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2,885 results for “transfers”
Dataset of maternal thyroid hormones transfer in Pied flycatchers
<p>This is the dataset of an experiment conducted on Pied flycatchers (<em>Ficedula hypoleuca</em>) in which we manipulated thyroid hormones in the mother's circulation. This dataset contains all the responses measured in this experiment.</p>
Within and cross species predictions of plant specialized metabolism genes using transfer learning
<p>Datasets for <em>Within and cross species predictions of plant specialized metabolism genes using transfer learning.</em> </p> <ol> <li>Dataset 1: All gene features used in the full feature machine learning models including expression, co-expression, evolutionary, duplication, and protein domain features.</li> <li>Dataset 2: All gene features used in shared feature machine learning models including expression, evolutionary, duplication, and protein domain features.</li> <li>Table S1: All gene annotations from TomatoCyc or manual annotation.</li> <li>Table S2: All model scores.</li> <li>Table S3: All gene scores and predictions from each model.</li> <li>Table S4: Feature importance for 5 models.</li> <li>Table S5: Statistical analysis between classes for binary and continuous feature data.</li> <li>Table S6: RNAseq datasets used in analysis.</li> </ol>
Trophic structure and mercury transfer in the subarctic fish community of Great Slave Lake, Northwest Territories, Canada
<p>In recent decades, mercury concentrations have increased in fish of Great Slave Lake (GSL), a subarctic great lake in northern Canada with important recreational, subsistence, and commercial fisheries. This study characterized habitat use and trophic position of common fish species in GSL near the City of Yellowknife (Northwest Territories, Canada), measured mercury concentrations in water and in taxa from lower trophic levels of the food web, and examined trophic and biological influences on mercury concentrations within and among fish species. Northern pike (<i>Exos lucius</i>) and lake whitefish (<i>Coregonus clupeformis</i>) fed predominantly nearshore, cisco (<i>Coregonus artedi</i>) and longnose sucker (<i>Catostomus catostomus</i>) fed predominantly offshore, and burbot (<i>Lota lota</i>) fed roughly equally in both habitats. Habitat-specific feeding did not influence mercury bioaccumulation in fish, in contrast with published studies of smaller lakes. Water concentrations of total mercury and methylmercury were low and showed little spatial variation among sites or depths. Zooplankton (>200 µm) had similarly low methylmercury concentrations to littoral and profundal amphipods, suggesting little habitat-variation of mercury exposure near the base of the food web. Age, size, and trophic position were significant explanatory variables for muscle total mercury concentrations within populations of fish species. Among fish species, size and trophic position explained 80% of the variation in muscle total mercury concentrations. This study generated the most comprehensive dataset to date on mercury bioaccumulation in the food web of GSL, which will serve as a baseline for future studies of this great lake.</p>
Dataset - Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2020) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.txt</strong></em> file.</p> <p>The dataset contains:</p> <p>- libRadtran input files</p> <p>- libRadtran output</p> <p>- name lists</p> <p>- GRAL simulation outputs</p>
On Transferability of Histological Tissue Labels in Computational Pathology
<p>This Zip file contains all Convolutional Neural Network (CNN) models of HistoNet trained on ADP database introduced in</p> <p><em>Hosseini et. al. “On Transferability of Histological Tissue Labels in Computational Pathology.” Accepted in the European Conference on Computer Vision (ECCV), Glasgow, UK, August 2020 </em></p> <p>For more information on the models and how to train them, please refer to <strong>https://github.com/mahdihosseini/HistoLabelTransfer</strong></p> <p> </p>
Fussy Transfer Learning dataset
<p>Companion dataset repository to be used with <a href="https://github.com/MatZar01/Fussy-Transfer-Learning">Fussy Transfer Learning - Github</a> project methods.</p>
Data from: Comparative genomics reveals high rates of horizontal transfer and strong purifying selection on rhizobial symbiosis genes
<p class="western"><span>Horizontal transfer (HT) alters the repertoire of symbiosis genes in rhizobial genomes and may play an important role in the on-going evolution of the rhizobia-legume symbiosis. To gain insight into the extent of HT of symbiosis genes with different functional roles (nodulation, N-fixation, host benefit, and symbiont fitness), we conducted comparative genomic and selection analyses of the full genome sequences from 27 rhizobial genomes. We find that symbiosis genes experience high rates of HT among rhizobial lineages but also bear signatures of purifying selection (low Ka:Ks). HT and purifying selection appear to be particularly strong in genes involved in initiating the symbiosis (e.g. nodulation) and in genome-wide association candidates for mediating variation in benefits provided to the host. These patterns are consistent with rhizobia adapting to the host environment through the loss and gain of symbiosis genes, but not with host-imposed positive selection driving divergence of symbiosis genes through recurring bouts of positive selection.</span></p>
Acoustic transfer function data for source and sensor placement
<p>Acoustic transfer function (ATF) data for the codes of source and sensor placement in sound field control. </p> <p>https://github.com/sh01k/SourceSensorPlacementSFC</p> <p>The ATF data in the 2D acoustic field was generated by the finite element method using FreeFem++ (<a href="https://freefem.org/">https://freefem.org/</a>).</p>
VerbaAlpina - oder: der Transfer der Geolinguistik in die digital humanities
<p>Seit der zügigen Durchsetzung interaktiver und kollaborativer Strukturen im Internet durchlaufen zahlreiche wissenschaftliche Disziplinen einen Prozess tiefgreifenden Wandels, denn im Gefolge der medialen Revolution haben sich die Rahmenbedingungen der Wissenschaftskommunikation substantiell verändert. In den Disziplinen, die sich mit kulturellen Techniken und ihrer geschichtlichen Entwicklung befassen, kann dieser Wandel mit dem Schlagwort der digital humanities identifiziert werden. Die seit je in der sprachlichen Empirie verankerte Geolinguistik ist in geradezu idealer Weise geeignet, um die Notwendigkeit des skizzierten Transfers zu illustrieren. Ausgehend von der detaillierten Vorstellung einer webbasierten Forschungsumgebung (<a href="https://www.verba-alpina.gwi.uni-muenchen.de/">https://www.verba-alpina.gwi.uni-muenchen.de/</a>) zeigt der Vortrag, wie sich die Methodologie der sprachwissenschaftlichen Arbeit ändert, wenn man sich der digitalen Herausforderung stellt. Es ist in der Konsequenz nicht zu übersehen, dass damit ein ganz neues Selbstverständnis der Disziplin einhergeht.</p> <p>Der Vortrag steht hier zur Verfügung: https://www.verba-alpina.gwi.uni-muenchen.de/?p=5969</p>
Simulating gas giant exoplanet atmospheres with Exo-FMS: Comparing semi-grey, picket fence and correlated-k radiative-transfer schemes.
<p>NetCDF dataset for paper titled: Simulating gas giant exoplanet atmospheres with Exo-FMS: Comparing semi-grey, picket fence and correlated-k radiative-transfer schemes.</p> <p> </p> <p>Contains:</p> <p>1. Heng benchmark GCM data</p> <p>2. Rauscher benchmark GCM data</p> <p>3. HD209 model using semi-grey RT</p> <p>4. HD 209 model using non-grey picket fence RT</p> <p>5. HD 209 model using corr-k RT scheme</p>
Detailed data sets of MMP-cliffs, SAR transfer series, RECAP-MMPs and compound activities
<p>An up-to-date version of three MMP-based data sets derived from compounds included in the latest release of ChEMBL is presented. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules. The structural data and information are provided in eight different files comprising the data sets of MMP-cliffs, SAR transfer series, and RECAP-MMPs. Compound activities are incorporated in files of RECAP-MMPs. For transfer series, substituted fragments are also provided. All MMP-cliffs, SAR transfer series with approximate or regular potency progression, and RECAP-MMPs are provided in canonical SMILES representation on a per-target basis separately for the Ki and IC50 subsets. The corresponding files are clearly designated.</p>
Supplementary Material: HIV-1-Induced Small T Cell Syncytia Can Transfer Virus Particles to Target Cells through Transient Contacts
<p>Videos supplementary to <em>Viruses</em> <strong>2015</strong>, <em>7</em>(12), 6590-6603; doi:10.3390/v7122959</p> <p><strong>Captions:</strong></p> <p><strong>Movie S1.</strong> <strong>HIV-1-infected cells in the lymph node of humanized mice.</strong></p> <p>Humanized BLT mice were injected in the footpad with HIV-nGFP, where GFP is highly enriched in cellular nuclei, and the draining popliteal lymph node prepared for MP-IVM at day 2. Representative infected cells (GFP+; green) that display one, two or three discernible nuclei are shown (<strong>top</strong>). In the bottom panels, green fluorescence signals above 80% of the intensity maximum were used to define cell nuclei, which are shown in white. The syncytium with two discernible nuclei remains elongated throughout the recording, while the syncytium with three discernible nuclei switches between coordinated and uncoordinated motility. Each individual frame is a maximum intensity projection of 11 <em>z</em>-stacks spaced 4 μm apart (for a total volume of 40 μm). Time is shown in minutes and seconds. Scale bar = 20 μm. See also Figure 1A.</p> <p><strong>Movie S2.</strong> <strong>Syncytia in the lymph node contact uninfected T cells without undergoing cell-cell fusion</strong>.</p> <p><em>In vitro</em>-generated central memory CD4+ T cells, either infected with HIV-GFP (GFP+; green) or uninfected (labeled with CellTracker Orange; red), were adoptively transferred by footpad injection into BLT mice pretreated with antiretroviral drugs (100 mg/kg FTC, 150 mg/kg TDF). After 12 h, the draining popliteal lymph node was prepared for MP-IVM. Two representative movies of T cell migration prior to (yellow circle) and during<br /> (blue circle) transient interactions with syncytia are shown, demonstrating cellular interactions without fusion. Each individual frame is a maximum intensity projection of 11 <em>z</em>-stacks spaced 4 μm apart (for a total volume of 40 μm). Time is shown in minutes and seconds. Scale bar = 40 μm. See also Figure 1F,G.</p> <p><strong>Movie S3.</strong> <strong>CD4</strong><strong>+ T cells in 3D culture form small syncytia with elongated morphology</strong>.</p> <p>Primary human CD4+ T cells isolated from a healthy donor were infected with VSV-G-pseudotyped NL4-3<sup>Gag-iGFP</sup> virus. The next day, cells were embedded in a 3D collagen gel as described in the Experimental Section, and 12 h later imaged live at 37 °C using a 20× objective on a DeltaVision widefield microscope. Six 3 µm-spaced Z-slices were taken every 20 s, and were subsequently projected into one image. The syncytium seen here in green has two nuclei located at opposite ends, and a central bulged region with high amounts of viral Gag. The diffuse fluorescence is a result of this syncytium being located at a higher part of the gel, where the limitations of widefield imaging become more prominent. See also Figure 2A.</p> <p><strong>Movie S4.</strong> <strong>Small CEM-SS syncytia in 3D culture can dynamically change their morphology</strong>.</p> <p>CEM-SS cells were infected with VSV-G-pseudotyped NL4-3<sup>Gag-iGFP</sup> virus. The next day, cells were embedded in a 3D Matrigel gel as described in the Experimental Section, and 24 h later imaged live at 37 °C using a<br /> 40× objective on a DeltaVision widefield microscope. Seven 2 μm-spaced Z-slices were taken every 5 min, and were subsequently projected into one image. A syncytium with two nuclei (dark areas within the cell in the GFP channel) begins with two lobes, which merge into a coordinated round morphology as the cell begins to migrate through the gel and leaves the plane of focus. See also Figure 2B.</p> <p><strong>Movie S5.</strong> <strong>CD4</strong><strong>+ T cells in 3D culture exhibit </strong><strong><em>in vivo</em>-like migratory behavior</strong>.</p> <p>Primary human CD4<sup>+</sup> T cells were infected, embedded in collagen, and imaged as in Movie S3 (though with a<br /> 10 s time lapse). The uninucleated infected cell migrating across the field moves by 108 μm over 560 s, for a mean velocity of 11.58 μm/min. Such fast directed amoeboid motility is not typically observed in classical 2D culture and requires the presence of a 3D ECM. See also Figure 2C.</p> <p><strong>Movie S6.</strong> <strong>Uninucleated infected cells and syncytia can transfer virus to target cells without fusion.</strong></p> <p>CEM-SS cells were infected, embedded in Matrigel, and imaged as in Movie S4. Virus transfer from a uninucleated infected cell (top) and a syncytium (bottom left) to a number of target cells (denoted by T) can be seen here.<br /> The uninucleated infected cell transfers virus to target cells T1–T5, and the syncytium transfers virus to target cells T6–T7. Images shown represent brightfield in gray (bottom right), or Gag-iGFP in green, shown either with normal scaling (bottom left, and merged with brightfield at top right), or with a 0.6 gamma correction applied and enhanced scaling to better show appearance of Gag-iGFP puncta on target cells (top left). Scale bar = 30 μm. A yellow arrow indicates the moment where the uninucleated infected cell begins transferring virus to target cells T1–T3, and all of the other transfer events in this field are happening at roughly the same time. At this time, Gag-iGFP puncta appear to distribute between cells T1–T3 in a progressive fashion, beginning from the point of contact with the infected cell (see also Figure 3A, middle panel). Cells T4 and T5 also receive virus particles from this infected cell, and T4 can be seen migrating away at the end. Also note a trail of released virus left behind by the uninucleated infected cell as it migrates from left to right from 08:00:00 to 09:10:00 (see also Movies S7 and S9, Figure 4 for similar events in syncytia). The syncytium’s targets, T6 and T7, are already in intimate contact with it at the start of the movie, and are obscured by lobes of the syncytium. At the 10:45:00 mark, cell T6 breaks free from the syncytium, now harboring a large amount of virus particles on its surface, as the syncytium slowly migrates away, and cell T7 also appears to harbor virus particles on its surface by the final time point.</p> <p><strong>Movie S7.</strong> <strong>A virus transfer event between a syncytium and two uninfected target cells.</strong></p> <p>CEM-SS cells were infected, embedded in Matrigel, and imaged as in Movies S4 and S6. The syncytium shown here initially has two nuclei, but soon fuses with an uninfected cell and now has three clearly visible nuclei. It remains stationary for several hours, before beginning to migrate towards a pair of uninfected target cells (top). Very soon after contact, virus particles can be seen covering the surface of both cells, one of which eventually migrates away. See also Figure 3A (left panel). Note also another instance of a dense accumulation of cell-free virus particles in what appears to be a pocket within the hydrogel that the syncytium moves one of its lobes out of, revealing the deposited free virus, before it moves back into the pocket. See also Figure 4A.</p> <p><strong>Movie S8.</strong> <strong>Cell-to-cell transfer of virus can take place while cells are migrating.</strong></p> <p>CEM-SS cells were infected, later mixed with CMAC-labeled uninfected CEM-SS cells (shown in blue), embedded in collagen, and imaged as above. A syncytium with two nuclei (one of which bears CMAC signal, indicating that it formed recently and not before the infected culture was mixed with the labeled uninfected cells) migrates across the field. Its trailing edge contacts an uninfected CMAC-labeled cell, which is then dragged along with it, and finally dropped in the corner of the field. The target cell now bears virus particles on its surface, and is no longer in contact with the syncytium, which has moved into a different focal plane and stopped migrating (not shown). The image was refocused at the 10:30:00 mark to better show the target cell and the virus particles on its surface. Note that this movie also shows an instance of newly synthesized Gag-iGFP appearing in a previously uninfected cell (not the target cell contacted by the syncytium). This non-CMAC labeled uninfected cell appears in the bottom left of the field at 07:20:00 and exhibits steadily increasing diffuse intracellular signal, as documented in Figure 3A (right) and Figure 3B,C (green traces).</p> <p><strong>Movie S9.</strong> <strong>Migrating infected cells can deposit a trail of released virus particles</strong>.</p> <p>CEM-SS cells were infected, embedded in Matrigel, and imaged as in Movies S4, S6, and S7. A syncytium with 3 nuclei switches into a coordinated morphology and begins migrating across the field. Released virus particles can be seen in its wake (also shown enlarged and with increased brightness as an inset). Shortly after the end of the movie, the cell-free virus accumulation appeared to dissipate (not shown), but it could not be determined whether this was because of photobleaching or if they had in fact diffused away. See also Figure 4B.</p>
Conceptual model of uptake of inorganic carbon by the sulfur-oxidizing γ1-symbionts and transfer to their host, the gutless marine worm Olavius algarvensis
<p>Conceptual model of uptake of inorganic carbon by the sulfur-oxidizing γ1-symbionts and transfer to their host, the gutless marine worm <em>Olavius algarvensis.</em></p>
Biological and environmental data for a study on transferability of statistical and machine learning models using North Sea Macrozoobenthos
<p>General</p> <p>Data documented here are not the product of our research but was scraped from various sources and processed - so no genuine reupload. This collection is a contribution to reproduceable reseach. All datasets are given in "RData" binary format</p> <p> </p> <p>Data description</p> <p>majornorthseabenthos </p> <p>This is macrozoobenthos data as data frame scraped from the GBIF repository (gbif.org). Species are Corbula gibba, Tellina fabula, Turritella communis, Euspira pulchella, Corystes cassive- launus, Upogebia deltaura, Lanice conchilega, Nephtys hombergii, Echinocardium cordatum, and Amphiura filiformis. data was postprocessed to have only single occurrence fon the approxinatel 1x1 km grid used for this study. Also, occurrences closer than 5 km close to shore were removed - including occurrences on land.</p> <p> </p> <p>Predictors</p> <p>A SpatialPixelsDataFrame in EPSG 4326 with five layers: Median grain size in micrometers, mud content in percent (both MUDAB database), water depth in meters above MSL (Weatherall et al, 2015), modelled average bottom shear stress from waves in N/sqrm (The Wamdi Group, 1988) and climatologival average winter bottom water temperature in deg. C (Stips et al, 2004).</p> <p> </p> <p> </p> <p>References</p> <p>Stips A, Bolding K, Pohlmann T, Burchard H (2004) Simulating the temporal and spatial dy- namics of the North Sea using the new model GETM (general estuarine transport model). Ocean Dynamics 54(2):266–283</p> <p>The Wamdi Group (1988) The WAM model-a third generation ocean wave prediction model. Journal of Physical Oceanography 18(12):1775–1810</p> <p>Weatherall P, Marks K, Jakobsson M, Schmitt T, Tani S, Arndt JE, Rovere M, Chayes D, Ferrini V, Wigley R (2015) A new digital bathymetric model of the world’s oceans. Earth and Space Science 2(8):331–345</p> <p> </p>
Data and script pipeline for: Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods
<p>The scripts and the data provided in this depository demonstrate how to apply the approach described in the paper "<strong>Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods</strong>" by Ovaskainen et al. Here we summarize how to use the software with a small, simulated dataset, with running time less than a minute in a typical laptop (Demo 1); (2) how to apply the analyses presented in the paper for a small subset of the data, with running time of ca. one hour in a powerful laptop (Demo 2); how to reproduce the full analyses presented in the paper, with running time up to several days, depending on the computational resources (Demo 3). The Demos 1 and 2 are aimed to be user-friendly starting points for understanding and testing how to implement CORAL. The Demo 3 is included mainly for reproducibility.</p> <p> </p> <p><strong><u>System requirements</u></strong></p> <p> </p> <p>· The software can be used in any operating system where R can be installed.</p> <p>· We have developed and tested the software in a windows environment with R version 4.3.1.</p> <p>· Demo 1 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0).</p> <p>· Demo 2 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0).</p> <p>· Demo 3 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0), jsonify (1.2.2), buildmer (2.11), colorspace (2.1-0), matlib (0.9.6), vioplot (0.4.0), MLmetrics (1.1.3) and ggplot2 (3.5.0).</p> <p>· The use of the software does not require any non-standard hardware.</p> <p> </p> <p><strong><u>Installation guide</u></strong></p> <p> </p> <p>· The CORAL functions are implemented in Hmsc (3.3-3). The software that applies the is presented as a R-pipeline and thus it does not require any installation other than installation of R.</p> <p> </p> <p><strong><u>Demo 1: Software demo with simulated data</u></strong></p> <p><strong> </strong></p> <p>The software demonstration consists of two R-markdown files:</p> <p> </p> <p>· <strong>D01_software_demo_simulate_data.</strong> This script creates a simulated dataset of 100 species on 200 sampling units. The species occurrences are simulated with a probit model that assumes phylogenetically structured responses to two environmental predictors. The pipeline saves all the data needed to data analysis in the file allDataDemo.RData: XData (the first predictor; the second one is not provided in the dataset as it is assumed to remain unknown for the user), Y (species occurrence data), phy (phylogenetic tree), studyDesign (list of sampling units). Additionally, true values used for data generation are save in the file trueValuesDemo.RData: LF (the second environmental predictor that will be estimated through a latent factor approach), and beta (species responses to environmental predictors).</p> <p>· <strong>D02_software_demo_apply_CORAL</strong>. This script loads the data generated by the script D01 and applies the CORAL approach to it. The script demonstrates the informativeness of the CORAL priors, the higher predictive power of CORAL models than baseline models, and the ability of CORAL to estimate the true values used for data generation.</p> <p> </p> <p>Both markdown files provide more detailed information and illustrations. The provided html file shows the expected output. The running time of the demonstration is very short, from few seconds to at most one minute.</p> <p> </p> <p><strong><u>Demo 2: Software demo with a small subset of the data used in the paper</u></strong></p> <p><strong> </strong></p> <p>The software demonstration consists of one R-markdown file:</p> <p> </p> <p><strong>MA_small_demo.</strong> This script uses the CORAL functions in HMSC to analyze a small subset of the Malagasy arthropod data. In this demo, we define rare species as those with prevalence at least 40 and less than 50, and common species as those with prevalence at least 200. This leaves 51 species to the backbone model and 460 rare species modelled through the CORAL approach. The script assess model fit for CORAL priors, CORAL posteriors, and null models. It further visualizes the responses of both the common and the rare species to the included predictors.</p> <p> </p> <p><strong><u>Scripts and data for reproducing the results presented in the paper (Demo 3)</u></strong></p> <p> </p> <p>The input data for the script pipeline is the file “allData.RData”. This file includes the metadata (meta), the response matrix (Y), and the taxonomical information (taxonomy). Each file in the pipeline below depends on the outputs of previous files: they must be run in order. The first six files are used for fitting the backbone HMSC model and calculating parameters for the CORAL prior:</p> <p> </p> <p>· <strong>S01_define_Hmsc_model</strong> - defines the initial HMSC model with fixed effects and sample- and site-level random effects.</p> <p>· <strong>S02_export_Hmsc_model</strong> - prepares the initial model for HPC sampling for fitting with Hmsc-HPC. Fitting of the model can be then done in an HPC environment with the bash file generated by the script. Computationally intensive.</p> <p>· <strong>S03_import_posterior</strong> – imports the posterior distributions sampled by the initial model.</p> <p>· <strong>S04_define_second_stage_Hmsc_model</strong> - extracts latent factors from the initial model and defines the backbone model. This is then sampled using the same S02 export + S03 import scripts. Computationally intensive.</p> <p>· <strong>S05_visualize_backbone_model</strong> – check backbone model quality with visual/numerical summaries. Generates Fig. 2 of the paper.</p> <p>· <strong>S06_construct_coral_priors</strong> – calculate CORAL prior parameters.</p> <p> </p> <p>The remaining scripts evaluate the model:</p> <p>· <strong>S07_evaluate_prior_predictionss </strong>– use the CORAL prior to predict rare species presence/absences and evaluate the predictions in terms of AUC. Generates Fig. 3 of the paper.</p> <p>· <strong>S08_make_training_test_split</strong><em> </em>– generate train/test splits for cross-validation ensuring at least 40% of positive samples are in each partition.</p> <p>· <strong>S09_cross-validate</strong><em> </em>– fit CORAL and the baseline model to the train/test splits and calculate performance summaries. Note: we ran this once with the initial train/test split and then again with on the inverse split (i.e., <em>training = ! training</em> in the code, see comment). The paper presents the average results across these two splits. Computationally intensive.</p> <p>· <strong>S10_show_cross-validation_results</strong><em> </em>– Make plots visualizing AUC/Tjur’s R<sup>2</sup> produced by cross-validation. Generates Fig. 4 of the paper.</p> <p>· <strong>S11a_fit_coral_models</strong><em> </em>– Fit the CORAL model to all 250k rare species. Computationally intensive.</p> <p>· <strong>S11b_fit_baseline_models</strong><em> </em>– Fit the baseline model to all 250k rare species. Computationally intensive.</p> <p>· <strong>S12_compare_posterior_inference</strong><em> ­­</em>– compare posterior climate predictions using CORAL and baseline models on selected species, as well as variance reduction for all species. Generates Fig. 5 of the paper.</p> <p> </p> <p>Pre-processing scripts:</p> <p>· <strong>P01_preprocess_sequence_data.R </strong>– Reads in the outputs of the bioinformatics pipeline and converts them into R-objects.</p> <p>· <strong>P02_download_climatic_data.R </strong>– Downloads the climatic data from "sis-biodiversity-era5-global” and adds that to metadata.</p> <p>· <strong>P03_construct_Y_matrix.R </strong>– Converts the response matrix from a sparse data format to regular matrix. Saves “allData.RData”, which includes the metadata (meta), the response matrix (Y), and the taxonomical information (taxonomy).</p> <p> </p> <p>Computationally intensive files had runtimes of 5-24 hours on high-performance machines. Preliminary testing suggests runtimes of over 100 hours on a standard laptop.</p> <p><em> </em></p> <p><strong><u>ENA Accession numbers</u></strong></p> <p>All raw sequence data are archived on mBRAVE and are publicly available in the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena; project accession number PRJEB86111; run accession numbers ERR15018787-ERR15009869; sample IDs for each accession and download URLs are provided in the file ENA_read_accessions.tsv).</p>
Relative Transfer Matrix for Low SNR Speech Separation from Noisy Sources in Reverberant Rooms
<p>This folder contains the supplementary audio files for the paper "Relative Transfer Matrix for Low SNR Speech Separation from Noisy Sources in Reverberant Rooms" submitted to <em>The Journal of the Acoustical Society of America</em>.</p>
Light spectrum matters: Interactive effects of light and nutrients on phytoplankton communities and trophic transfer
<p>Phytoplankton is highly sensitive to both the quantity (intensity) and quality (spectrum) of available light, due to a large range of light-absorbing pigments. While light is considered as a main driver for trophic energy transfer, focus has been primarily on the intensity of light. This study investigated the role of different light spectra on the community structure and biochemical composition of natural phytoplankton. We found that light spectrum affects the species composition and biochemistry of phytoplankton communities with these effects being highly dependent on light intensity and nutrients. Additionally, we tested for potential nutritional constraints for the zooplankton species <em>Daphnia pulex</em> in response to changes in the phytoplankton community and found different growth responses of <em>D. pulex</em> as a result of changing mineral and biochemical growth contraints for the <em>Daphnia</em> species. We conclude that, beyond nutrients and light intensity, also the light spectrum influences plankton dynamics and trophic transfer in aquatic systems.</p>
Transferability of atomic energies from alchemical decomposition - additional data
<p>Additional data to the paper "Transferability of atomic energies from alchemical decomposition"</p><p><a href="https://doi.org/10.48550/arXiv.2311.04784">https://doi.org/10.48550/arXiv.2311.04784</a></p><p>Atomic energies of selected QM9 compounds calculated with different decomposition schemes (alchemy, IQA and IBO/IAO).</p><p>Code to generate the input-file and the rescaled pseudopotential files for DFT calculations with fractional core charges with the CPMD program and to calculate alchemical atomic energies from the CPMD output.</p><p> </p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
Open the record for dataset details and reuse information.
Blue Transfer-printed Plate (2445p3192)
**Blue transfer-printed plate** Location: Eagle Tavern site on UNC Campus (RLA-Or257), Orange County, North Carolina. Period: Historic (19th century). Material: ceramic. Dimensions: height, 2.5 cm; diameter, 23.3 cm. Notes: Catalog no. 2445p3192. North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Chris LaMack. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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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.