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86 results for “dark matter”
Dark Matter-Induced Stellar Oscillations
<p>Reproduction package for the paper "Dark Matter-Induced Stellar Oscillations".</p>
Collider Datasets for Simplified Dark Matter models
<p>This is a set of datasets containing MonoJet and DiJet interpolation grids. When using the simplified dark matter models in gambit, the backend "DMsimp_data" must be made first, which will download these datasets to the correct folder.</p> <p>If using monojet data outside of GAMBIT, please use the files in the DMsimp_monojet_data folder, which have been formatted with appropriate headings. Vector DM data is not included in this additional folder due to additional unitarity considerations required to use the data.</p> <p>If using this data, please cite this zenodo record, along with the gambit simplified model study (https://arxiv.org/abs/2209.13266).</p>
Data for: Dark matters – contrasting responses of stream biofilm to browning and loss of riparian shading
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Simulations of magnetized quark nugget dark matter in three-layer witness plate
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Dark matter multisensory experience: Voiceover (narrated by Gareth Mitchell)
<p>This is the audio component to the "Multisensory Dark Matter Experience" project. The audio (with no changes) is freely available and can be used (without modifications) at no charge, provided the authors are acknowledged in full. For further details about the experience, see: https://www.youtube.com/watch?v=zKRsjGqz5Ls </p> <p>The audio track is described in the following publication: </p> <p>Trotta, R., Hajas, D., Camargo-Molina, J. E., Cobden, R., Maggioni, E. and Obrist, M. (2020). ‘Communicating cosmology with multisensory metaphorical experiences’. JCOM 19 (02), N01. </p> <p><a href="https://doi.org/10.22323/2.19020801">https://doi.org/10.22323/2.19020801</a></p> <p> </p>
MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Spins
<p>MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo spin model of Benson, Behrens, & Lu (2020; https://arxiv.org/abs/2001.09208). See the README file for details.</p>
MCMC Chains and Maximum Likelihood Parameters for a Random Walk Model of Dark Matter Halo Concentrations
<p>MCMC chains and the maximum likelihood model parameter file associated with the random walk dark matter halo concentration model of Johnson, Benson, & Grin (2020; https://arxiv.org/abs/2006.15231). See the README file for details.</p>
Dark Matter Halo Merger Trees
<p>This dataset is generated from the Genesis suite of dark matter only N-body simulations (C. Power et al in prep). L10_N2048 is a periodic cubical simulation of side 10 h^-1 Mpc, with 2048^3 particles each of mass 1.0e4 h^-1 solar units. It is used as the initial condition for models presented in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240107396V" target="_blank" rel="noopener">E. M. Ventura et al. (2024)</a> S. Balu, along with input from C. Power ran the simulation using the SWIFT (<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230513380S" target="_blank" rel="noopener">Schaller et al. 2023</a>) cosmological code, identified haloes using the six-dimensional phase-space halo-finder VELOCIraptor (<a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft11020E" target="_blank" rel="noopener">Elahi et al. 2019a</a>), and constructed the merger trees with TREEFROG (<a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft11021E" target="_blank" rel="noopener">Elahi et al. 2019b</a>). During the halo-finding, Friends-of-Friends group require a minimum of 32 particles and the subhaloes need atleast 20 particles. S. Balu converted the original files into the format required by <a href="https://github.com/meraxes-devs/meraxes" target="_blank" rel="noopener">Meraxes</a> using codes developed by S. Mutch with help from Y. Qin and E. M. Ventura.</p> <p>After decompressing, the dataset has the following structure.</p> <pre>├── a_list.txt (the scale factor at all 120 snapshots) ├── grids (average density and velocity) │ ├── snap │ ├── snap_0000.hdf5 │ ├── snap_0001.hdf5 │ ├── ... │ ├── snap_0119.hdf5 └── trees ├── meraxes_augmented_stats.h5 (forest information) └── trees_trimmed.hdf5 (halo properties)</pre>
Raw data for 'Long-baseline Quantum Sensor Network as Dark Matter Haloscope'
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Supplementary material 4 from: Zafeiropoulos H, Gargan L, Hintikka S, Pavloudi C, Carlsson J (2021) The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data. Metabarcoding and Metagenomics 5: e69657. https://doi.org/10.3897/mbmg.5.69657
Figure S2
Supplementary material 3 from: Zafeiropoulos H, Gargan L, Hintikka S, Pavloudi C, Carlsson J (2021) The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data. Metabarcoding and Metagenomics 5: e69657. https://doi.org/10.3897/mbmg.5.69657
Figure S1
Supplementary material 2 from: Zafeiropoulos H, Gargan L, Hintikka S, Pavloudi C, Carlsson J (2021) The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data. Metabarcoding and Metagenomics 5: e69657. https://doi.org/10.3897/mbmg.5.69657
Table S2
Supplementary material 1 from: Zafeiropoulos H, Gargan L, Hintikka S, Pavloudi C, Carlsson J (2021) The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data. Metabarcoding and Metagenomics 5: e69657. https://doi.org/10.3897/mbmg.5.69657
Table S1
UFO model for universal framework for t-channel dark matter models
<p>t-channel dark matter simplified models</p>
Data for "HSTPROMO Internal Proper Motion Kinematics of Dwarf Spheroidal Galaxies: I. Velocity Anisotropy and Dark Matter Cusp Slope of Draco"
<p>DATA FOR:</p> <p>HSTPROMO Internal Proper Motion Kinematics of Dwarf Spheroidal Galaxies: I. Velocity Anisotropy and Dark Matter Cusp Slope of Draco</p> <p>THE PUBLIC FILES THAT ARE SHARED HERE ARE:</p> <p>1. DRACO_STARS: This file contains measured proper motions, uncertainties and respective instrumental magnitudes of stars with proper motion uncertainties smaller than Draco's intrinsic velocity dispersion. The detailed explanation of this dataset is given in Vitral et al. (2024, Section 2.3.5).<br>2. DRACO_BINS: This file corresponds to the data points in Figure 11 from Vitral et al. (2024), corresponding to 3D velocity dispersion profiles and respective line-of-sight rotation curve from Draco.<br>3. DRACO_LOS: This file contains the field "OBJ_ID" from the line-of-sight catalog from Walker et al. (2023) concerning the stars retained for internal mass-modeling in Vitral et al. (2024).</p> <p>Please read the "Notes" section of each file for further information.</p> <p>Please cite the following papers when using these catalogs:</p> <p>- Vitral et al. (2024), ApJ, DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad571c" target="_blank" rel="noopener">10.3847/1538-4357/ad571c</a><br>- Walker et al. (2023), ApJS, 268, 19, DOI: <a href="https://doi.org/10.3847/1538-4365/acdd79">10.3847/1538-4365/acdd79</a></p>
Data from: Viral dark matter and virus–host interactions resolved from publicly available microbial genomes
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Data from: Exploring microbial dark matter to resolve the deep archaeal ancestry of eukaryotes
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Analysis of bacterial pangenomes reduces CRISPR dark matter and reveals strong association between membranome and CRISPR-Cas systems
<p><strong>Scripts for the paper "Analysis of bacterial pangenomes reduces CRISPR dark matter and reveals strong association between membranome and CRISPR-Cas systems"</strong></p> <p>DOI (BioRxiv): <a href="https://doi.org/10.1126/sciadv.add8911">10.1126/sciadv.add8911 </a></p> <p>In this project we have searched for genes associated with CRISPR-Cas systems from bacteria of the ESKAPE group using Random Forest and we have been able to reduce the percentage of CRISPR black matter, as well as to propose a triad 'Membrane Proteins - Phages - CRISPR' by which bacterial genomes would acquire CRISPR-Cas systems when they have certain useful membrane proteins that can act as viral receptors.</p> <p><strong>Python scripts</strong></p> <p>Package versions: matplotlib 3.5.0 numpy 1.19.2 pandas 1.3.5 scikit-learn 1.0.2 scipy 1.6.2 seaborn 0.11.2</p> <p><strong>Random forest inference</strong></p> <p>The script utilizes scikit-learns mean decrease in impurity feature importance measre to infer genes that set crispr containing strains apart from those without crispr systems. Cas-genes have been removed in the data, to allow to detect non Crispr-Cas related genes.</p> <p><strong>Jaccard distances</strong></p> <p>This script computes the Jaccard dissimilarity between the MLST groups which is used as distance measurement for a hierarchical clustering and dendrograms available in the papers supplementary data.</p> <p><strong>Correlation plot</strong></p> <p>This script uses the Jaccard indices of the jaccard-panel.py script, so the latter needs to be run beforehand. It plots the correlation plot featured in the paper.</p> <p><strong>Perl scripts</strong></p> <p><strong>addColumnFromABfile.pl</strong><br> Create an output to add to the metadata table, from a file with 2 columns: metadata ID.</p> <p><strong>addColumnsFromABfile.pl</strong><br> Creates an output with several columns to add to the metadata table, from a list of files, each with a list of IDs.</p> <p><strong>analyseSpacers.pl</strong><br> Calculate proportions of genomes with unique spacer/phages in two different clusters.</p> <p><strong>calculatePercentGenesInStrainsID.pl</strong><br> Calculate both absolute and relative frequency of pangenome genes in two clusters of genomes.</p> <p><strong>collectGFFMetadata.pl</strong><br> Collect all the information of the different strains and groups them in a final table</p> <p><strong>countGenesAndShareGenesFromPangenome.pl</strong><br> Count genes and average number of shared genes for each genome in the pangenome.</p> <p><strong>createComparisonMatrix.pl</strong><br> Create matrix for heatmaps comparing genes vs genomes.</p> <p><strong>createMatrixPresenceAbsence.pl</strong><br> Create gene presence/absence matrix.</p> <p><strong>delete_FP.pl</strong><br> Identify false positives by comparing spacers and direct repeats</p> <p><strong>extractSpacers.pl</strong><br> Extract spacers with evidence code 4 from CRISPRCasFinder results.</p> <p><strong>filterBlast_vs_db.pl</strong><br> Filter Blast hits with a given identity and subject coverage.</p> <p><strong>filter_spacers_vsall.pl</strong><br> Filter the results obtained from the comparison of spacers with different databases</p> <p><strong>find_cluster_cas.pl</strong><br> Find cas gene cluster from ccfinder results</p> <p><strong>getGroupsFromPangenome.pl</strong><br> Count appearances of each gene in the pangenome.</p> <p><strong>joinGFFwithCRISPR_v2.pl</strong><br> Prepare contigs from each genome as a string of gene names.</p> <p><strong>phage_vs_spacers.pl</strong><br> Compare a list of spacers against a phages database.</p> <p><strong>putSma3sGenenames2roaryClusters.pl</strong><br> Modify gene names assigned by Sma3s to avoid redundant names.</p> <p><strong>removeAssemblies.pl</strong><br> Check assembly files and remove those from GenBank when RefSeq is available.</p> <table> <tbody> <tr> <td><strong>Pangenomes files</strong></td> </tr> <tr> </tr> </tbody> </table> <p>If you want to download these files, please see "<em>url pangenomes files</em>"</p>
Assessing marine biogenic matter Production, Export and Remineralization: from the surface to the dark Ocean
The APERO project proposes a mechanistic approach to the biological carbon pump (export of surface biogenic carbon production and fate in the water column -200/2000m). APERO aims to reduce the gap between the amount of photosynthetically produced organic carbon transferred to the deep ocean and the metabolic demand for carbon in the water column. The project is built around a campaign with two oceanographic vessels in the Northeast Atlantic, at the level of the British permanent station PAP (58N, 16W). It lasted 40 days and took place in June and July of 2023, at the time of maximum particulate carbon export to the deep ocean. The three major contributions of APERO are the study of the role of small-scale dynamics (~1-10km) on the water column using autonomous platforms, imagery and innovative instrumentation, the construction of a comprehensive database based on the simultaneous multidisciplinary observation of all processes regulating the attenuation of carbon flux in the water column and the quantification of the fluxes associated with these processes. Relying on a significant international collaboration with the JETZON consortium (https://www.jetzon.org/) and an ambitious observation strategy, complemented by molecular biology and innovative modeling approaches, this study will contribute to a significant reduction of uncertainties on carbon storage by the ocean.
Extensive binding of uncharacterized human transcription factors to genomic dark matter [GSE58341 Reanalysis]
GEO Series GSE280247. Homo sapiens. 0 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.