Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,157
datasets available to search
ShareScore release 0.9.0
Dataset results
1,157 results for “Limited data”
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Code and data: Slush limits of the western flank of the Greenland Ice Sheet, mapped from MODIS, 2000 - 2021
<p>Code and output of the slush limit detection for the western flank of the Greenland Ice Sheet, as described in Machguth, H., A. Tedstone and E. Mattea (2022), <strong>Daily Variations in Western Greenland Slush Limits, 2000 to 2021, mapped from MODIS</strong>, <em>Journal of Glaciology, </em>https://doi.org/10.1017/jog.2022.65<em>.</em></p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
Measurement data of a three-phase grid-side converter with a grid-forming synchronverter-based control method with current limitation
<p>The data set was recorded for a publication currently undergoing the submission process. The published data correspond to the data presented in the figures. The first column represents the time vector. All other columns are linked to the corresponding scenario by an identifier in the column name. The column name also contains the name of the recorded signal and the associated unit. The naming convention is <identifier_to_figure>_<recorded_signal>_<unit>.</p>
Supporting data for "Fundamental limitations of cavity-assisted atom interferometry"
<p>Supporting data with code to generate Fig. 2 Cavity-induced deformation of a Gaussian input. Publication: DOI:https://doi.org/10.1103/PhysRevA.96.053820</p> <p>arXiv:1710.02448</p> <p>This dataset contains a zip file with raw data sets of all relevant measurements to plot figure 2.</p> <p>Figure 2. Envelope functions of the intracavity field for a 1 m cavity injected with<br> a 1μs pulse for different cavity finesses. All areas are normalized<br> to the input pulse area for comparison. When the pulse duration is<br> comparable to the photon lifetime of the cavity, its envelope function<br> is elongated. Inset: Envelopes without normalization.</p> <p> </p> <p>Further data and information are available from Miguel Dovale <mdovale@star.sr.bham.ac.uk> at reasonable request.</p> <p>School of Physics and Astronomy and Institute of Gravitational Wave Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom</p>
New limit on dark photon dark matter from searches using LIGO O1 data.
<p>The three files contain the three limits in Fig.4 of the published paper.</p> <p>For each file, there are 2 columns of data, with the first column being the frequency in Hz and the second column the</p> <p>corresponding upper limit on the dimensionless dark photon squared coupling.</p> <p>limit_red.txt: the red line, O1 95% CL limits (1800s SFTs) </p> <p>limit_blue.txt: the blue line, average optimal O1 limits (893 hours)</p> <p>limit_yellow.txt: the yellow line, Nominal SNR O1 95% CL limits</p>
Climate change impact and mitigation cost data - The economically optimal warming limit of the planet
<p>This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:</p> <p>Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. <a href="https://doi.org/10.5194/esd-10-741-2019">https://doi.org/10.5194/esd-10-741-2019</a></p> <p>Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).</p> <p> </p> <p><strong>Climate change impact data</strong></p> <p>File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.</p> <p> </p> <p>File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p> </p> <p>File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv</p> <p>Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).</p> <p><br> In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, <a href="https://tntcat.iiasa.ac.at/SspDb/">https://tntcat.iiasa.ac.at/SspDb/</a>, last accessed 15Nov 2019).</p> <p> </p> <p><strong>Climate change mitigation cost data</strong></p> <p>The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].</p> <p>File 4: REMIND_scenario_results_economic_data.csv</p> <p>File 5: REMIND_scenarios_climate_data.csv</p> <p>Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.</p> <p>In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.</p> <p>The first dimension specifies the climate policy regime (delayed action, baseline scenarios):</p> <p>1xx: climate action from 2010<br> 5xx: climate action from 2015<br> 2xx climate action from 2020 (used in this study)<br> 3xx climate action from 2030<br> 4x1 weak policy baseline (before Paris agreement)</p> <p>The second dimension specifies the technology portfolio and assumptions:</p> <p>x1x Full technology portfolio (used in this study)<br> x2x noCCS: unavailability of CCS<br> x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed<br> x4x NucPO: phase out of investments into nuclear energy<br> x5x Limited SW: penetration of solar and wind power limited<br> x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)<br> x6x noBECCS: unavailability of CCS in combination with bioenergy</p> <p>The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).</p> <p>xx1 0$/tCO2 (baseline)<br> xx2 10$/tCO2<br> xx3 30$/tCO2<br> xx4 50$/tCO2 <br> xx5 100$/tCO2<br> xx6 200$/tCO2<br> xx7 500$/tCO2<br> xx8 40$/tCO2<br> xx9 20$/tCO2<br> xx0 5$/tCO2</p> <p>For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).</p> <p> </p> <p>[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.</p> <p>[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.</p>
Supplemental Data for "Stability of fractional Chern insulators with a non-Landau level continuum limit"
<p>Scripts and data to supplement the paper "Stability of fractional Chern insulators with a non-Landau level continuum limit".</p>
SU(2) gauge theory with one and two adjoint fermions towards the continuum limit—data release
<p>This package contains all data generated in preparing the publication <a href="https://arxiv.org/abs/2408.00171">SU(2) gauge theory with one and two adjoint fermions towards the continuum limit</a>. It includes four classes of data:</p> <ol> <li>Raw data, as generated from the measurement code running on HPC, in their native formats (raw_data.zip).</li> <li>Metadata around the analysis of the ensembles, in YAML format (ensembles.yaml).</li> <li>Data obtained by analysing the above data and presented in <a href="https://arxiv.org/abs/2408.00171">arXiv:2408.00171</a>, for specific ensembles, in sqlite3 format (su2.sqlite).</li> <li>The above data in (3), and additional data obtained by further analysing them, in CSV format (ensemble_results.csv and gammastar_results.csv).</li> <li>For convenience, the data in (1) above, repackaged in HDF5 format (package.h5).</li> </ol> <p>Each of these is documented in more detail in the file README.md.</p> <p>Due to their size, raw gauge configurations are not included in this package.</p> <p> </p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a vertical concentrated live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from: Oxygen limited thermal tolerance is seen in a plastron breathing insect, and can be induced in a bimodal gas exchanger
<p>Dataset on respiration and ctmax in two freshwater bugs, associated with the paper:<br> <strong>Verberk WCEP & Bilton DT (2015) </strong>Oxygen limited thermal tolerance is seen in a plastron breathing insect, and can be induced in a bimodal gas exchanger. <em>Journal of Experimental Biology </em>218: 2083-2088. doi: 10.1242/jeb.119560</p>
Experimental data of "Magneto-exciton limit of quantum Hall breakdown in graphene"
<p>These files are the measurements presented in A. Schmitt et al. manuscript. AuS2_xxT.csv files are the breakdown measurements with current and noise function of bias. Each file is obtained for a different magnetic field in teslas (T).</p> <p>The low-bias_AuS2.csv file contains all the data for the low bias fan-charts of fig. 1</p>
Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases
<p>These data were used for the development of the paper "<strong>Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases</strong>". Especifically, we added ecological and environmental data that were used for modeling.</p>
Data from: The genomics and evolution of inter-sexual mimicry and female-limited polymorphisms in damselflies
<p>The dataset contains intermediate output files required to reproduce the figures in the main text and Supporting Material of Willink <em>et al</em>. 2023. The genomics and evolution of inter-sexual mimicry and female-limited polymorphisms in damselflies.</p> <p>FILE OVERVIEW:</p> <p>1. Morph-specific assemblies<br> A. File names: Afem_1354_ragtag.fasta.gz, Ifem_1049_ragtag.fa.gz, Ofem_0081_ragtag.fa.gz, O054_Shasta_run2.PMDV.HAP1.purged.fasta.gz, A059_Shasta_run1.PMDV.HAP1.purged.fa.gz<br> B. Description: genome assemblies for different morphs of <em>Ischnura elegans</em> (Afem_1354, Ifem_1049, and Ofem_0081) and <em>Ischnura senegalensis</em> (A059 and O054), generated in this study from long-read Nanopore data using Shasta v 0.7.0 (https://github.com/paoloshasta/shasta).</p> <p>2. Assembly statistics<br> A. File names: Assembly_statistics.csv, Assembly_statistics_sen.csv<br> B. Description: Completeness and quality metrics for <em>de novo</em> genome assemblies of <em>I. elegans</em> and <em>I. senegalensis</em> female morphs. See Fig. S1-S2.</p> <p>3. Repetitive content annotation<br> A. File names: A1354_ragtag_RED.bed.repeats.bed.gz, Afem_Shasta1_polished_ragtag_UPPER.fa.out.gz, Ifem_Shasta2_polished_ragtag_UPPER.fa.out.gz, ioIscEleg1.1.primary_UPPER.fa.out.gz, ToL_RED.repeats.bed.gz<br> B. Description: Annotation of repetitive sequences in morph-specific assemblies. All morph assemblies (A, I and Darwin Tree of Life assemblies) were annotated using RepeatModeler v 2.0.1 and RepeatMasker v 1.0.93 (http://www.repeatmasker.org). The A morph and DToL assemblies were additionally annotated using Red v 0.0.1 (https://github.com/BioinformaticsToolsmith/Red). RepeatMasker annotations were then used to estimate TE coverage. See Extended Data Fig. 4 and Fig. S7.</p> <p>4. GWAS output<br> A. File names: A1354_ragtag_AvI.assoc_filtered.txt.gz, A1354_ragtag_AvO.assoc_filtered.txt.gz, A1354_ragtag_IvO.assoc_filtered.txt.gz, ToL_AvI.assoc_filtered.txt.gz, ToL_AvO.assoc_filtered.txt.gz, ToL_IvO.assoc_filtered.txt.gz<br> B. Description: filtered SNPs in pairwise association tests between morphs (n = 19 resequencing samples per morph) of<em> I. elegans</em>. Analyses were conducted in PLINK v 1.9 (http://pngu.mgh.harvard.edu/purcell/plink/), using either the A morph assembly (Fig. 2a-b), or the Darwin Tree of Life (DToL) reference assembly (Extended Data Figure 8a-b) as mapping reference.</p> <p>5. Population statistics<br> A. File names: Afem_pixy_30K_fst.txt.gz, A1354_30kb.Tajima.D.gz, Afem_pi_30K_pi.txt.gz, ToL_30K_fst.txt.gz, ToL_30kb.Tajima.D.gz, ToL_30K_onepop_pi.txt.gz<br> B. Description: Genetic differentiation (fst) between morphs, Tajima's D statistics, and nucleotide diversity across 30 kb windows of the<em> I. elegans</em> genome. Population statistics were computed using either the A morph assembly (Fig. 2c-e), or the DToL reference assembly (Extended Data Figure 8c-e) as mapping reference.</p> <p>6. k-mer based GWAS<br> A. File names: AvI_kmers.fa.gz, AvO_kmers.fa.gz, OvAI_kmers.fa.gz, AvI_kmers.fa_v_A1354_Shasta_run1_table.tsv.gz, AvO_kmers.fa_v_A1354_Shasta_run1_table.tsv.gz, OvAI_kmers.fa_v_A1354_Shasta_run1_table.tsv.gz, OvAI_kmers.fa_v_Ifem_1049_ragtag_table.tsv.gz<br> B. Description: List of significant k-mers (in fasta format) in three k-mer based association analyses (n = 19 resequencing samples per morph) between morphs of<em> I. elegans</em>. Significant k-mers were then mapped to morph-specific assemblies using Blast v 2.22.28 (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for short sequences. We include mapping results shown in Fig. 3a-b.</p> <p>7. Read-depth coverage<br> A. File names: reseq_coverage_norepeat_500_window.bed.gz, nano_coverage_norepeat_500_window.bed.gz, Ifem_nano_coverage_norepeat_500_window.bed.gz, Ifem_reseq_coverage_norepeat_500_window_15Mb.bed.gz, poolseq_coverage_norepeat_500_window.bed.gz, morph_coverage_norepeat_diff_500.tsv.gz, SwD_popmap<br> B. Description: Read depth coverage of the morph locus and a 15 mb region used to estimate baseline read depths. 19 Illumina resequencing samples, and one long-read Nanopore sample of each morph of <em>I. elegans</em> were mapped to both the A and I assemblies to estimate read depth. Two poolseq samples (each pool consisting of 30 females of each morph) of<em> I. senegalensis</em> were mapped to the A assembly of<em> I. elegans</em> to estimate read depth. Read depth was estimated in mosdepth v 0.2.8 (https://github.com/brentp/mosdepth) across 500 bp windows after filtering windows with more than 10% repetitive content. For poolseq samples, the difference in coverage values between the A and O pools was computed across the entire genome. Sample information for resequencing samples is recorded in the file SwD_popmap. See Fig. 3c-d, 5b, and S8.</p> <p>8. Assembly alignment<br> A. File names: nucmer_aln_Ifem_1049_ragtag_Afem_1354_ragtag.qr1_filter.reformat.coords.gz, nucmer_aln_Ofem_0081_ragtag_Afem_1354_ragtag.qr1_filter.reformat.coords.gz, nucmer_aln_Afem_Isen_Afem_Iele.qr1_filter.reformat.coords.gz, nucmer_aln_Ofem_Isen_Afem_Iele.qr1_filter.reformat.coords.gz, karyotype_AI_RagTag.csv, karyotype_AO_RagTag.csv, karyotype_AIsen_AIele.cs, karyotype_OIsen_AIele.csv<br> B. Description: Assembly alignments using nucmer v 4.0.0 (https://github.com/mummer4/mummer) and contig synteny for plotting using RIdeogram v 0.2.2 (https://cran.r-project.org/web/packages/RIdeogram/vignettes/RIdeogram.html) in R v 4.2.2 (https://www.r-project.org/). The A morph assembly of <em>I. elegans</em> was aligned to the I and O morph assemblies of<em> I. elegans</em> and to the A and O-like assemblies of <em>I. senegalensis</em>. See Fig. 4a, 5c.</p> <p>9. Genotyping the Darwin Tree of Life assembly<br> A. File names: nucmer_aln_Afem_ragtag_ToL-haplotigs.qr1_filter.reformat.coords.gz, nucmer_aln_Afem_ragtag_ToL-primary.qr1_filter.reformat.coords.gz, ToL_500_norepeat.regions.bed.gz, karyotype_AToL_13_unloc_RagTag.csv, karyotype_AToL_RagTag_haplotigs.csv<br> B. Description: To genotype the DToL reference assembly of<em> I. elegans</em>, we estimated read-depth coverage of the DToL long-read Pacbio data mapped to the A morph assembly of <em>I. elegans</em> generated in this study, and aligned the A morph assembly to both the primary DToL assembly and to the purged haplotigs. Read depth was estimated in mosdepth v 0.2.8 (https://github.com/brentp/mosdepth) and assembly alignments were conducted using nucmer v 4.0.0 (https://github.com/mummer4/mummer). See Fig. S3.</p> <p>10. SV calling<br> A. File names: A_to_A.bam, A_to_A.bam.bai, A_to_I.bam, A_to_I.bam.bai, A_to_O.bam, A_to_O.bam.bai, A_to_ToL_2mb.bam, A_to_ToL_2mb.bam.bai, I_to_A.bam, I_to_A.bam.bai, I_to_I.bam, I_to_I.bam.bai, I_to_O.bam, I_to_O.bam.bai, I_to_ToL_2mb.bam, I_to_ToL_2mb.bam.bai, O_to_A.bam, O_to_A.bam.bai, O_to_I.bam, O_to_I.bam.bai, O_to_O.bam, O_to_O.bam.bai, O_to_ToL_2mb.bam, O_to_ToL_2mb.bam.bai<br> B. Description: mergede alignements of resequencing samples (n = 19 per morph) to alternative reference assemblies (A, I, O, and DToL) for<em> I. elegans</em>. The alignments have been filtered by quality and to contain only the unlocalized scaffold 2 of chromosome 13, which includes the morph locus. These files were used to call morph-specific structural variants using samplot v 1.3.0 (https://github.com/ryanlayer/samplot). See Extended Data Figs 2, 7, and Fig. S5-S6.</p> <p>11. Mapping of inversion breakpoint reads<br> A. File names: AvO_3K.tsv.gz, AvO_22K.tsv.gz, AvO_sen_3K.tsv.gz, AvO_sen_22K.tsv.gz, IvO_3K.tsv.gz<br> B. Description: Signatures of an inversion with breakpoints at ~ 3 kb and ~ 22 kb of the unlocalized scaffold 2 of chromosome 13 on the O assembly were found in A and I resequencing samples of <em>I. elegans</em> and in poolseq samples of A females of <em>I. senegalensis</em>. We queried the reads mapping to the inversion breakpoints and then tabulated their mapping locations of the A morph assembly of<em> I. elegans</em> (Fig. 6 and Extended Data Fig. 3, 7b-c). For the first inversion breakpoint, we also mapped reads on the I morph assembly of <em>I.</em> elegans (Fig. S12).</p> <p>12. Evidence of translocation in I<br> A. File names: Ifem_nano_SUPER_13_unloc_2.bam, Ifem_nano_SUPER_13_unloc_2.bam.bai<br> B. Description: Long-read Nanopore data of a I morph female of <em>I. elegans</em> mapped to the A morph of <em>I. elegans</em> and filtered to contain the entire unlocalized scaffold 2 of chromosome 13. Read mapping was conducted in minimap2 v 2.22-r1110 (https://github.com/lh3/minimap2) and used to identify a translocation signature in the I morph, relative to the A morph of <em>I. elegans</em>. See Extended Data Fig. 6.</p> <p>13. PCA output<br> A. File names: A1354_all.eigenval, A1354_all.eigenvec, I1049_all.eigenval, I1049_all.eigenvec<br> B. Description: Eigenvectors and eigenvalues of PCA analyses of population structure between morphs of <em>I. elegans</em>. PCA analysis were conducted on morph locus, using either the A morph or the I morph assembly as mapping reference in PLINK v 1.9 (http://pngu.mgh.harvard.edu/purcell/plink/). See Fig. S4.</p> <p>14. Linkage disequilibrium<br> A. File names: A1354_SUPER_1_allr.ld.gz, A1354_SUPER_2_allr.ld.gz, A1354_SUPER_3_allr.ld.gz, A1354_SUPER_4_allr.ld.gz, A1354_SUPER_5_allr.ld.gz, A1354_SUPER_6_allr.ld.gz, A1354_SUPER_7_allr.ld.gz, A1354_SUPER_8_allr.ld.gz, A1354_SUPER_9_allr.ld.gz, A1354_SUPER_10_allr.ld.gz, A1354_SUPER_11_allr.ld.gz, A1354_SUPER_12_allr.ld.gz, A1354_SUPER_13_allr.ld.gz, A1354_SUPER_13_unloc_1_allr.ld.gz, A1354_SUPER_13_unloc_2_allr.ld.gz, A1354_SUPER_13_unloc_3_allr.ld.gz, A1354_SUPER_13_unloc_4_allr.ld.gz, A1354_SUPER_X_allr.ld.gz<br> B. Description: Estimates of recombination rate (R2) between SNPs across the first 15 mb of each chromosome and unlocalized segments of chromosome 13 of <em>I. elegans</em>. Recombination rates were estimated based on 57 resequencing samples and using the A morph assembly as mapping reference in PLINK v 1.9 (http://pngu.mgh.harvard.edu/purcell/plink/). See Extended Data Fig. 5.</p> <p>15. Gene annotations<br> A. File names: Afem_all_ragtag.gtf.gz, Afem_all_transcripts.transdecoder.genome.gff3.gz, Isen.gtf.gz<br> B. Description: Annotation of the A morph assembly of <em>I. elegans</em> using RNAseq data to assemble transcripts <em>de novo </em>for <em>I. elengans</em> and <em>I. senegalensis</em> in Stringtie v 2.1.4 (https://ccb.jhu.edu/software/stringtie/). Peptide sequences for the <em>I. elegans</em> transcripts were then predicted using Transdecoder v 5.5.0 (https://github.com/TransDecoder/TransDecoder).</p> <p>16. Gene annotations in the morph locus<br> A. File names: gene_models_shared_trancripts_simple.csv, gene_models_shared_trancripts_simple_I.csv<br> B. Description: locations of exon features for genes in the morphs locus and expressed in at least one adult sample of both<em> I. elegans</em> and <em>I. senegalensis</em>. Locations are given for the A and I assemblies. See Fig. 6 and S12.</p> <p>17. Gene expression<br> A. File names: DToL_gene_count_matrix.csv.gz, DToL_transcript_count_matrix.csv.gz, gene_count_matrix.csv.gz, transcript_count_matrix.csv.gz, Isen_gene_count_matrix.csv.gz, Isen_transcript_count_matrix.csv.gz, Iele_phenodata.csv, Isen_phenodata.csv<br> B. Description: Sample information (phenodata), gene and transcript count matrices for gene expression analysis. For <em>I. elegans</em>, gene expression was quantified on thoracic tissue of six adult females of each morph and six adult males (three sexually mature and three sexually immature in each group). Reads were mapped to both the A morph assembly and the DToL reference assembly. For <em>I. senegalensis</em>, we used previously published data (NCBI BioProject PRJDB11387) from different tissues of adult females of each morph and males (one upon emergence and one two days after emergence for each group) mapped to the A morph assembly. Gene and transcript counts were generated using Stringtie v 2.1.4 (https://ccb.jhu.edu/software/stringtie/). See Fig. 6, S9-S11, S13.</p> <p>18. SNPs in the morph locus<br> A. File names: A1354-ragtag-allsites-candidate_gene_cds.vcf.gz, A1354-ragtag-allsites-candidate_gene_cds.vcf.gz.tbi, vcf_popmap<br> B. Description: SNPs in 57 resequencing samples across coding sequences of the morph locus of <em>I. elegans</em>. The A morph assembly was used as mapping reference. Sample information for resequencing samples is recorded in the file vcf_popmap. See Fig. S14a.</p> <p>19. Domains and orthologues of Gastrula zinc-finger transcription factor in the morph locus<br> A. File names: GZnf_domain_annot.csv, GZnF_orthologue.tre, GZnf_orthologue_annot.txt<br> B. Description: Functional domains were annotated using InterProScan (https://www.ebi.ac.uk/interpro/). The gene orthologue tree was inferred using OrthoFinder v 2.5.2 (https://github.com/davidemms/OrthoFinder). See Fig. S14.</p>
Data from: Genomic analysis reveals limited hybridization among three giraffe species in Kenya
<p>The data deposited here was generated by and reported in Coimbra <em>et al.</em> (2023).</p> <p><em>SNP calling and linkage pruning</em></p> <ul> <li><strong>snp_calling_per_species.tar.gz:</strong> includes a genotype likelihoods (GL) file estimated with ANGSD for each giraffe species.</li> <li><strong>sampled_ld.tar.gz:</strong> contains a random sample of estimated pairwise <em>r<sup>2</sup></em> values for each species used to fit linkage disequilibrium (LD) decay curves.</li> <li><strong>ld_pruned_snps.tar.gz:</strong> contains an LD-pruned ANGSD GL file per species.</li> <li><strong>snp_calling_combined.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all sampled individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Relatedness</em></p> <ul> <li><strong>relatedness.tar.gz:</strong> contains the input and output files used with NGSremix to estimate relatedness among giraffe in the dataset.</li> <li><strong>snp_calling_combined_unrelated.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all unrelated individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Population structure and admixture</em></p> <ul> <li><strong>pcangsd.tar.gz:</strong> contains the covariance matrix generated by PCAngsd.</li> <li><strong>ngsadmix.tar.gz:</strong> includes run likelihood lists for each K value ranging from 1 to 11, as well as the admixture proportions (stored in '.qopt' files) inferred from the run with the highest log-likelihood for each K in NGSadmix.</li> <li><strong>evaladmix.tar.gz:</strong> contains the pairwise correlation of residuals between individuals estimated with evalAdmix for the NGSadmix runs with the highest log-likelihood run for each K.</li> </ul> <p><em>SNP-based phylogenomic inference</em></p> <ul> <li><strong>snp_phylogeny.tar.gz:</strong> contains the input PHYLIP file and the IQ-TREE output tree and log files.</li> </ul> <p><em>Phylogeny of mitochondrial genomes</em></p> <ul> <li><strong>mtdna_phylogeny.tar.gz:</strong> includes the 13 mitochondrial protein-coding gene alignments, the partitions file, and the IQ-TREE output tree and log files.</li> </ul> <p><em>Inference of migration events</em></p> <ul> <li><strong>admixture_graphs.tar.gz:</strong> contains the TreeMix / OrientAGraph input file ('treemix.frq.strat.gz'), the output files for all TreeMix and OrientAGraph runs, and the OptM summary table of TreeMix runs ('optm.tsv').</li> </ul> <p><em>Test for introgression</em></p> <ul> <li><strong>dsuite_introgression.tar.gz:</strong> includes the input VCF, the admixture graph topology reconstructed by OrientAGraph, and the Dsuite output files for the estimation of Patterson's D, f4-ratio, and f-branch statistics.</li> </ul> <p><em>Contemporary migration rates</em></p> <ul> <li><strong>ba3-snps.tar.gz:</strong> contains the input and output files for the BA3-SNPs-autotune and BA3-SNPs runs.</li> </ul> <p><em>Demographic reconstruction</em></p> <ul> <li><strong>demographic_inference.tar.gz:</strong> includes the SFS files generated with ANGSD and realSFS and the StairwayPlot2 blueprint and output files.</li> </ul> <p>Other:</p> <ul> <li><strong>metadata.csv:</strong> a companion file containing sample information used in conjunction with R scripts to plot the figures in the paper.</li> </ul>
Chlorophyll data from experiments testing for nitrogen, phosphorus, and thiamine limitation of phytoplankton in 39 Ohio lakes of varying trophic status during the growing seasons (April – October) of 2008–2009
Although nitrogen and phosphorus deficiency of algal blooms have been the focus of substantial attention, organic nutrients can limit algal growth in aquatic systems. Growing evidence indicates thiamine (vitamin B1) can influence the community of primary producers in marine systems, but comparatively little is known about the effect of thiamine on freshwater algal productivity. We conducted 106 nutrient deficiency experiments with water from 39 Ohio lakes of varying trophic status during the growing seasons (April – October) of 2008–2009. Specifically, we tested the response of phytoplankton biomass (as chlorophyll a, chl-a) relative to controls to added nitrogen (N), phosphorus (P), thiamine (Th), or combinations of N+P and N+P+Th in integrated surface water collected from the inflow and outflow of each lake. The data presented here show the average chl-a of two replicate samples of each treatment (control, N, P, Th, N+P, N+P+Th), ratio of treatment/control response, and growth response ratio as ln(treatment chl-a/control chl-a). Each entry also includes lake surface water pH at time of collection and the initial chl-a concentration at time of experiment start.
Mammalian herbivores restrict the altitudinal range limits of three alpine grass species (transplant and herbivore exclusion experiment and demographic data from natural populations), West Elk Mountains, Colorado, USA 2015-2018
Though rarely experimentally tested, biotic interactions have long been hypothesized to limit low-elevation range boundaries of species. We tested the effects of herbivory on three alpine-restricted plant species by transplanting plants below (novel), at the edge (limit), or in the center (core) of their current elevational range and factorially fencing-out above- and belowground mammals in the West Elk Mountains, Colorado, USA from 2015-2018. Herbivore damage was greater in range limit and novel habitats than in range cores. Exclosures increased plant biomass and reproduction more in novel habitats than in range cores, suggesting demographic costs of novel interactions with herbivores. We then used demographic models to project population growth rates, which increased 5-20% more under herbivore exclosure at range limit and novel sites than in core habitats. Our results identify mammalian herbivores as key drivers of the low-elevation range limits of alpine plants and indicate that upward encroachment of herbivores could trigger local extinctions by depressing plant population growth.
The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data
<p>This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER</p>
ScienceDex guides
Understand access before you commit
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.