Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Content Analysis Data set

<p><strong>Assessment of the Global Healthcare Industry during COVID-19 pandemic: A Content Analysis Approach</strong></p>

opencc-by-4.0Mar 2023View details →

Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand)

<p>A Data Collection and Manipulation Template for MAED (Model for Analysis of Energy Demand). </p>

opencc-zeroApr 2023View details →

Data from: A meta-analysis of plant tissue O2 dynamics

<p><span>This dataset includes 1,567 recorded tissue O<sub>2</sub> levels from 112 plant species extracted from published literature. The data forms the basis of the publication "A meta-analysis of plant tissue </span><span>O<sub>2</sub></span><span> dynamics" with the following abstract:</span><br><br><span>Adequate tissue </span><span>O<sub>2</sub></span><span> supply is crucial for plant functioning. We therefore aimed at identifying environmental conditions and plant characteristics affecting plant tissue </span><span>O<sub>2</sub></span><span> status. We extracted data and performed meta-analysis on > 1,500 published tissue </span><span>O<sub>2</sub></span><span> measurements from 112 species. Tissue </span><span>O<sub>2</sub></span><span> status ranged from anoxic conditions in especially roots, to > 53 kPa in submerged, photosynthesizing shoots. Using </span><span>information-theoretic</span><span> model selection</span><span>, we identified 'submergence', 'light', 'tissue type' as well as 'light × submergence' interaction as significant drivers of tissue </span><span>O<sub>2</sub></span><span> status. Median </span><span>O<sub>2</sub></span><span> status was especially low (< 50% of atmospheric equilibrium) in belowground rhizomes, potato tubers and root nodules. Mean shoot and root </span><span>O<sub>2</sub></span><span> was ~25% higher in light than in dark when shoots had atmospheric contact. However, light showed a significant interaction with submergence on plant </span><span>O<sub>2</sub></span><span>, with a submergence-induced 44% increase in light, compared with a 42% decline in dark, relative to plants with atmospheric contact. During submergence, ambient water column </span><span>O<sub>2</sub></span><span> and shoot tissue </span><span>O<sub>2</sub></span><span> correlated stronger in darkness than in light conditions. Although miniaturised Clark-type </span><span>O<sub>2</sub></span><span> electrodes in particular have resulted in enhanced understanding of plant </span><span>O<sub>2</sub></span><span> dynamics, the use of non-invasive methods is still lacking behind</span> <span>its widespread use in</span><span> mammalian tissues. </span></p>

opencc-zeroApr 2023View details →

Data from: Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in sage grouse Centrocercus urophasianus

<p><span>Sex ratio, and the extent to which it varies over time, is an important factor in the demography, management, and conservation of wildlife populations. We estimated pre-breeding sex ratio of greater sage-grouse (Centrocercus urophasianus) in a peripheral, geographically isolated population in northwestern Colorado during two consecutive winters using closed-population, robust-design, multi-state, genetic mark-recapture models in program MARK (White and Burnham 1999). This data release includes the data files (.inp format) used in those models, as described in Shyvers et al. 2023. The data include capture histories and auxiliary data for individual greater sage-grouse collected during two study seasons: Season 1 (winter 2012-2013) and Season 2 (winter 2013-2014) and are readable using program MARK or notepad. Each data row includes the unique bird identification number (GMR-ID); the bird's encounter history for n= sampling occasions coded as a static state (M = male, F = female); the group ID; and a region covariate (0 = North, 1 = South). The data were adapted from those originally developed for Shyvers et al. 2020 and applied using Closed Robust Design Multi-state (CRDMS) Huggins' p and c w/state probabilities in program MARK to obtain estimates of Omega, enabling estimation of sex ratio with associated confidence intervals (see Shyvers et al. 2023).</span></p> <p>References:</p> <p>Shyvers, J.E., Walker, B.L., Oyler-McCance, S.J., Fike, J.A. and Noon, B.R. 2023. Genetic mark-recapture analysis reveals large annual variation in pre-breeding sex ratio of greater sage-grouse. Wildlife Biology (https://doi.org/10.1002/wlb3.01085)</p> <p>Shyvers, J.E., Walker, B.L., Oyler‐McCance, S.J., Fike, J.A. and Noon, B.R., 2020. Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in Sage Grouse Centrocercus urophasianus. Ibis, 162(3), pp.749-765.</p> <p>White, G. C., and K. P. Burnham. 1999. Program Mark: survival estimation from populations of marked animals. – Bird Study 46:120–139.</p>

opencc-by-4.0Dec 2019View details →

The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization

<h1>The SIGMA templates and atlases for the Wistar Rat Brain</h1> <p>The current document is a short description of the second version of the SIGMA resources for the Wistar rat brain. For a full description of the resources and the methodologies used to create them please consult the main publication [Barriere D.A. et al 2019].</p> <p>The SIGMA resources are a set of standardized MRI compatible templates and atlases meant to support the analysis of multimodal MRI data of the rat brain. They were developped as part of the SIGMA project, a collaborative project between French (CEA and INSERM) and Portuguese (ICVS) institutions (FCT-ANR/NEU-OSD/0258/2012). They provide a unified and standardized framework for the analysis of multimodal rat brain imaging data, allowing the reporting of results within the coordinate system of the Paxinos-Watson atlas.</p> <p>In this second version, standardized MRI compatible templates have been built from the original acquired data (11.7 Tesla Bruker Scanner at Neuropsin center <a href="https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx" rel="nofollow">https://www.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx</a>) and emulated using the methods developed by Gabriel A. Devenyi (<a href="https://github.com/gdevenyi">https://github.com/gdevenyi</a>) and available here : <a href="https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction">https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction</a>. This pipeline is a re-implementation of the ANTs template construction pipeline requiring ANTs for the primary commands, and running on our cluster facilities using qbatch (<a href="https://islande.hub.inrae.fr/infrastructure" rel="nofollow">https://islande.hub.inrae.fr/infrastructure</a>).</p> <p>Using this methodology we firstly, updated the previous SIGMA spaces (T2sw, T2w, T1w) previously generated using the DARTEL Methods implemented in SPM8 and normalized the whole head images instead of brain.</p> <p>Secondly, we updated the probabilistic maps of the rat brain which are mandatory for the automatic segmentation of the rat brain and standardisation of morphometric analysis. Namely, we created new maps of Grey Matter, White Matter, CSF, Skull and outbrain. Those maps allow the use of SIGMA with the latter release of SPM12, a popular neuroimaging software dedicated to brain imaging analysis but also with ANTs, FSL and AFNI. Additionnally, we revised the Grey Matter/White Matter segmentations since the limits of which (particularly at the thalamic level) were a matter to debate with some users in the previous version of SIGMA.</p> <p>Thirdly, additionnal templates have been created using the optimized ANTs methodology to create from original unpublished data diffusion templates (B0, FA, etc.) at both ex-vivo and in-vivo resolutions.</p> <p>Eventually, using the same strategy, we created a CT/18FDG reference space from data obtained previously [Barrière D.A. et al 2018] which has been normalized with the MRI ex-vivo SIGMA template allowing to the SIGMA resource to propose a multimodal space for CT/TEP/MRI normalisation.</p> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#organisation-of-the-sigma-resources"></a></h2> <h2>Organisation of the SIGMA resources</h2> <p>The SIGMA resources have been organized as four sections : anatomical Imaging, functional imaging, atlases and TEP/CT imaging</p> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#anatomical-imaging"></a></h3> <h3>Anatomical Imaging</h3> <p>In this section a set of templates, priors and brain masks is available for ex-vivo and in-vivo data normalization</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-t2-weighted"></a></h4> <h4>Ex-vivo T2*-weighted</h4> <p>T2*-weighted template + T2*-weighted map + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_csf.nii.gz SIGMA_ExVivo_Anatomical_Brain_gm.nii.gz SIGMA_ExVivo_Anatomical_Brain_mask.nii.gz SIGMA_ExVivo_Anatomical_Brain_out.nii.gz SIGMA_ExVivo_Anatomical_Brain_skull.nii.gz SIGMA_ExVivo_Anatomical_Brain_t2starmap.nii.gz SIGMA_ExVivo_Anatomical_Brain_template.nii.gz SIGMA_ExVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-diffusion"></a></h4> <h4>Ex-vivo diffusion</h4> <p>B0 template + FA template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.25x0.25x0.25mm.</p> <div> <pre><code> SIGMA_ExVivo_Diffusion_Brain_b0.nii.gz SIGMA_ExVivo_Diffusion_Brain_csf.nii.gz SIGMA_ExVivo_Diffusion_Brain_fa.nii.gz SIGMA_ExVivo_Diffusion_Brain_gm.nii.gz SIGMA_ExVivo_Diffusion_Brain_mask.nii.gz SIGMA_ExVivo_Diffusion_Brain_out.nii.gz SIGMA_ExVivo_Diffusion_Brain_skull.nii.gz SIGMA_ExVivo_Diffusion_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-t2-weighted"></a></h4> <h4>In-vivo T2-weighted</h4> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_csf.nii.gz SIGMA_InVivo_Anatomical_Brain_gm.nii.gz SIGMA_InVivo_Anatomical_Brain_mask.nii.gz SIGMA_InVivo_Anatomical_Brain_out.nii.gz SIGMA_InVivo_Anatomical_Brain_skull.nii.gz SIGMA_InVivo_Anatomical_Brain_template.nii.gz SIGMA_InVivo_Anatomical_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-diffusion"></a></h4> <h4>In-vivo diffusion</h4> <p>T2-weighted template + B0 template + FA template + ADC template + brain mask.</p> <p>Spatial resolution 0.375x0.375x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Diffusion_Brain_adc.nii.gz SIGMA_InVivo_Diffusion_Brain_b0.nii.gz SIGMA_InVivo_Diffusion_Brain_fa.nii.gz SIGMA_InVivo_Diffusion_Brain_mask.nii.gz SIGMA_InVivo_Diffusion_Brain_t2.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#functional-imaging"></a></h3> <h3>Functional Imaging</h3> <p>T2-weighted template + associated probabilistics maps (GM, WM, CSF, Skull, outbrain) + brain mask.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_InVivo_Functional_Brain_csf.nii.gz SIGMA_InVivo_Functional_Brain_epi.nii.gz SIGMA_InVivo_Functional_Brain_gm.nii.gz SIGMA_InVivo_Functional_Brain_mask.nii.gz SIGMA_InVivo_Functional_Brain_t2.nii.gz SIGMA_InVivo_Functional_Brain_wm.nii.gz </code></pre> <div> </div> </div> <h3><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-rat-brain-atlas-version-20--waxholm-atlas-feat-sigma"></a></h3> <h3>SIGMA Rat Brain Atlas Version 2.0 : Waxholm atlas Feat. SIGMA</h3> <p>In this second version of the SIGMA resources we deliver a new SIGMA brain atlas obtained by the normalization of the Waxholm space published by Kleven, H. et al. Nat Methods (2023, <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41592-023-02034-3.pdf" target="_blank" rel="noopener"></a>). The Waxholm rat brain atlas is currently the best numerical 3D atlas of the rat brain. In accordance with authors of this paper we are authorized to modify and embed the WHS atlas within the SIGMA environement to standardize the identification of brain territories. We provide a normalized version the WHS for both ex-vivo and in-vivo of the anatomical SIGMA templates. Finally, we offer linear and non-linear transformations to enable your data to commute between the SIGMA and WHS ex-vivo environments using ANTs commands.</p> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#ex-vivo-atlas"></a></h4> <h4>Ex-vivo atlas</h4> <p>WHS rat brain atlas normalized in ex-vivo T2*-weighted SIGMA template + List of 222 labels created in ITKSnap Format + linear and non-linear transformations for SIGMA-WHS journeys (WHS-to-SIGMA_Transformations folder).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_ExVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_ExVivo_Anatomical_Brain_Atlas.txt ./WHS-to-SIGMA_Transformations/reference_SIGMA.nii.gz ./WHS-to-SIGMA_Transformations/reference_WHS.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_InverseWarp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_01_Warp.nii.gz ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_02_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS-in-SIGMA_transform_03_GenericAffine.mat ./WHS-to-SIGMA_Transformations/WHS_SD_rat_atlas_v4.nii.gz ./WHS-to-SIGMA_Transformations/WHS-to-SIGMA_byANTS.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#in-vivo-atlas"></a></h4> <h4>In-vivo atlas</h4> <p>WHS rat brain atlas normalized in in-vivo T2 SIGMA anatomical template + List of 222 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.15x0.15x0.15mm.</p> <div> <pre><code> SIGMA_InVivo_Anatomical_Brain_Atlas.nii.gz SIGMA_InVivo_Anatomical_Brain_Atlas.txt </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-brain-meshes"></a></h4> <h4>SIGMA brain meshes</h4> <p>Rat brain mesh created using BrainNet viewers commands in matlab (<a href="https://www.nitrc.org/projects/bnv/" rel="nofollow">https://www.nitrc.org/projects/bnv/</a>).</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_Anatomical_Brain_Atlas_mesh.nv </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-functional-atlas"></a></h4> <h4>SIGMA functional atlas</h4> <p>In the original publication of the SIGMA resources, we developed a functional atlas for the rat brain, using a group ICA analysis validated through a RAICAR approach. From this analysis, we identified 59 bilateral ROIs covering cortical, sub-cortical and brainstem structures that are functionally distinct. Despite having been derived from purely functional data, this atlas broadly, if not precisely, correlates with the general anatomical boundaries and many are associated with specific anatomical structures. A primary motivation for the creation of this atlas is derived from the need to perform brain segmentations which is optimized for functional MRI analysis, since the signal sources do not necessarily match typical anatomical boundaries. A similar requirement has been identified by those performing human studies, resulting in efforts to generate more diverse, multi-modal atlases.</p> <p>SIGMA rat brain functional atlas normalized in in-vivo T2 SIGMA functional template + List of 59 labels created in ITKSnap Format.</p> <p>Spatial resolution 0.375x1x0.375mm.</p> <div> <pre><code> SIGMA_Functional_Brain_Atlas_Labels.txt SIGMA_Functional_Brain_Atlas_ListOfStructures.csv SIGMA_InVivo_Functional_Brain_Atlas.nii.gz </code></pre> <div> </div> </div> <h4><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#sigma-cttep-template"></a></h4> <h4>SIGMA CT/TEP template</h4> <p>In this version of the SIGMA resources we included a CT/TEP template built from the data that previously published (Barriere D.A. et al 2018 , Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5) and acquired on a Triumph™ PET/CT dual modality imaging platform (Gamma Medica, Inc., Northridge, CA, USA), consisting of a LabPET™ avalanche photodiode-based digital PET scanner with a 7.5 cm axial field-of-view capable of achieving an isotropic spatial resolution. A caudal injection of approximately 30 MBq of [18F]-FDG was applied followed by a static acquisition to evaluate [18F]-FDG uptake within brain. CT images were acquired from the high-resolution X-ray computed tomography (CT) modality. Images were reconstructed using the Triumph™ PET/CT software. using the following parameters: 20 iterations, span of 63, field of view of 80 mm with a final matrix resolution of 160 × 160 × 128 and a voxel size of 0.5 × 0.5 × 0.597 mm. Brain dynamic [18F]-FDG images were reconstructed using the same protocol but we generated 32 frames (10 for 5 s, 7 for 10 s, 6 for 30 sec, 6 for 120 s, 2 for 240 s and 1 for 300 s). [18F]-FDG images were reconstructed using 3-D MLEM algorithm providing 0.5 × 0.5 × 0.597 mm images. CT scans were reconstructed using the standard FBP kernel analytical reconstruction algorithms, providing an isotropic image of 512 slices with a final resolution of 0.165 µm isotropic. Both [18F]-FDG and CT data were spatially normalized to the SIGMA ex-vivo template using the previously described methods.</p> <p>Spatial resolution 0.09x0.09x0.09mm.</p> <div> <pre><code> SIGMA_InVivo_18FDG_Brain_template.nii.gz SIGMA_InVivo_CT_Brain_template.nii.gz </code></pre> <div> </div> </div> <h2><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#important-note"></a></h2> <h2>Important Note</h2> <p>SIGMA ressources are provided at the scanner resolution and are oriented in anterior commisure/posterior commisure axis. Center of the images have been set at the anterior commisure level (Bregma 0 mm). Nevertheless, users are invited to increase the resolution of the current images for using in SPM or FSL for accurate coregistration and normalization steps (we recommand x10 increasing). No manipulation of image resolution are required with ANTs. Not tested with AFNI.</p> <p>For any questions regarding the SIGMA ressource, please email the SIGMA Team (<a href="mailto:sigma.preclinical.resources@gmail.com">sigma.preclinical.resources@gmail.com</a>) or Email directly David A. Barrière (<a href="mailto:david.barriere@cnrs.fr">david.barriere@cnrs.fr</a>).</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#references"></a></h1> <h1>REFERENCES</h1> <p>Barrière, D.A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat Commun 10, 5699 (2019). <a href="https://doi.org/10.1038/s41467-019-13575-7" rel="nofollow">https://doi.org/10.1038/s41467-019-13575-7</a><a title="La ressource a été trouvée dans UNPAYWALL" href="https://www.nature.com/articles/s41467-019-13575-7.pdf" target="_blank" rel="noopener"></a></p> <p>Kleven, H. et al. Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration. Nat Methods 20, 1822–1829 (2023). <a href="https://doi.org/10.1038/s41592-023-02034-3" rel="nofollow">https://doi.org/10.1038/s41592-023-02034-3</a></p> <p>Barrière, D.A. et al. Combination of high-fat/high-fructose diet and low-dose streptozotocin to model long-term type-2 diabetes complications. Sci Rep. 2018 Jan 11;8(1):424. doi: 10.1038/s41598-017-18896-5. PMID: 29323186; PMCID: PMC5765114.</p> <h1><a href="https://github.com/DavidBarriere/SIGMA-Rat-Brain-Templates-and-Atlases#related-works-using-the-sigma-ressources"></a></h1> <h1>RELATED WORKS USING THE SIGMA RESSOURCES</h1> <p>Grandjean J. et al. A consensus protocol for functional connectivity analysis in the rat brain. Nat Neurosci. 2023 Apr;26(4):673-681. doi: 10.1038/s41593-023-01286-8. Epub 2023 Mar 27. Erratum in: Nat Neurosci. 2023 Jun;26(6):1127-1128. PMID: 36973511; PMCID: PMC10493189.</p> <p>Vidal B. et al. Inter-subject registration and application of the SIGMA rat brain atlas for regional labeling in functional ultrasound imaging. J Neurosci Methods. 2021 May 1;355:109139. doi: 10.1016/j.jneumeth.2021.109139. Epub 2021 Mar 16. PMID: 33741345.</p> <p>Barrière D.A. et al. Paracetamol is a centrally acting analgesic using mechanisms located in the periaqueductal grey. Br J Pharmacol. 2020 Apr;177(8):1773-1792. doi: 10.1111/bph.14934. Epub 2020 Jan 22. PMID: 31734950; PMCID: PMC7070177</p> <p>Barrière D.A. et al. Structural and functional alterations in the retrosplenial cortex following neuropathic pain. Pain. 2019 Oct;160(10):2241-2254. doi: 10.1097/j.pain.0000000000001610. PMID: 31145220.</p> <p>Magalhães, R. et al Resting-State Functional MR Imaging and Spectroscopy Study of the Dorsal Hippocampus in the Chronic Unpredictable Stress Rat Model. J Neurosci. 2019 May 8;39(19):3640-3650. doi: 10.1523/JNEUROSCI.2192-18.2019. Epub 2019 Feb 25. PMID: 30804096; PMCID: PMC6510342.</p> <p>Magalhães, R. et al The dynamics of stress: a longitudinal MRI study of rat brain structure and connectome. Mol Psychiatry. 2018 Oct;23(10):1998-2006. doi: 10.1038/mp.2017.244. Epub 2017 Dec 5. PMID: 29203852.</p>

opencc-zeroApr 2023View details →

Data and analysis code for Norton and DuVal - Causes and consequences of nest site fidelity in a tropical lekking bird: win-stay-lose-shift tactics are unrelated to subsequent success, but site-faithful females nest earlier

<p>Nest site selection influences the survival of care-giving parents and their offspring, but search costs and site availability may limit site choices. Returning to previous nest sites may reduce costs and allow parents to better avoid local predators or access familiar resources. We investigated nest site fidelity in the Lance-tailed Manakin (Chiroxiphia lanceolata), in which long-lived females raise offspring without male assistance, and found that site choices are responsive to past success but do not predict future outcomes. We compared georeferenced nest locations for the same females detected in consecutive years (245 comparisons for 138 females) and females nesting repeatedly within a year (137 comparisons for 97 females). Females were faithful to nesting sites in 13.9% of comparisons across years and 10.2% within years, and were more likely to nest again in the same site if their offspring fledged. When switching sites, females moved farther if their previous nest failed. Nest-site fidelity was unrelated to mate fidelity or female age. We then assessed whether site choice related to subsequent female survival, nest timing, or nest survival. Contrary to the hypothesis that win-stay-lose-shift tactics improve subsequent nesting outcomes, we found females were no more likely to fledge chicks or survive to a later year after they reused nest-sites. However, across years, site-faithful females nested earlier on average than females that switched sites. Early nests were more likely to fledge chicks, and early-nesting females were more likely to renest when their first nesting effort was complete. Win-stay-lose-shift tactics may allow females to avoid areas where predation is likely, but new nest sites are not safer. Females that reuse nest sites benefit from early nest initiation, which both correlates with immediate success and creates potential for longer-term benefits of fidelity through increased opportunities to renest throughout the breeding season.</p>

opencc-by-4.0Apr 2023View details →

FLUTE: a Python GUI for interactive phasor analysis of FLIM data

<p>This repository contains the Fluorescence lifetime imaging microscopy (FLIM) data relative to the following publication <em>"FLUTE: a Python GUI for interactive phasor analysis of FLIM data" </em>https://www.biorxiv.org/content/10.1101/2023.03.31.534529v1</p> <p><em><strong>Fluorescein.tif</strong> </em>stack contains the fluorescence intensity decay of fluorescein solution with a known lifetime of 4ns, used as calibration.</p> <p><strong><em>Embryo.tif</em></strong>  file contains the fluorescence intensity decay of a zebrafish embryo at 3 days post fertilisation.</p> <p>Both files have been acquired with the following parameters:</p> <ul> <li>temporal bin number = 56</li> <li>laser repetition rates = 80 MHz</li> <li>bin width = 0.223ns</li> </ul>

opencc-by-nc-4.0Jan 2023View details →

ODNA Data and Analysis Source Code

<p>This upload contains the analysis source code and processed data for developing the software ODNA, including the machine learning pipeline. ODNA is software for identifying organellar DNA sequences from genome assemblies using genome annotation derived from the Modular Open-Source Genome Annotator (MOSGA).</p>

opencc-by-4.0Dec 2022View details →

Sentiment analysis data and word embeddings for Erzya, Komi-Zyrian, Moksha and Udmurt

<p>The aligned sentiment annotated data is in setiment_eval_data.json, vectors.zip has the word embeddings in a textual Gensim format, code.zip has the code and models.zip the sentiment analysis model.</p> <p>Please cite the following paper:</p> <p><strong>Alnajjar, K., Hämäläinen, M., & Rueter, J, (2023) Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages. In <em>Proceedings of the Second Workshop on Resources and Representations for Under-resourced Languages and Domains (RESOURCEFUL-2023)</em></strong></p>

opencc-zeroMay 2023View details →

Correlation between body size and longevity: new analysis and data covering six taxonomic classes of vertebrates

<p class="MsoNormal"><span>Large bodied species are known to live longer than small bodied species. However, it is less clear whether the positive correlation varies across taxa. In this short communication, we combine data entries from literature and databases on body mass and maximum life span for 3722 species covering </span><span>taxonomic Classes <span><em><span>Chondrichthyes</span></em><span>, <em>Teleostei, Amphibia</em>, <em>Reptilia, Aves</em><span>, and</span><em> Mammalia</em>.</span> </span>We then analyze the log(maximum life span) – log(body mass) relationship using generalized linear model with nested random intercepts and slopes for Class/Order/Family. Our analyses generally demonstrate the positive longevity – body mass relationship but also reveal that slopes and intercepts differ slightly among all Classes except <em>Reptilia</em> and <em>Amphibia</em>. Highest slopes can be found in Classes <em>Aves</em> and <em>Chondrichthyes</em>. Differences between the smallest and largest Family-level slopes was more than threefold. While these preliminary analyses provide a brief overview of body size – longevity relationships across taxa, the dataset collated in the present study could serve as a start point for in-depth phylogenetic analyses to uncover complex pathways through which body size and its correlates might have evolved. </span></p>

Chapter 11 Image set for the book, "Data Science for Nano Image Analysis"

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Data in support of: `Two-Dimensional Strain Mapping with Scanning Precession Electron Diffraction: An Investigation into Data Analysis Routines'

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