features a distinct number of indicators. The data correspond to the following scales: AC (Lifelong Learning), AF (Project Management and Finance), AP (Problem Analysis), DI (Design/Development of Solutions), EE (Ethics), HC (Communication), HI (Tool Usage), IN (Investigation), and TE (Individual and Collaborative Teamwork).</p>
Pre- and postnatal noise directly impairs avian development, with fitness consequences
<p><span>Noise pollution is expanding at an unprecedented rate and </span><span>is</span><span> increasingly associated with impaired reproduction and development across taxa. However, whether noise soundwaves are intrinsically harmful for developing young – or merely disturb parents – and the fitness consequences of early exposure </span><span>remains</span><span> unknown. Here, </span><span>by only manipulating the offspring</span><span>, we </span><span>show</span><span> that sole exposure to noise in early-life </span><span>in zebra finches </span><span>has fitness consequences, </span><span>and causes</span><span> embryonic death during exposure. </span><span>Exposure to </span><span>pre- and postnatal traffic noise cumulatively impaired nestling growth and physiology, and </span><span>aggravated telomere shortening </span><span>across life stages </span><span>until adulthood</span><span>. Consistent with a long-term somatic impact, early-life noise exposure, especially prenatally, decreased individual offspring production throughout adulthood. Our findings </span><span>suggest the </span><span>effects of noise pollution </span><span>are more pervasive </span><span>than previously realized.</span></p>
High genetic gains in wood volume and fecundity can be both achieved by direct selection in half-sib families of Pinus yunnanensis Franch.
<p><strong><span>Experiment background</span></strong></p> <p><span>This study focused on characterizing phenotypic variation among and within provenances of <em>Pinus yunnanensis</em><span> Franch. aged 16 years in </span></span><span>a common garden</span><span>, with an emphasis on key traits such as cone production, trunk straightness, and crown health, as well as their relationships with traditional growth traits like tree height, diameter at breast height, and wood volume. Specifically, the objectives were to (1) characterize the variation of each trait within and among provenances; (2) assess inter-trait relationships, exploring patterns of co-variation and potential trade-offs; and (3) evaluate the feasibility of multi-trait selection strategies that aim for simultaneous improvements in growth, trunk straightness, and fecundity, contributing valuable insights for advancing <em>P. yunnanensis</em><span> </span>breeding efforts.</span></p> <p><strong><span>Experimental Design</span></strong></p> <p><span>This study was conducted in a common garden for <em>P. yunnanensis</em> located in Lufeng County, central Yunnan Province (102°12' E, 25°13' N) at an altitude of 1860 meters. The site lies in the transition zone between the subtropical humid climate of eastern Yunnan and the sub-humid climate of southwest Yunnan. The climate is characterized by warm and dry winters, humid and hot summers, with a mean annual temperature of 15.5°C and annual precipitation ranging between 900–1000 mm. The dry season extends from November to April, accounting for 6%-17% of the total annual precipitation.</span></p> <p><span>The common garden was established in 2006, with progeny from 179 superior trees selected from six provenance regions, including Anning County (AN), Qujing City (QJ), Yongren County (YR), Yulong County (YL), Tengchong County (TC), and Ninglang County (NL). Each provenance includes 30 families, except for one provenance with 29 families. </span></p> <p><span>The common garden has an area of about 3 ha, with a random block design, and a planting scheme of 2 m × 3 m</span><span>. </span><a name="_Hlk181695359"></a><span>To minimize environmental variation across the study site, a horizontal banding method was used for land preparation prior to planting.</span><span> </span><span>To reduce environmental variation across the study site, a horizontal banding method was used during land preparation. In each block, six provenances were randomly arranged, and families were randomly assigned within each provenance. Five plants from each family were planted in rows, and the design was replicated four times. A total of 3467 progeny from 179 superior trees across six provenances were included in the trial.</span></p> <p><strong><span>Experimental Variables</span></strong></p> <p><span>The study measured nine phenotypic traits, which included both quantitative and qualitative traits, as outlined below:</span></p> <p><span>Tree Height (H): Measured directly with a Vertex Laser instrument (DZH-30, Harbin, China) in meters (m).</span></p> <p><span>Diameter at Breast Height (D): Measured using a circumference tape in centimeters (cm).</span></p> <p><span>Crown Diameter (LCD, SCD): Long crown diameter (LCD) and short crown diameter (SCD), representing the maximum and minimum tree crown diameter, respectively, measured in meters (m) using a tower ruler.</span></p> <p><span>Height Under the Branch (TH): Measured in meters (m) using a tower ruler.</span></p> <p><span>Wood Volume (V): Estimated using the formula based on the forestry industry standard for <em>P. yunnanensis</em> (Agriculture and Forestry Ministry of China, 1977), with units in cubic meters (m³).</span></p> <p><span>Cone Production (CP): The number of open and closed cones in the canopy, including both serotinous and non-serotinous cones, recorded in counts to assess fecundity.</span></p> <p><span>Trunk Straightness (ST): A subjective visual assessment using a classification system: 1 for a highly twisted stem, 5 for a perfectly straight stem.</span></p> <p><span>Crown Health (CH): Visual assessment of the tree's crown, considering damage from abiotic and biotic stresses, with a grading scale from 1 (severely damaged) to 5 (perfectly healthy).</span></p> <p><strong><span>Data Analysis Methods</span></strong></p> <p><span>Data analysis was performed using R (version 3.6.3). The following statistical methods were employed:</span></p> <p><span>Variance Analysis: Nested variance analysis was used to evaluate the significance of differences and partition phenotypic variation among and within provenances. </span></p> <p><span>Principal Component Analysis (PCA): PCA was performed on the standardized matrix of nine phenotypic traits to reveal the dimensional structure and patterns of the data.</span></p> <p><span>Structural Equation Modeling (SEM): SEM was used to examine the direct and indirect relationships among traits, such as growth (H, D, V), crown size (LCD, SCD, TH), fecundity (CP), trunk straightness (ST), and crown health (CH). This analysis helped identify the causal pathways between the traits.</span></p> <p><span>Random Forest Analysis (RF): RF analysis was conducted to assess the importance of specific traits in predicting fecundity (CP) and trunk straightness (ST). Regression and classification methods were used for these analyses, with 1000 decision trees to ensure stable importance measures.</span></p> <p><strong><span>Dataset Description</span></strong></p> <p><span>The excel file (Raw Data) includes the following sheets: 1- Variables: Details on all the variables. 2- </span><span>Values of phenotypic traits</span><span>. 3- </span><span>Variance components </span><span>of phenotypic traits among and within provenances</span><span>. 4-</span><span> </span><span>Coefficient of variance</span><span> for phenotypic traits</span><span>. 5-</span><span> <span>The</span> <span>average membership function values (SFM) and </span>comprehensive weight of each principal component (PCA)<span> of </span></span><span>phenotypic traits</span><span>.</span></p>
Assessing size at sexual maturity and fine-scale population structure in a direct developing whelk (Buccinum undatum) in Southern Newfoundland, Canada
<p>R script file used to filter genotype data, estimate L50, and analyze patterns of population structure of <em>Buccinum undatum </em>in Southern Newfoundland, Canada. Also included are the following files required to run the script:</p> <p>populationsNWA.snps.vcf - Northwest Atlantic group output at the conclusion of the Stacks de novo pipeline<br>pop_map_NWA.txt - Population map for the Northwest Atlantic group<br>genlightNWAFullFilt.rds - Filtered genotype data for the Northwest Atlantic group<br>populations3Ps.snps.vcf - 3Ps group output at the conclusion of the Stacks de novo pipeline <br>pop_map_3Ps.txt - Population map for the 3Ps group<br>genlight3PsFullFilt.rds - Filtered genotype data for the 3Ps group<br>maturity_data.csv - Data set containing, shell length, sex, and maturity status for samples.<br>sample_site_coordinates_3Ps.csv - Data set containing coordinates of 3Ps sample sites</p> <p> </p>
Dataset for Direct visualization of quasiparticle concentration around superconducting vortices
<p>Data for Jian-Feng Ge, et al. “Direct visualization of quasiparticle concentration around superconducting vortices”.</p> <p>The following data files are used for the following figures.</p> <p> Fig. 1 a Illustration figure, no data used<br> b qeff_vs_y_sim.py</p> <p> Fig. 2 a NbSe2_04_220202_0189.txt<br> b NbSe2_05_220503_dIdV_0017.txt<br> NbSe2_07_220822_dIdV_0045.txt<br> c 220210_NbSe2_04_2.3K_map_03.txt<br> d 220824_NbSe2_07_2.3K_spectrum_01.txt<br> 220910_NbSe2_07_2.3K_spectrum_06.txt<br> e 220210_NbSe2_04_2.3K_map_03_qeff.txt<br> f 220824_NbSe2_07_2.3K_spectrum_01_qeff.txt<br> 220910_NbSe2_07_2.3K_spectrum_06_qeff.txt</p> <p> Fig. 3 a NbSe2_04_220202_0180.txt<br> b 220210_NbSe2_04_2.3K_map_01_Rdyn.txt<br> c 220210_NbSe2_04_2.3K_map_01_noise.txt<br> d NbSe2_04_220202_0180_radave.txt<br> e 220210_NbSe2_04_2.3K_map_01_Rdyn_radave.txt<br> f 220210_NbSe2_04_2.3K_map_01_noise_radave.txt</p> <p> Fig. 4 a 220824_NbSe2_07_2.3K_map_01.txt<br> b 220824_NbSe2_07_2.3K_map_01_cuts.txt<br> c qmax_vs_B.txt</p>
Erythropoietin directly remodels the clonal composition of murine hematopoietic multipotent progenitor cells
<p>## In version 1 of the repository the scRNAseq data was corrupted -- in version 2 this has been corrected and the bam files of both scRNAseq runs have been uploaded ##</p> <p>This dataset consists of the raw sequencing files of 9 independent barcoding experiments and of one 10X Genomics scRNAseq experiment. These are the source files for the main figures of the associated publication.</p> <p>HSPCs (C-Kit+ Sca1+ CD150+ Flt3-) or MPP2 (C-Kit+ Sca1+ Flt3- CD150+ CD48+) were isolated form mouse bone marrow through flushing, MACS-enrichment and sorting. The cells were barcoded by spin infection for 6h, and cultured in StemSpanMedium SFEM with 50 ng/ml mSCF (STEMCELL Technologies) for 16h with or without human recombinant EPO (Eprex, erythropoietin alpha, Janssen) at 1000 ng/ml or 160 ng/ml. At this stage scRNAseq was perfromed on the 10X Chromium platfom (10X Genomics), or cells were transplanted by tail vein injection into 6Gy irradiated recipient mice. When appropirate an additional injection of EPO 133ug/kg was given at the moment of transplantation. Different mature hematopoietic cells were isolated form the transplanted mice after 4 weeks or 16 weeks.</p> <p>For barcoding experiments, a three-step PCR was performed to amplify barcode sequences, to add Read1 and Read2 Illumina sequencing adapters, P5 and P3 flow cell attachment sites as well as plate and sample indices. Libraries were sequenced on an Illumina HiSeq SR65 with 10% of PhiX spike-in. For scRNAseq experiment, libraries were made using the Chromium SIngle Cell 3' v2 kit and sequencing was performed on a HiSeq PE26-98.</p> <p>The repository encompasses the following datasets:</p> <ul> <li>A1006.tar.gz -- AE05Low -- HSPCs EPO 160 ng/ml + injection 4 weeks -- main figure 2</li> <li>A1007.tar.gz -- AE05High -- HSPCs EPO 1000 ng/ml + injection 4 weeks -- main figure 2</li> <li>A984.tar.gz -- AE03Low -- HSPCs EPO 160 ng/ml 4 weeks -- main figure 2 and HSPC part of main figure 4</li> <li>A1008.tar.gz -- AE03High -- HSPCs EPO 1000 ng/ml 4 weeks -- main figure 1, 2 and DC part of main figure 3</li> <li>A1012.tar.gz -- AE04Low -- HSPCs EPO 160 ng/ml 4 months -- main figure 8</li> <li>A1013.tar.gz -- AE04High -- HSPCs EPO 1000 ng/ml 4 months -- main figure 8</li> <li>A1105.tar.gz -- AE07part1 -- HSPCs EPO 1000 ng/ml 4 weeks part 1 -- MkP part of main figure 3</li> <li>A1107.tar.gz -- AE07part2 -- HSPCs EPO 1000 ng/ml 4 weeks part 2 -- MkP part of main figure 3</li> <li>A1166.tar.gz -- AE13part1 -- HSPCs EPO 1000 ng/ml 4 weeks part 1 -- HSPC part of main figure 4</li> <li>A1166.tar.gz -- AE13part2 -- HSPCs EPO 1000 ng/ml 4 weeks part 2 -- HSPC part of main figure 4</li> <li>D757_3697.tar.gz -- LP26MPP2 -- MPP2 EPO 1000 ng/ml 4 weeks -- main figure 7</li> </ul> <p>In version two of the repository the scRNAseq data is changed into:</p> <ul> <li>possorted_genome_bam_T1.bam -- 10X of EPO exposed (T1) HSPCs -- main figure 5 and 6</li> <li>possorted_genome_bam_T2.bam -- 10X of control (T2) HSPCs -- main figure 5 and 6</li> </ul> <p>The folder for each barcode experiment encompasses fastq files for each sample index used in the experiment. These have to be further de-multiplexed by plate index and barcode reads have to be called. These, and all subsequent processing steps till the making of the main figures are described in the github folder accompanying the associated publication.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Using species distribution models and decision tools to direct surveys and identify potential translocation sites for a critically endangered species
<p>Aim: Occurrence records for cryptic species are typically limited or highly uncertain, leaving their distributions poorly resolved and hampering conservation. This can apply to well‐studied species, and increased survey effort and/or novel methods are required to improve distribution data. Here, we paired species distribution modelling (SDM) with decision tools to direct surveys for the critically endangered Leadbeater's possum (Gymnobelideus leadbeateri) outside its current restricted range. We also assessed survey areas for their suitability to host translocations.</p> <p>Location: Victoria, Australia.</p> <p>Method: We used both recent and historic records (now out of range and spatially uncertain) of Leadbeater's possum to build SDMs using MaxEnt. The SDMs informed an initial multi‐criteria decision analysis (MCDA) that enabled prioritization of 80 survey sites across seven forest patches (13–145 km outside the known range), which we surveyed using camera traps. Site and vegetation data were used in a post‐survey MCDA to rank their potential translocation suitability.</p> <p>Results: The SDM predictions were consistent with the species' ecology, identifying cold areas with high rainfall that had not recently burnt as suitable. The spatial uncertainty of records did not exert a strong influence on either model predictions or the ranking of patches for surveys. Camera trap surveys yielded records of 19 native species, with Leadbeater's possum detected in only one survey patch, 13 km outside of its previously known range. The post‐survey MCDA identified three forest patches as potentially suitable for conservation translocations, and these priorities were not sensitive to the decision criteria used.</p> <p>Main conclusions: The approach outlined here prioritized survey effort over a large area, resulting in detection of Leadbeater's possum in one new patch. The potential translocation sites identified could present an important risk‐spreading measure for the species given the threat posed by bushfire. Combining SDMs and decision tools can help target surveys and guide subsequent conservation strategies.</p>
Data and custom codes from "Rapid evolution in salmon life-history induced by direct and indirect effects of fishing"
<p>Data and custom codes from Czorlich, Y., Aykanat, T., Erkinaro, J., Orell, P. & Primmer, C.R. (2021) <em>Rapid evolution in salmon life-history induced by direct and indirect effects of fishing. </em>Science.</p> <p><strong>Codes:</strong></p> <p>The R file "Fishing_effort_parallel.R" was used to estimate fishing effort/intensity (beta in equation 8) given the length distribution, the gear-specific catchability and harvest rate</p> <p>"Fishing_selection_estimate.R" was used to estimate fishery-induced selection at <em>vgll3.</em></p> <p><strong>Datasets:</strong></p> <p>Genetic_phenotypic_data.xlsx: Genetic and phenotypic data about salmon from the Teno mainstem population</p> <p>sonar_data.xlsx: Number of salmon per length class entering the river in 2018 and 2019. The length classes of salmon caught in those years by one of the fishing methods are also included</p> <p>annual_catch_data.xlsx: Total mass (kg) of salmon caught by each fishing method between 1975 to 2014.</p> <p>Environmental_data.xlsx: Data about Barents Sea temperature, biomass of key species, fishing data</p> <p>individual_weight_salmon_catches.xlsx: Individual weight of salmon caught with different fishing gears in the last decades</p> <p><strong>Data sources:</strong></p> <p>- Genetic data (Tenojoki population, random sampling): From Czorlich et al. 2018, https://datadryad.org/stash/dataset/doi:10.5061/dryad.7hm4708</p> <p>- Data about krill biomass (1980 – 2013) were taken from (<em>1</em>, <em>2</em>).</p> <p>- Capelin biomass estimated from acoustic survey and the landed capelin catches were derived from (<em>3</em>) for 1973 – 2013.</p> <p>- Herring biomass data were retrieved from (<em>4</em>) for the 1973-1998 period. Herring biomass was calculated from the number of 1-2 year old herring and the mean weight per age as reported in (<em>3</em>) for 1988 – 2013.</p> <p>- The annual biomass of cod (a predator of forage fish) was derived from VPA analyses ((<em>5</em>), table 3.24). Landed cod biomass was also taken from (<em>5</em>).</p> <p>- An index for mesozooplankton (a forage fish food source) corresponding to the sum of <em>Calanus</em> biomass indices from different parts of the Barents Sea was used (<em>6</em>).</p> <p>- The annual sea temperature in the Kola section of the Barents Sea measured in the upper 200 meters was from <a href="http://www.pinro.vniro.ru/">pinro.vniro.ru</a></p> <p>- The total number of nets used to catch salmon in the Finnmark coastal region was calculated for each year using data from (7)</p> <p>- Other data were generated for this study, please check the Material and Methods. </p> <p><em>References:</em></p> <p>1. E. Eriksen, P. Dalpadado, Long-term changes in Krill biomass and distribution in the Barents Sea: Are the changes mainly related to capelin stock size and temperature conditions? <em>Polar Biology</em>. <strong>34</strong>, 1399–1409 (2011).</p> <p>2. ICES, “Report of the Working Group on the Integrated Assessments of the Barents Sea. ICES CM 2017/SSGIEA:04. 186 pp.” (2017).</p> <p>3. ICES, “Report of the Arctic Fisheries Working Group (AFWG). ICES CM 2015/ACOM:05. 639 pp.” (2015).</p> <p>4. R. Toresen, O. J. Østvedt, Variation in abundance of Norwegian spring-spawning herring (Clupea harengus, Clupeidae) throughout the 20th century and the influence of climatic fluctuations. <em>Fish and Fisheries</em>. <strong>85</strong>, 385–391 (2000).</p> <p>5. ICES, “Report of the Arctic Fisheries Working Group (AFWG). ICES CM 2016/ACOM:06. 621 pp.” (2016).</p> <p>6. L. C. Stige et al., Spatiotemporal statistical analyses reveal predator-driven zooplankton fluctuations in the Barents Sea. <em>Progress in Oceanography</em>. <strong>120</strong>, 243–253 (2014).</p> <p>7. E. Niemelä, T. Kalske, E. Hassinen, “Numbers of fishing gears used in Kolarctic salmon project area, numbers of allowed sites for salmon fishing and numbers of salmon fishermen in Finnmark; development until the year 2013” (2013).</p>
Influence of the imperfection direction on the strength of steel and stainless steel frames
<p>This file includes finite element simulation data (ultimate load factor) carried out on 60 steel and stainless steel frames, including regular and irregular frame configurations with different section sizes.</p>
No evidence that grooming is exchanged for coalitionary support in the short- or long-term via direct or generalized reciprocity in unrelated rhesus macaques
<p>Reciprocity is a prominent explanation for cooperation between non-kin. Studies seeking to demonstrate reciprocity often focus on direct reciprocity in the timescale of minutes to hours, whereas alternative mechanisms like generalised reciprocity and the possibility of reciprocation over longer timescales of months and years are less often explored. Using a playback experiment, we tested for evidence of direct and generalised reciprocity, across short and longer timescales. We examined the exchange of grooming for coalitionary support between female rhesus macaques in a population with a complete genetic pedigree. Females that received grooming were not more responsive to calls for coalitionary support from female groupmates compared to control females that received agonism or no interaction – even when the call belonged to a females' most recent grooming partner. Similarly, females were not more responsive to calls for support from their most frequent grooming partner of the last two years, nor if they received large amounts of grooming from all other females in their group. We interpret these results as an absence of evidence for direct or generalised reciprocity on any timescale in the exchange of grooming for coalitionary support in rhesus macaques. If grooming is exchanged for support in this population, it is with an intensity below our ability to detect or over a longer timescale than we examined. We propose by-product explanations may be responsible and highlight the importance of investigating multiple mechanisms when testing apparently cooperative behaviours.</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Directional excitation of a high-density magnon gas using coherently driven spin waves
<p>Data corresponding to the figures of the main text of: "Directional excitation of a high-density magnon gas using coherently driven spin waves "</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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