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CESM2 simulation output used in the study "On the links between ice nucleation, cloud phase, and climate sensitivity in CESM2"
<p>Provided is all CESM2 model output used to generate figures in the study, for which a preprint is at 'https://doi.org/10.22541/essoar.167214452.25853014/v1'. File names indicate the experiment names used in the study. For each model experiment, there is one file containing variables in a present-day (PD) simulation, plus a second file containing cloud feedbacks calculated by the Zelinka et al 2012 kernel method (comparing PD to PD with 4K warming uniformly added to sea surface temperatures).</p>
Model output from the Bern3D model of 600 kyr weathering step-change experiments
<p>Model output data from the Bern3D model that were used for the manuscript "Carbon cycle responses to changes in weathering and the long-term fat of stable carbon isotopes".</p> <p>Information on the different data files is included in the README.</p>
Modèle Atmosphérique Régional (MAR) v3.12.0 15km output for Greenland (JJA 1980-2020, 1-hourly)
<p>The Modèle Atmosphérique Régional (MAR) is a regional climate model especially developed for studying the near surface climate and surface mass balance of both polar ice sheets. This dataset includes MAR outputs from version 3.12.0 of the model (MARv3.12) forced by the ERA5 reanalysis for a domain surrounding Greenland. The data are provided at a spatial resolution of 15km and temporal resolution of 1 hour, for the June-July-August (JJA) summer months during 1980-2020.</p> <p>With respect to MARv3.11, the main improvements of MARv3.12 are the geographical projection used by MAR which is now the standard Polar Stereographic EPSG 3413, a correction of an important bug impacting the snow temperature at the base of the snowpack, a conservation of water mass in the soil impacting water fluxes over the tundra, and a continuous conversion from rainfall to snowfall from 0°C to -2°C as input of the snow model instead as a fixed one at -1°C.</p> <p>The following MAR variables are included in this dataset: lat/lon coordinates, ice sheet mask, surface height, meltwater, 10-meter u/v-wind components, and relative humidity.</p>
STRT-N mouse library output files
<p><strong>Output files from STRT-N data analysis</strong></p> <p>These files were obtained from a successful STRT-N mouse library using STRTN.sh, STRTN-UCSC-Allas.sh and STRTN-Seurat.sh. Details are below:</p> <ul> <li> <p><strong><code>OUTPUT</code>-QC.txt </strong><br> Quality check report for all samples. It is provided in <code>out</code> directory by STRTN.sh.</p> <table> <thead> <tr> <th>Column</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td><code>Barcode</code></td> <td>Sample name. <code>OUTPUT</code> with numbers</td> </tr> <tr> <td><code>Qualified_reads</code></td> <td>Primary aligned read count</td> </tr> <tr> <td><code>Total_reads</code></td> <td>Read count without redundant (duplicate) reads</td> </tr> <tr> <td><code>Redundancy</code></td> <td>Qualified reads / Total reads</td> </tr> <tr> <td><code>Mapped_reads</code></td> <td>Mapped read count (Total reads without unmapped reads)</td> </tr> <tr> <td><code>Mapped_rate</code></td> <td>Mapped reads / Total reads</td> </tr> <tr> <td><code>Spikein_reads</code></td> <td>Read count mapped to ERCC spike-ins</td> </tr> <tr> <td><code>Spikein-5end_reads</code></td> <td>Read count mapped to the 5'-end 50 nt region of ERCC spike-ins</td> </tr> <tr> <td><code>Spikein-5end_rate</code></td> <td>Spikein-5end reads / Spikein reads</td> </tr> <tr> <td><code>Coding_reads</code></td> <td>Read count aligned within any exon or the 500 bp upstream of coding genes</td> </tr> <tr> <td><code>Coding-5end_reads</code></td> <td>Read count aligned the 5′-UTR or 500 bp upstream of coding genes</td> </tr> <tr> <td><code>Coding-5end_rate</code></td> <td>Coding-5end reads / Coding reads</td> </tr> </tbody> </table> </li> <li> <p><strong><code>OUTPUT</code>-QC-plots.pdf</strong><br> Quality check report by boxplots. <code>Mapped_reads</code>, <code>Mapped_rate</code>, <code>Spikein_reads</code>, <code>Mapped / Spikein</code>, <code>Spikein-5end_rate</code>, and <code>Coding-5end_rate</code> are shown for all samples. Barcode numbers of outlier samples are marked with red characters. It is provided in <code>out</code> directory by STRTN.sh.<br> Please consider these outlier samples for the further downstream analysis.</p> </li> <li> <p><strong><code>OUTPUT</code>_byGene-counts.txt</strong><br> Read count table output from. It is provided in <code>out</code> directory by STRTN.sh.<br> featureCounts. <a href="https://bioconductor.org/packages/release/bioc/vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf">https://bioconductor.org/packages/release/bioc/vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf</a></p> </li> <li> <p><strong><code>OUTPUT</code>_byGene-counts.txt.summary</strong><br> Filtering summary from. It is provided in <code>out</code> directory by STRTN.sh. <br> featureCounts. <a href="https://bioconductor.org/packages/release/bioc/vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf">https://bioconductor.org/packages/release/bioc/vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf</a></p> </li> <li> <p><strong>Output_bam</strong><br> Resulting BAM files including unmapped, non-primary aligned, and duplicated (marked) reads. Files are provided in <code>out</code> directory by STRTN.sh.</p> </li> <li> <p><strong>Output_bai</strong><br> Index files (.bai) of the resulting BAM files in the <code>Output_bam</code> directory. Files are provided in <code>out</code> directory by STRTN.sh.</p> </li> <li> <p><strong><code>OUTPUT</code>.output.bam</strong><br> BAM files containing reads except for duplicate and non-primary reads. Files are provided in the working directory by STRTN.sh.</p> </li> <li> <p><strong><code>OUTPUT</code>.minus.bw</strong> and <strong><code>OUTPUT</code>.plus.bw</strong><br> BigWig files for each strands of each sample. Files are provided in the working directory by STRTN.sh.</p> </li> <li> <p><strong>coding_5end.bb</strong><br> BigBed file for coding-5'end annotation file. It is provided in the working directory by STRTN.sh</p> </li> </ul> <ul> <li> <p><strong>hub.txt</strong><br> Parameters for each tracks. It is provided in the working directory by STRTN-UCSC-Allas.sh.</p> </li> <li> <p><strong>Link of hub.txt file</strong><br> Provided by STRTN-UCSC-Allas.sh.</p> </li> <li> <p><strong><code>OUTPUT</code>-QC-BeeswarmPlots.pdf</strong><br> Visualization quality check values for each developmental stage using BeeswarmPlots. It is provided in <code>out</code> directory by STRTN-Seurat.sh.</p> </li> <li> <p><strong>Rplots.pdf</strong><br> Elbow, JackStraw, PCA, UMAP and violin plots. It is provided in <code>out</code> directory by STRTN-Seurat.sh.</p> </li> <li> <p><strong>ExtractIlluminaBarcodes_Metrics</strong><br> Metrics file produced by the Picard ExtractIlluminaBarcodes program. The number of matches/mismatches between the barcode reads and the actual barcodes is shown per lane. <a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037426491-ExtractIlluminaBarcodes-Picard-">https://gatk.broadinstitute.org/hc/en-us/articles/360037426491-ExtractIlluminaBarcodes-Picard-</a></p> </li> <li> <p><strong>HISAT2_Metrics</strong><br> Alignment summary of samples from each lane produced by the HISAT2 program. <a href="https://daehwankimlab.github.io/hisat2/manual/">https://daehwankimlab.github.io/hisat2/manual/</a></p> </li> <li> <p><strong>MarkDuplicates_Metrics</strong><br> Metrics file indicating the numbers of duplicates produced by the Picard MarkDuplicates program. <a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037052812-MarkDuplicates-Picard-">https://gatk.broadinstitute.org/hc/en-us/articles/360037052812-MarkDuplicates-Picard-</a></p> </li> <li> <p><strong><code>OUTPUT_</code>MultiQC_report.html </strong><br> For each sample, fastq files from the output BAM files are generated by fastq-fastQC.sh in the <code>fastq</code> directory. These fastq files (without duplicated reads) can be submitted to public sequence databases. <a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/">FastQC</a> files are also generated for each fastq file in the <code>fastqc</code> directory. Based on the FastQC results, <a href="https://multiqc.info/">MultiQC</a> report (<strong>MultiQC_report.html</strong>) is generated.</p> </li> <li> <p><strong><code>OUTPUT_</code>byTFE-counts_annotation.txt</strong><br> Read count table output from featureCounts with genomic annotations, produced by STRTN-TFE.sh.</p> </li> <li> <p><strong><code>OUTPUT_</code>peaks.bed</strong><br> Peak position information of TFEs, produced by STRTN-TFE.sh.</p> </li> <li> <p><strong><code>OUTPUT_</code>annotation</strong><br> Annotation of TFEs, produced by STRTN-TFE.sh.</p> </li> </ul>
Sample output from from Oktoberfest
<p>Oktoberfest output for the 3 available tasks:</p> <p>1- Spectral Library Generation</p> <p>2- CE Calibration</p> <p>3- Rescoring</p> <p>This is the result of running Oktoberfest with the input files available at <a href="https://zenodo.org/deposit/7600139">https://zenodo.org/deposit/7600139</a></p>
Output of several experiments with Fenics_ice over Smith, Pope, and Kohler Glaciers
<p>Output of several experiments with <a href="https://github.com/EdiGlacUQ/fenics_ice">Fenics_ice</a> over Smith, Pope, and Kohler Glaciers. The code to produce this output can be found in the following repository; <a href="https://github.com/bearecinos/smith_glacier">Smith_glacier</a></p> <p>More information on how to read and plot this data can be found in the smith glacier repository <a href="https://github.com/bearecinos/smith_glacier/wiki">wiki</a>: </p> <p>Citation for the smith glacier repository used to produce this data:</p> <pre>https://zenodo.org/badge/latestdoi/101511241</pre> <p>Citation for the Fenics_ice version used to produce this data:</p> <pre>https://zenodo.org/badge/latestdoi/417440075</pre> <p> </p>
SAM simulation output
<p>This is the simulation output of the submitted manuscript "Between Broadening and Narrowing: How Mixing Affects the Width of the Droplet Size Distribution" to the Journal of Geophysical Research: Atmospheres. </p> <p>tar file includes model output, including all variables used in the paper. The description of each variable is indicated in the nc file header. Each nc file is an output of a single time step in the whole model domain, where the simulation timestep is indicated as (time) at the end of each file (e.g., 000001440). The model timestep (dt) is 0.5s. </p> <p>Files named SCMS_REVISON_200_(time)_mod.nc are the results from simulation with a linear eddy model (LEM) and SCMS_REVISION_NOLEM_200_(time)_mod.nc are the results from the simulation without a LEM.</p> <p> </p>
Model output for "Enrichment of ammonium in the future ocean threatens diatom productivity"
<p>Each netcdf file (.nc) contains model output from simulations performed with the<br> NEMO-PISCES global ocean-biogeochemistry model. These simulations were<br> forced by physical output from the IPSL-CM5A Earth System Model, which <br> performed both the natural (no anthropogenic activities) and RCP8.5 scenarios.</p> <p>Variables in spin-up "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p> </p> <p>Variables in scenario "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> ZOO (Micro)Zooplankton Concentratio 1:360 1:180 1:31 1:12<br> ZOO2 Mesozooplankton Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> DOC Dissolved organic Concentration 1:360 1:180 1:31 1:12<br> POC Small organic carbon Concentrat 1:360 1:180 1:31 1:12<br> GOC Big organic carbon Concentratio 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> PHY_15 15N Nanophytoplankton Concentra 1:360 1:180 1:31 1:12<br> PHY2_15 15N Diatoms Concentration 1:360 1:180 1:31 1:12<br> DOC_15 15N Dissolved organic Concentra 1:360 1:180 1:31 1:12<br> POC_15 15N Small particulate Concentra 1:360 1:180 1:31 1:12<br> GOC_15 15N Large particulate Concentra 1:360 1:180 1:31 1:12<br> ZOO_15 15N Microzooplankton Concentrat 1:360 1:180 1:31 1:12<br> ZOO2_15 15N Mesozooplankton Concentrati 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p>Variables in the scenario "diad" files are:</p> <p> name title I J K L<br> PH PH 1:360 1:180 1:31 1:12<br> HEUP Euphotic layer depth 1:360 1:180 ... 1:12<br> PAR Photosynthetically Available Ra 1:360 1:180 1:31 1:12<br> PARDM Daily mean PAR 1:360 1:180 1:31 1:12<br> PPPHYN Primary production of nanophyto 1:360 1:180 1:31 1:12<br> PPPHYD Primary production of diatoms 1:360 1:180 1:31 1:12<br> PPNEWN New Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNEWD New Primary production of diato 1:360 1:180 1:31 1:12<br> PPNO2N NO2 Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNO2D NO2 Primary production of diato 1:360 1:180 1:31 1:12<br> NITRNH4 Ammonia-oxidation rate (NH4-->N 1:360 1:180 1:31 1:12<br> NITRNO2 Nitrite-oxidation rate (NO2-->N 1:360 1:180 1:31 1:12<br> MUAOA Growth rate of ammonia oxidiser 1:360 1:180 1:31 1:12<br> MUAOAMAX Max potential ammonia oxidation 1:360 1:180 1:31 1:12<br> LAOANH4 Substrate limitation of NH4 oxi 1:360 1:180 1:31 1:12<br> LAOAFER Iron limitation of NH4 oxidatio 1:360 1:180 1:31 1:12<br> LAOAPAR Light limitation of NH4 oxidati 1:360 1:180 1:31 1:12<br> LAOAPH pH limitation of NH4 oxidation 1:360 1:180 1:31 1:12<br> LNOBNO2 Substrate limitation of NO2 oxi 1:360 1:180 1:31 1:12<br> LNOBFER Iron limitation of NO2 oxidatio 1:360 1:180 1:31 1:12<br> LNOBPAR Light limitation of NO2 oxidati 1:360 1:180 1:31 1:12<br> NFIX Nitrogen fixation 1:360 1:180 1:31 1:12<br> RIVER_NO3<br> Nitrate added by rivers 1:360 1:180 ... 1:12<br> NDEP_NO3 Nitrate added by deposition 1:360 1:180 ... 1:12<br> REMIN Oxic remineralization of OM (DO 1:360 1:180 1:31 1:12<br> EXCR1 Excretion by microzooplankton 1:360 1:180 1:31 1:12<br> EXCR2 Excretion by mesozooplankton 1:360 1:180 1:31 1:12<br> DENITNO3 Denitrification rate (NO3-->NO2 1:360 1:180 1:31 1:12<br> DENITNO2 Denitrification rate (NO2-->N2) 1:360 1:180 1:31 1:12<br> ANAMMOX Anaerobic oxidation of NH4 (NH4 1:360 1:180 1:31 1:12<br> ALTREM Alternative anaerobic remin (DO 1:360 1:180 1:31 1:12<br> SDEN3D Sed denitrification of OM (NO3- 1:360 1:180 1:31 1:12<br> SREM3D Sed remineralisation of OM (DOC 1:360 1:180 1:31 1:12<br> <br> Files:</p> <ul> <li> ETOPO_nitr_kaoafer00_1m_ptrc.nc</li> <li> ETOPO_nitr_kaoafer00_1m_diad.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_diad_2081-2100_ave.nc</li> </ul> <p> </p> <p>Naming convention:<br> "ETOPO" - refers to being on a regular 1x1 degree horizontal grid<br> "nitri" - refers to the developed PISCES model with explicit two-step nitrification<br> "kaoafer00" - refers to no iron limitation of AOA <br> "1m" - refers to the timestep resolution, here 1 month. Thus, all data presented here is monthly averaged values.<br> "2ndpicontrol" - refers to preindustrial control run<br> "acid" - refers to the control run + ocean acidification<br> "warm" - refers to the control run + warming<br> "circ" - refers to the control run + circulation change<br> "full" - refers to the control run + ocean acidification + warming + circulation change<br> "picontrolalt" - refers to preindustrial control run (alternative pH parameterisation)<br> "acidalt" - refers to the control run + ocean acidification (alternative pH parameterisation)<br> "fullalt" - refers to the control run + ocean acidification + warming + circulation change (alternative pH parameterisation)<br> <br> Contact: Pearse.Buchanan@liverpool.ac.uk or pbuchanan@carnegiescience.edu</p> <p> </p>
Synaptic gradients transform object location to action: data and outputs
<p>Data an outputs associated with the following article:</p> <p>Dombrovski, M., Peek, M.Y., Park, JY. <em>et al.</em> Synaptic gradients transform object location to action. <em>Nature (2023)</em>. <a href="https://doi.org/10.1038/s41586-022-05562-8">https://doi.org/10.1038/s41586-022-05562-8</a></p> <p>and the following software:</p> <p><a href="https://github.com/avaccari/DrosophilaVPNWiring">https://github.com/avaccari/DrosophilaVPNWiring</a></p>
Code for the publication "DOSE - Global data set of reported sub-national economic output"
<p>The zipped file of this repository contains code and auxiliary data to reproduce the results of the publication:</p> <p>Wenz et al, "DOSE - Global data set of reported sub-national economic output"</p> <p>The respective DOSE database, version2 can be found here: <a href="http://doi.org/10.5281/zenodo.4681305">https://zenodo.org/record/7573249#.Y_RYTnbMI2w</a></p> <p>Please see the README document for descriptions of the required dependencies.</p>
MAGICC7 SSP carbon cycle output using AR6 tuning
<p>Output from the MAGICC7 carbon cycle model under the SSP scenarios with the configuration used in IPCC AR6. For further details on MAGICC and expectations re use of data and the model, please see https://magicc.org/download/magicc7.</p>
Bioclimatic outputs for Last Glacial Maximum South America for LPX, bias-corrected to pollen records
<p>LPX model output for South America for the Last Glacial Maximum. We provide outputs for LPX driven by four GCM simulated climates along with an ensemble average:</p> <ul> <li>MIROC.tar.gz: LPX driven by MIROC3.2</li> <li>FGOALS.tar.gz: driven by FGOALS-1.0g</li> <li>HAD.tar.gz: HadCM3M2</li> <li>CNRM.tar.gz: CNRM-CM33</li> <li>Ensemble.tar.gz: The mean of each output variable for the four models.</li> </ul> <p> </p> <p>Driving data comes from the Palaeoclimate Modelling Intercomparison Project Phase II (PMIP2)<sup>1,2</sup>. See Sato et al. <sup>3</sup> for modelling protocol.</p> <p>Each model’s directory contains “uncorrected” and “corrected” directories. With each of these are bioclimatic maps outputted from LPX and biome information:</p> <ul> <li>fpc.nc: fractional projected cover of all vegetation</li> <li>height.nc: mean height of vegetations</li> <li>gdd.nc: Growing Degree Days base 2</li> <li>tropical.nc: proportion of vegetated areas taken up by tropical trees and c4 grasses</li> <li>temperate.nc: proportion of vegetated areas taken up by temperate trees and c3 grasses</li> <li>evergreen.nc: proportion of tree cover that is composed of evergreen trees</li> <li>biome.nc: the assigned biomes from these data, based on a modified version of Sato et al. <sup>3</sup>. See “biomisation” below.</li> <li>cluster.nc: In “corrected” only. The spatial location of kmean clusters of fpc vs height. See <sup>4</sup> for details.</li> </ul> <p> </p> <p>“Uncorrected” is from Sato et al. <sup>3</sup>. “Corrected” is bias-corrected to match the 42 pollen-core observations taken from Marchant et al. <sup>5</sup> We do this by shifting the total vegetation cover and composition, height, and growing degree day (GDD) DVM output to the closest boundary of the corresponding biome of the pollen core in that specific location. We then extrapolate this correction between pollen-core locations across the Neotropics. See Kelley et al. <sup>4</sup> for details.</p> <p> </p> <p><strong>Biomeisation</strong></p> <p>"Biomeisation.png" displays the scheme. We primarily split biomes by FPCs of 0.3 and 0.6, with biomes > 0.6 split by a height of 10m. Forests (>0.6 FPC and > 10m) is split by GDD, Evergreen FPC (EG) and Tropical or temperate FPC (TR, TM). Likewise, we split FPCs> 0.6 and heights <10m into savanna, woodland and parkland using EG and TR. We additionally assign Tropical savanna >5m to Woodland/Tropical savanna. We divided desert, dry grassland and (shrub)-tundra by FPC of 0.3 and GDD of 350°C. See Kelley et al. <sup>4</sup> for details.</p>
Replication data in the form of simulation outputs for "The joint evolution of animal movement and competition strategies"
<p>This version of the data is a re-upload of an identical dataset uploaded to DataverseNL. The data are being uploaded here for uniformity of archiving with the other supplementary materials accompanying the manuscript, "The joint evolution of animal movement and competition strategies", which is accepted for publication in <em>The American Naturalist</em>.</p> <p>The dataset consists of a single zipped folder, data/, which contains subfolders with the simulation specific data. These subfolders are named 'sim_SCENARIO_rep_NNN_gro_RMAX', where 'SCENARIO' refers to the three scenarios of the model described in our manuscript, 'NNN' is the replicate number, and 'RMAX' is the maximum cell productivity in that simulation.</p> <p>Each of the subfolders consists of the following:</p> <ol> <li>The directory ‘depends/’, which holds the ‘extract.exe’ program. This program is used to extract specific data from the stored simulation output.</li> <li>The ‘sourceMe.R’ file allows the ‘extract.exe’ program to be linked to R, delivering the required data as a list object that can be handled in R.</li> <li>The stored simulation data on agents, as a series of ‘.arc’ and ‘.bin’ files.</li> <li>The ecological snapshots of the landscape, with prey items, foragers, kleptoparasites, and handlers (depending on the scenario), which are stored as PNG files named ‘00NNN.png’, where NNN is a three digit representation of the generation number (e.g. 001 for generation 1).</li> <li>Cumulative sums of the numbers of prey items, foragers, kleptoparasites, the intake from foraging (searching for prey), and the intake from the kleptoparasitic strategy (searching for handlers), on each cell of the landscape, for each of the last 9 generations of the simulation, as ‘NNN.txt’, where LAYER may be ‘items’ (prey items), ‘foragers’ (foragers), ‘klepts’ (kleptoparasites), ‘foragers_intake’ (the intake from the foraging strategy), or ‘klepts_intake’ (the intake due the kleptoparasite strategy. NB: These files are not used in our analyses, and may be ignored.</li> </ol> <p>'SCENARIO' may be one of “foragers” (scenario 1), “obligate” (scenario 2), or “facultative” (scenario 3).</p> <p>'NNN' may be one of “001”, “002”, or “003”.</p> <p>'RMAX' may be one of “0.001”, “0.005”, “0.01”, “0.02”, “0.03”, “0.04”, or “0.05”. The manuscript presents results for 'RMAX' = 0.01.</p>
Output files of the computationally reviewed literature data on enantiomeric metabolism
<p>The computationally reviewed literature data on enantiomeric metabolism. The chiral drug selection based on quantum chemistry's role in the enantiomeric metabolism.</p>
Outputs of current speed and sea otter abundance models in Glacier Bay, Alaska
<p>Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator-prey interaction between sea otters and butter clams <em>(Saxidomus</em> <em>gigantea</em>), one of the dominant large bivalves in Glacier Bay and a favored prey of sea otters. To do so, we developed a modeling framework to account for both bottom-up and top-down drivers of butter clam abundance and dynamics. For the bottom-up driver, we used the root-mean-square current speed (m/s) predicted by a tidal circulation model of Glacier Bay developed by Drew <em>et al. </em>(2013). For top-down sea otter dynamics, we used the posterior mean sea otter abundance estimates from Lu <em>et al. </em>(2019). This repository contains the current speed raster (100m x 100m resolution) produced by Drew <em>et al. </em>(2013) and the files and model output from Lu <em>et al.</em> (2019) necessary to generate a time series of rasters (400m x 400m resolution raster brick with 26 layers for the years 1993-2018) of estimated posterior mean sea otter abundance. These data layers are used in Leach <em>et al. </em>(2023) to model butter clam dynamics at sampling sites across Glacier Bay.</p>
BioStars Posts API Output
<p>A dataset is extracted from BioStars (https://www.biostars.org/). Biostars is a question and answer forum that focuses on bioinformatics, computational genomics, and biological data analysis. </p> <p>This dataset contains output from the BioStars API (https://www.biostars.org/info/api/) through post ID (UID) 9557161. </p> <p>This dataset is licensed with the same license as BioStars content: https://www.biostars.org/info/about/ </p> <p>The author of this Zenodo dataset has no affiliation with the BioStars team. </p> <p>NOTE: No content was available for IDs from 9463943 to 494831. </p>
Selected simulation input and output for patchoulol synthase and germacradien-11-ol synthase
<p><em><strong>Files related to the biomolecular simulations in the following publication:</strong></em></p> <p><strong>Active site loop engineering abolishes water capture in hydroxylating sesquiterpene synthases</strong></p> <p>Prabhakar L. Srivastava,<sup>[a]</sup> Sam T. Johns,<sup>[b]</sup> Rebecca Walters,<sup>[b]</sup> David J. Miller,<sup>[a]</sup> Rudolf K. Allemann*<sup>[a]</sup> and Marc W. Van der Kamp*<sup>[b]</sup></p> <p> </p> <p><sup>[a]</sup>School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom</p> <p><sup>[b]</sup>School of Biochemistry, University of Bristol, University Walk, Bristol BS8 1TD, United Kingdom</p> <p><br> See the README.docx document for details.</p>
Theoretical DFT calculation based output file of esters in water
<p>The presented theoretical DFT study resolves the question of why the hydrolysis of simple ester can occur in acidic as well as basic medium while that of S-naproxen ester in acidic medium only.</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases -- Output Files
<p>These are the output files generated using the input files and Galaxy workflows for coronavirus helicase simulations, from: </p> <pre>https://doi.org/10.5281/zenodo.7492987</pre>
Simulating the Laurentide Ice Sheet of the LGM (Datasets from Yelmo_v1.751 output simulations)
<p>Model output presented in Moreno-Parada, D., Alvarez-Solas, J., Blasco, J., Montoya, M., and Robinson, A.: Simulating the Laurentide ice sheet of the Last Glacial Maximum.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.