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FIGURE 38 in A new blind Lamyctes (Chilopoda: Lithobiomorpha) from Tasmania with an analysis of molecular sequence data for the Lamyctes Henicops Group
FIGURE 38. Cladograms based on molecular sequence data. Cladograms at left are shortest based on parameter set (121) that minimises incongruence between genes; cladograms at right are strict consensus of all 15 explored parameter sets. Numbers at nodes are parsimony jackknife frequencies. From left to right, top to bottom: cladograms based on combined molecular data (2198 steps); cladograms based on 18S rRNA (562 steps); cladograms based on 28S rRNA (98 steps); cladograms based on 16S rRNA (616 steps); cladograms based on COI (901 steps).
FIGURES 3437. Lamyctes hellyeri n in A new blind Lamyctes (Chilopoda: Lithobiomorpha) from Tasmania with an analysis of molecular sequence data for the Lamyctes Henicops Group
FIGURES 3437. Lamyctes hellyeri n. sp. QVMAG 23:23048, female, pretarsus of leg 14, scales 10 m. 3436, anterior, posterior, and ventral views; 37, detail of lateral pore and ornament on scutes of main claw.
FIGURES 1825. Lamyctes hellyeri n in A new blind Lamyctes (Chilopoda: Lithobiomorpha) from Tasmania with an analysis of molecular sequence data for the Lamyctes Henicops Group
FIGURES 1825. Lamyctes hellyeri n. sp. QVMAG 23:23046, female. 18, ventral view of maxillipede, scale 100 m; 1920, dental margin of maxillipede coxosternite, scales 50 m, 10 m; 21, tarsus and claw of second maxilla, scale 50 m; 22, distal part of tarsus and claw of second maxilla, scale 10 m; 23, coxal projections and telopods of first maxillae, scale 50 m; 24, first maxillae, scale 100 m; 25, plumose setae on inner margins of telopods of first maxillae, scale 10 m.
FIGURES 1117. Lamyctes hellyeri n in A new blind Lamyctes (Chilopoda: Lithobiomorpha) from Tasmania with an analysis of molecular sequence data for the Lamyctes Henicops Group
FIGURES 1117. Lamyctes hellyeri n. sp. 11, 1417, QVMAG 23:23046, female. 11, anterior part of head shield and basal part of antennae, scale 100 m; 14, sensilla on dorsal side of antenna, scale 10 m; 1516, antennal articles, dorsal side, scales 50 m; 17, cephalic pleurite with Tömösváry organ, scale 50 m. 1213, QVMAG 23:23047, female. 12, ventral view of clypeus and labrum, scale 100 m; 13, labral midpiece and inner parts of sidepieces, scale 30 m.
FIGURES 14 in A new blind Lamyctes (Chilopoda: Lithobiomorpha) from Tasmania with an analysis of molecular sequence data for the Lamyctes Henicops Group
FIGURES 14. Lamyctes coeculus (Brölemann). 1, 3, AM KS57961, female, Mellong Range, NSW, Australia. 2, 4, MCZ DNA100472, female, Cerro San Javier, Tucumán, Argentina. 12, ventral view of head, scales 100 m; 34, dental margin of maxillipede coxosternite, scales 50 m.
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
CMIP5 P50 Analysis v1.0 for Tuna Species: Source Data
<p><strong>Model results and data used to make future projections of the effects of climate change on the physiology of tuna in the global ocean</strong></p> <p>-------------------------------------------------</p> <p><strong>Description:</strong></p> <p>Coupled Model Intercomparison Project Phase 5 (CMIP5) model results were downloaded from here:<br> https://esgf-node.llnl.gov/search/cmip5/</p> <p>World Ocean Atlas (WOA) 2009 data were downloaded from here:<br> https://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html</p> <p>The model results and data should only be used to reproduce the analysis described in this publication:</p> <p>Mislan, K. A. S., C. A. Deutsch, R. W. Brill, J. P. Dunne, and J. L. Sarmiento. (2017) Projections of climate driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity. Global Change Biology.</p> <p><strong>The Zenodo archive of the code is here:<br> https://doi.org/10.5281/zenodo.808742</strong></p> <p> </p> <p>-------------------------------------------------</p> <p><strong>Instructions:</strong></p> <p>Download the tar.gz file, unzip, and put the folders in the data folder of the CMIP5_p50_tuna code.</p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments" (MATLAB format)
<p>See 10.5281/zenodo.834787 for a more complete description.</p>
FIGURE 9. Principal Component Analysis showing morphometric data from C in Two new troglobitic Coarazuphium Gnaspini, Godoy & Vanin 1998 species of ground beetles from iron ore Brazilian caves (Coleoptera: Carabidae: Zuphiini)
FIGURE 9. Principal Component Analysis showing morphometric data from C. spinifemur new species (red dots); C. amazonicus new species (green triangles) and C. tapiaguassu (purple dots): AL, Antenna length; OBL, Overall body length; HL, Head length; HW, Head width; PL, Pronotum length; PW, Pronotum width; EL, Elytra length; EW, Elytra width; PF, Profemur length; PTI, Protibia length; PTA, Protarsus length; MSF, Mesofemur length; MSTI, Mesotibia length; MSTA, Mesotarsus length; MTF, Metafemur length; MTTI, Metatibia length; MTTA, Metatarsus length.
Per-chalcopyrite particle morphological analysis data obtaining from three time-lapse micro-CT images and the PhreeqcRM simulation data
<p>The file "overall_particles.xlsx" includes all the image-based quantifications of all chalcopyrite particles extracted from three time-lapse micro-CT images. These image-based quantifications include specific surface area, volume, liberation ratio, mass before leaching, in the middle of leaching, and after leaching.</p> <p> </p> <p>The file "PhreeqcRM simulation.xlsx" includes the PhreeqcRM simulation results using experimental and image-based data.</p> <p> </p> <p>The file "<a href="https://zenodo.org/api/records/15239075/draft/files/Laplace_Solver-master-main.zip/content" target="_blank" rel="noopener noreferrer">Laplace_Solver-master-main.zip</a>" includes the diffusion simulation source code.</p>
D6.1 Review, analysis and crosswalk of the data requirements of key policies
Open the record for dataset details and reuse information.
Data of paper "Grid ,Hydrodynamic boundary and Uncertainty analysis of 2D-SWEs in the context of digital twins: Taking numerical simulation of river networksas an example"
<p>论文数据 “数字孪生背景下2D-SWEs的网格、水动力边界和不确定性分析:以河流网络数值模拟为例”</p>
Natural river data and analysis code for the correlation of channel threads of the Brahmaputra-Jamuna River (v2)
<p>This is the archive of the data of processed water masks and channel-thread centerlines, code used for analyzing the coherent motion of channel threads, results of channel thread migration and numerical modeling for the braided Brahmaputra-Jamuna River, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Coherent motion of channel threads in the braided Brahmaputra-Jamuna River.</p> <p>Running the analyze code needs a MATLAB® software environment. The MATLAB script 'demo_dtw.m' in folder 'braided_dataRepo/Code' recreates the correlation for the braided channel threads in Figure 5 of the manuscript.</p>
Neutron activation analysis data of pottery from Ziyaret Tepe, Turkey
<p>Neutron activation analysis data of pottery from Ziyaret Tepe, Diyarbakır Province, Turkey.</p>
Data analysis scripts for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'
<p>Data analysis scripts for the manuscript <strong>Marsh<em> </em>et<em> </em>al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'</strong></p> <p><strong>Update for Version 2:</strong> The calculation of confidence intervals around the mean effects in Figure 2 has been updated to use the <code>marginaleffects</code> package (many thanks to Biao Wang and Shuang Zhang for pointing out an error in the original code). Using the Satterthwaite method for determining degrees of freedom, the updated confidence intervals are around 32% smaller than our original estimates (MLF = 32.0%, HLF = 32.1%, OP = 21.6%). Note, this change is only relevant to fig. 2 and figs. S2-4; the mean effect sizes and trends along the disturbance gradient, all statistical comparisons, and the constrast analyses in fig. 3 remain unaffected. The updated figures S2-4 and Table S6 can be seen in the file 'Updated figures S2-4 with recalculated confidence intervals.pdf'.</p> <p>In the zip file 'BALI_synthesis_analysis.zip' there are outputs from RMarkdown scripts that include all steps of the analysis for each dataset, including R code, incorporating data visualisation, exploration and standardisation, model building and evaluation, and visualisation of results. Fig. 2b can be regenerated using code in the zip file 'Marsh_etal_2024_Science_fig1b_chm_and_canopy_profiles-main.zip'.</p> <p>Each dataset presented in the manuscript has an html file within the folder 'Analyses'. For datasets involving bat, bird, dung beetle and tree traits additional markdown documents are available for steps take during data preparation in the folder 'Data preparation'.</p> <p>In the zip file 'BALI_synthesis_data.zip' are .rds data files that have been cleaned, prepared and z-score standardised following the procedures outlined in the respective markdown files.</p> <p>To repeat any given analysis, follow the respective rmarkdown document, excluding the data manipulation steps:</p> <ol> <li>Read in the data file as described above: dd <- readRDS(paste0("path/to/rds/file/", "name_of_file.rds"))</li> <li>Run the code at the top of the markdown workflow (sections "Data information" and "Load in necessary libraries")</li> <li>Do not run the sections "Read in data" through to "Visual inspection of the data"</li> <li>Continue the analysis from the 'Modelling' section</li> </ol> <div> <h3> </h3> <h3>Level 1 - Structure & Environment</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Above-ground carbon</td> <td>Above ground carbon</td> <td>Above_ground_carbon</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf-area index</td> <td>Leaf-area index</td> <td>Leaf_area_index</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil temperature</td> <td>Soil temp.</td> <td>Soil_temperature</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil moisture</td> <td>Soil moisture</td> <td>Soil_moisture</td> <td>Dafydd Elias</td> </tr> <tr> <td>Air temperature: Minimum</td> <td>Air temp.: Min.</td> <td>Air_temperature_minimum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Mean</td> <td>Air temp.: Mean</td> <td>Air_temperature_mean</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Maximum</td> <td>Air temp.: Max.</td> <td>Air_temperature_maximum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Soil bulk density</td> <td>Soil bulk density</td> <td>Soil_bulk_density</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil horizon depth</td> <td>Soil horizon depth</td> <td>Soil_horizon_depth</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil pH</td> <td>Soil pH</td> <td>Soil_pH</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon</td> <td>Soil nutrients (C)</td> <td>Soil_nutrients_C</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Nitrogen</td> <td>Soil nutrients (N)</td> <td>Soil_nutrients_N</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Inorganic Phosporous</td> <td>Soil nutrients (Inorganic P)</td> <td>Soil_nutrients_Inorganic_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Phosphorous</td> <td>Soil nutrients (C:P)</td> <td>Soil_nutrients_C_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Nitrogen</td> <td>Soil nutrients (C:N)</td> <td>Soil_nutrients_C_N</td> <td>Dafydd Elias</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 2 - Tree traits</h3> </div> <p>All tree traits were collected as part of the following study (details in this table have been extracted from table S1 of that publication): S. Both, T. Riutta, C.E.T. Paine, D.M.O. Elias, R.S. Cruz, A. Jain, D. Johnson, U.H. Kritzler, M. Kuntz, N. Majalap-Lee, N. Mielke, M.X. Montoya Pillco, N.J. Ostle, Y. Arn Teh, Y. Malhi, D.F.R.P. Burslem (2019) Logging and soil nutrients independently explain plant trait expression in tropical forests. New Phytologist. 221:4, 1853–1865.</p> <p> </p> <p><em><strong>Photosynthesis Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated photosynthesis traits</td> <td>Photosyn. traits</td> <td>Photosynthesis traits</td> </tr> <tr> <td>δ<sup>13</sup>C</td> <td>δ<sup>13</sup>C</td> <td>Traits_13C</td> </tr> <tr> <td>Light-saturated photosynthetic rate</td> <td>Photosyn. rate: A<sub>sat</sub></td> <td>Traits_Asat</td> </tr> <tr> <td>Maximum photosynthetic rate</td> <td>Photosyn. rate: A<sub>max</sub></td> <td>Traits_Amax</td> </tr> <tr> <td>Maximum photosynthetic rate: Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_conc</td> </tr> <tr> <td>Maximum photosynthetic rate: Phosphorous mass (area)</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_area</td> </tr> <tr> <td>Dark respiration (Rd)</td> <td>Dark respiration</td> <td>Traits_Dark_resp</td> </tr> <tr> <td>Specific leaf area (SLA)</td> <td>Specific leaf area</td> <td>Traits_SLA</td> </tr> <tr> <td>Carotenoids (area)</td> <td>Carotenoids: Area</td> <td>Traits_Carot_area</td> </tr> <tr> <td>Carotenoids (mass)</td> <td>Carotenoids: Mass</td> <td>Traits_Carot_mass</td> </tr> <tr> <td>Chlorophyll a (area)</td> <td>Chlorophyll a: Area</td> <td>Traits_Chl_a_area</td> </tr> <tr> <td>Chlorophyll a (mass)</td> <td>Chlorophyll a: Mass</td> <td>Traits_Chl_a_mass</td> </tr> <tr> <td>Chlorophyll b (area)</td> <td>Chlorophyll b: Area</td> <td>Traits_Chl_b_area</td> </tr> <tr> <td>Chlorophyll b (mass)</td> <td>Chlorophyll b: Mass</td> <td>Traits_Chl_b_mass</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Nutrient Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated nutrient traits</td> <td>Nutrient traits</td> <td>Nutrient_traits</td> </tr> <tr> <td>δ<sup>15</sup>N</td> <td>δ<sup>15</sup>N</td> <td>Traits_15N</td> </tr> <tr> <td>Carbon concentration</td> <td>Carbon conc.</td> <td>Traits_Carbon_conc</td> </tr> <tr> <td>Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_perc</td> </tr> <tr> <td>Phosphorous concentration</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_mass</td> </tr> <tr> <td>Magnesium concentration</td> <td>Regulat. nutrients: Total Mg</td> <td>Traits_Total_Mg</td> </tr> <tr> <td>Potassium concentration</td> <td>Regulat. nutrients: Total K</td> <td>Traits_Total_K</td> </tr> <tr> <td>Calcium concentration</td> <td>Regulat. nutrients: Total Ca</td> <td>Traits_Total_Ca</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Structural Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated structural traits</td> <td>Structural traits</td> <td>Structural_traits</td> </tr> <tr> <td>Branch specific density</td> <td>Branch wood density</td> <td>Traits_Branch_WD</td> </tr> <tr> <td>Leaf cellulose concentration</td> <td>Leaf fibre conc.: Cellul.</td> <td>Traits_Cellulose</td> </tr> <tr> <td>Leaf lignin concentration</td> <td>Leaf fibre conc.: Lignin</td> <td>Traits_Lignin</td> </tr> <tr> <td>Leaf hemicellulose concentration</td> <td>Leaf fibre conc.: Hemicel.</td> <td>Traits_Hemicellulose</td> </tr> <tr> <td>Leaf area</td> <td>Leaf size: Area</td> <td>Traits_Leaf_area</td> </tr> <tr> <td>Leaf dry weight</td> <td>Leaf size: Dry wgt</td> <td>Traits_Dry_weight</td> </tr> <tr> <td>Leaf force to punch</td> <td>Leaf strength: Tough.</td> <td>Traits_Leaf_toughness</td> </tr> <tr> <td>Leaf thickness</td> <td>Leaf strength: Thick.</td> <td>Traits_Leaf_thickness</td> </tr> <tr> <td>Leaf dry matter content</td> <td>Leaf strength: Dry mat.</td> <td>Traits_LDMC</td> </tr> <tr> <td>Total phenol concentration</td> <td>Leaf defence: Phenol</td> <td>Traits_Phenol</td> </tr> <tr> <td>Total tannin concentration</td> <td>Leaf defenct: Tannin</td> <td>Traits_Tannin</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 3 - Biodiversity</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil bacterial richness</td> <td>Soil microbial richness: Bacteria</td> <td>Soil_richness_Bacteria</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil protist richness</td> <td>Soil microbial richness: Protists</td> <td>Soil_richness_Protist</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil ectomycorrhizal richness</td> <td>Soil fungal richness: Ectomycorrhiza</td> <td>Soil_richness_Ectomycorrhiza</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil fungal richness</td> <td>Soil fungal richness: Fungi</td> <td>Soil_richness_Fungi</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil arbuscular mycorrhizal richness</td> <td>Soil fungal richness: Arbuscular mycorrhiza</td> <td>Soil_richness_Arbuscular_mycorrhizal</td> <td>Dafydd Elias</td> </tr> <tr> <td>Leaf spectral diversity</td> <td>Spectral diversity</td> <td>Spectral_diversity</td> <td>Matheus Nunes</td> </tr> <tr> <td>Liana abundance</td> <td>Liana abundance</td> <td>Liana_abundance</td> <td>Boris Bongalov</td> </tr> <tr> <td>Dung beetle abundance</td> <td>Dung beetle abund.</td> <td>Dung_beetle_abundance</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: richness</td> <td>Dung beetle diversity: q=0</td> <td>Dung_beetle_diversity_q=0</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Shannon diversity</td> <td>Dung beetle diversity: q=1</td> <td>Dung_beetle_diversity_q=1</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Simpson diversity</td> <td>Dung beetle diversity: q=2</td> <td>Dung_beetle_diversity_q=2</td> <td>Eleanor Slade</td> </tr> <tr> <td>Bird abundance</td> <td>Bird abund.</td> <td>Bird_abundance</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: richness</td> <td>Bird diversity: q=0</td> <td>Bird_diversity_q=0</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Shannon diversity</td> <td>Bird diversity: q=1</td> <td>Bird_diversity_q=1</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Simpsons diversity</td> <td>Bird diversity: q=2</td> <td>Bird_diversity_q=2</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bat abundance</td> <td>Bat abund.</td> <td>Bat_abundance</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (small scale)</td> <td>Bat diversity (sm scale)</td> <td>Bat_diversity_small_scale</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): richness</td> <td>Bat diversity (lg scale): q=0</td> <td>Bat_diversity_large_scale_q=0</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Shannon diversity</td> <td>Bat diversity (lg scale): q=1</td> <td>Bat_diversity_large_scale_q=1</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Simpson diversity</td> <td>Bat diversity (lg scale): q=2</td> <td>Bat_diversity_large_scale_q=2</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Nestedness</td> <td>Bat β-diversity: Nested.</td> <td>Bat_beta_diversity_Nestedness</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Turnover</td> <td>Bat β-diversity: Turn.</td> <td>Bat_beta_diversity_Turnover</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Total</td> <td>Bat β-diversity: Total</td> <td>Bat_beta_diversity_Total</td> <td>David Hemprich-Bennett</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 4 - Functioning</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil respiration</td> <td>Respiration: Soil</td> <td>Soil_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Stem respiration</td> <td>Respiration: Stem</td> <td>Stem_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Net primary productivity</td> <td>NPP</td> <td>NPP</td> <td>Terhi Riutta</td> </tr> <tr> <td>Litterfall</td> <td>Litterfall</td> <td>Litterfall</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf litter decomposition</td> <td>Litter decomposition</td> <td>Litter_decomposition</td> <td>Sabine Both</td> </tr> <tr> <td>Soil mycelial production</td> <td>Mycelial production</td> <td>Hyphal_length</td> <td>Samuel Robinson</td> </tr> <tr> <td>Dung removal</td> <td>Dung removal</td> <td>Dung_removal</td> <td>Eleanor Slade</td> </tr> </tbody> </table> <p> </p> <h2>Funding</h2> <p>Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) and LOMBOK (Land-use Options for Maintaining BiOdiversity & eKosystem functions) projects using the following funding:</p> <ul> <li>NERC Human-modified Tropical Forests Programme (NE/K016377/1, NE/K016261/1, NE/K016148/1, NE/K016407/1);</li> <li>NERC grant (NE/I028068/1);</li> <li>British Ecological Society Small Ecological Project Grant (No.: 3256/4035);</li> <li>Varley-Gradwell Travelling Fellowship in Insect Ecology;</li> <li>Bat Conservation International Student Research Scholarship;</li> <li>NOMIS Foundation;</li> <li>ERC European Union's Horizon 2020 research and innovation programme (grant agreement No 865403);</li> <li>ERC Advanced Investigator Grant, GEM-TRAIT (321131);</li> <li>The SAFE Project is funded by the Sime Darby Foundation.</li> </ul>
Data underlying "EVE is an open modular data analysis software for event-based localization microscopy"
<p>Data underlying the manuscript "EVE is an open modular data analysis software for event-based localization microscopy"</p> <p>Contains raw EBS-recorded SMLM (eveSMLM) data of DNA-PAINT nanoruler, E.coli cell, and aTubulin network in Cos-7 cells (3D and high density acquisitions).</p>
Replication data and analysis code for the article "Insect-habitat-plant interaction networks provide guidelines to mitigate the risk of transmission of Xylella fastidiosa to grapevine in Southern France"
<p>This deposit contains the dataset used in the article "Insect-habitat-plant interaction networks provide guidelines to mitigate the risk of transmission of Xylella fastidiosa to grapevine in Southern France" in the form of a RData file, directly loadable in R, as well as the Rmd script used to analyse the data and produce the figures.</p>
data for R competition analysis
<p>data of freshwater snails for competition analysis in R</p>
Statistical Analysis of Feature-based Molecular Networking Results from Non-Targeted Metabolomics Data
<p>This folder contains the following used for the publication:</p><ul><li>MASSIVE Repositories: MSV000082312 and MSV000085786. This contains the original data in both .raw and .mzxml formats.</li><li>MZmine 3 files: The feature table (SD_BeachSurvey_GapFilled_quant.csv), the associated mgf file, the batch file (.xml) used for MZmine 3 to obtain the feature table, the mgf file for SIRIUS annotations (SD_BeachSurvey_SIRIUS_fixed.mgf)</li><li>SIRIUS and CANOPUS summary files (.tsv files)</li><li>FBMN Result files</li></ul>
Data from: Unexpected discovery: A new 3,3'-bipyrazolo[3,4-b]pyridine scaffold and its comprehensive analysis
<p>A novel 3,3'-bipyrazolo[3,4-b]pyridine scaffold was obtained as a product of serendipity. This article describes the synthesis and characterization this new compound, 3,3'-dimethyl-1,1'-diphenyl-1H,1'H-[6,6'-bipyrazolo[3,4-b]pyridine]-5-carbaldehyde, using various analytical techniques such as NMR, IR, HRMS and melting point measurements. The compound was synthesized by reacting Acetamidopyrazole 2 with the Vilsmeier-Haack reagent, resulting in a 40% yield of the final product. From a theoretical viewpoint, the conformational barrier of the compound was studied using ab-initio calculation methodology based on density functional theory. The equilibrium structures associated with the barrier were optimized at a reasonable calculation level (B3LYP/6-311+G(2d,p)), and subsequently, the energy profile corresponding to the conformational barrier was constructed (energies were obtained at the CAM-B3LYP/aug-ccpVTZ level of calculation). Once the most probable structures were identified, theoretical IR and NMR spectra for these structures were obtained for comparison with the corresponding experimental spectra.</p>
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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.