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348 results for “Core data”
Virtual memory on a many-core NoC: experimental data
<p>Experimental data that accompanies the thesis "Virtual Memory on a Many-Core NoC" (http://etheses.whiterose.ac.uk/25675/). The data is textual and compressed.</p>
Latitudinal core habitat prediction data for the manuscript: "Seascape topography slows predicted range shifts in fish under climate change"
<p>Latitudinal locations of core environmental habitat for yellowtail kingfish (<em>Seriola lalandi</em>), Australian bonito (<em>Sarda australis</em>), Australian spotted mackerel (<em>Scomberomorus munroi</em>), narrow-barred Spanish mackerel (<em>Scomberomorus commerson</em>) and common dolphinfish (<em>Coryphaena hippurus</em>) nearshore of the continental shelf break (i.e. 200-m isobath) within 145 – 160°E, 15 – 45°S and between years 1998 – 2018.</p>
Piburgersee core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>The core meta data belongs to a 8m long sediment core composed of 12 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar), XRF data, (_XRF.txt) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Plansee seismic and core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the raw seismic data and core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>Seismic profiles are provided as .SGY files (Plansee_seismics_SGYfiles.rar)</p> <p>The core meta data belongs to a 7m long sediment core composed of 10 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Data for: A Comparison of the Influence of Different Multi-Core Processors on the Runtime Overhead for Application-Level Monitoring
<p>Application-level monitoring is required for continuously operating software systems to maintain their performance and availability at runtime. Performance monitoring of software systems requires storing time series data in a monitoring log or stream. Such monitoring may cause a significant runtime overhead to the monitored system.</p> <p>In this paper, we evaluate the influence of multi-core processors on the overhead of the Kieker application-level monitoring framework. We present a breakdown of the monitoring overhead into three portions and the results of extensive controlled laboratory experiments with microbenchmarks to quantify these portions of monitoring overhead under controlled and repeatable conditions. Our experiments show that the already low overhead of the Kieker framework may be further reduced on multi-core processors with asynchronous writing of the monitoring log.</p> <p>Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Changes in core-mantle boundary heat flux patterns throughout the supercontinent cycle: Data
<p>This repository accompanies the paper</p> <p> </p> <p>```</p> <p>Dannberg, J., Gassmoeller, R., Thallner, D., LaCombe, F., Sprain, C.: Changes in core-mantle boundary heat flux patterns throughout the supercontinent cycle.</p> <p>```</p> <p> </p> <p>This repository contains instructions for how to obtain the boundary conditions from GPlates, ASPECT code, data and model setups, and scripts for converting the ASPECT model output to spherical harmonics so it can be used in geodynamic simulations. To reproduce the workflow follow the steps:</p> <p> </p> <p>- To create the velocity boundary conditions for the ASPECT models, download the plate reconstruction from 'Merdith, A.S., Williams, S.E., Collins, A.S., Tetley, M.G., Mulder, J.A., Blades, M.L., Young, A., Armistead, S.E., Cannon, J., Zahirovic, S. and Müller, R.D., 2021. Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. Earth-Science Reviews, 214, p.103477', which can be found here:</p> <p> </p> <p>https://doi.org/10.5281/zenodo.4485738</p> <p> </p> <p>To create the 'lat_lon_velocity' files, take the following steps in GPlates:</p> <p> </p> <p>1. Load all of the files from the Merdith et al, 2021 plate reconstruction into a Feature Collection which can be saved as a project (the project for our visualization is 'project.gproj').</p> <p>2. Establish the output grid (ours is lat_lon_velocity_domain_91_181): Features -> Generate Velocity Domain Points -> Latitude Longitude -> Number of latitudinal grid intervals=91, Number of longitudinal grid intervals=181, number of nodes=16652.</p> <p>3. To output the point velocities we used for the models: Reconstruction -> Export -> Add Export -> Velocities, GPML(*.gpml), velocity_%nMa</p> <p>- The data files we created following this workflow are part of this data publication and can be found in the `lat_lon_velocity` folder.</p> <p> </p> <p>- The global spherical convection models of the publication were created using two different ASPECT configurations:</p> <p> </p> <p>Models `thermal`, `thermochemical`, and `p-T-dependent` were run using:</p> <p> </p> <p>```</p> <p>-----------------------------------------------------------------------------</p> <p>-- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion.</p> <p>-- . version 2.4.0-pre (limit_shear_heating, 4f45a72fe)</p> <p>-- . using deal.II 9.4.0-pre (3d869ba6cd1fd462624e09dc232e34ed17880701)</p> <p>-- . with 64 bit indices and vectorization level 2 (256 bits)</p> <p>-- . using Trilinos 12.18.1</p> <p>-- . using p4est 2.3.2</p> <p>-----------------------------------------------------------------------------</p> <p>```</p> <p> </p> <p>Models `strong basalt` and `weak ppv` were run using:</p> <p> </p> <p>```</p> <p>-----------------------------------------------------------------------------</p> <p>-- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion.</p> <p>-- . version 2.5.0-pre (limit_shear_heating_and_ppv, a4812c95a)</p> <p>-- . using deal.II 9.4.2</p> <p>-- . with 64 bit indices and vectorization level 3 (512 bits)</p> <p>-- . using Trilinos 13.2.0</p> <p>-- . using p4est 2.3.2</p> <p>-----------------------------------------------------------------------------</p> <p>```</p> <p> </p> <p>- The two modified ASPECT versions are included in this data package. The repository including full</p> <p>version history is until further notice available as branch `limit_shear_heating` and branch `limit_shear_heating_and_ppv`</p> <p>in the repository `https://github.com/jdannberg/aspect.git`.</p> <p> </p> <p>- Running these models also requires plugins that are located in the `shared_libs` folder in this repository and that need to be compiled. Navigate into this directory and follow the steps:</p> <p> </p> <p>1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT).</p> <p>2. `make`</p> <p>- Now the models in this repository can be started. You should start them from the `input_files` directory so that all paths are set correctly and you can start them with the ASPECT executable in your build folder.</p> <p> </p> <p>- The files in `aspect_input_files` correspond to the models presented in the paper following the same naming scheme.</p> <p> </p> <p>- To convert the ASPECT heat flux output to spherical harmonics we used the script `analyze_heatflux_mpi_gmt.py` in `SPH_scripts`,</p> <p>which requires modification to point to the correct ASPECT statistics file and the correct output directory.</p> <p> </p> <p>- The final heat flux output is included in this data package in the `heat_flux` folder, which includes archives of the processed heat flux output in 1 Myr time intervals with 0 being the start of the model run and the highest timestep number representing the present day state.</p>
Dinoflagellate cyst assemblage data from core JM09-020
<p>Absolute abundances (cysts g<sup>-1</sup>) of dinoflagellate cysts (dinocysts) species, total dinocyst abundance (cysts g<sup>-1</sup>), relative abundance of dinocysts produced by auto- and heterotrophic dinoflagellate species (%), and dry bulk density (g cm<sup>-3</sup>) for flux calculation in core JM09-020.</p>
Core individual-based model simulation script and landscape data
<p>Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally ("residents") were often more resilient to fragmentation than long-ranging "migrants". This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.</p>
Gravity core XRF data from the Porcupine Abyssal Plain
<p>X-ray Fluorescence (XRF) data from three gravity cores obtained during NOC expedition JC231 (2022) for the "Time-series studies at the Porcupine Abyssal Plain Sustained Observatory". These three gravity cores (GC050; GC073; GC076) were collected from different sites at the Porcupine Abyssal Plain (AESA Hill; AESA North Plain; PAP Central respectively). </p> <table> <tbody> <tr> <td>Ship/Platform</td> <td>Cruise Identifier</td> <td>Sample Identifier</td> <td>Site</td> <td>Number of Sections</td> <td>Section Identifier</td> <td>Date Sample Collected</td> <td>Decimal Latitude</td> <td>Decimal Longitude</td> <td>Water Depth (m)</td> <td>Sampling Device</td> <td>Storage Method</td> <td>Core Length (cm)</td> <td>Core Diameter (cm)</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC050</td> <td>AESA Hill</td> <td>2</td> <td>Section 1</td> <td>09/05/2022</td> <td>48 59.103</td> <td>16 33.17</td> <td>4795</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC050</td> <td>AESA Hill</td> <td>2</td> <td>Section 2</td> <td>09/05/2022</td> <td>48 59.103</td> <td>16 33.17</td> <td>4795</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>50</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 1</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 2</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC073</td> <td>AESA North Plain</td> <td>3</td> <td>Section 3</td> <td>12/05/2022</td> <td>49 0.657</td> <td>16 33.221</td> <td>4846</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 1</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 2</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>100</td> <td>6.5</td> </tr> <tr> <td>RRS James Cook</td> <td>JC231</td> <td>GC076</td> <td>PAP Central </td> <td>3</td> <td>Section 3</td> <td>12/05/2022</td> <td>48 50.095</td> <td>16 31.331</td> <td>4843</td> <td>Gravity core</td> <td>room temperature, dry (D)</td> <td>80</td> <td>6.5</td> </tr> </tbody> </table>
Training and Testing Data, Associated Code, and WRF Code for ML-based nonhydrostatic alternative scheme in dynamical core of atmosphere
<p>Data and codes for a nonhydrostatic alternative scheme (NAS) in dynamical core of atmosphere based on machine learning.</p> <p>In this new version, the randomly sampled training data samples testing data samples from nonhydrostatic simulations in WRF baraclinic wave test are provided. They are processed into a new data structure, which can be directly utilized in training and testing. </p> <p>Follow the instructions in README.txt and download the training and testing data, and the associated codes.</p> <p>Here we provide 3 parts of data and codes:</p> <p>1, Training and testing data from WRF;</p> <p>2, Training and testing codes for two machine learning emulators: machine learning and neural network</p> <p>3, WRF application.</p>
Palaeoecological data of KP core, Kampar Peninsula, Riau, Sumatra, Indonesia
<p>Southeast Asian peatlands, along with their various important ecosystem services, are mainly distributed in the coastal areas of Sumatra and Borneo. These ecosystems are threatened by coastal development, global warming and sea level rise (SLR). Despite receiving growing attention for their biodiversity and as massive carbon stores, there is still a lack of knowledge on how they initiated and evolved over time, and how they responded to past environmental change, i.e., precipitation, sea level and early anthropogenic activities. To improve our understanding thereof, we conducted multi-proxy palaeoecological studies in the Kampar Peninsula and Katingan peatlands in the coastal area of Riau and Central Kalimantan, Indonesia. The results indicate that the initiation timing and environment of both peatlands are very distinct, suggesting that peat could form under various vegetation as soon as there is sufficient moisture to limit organic matter decomposition. The past dynamics of both peatlands were mainly attributable to natural drivers, while anthropogenic activities were hardly relevant. Changes in precipitation and sea level led to shifts in peat swamp forest vegetation, peat accumulation rates, and fire regimes at both sites. We infer that the simultaneous occurrence of El Niño-Southern Oscillation (ENSO) events and SLR resulted in synergistic effects which led to the occurrenceere fires in a pristine coastal peatland ecosystem, however, it did not interrupt peat accretion. In the future, SLR, combined with the projected increase in frequency and intensity of ENSO, can potentially amplify the negative effects of anthropogenic peatland fires. This prospectively stimulates massive carbon release, thus could, in turn, contribute to worsening the global climate crisis especially once an as yet unknown threshold is crossed and peat accretion is halted, i.e., peatlands lose their carbon sink function. Given the current rapid SLR, coastal peatland managements should start develop fire risk reduction or mitigation strategies.</p>
Millstätter See seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"
<p>This dataset comprises the core data and the 3.5 kHz seismic data of Millstätter See, a lake in the Eastern European Alps, Austria. Together with a bathymetric dataset (10.5281/zenodo.5875923) and a core/seismic dataset from Wörthersee (10.5281/zenodo.5875576), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)".</p> <p>28 core sections (individual short cores or sections of long cores - see <em>MillstaetterSee_core_data.xlsx</em> for information) were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders <em>MSCL.zip</em> and <em>Photos.zip</em>. Some core sections were also analysed with a Malvern Mastersizer 3000 and/or CT scanning. The generated data are provided in the folders <em>Grain Size.zip </em>and<em> CT data MI17-04.zip </em>(as .dcm files).</p> <p>The seismic profiles are provided as .SGY files (<em>Seismic Pinger Data.zip</em>).</p>
Woerthersee seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"
<p>This dataset comprises the core data and the 3.5 kHz seismic data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset from Millstättersee (core and seismic data: 10.5281/zenodo.5875911; bathymetric data: 10.5281/zenodo.5875923), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)".</p> <p>24 short cores were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders <em>MSCL.zip</em> and <em>Photos.zip</em>. Some core sections were also analysed with a Malvern Mastersizer 3000. The generated grain-size data are provided in the folder <em>Grain Size.zip</em>.</p> <p>The seismic profiles are provided as .SGY files (<em>Seismic Pinger Data.zip</em>).</p>
Ice core and model data for Moseid et al. 2022
<p>These datasets are used in the publication "Using ice cores to evaluate CMIP6 aerosol concentrations over the historical era" with the authors Kine Onsum Moseid, Michael Schulz, Anja Eichler, Margit<br> Schwikowski, Joseph R. McConnell, Dirk Olivi ́e, Alison S. Criscitiello, Karl J. Kreutz, and Michel Legrand.</p> <p>The paper is currently in review when this data is published.</p> <p>The excel sheet dataset contains sulfate and black carbon records from 15 ice cores as presented in the paper. </p> <p>One zip file contain the part of the data from NorESM2-LM experiments as described in the paper. Another dataset will be published to compliment this dataset. </p>
Data from: Bratzel et al. (2022) Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (Bromeliaceae). Taxon
<p>DNA sequence alignments used for phylogenetic analyses in Bratzel et al. (2022) Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (Bromeliaceae). Taxon.</p>
Data for "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure"
<p>This file is the dataset used in the article "Sound velocity of hexagonal close-packed iron to the Earth's inner core pressure", Nat. Commun. 13, 7211 (2022). https://doi.org/10.1038/s41467-022-34789-2</p>
Supplementary data accompanying Hess et al. (2025) 'The I/Ca paleo-oxygenation proxy in planktonic foraminifera: A multispecies core-top calibration' published in Geochimica et Cosmochimica Acta
<p>This data accompanies Hess et al. (2025) 'The I/Ca paleo-oxygenation proxy in planktonic foraminifera: A multispecies core-top calibrations' published in Geochimica et Cosmochimica Acta.</p> <p>Data columns and explanation:</p> <table> <tbody> <tr> <td>reference</td> <td>reference for the I/Ca and Mg/Ca data</td> </tr> <tr> <td>site</td> <td>site name</td> </tr> <tr> <td>sample_depth_cm</td> <td>sample depth (cm below sediment surface)</td> </tr> <tr> <td>basin</td> <td>ocean basin</td> </tr> <tr> <td>site_depth_km</td> <td>site water depth (km)</td> </tr> <tr> <td>species</td> <td>foraminifera species</td> </tr> <tr> <td>calcification_depth</td> <td>foraminifera calcification depth</td> </tr> <tr> <td>size_fraction</td> <td>foraminifera size fraction</td> </tr> <tr> <td>cleaning_oxidative_reductive</td> <td>cleaning applied to sample before trace element analysis (O = oxidative, O+R = oxidative and reductive)</td> </tr> <tr> <td>local_O2_variability</td> <td>indication of whether the site experiences local O2 variability, rows with "yes" are excluded from Figure 4</td> </tr> <tr> <td>MgCa</td> <td>Mg/Ca (mmol/mol)</td> </tr> <tr> <td>MgCa_corr</td> <td>Mg/Ca corrected for effect of reductive cleaning, as necessary (mmol/mol)</td> </tr> <tr> <td>T_anand</td> <td>calcification temperature calculated from Mg/Ca using Anand et al. (2003) multispecies equation</td> </tr> <tr> <td>T_Hollstein_multispec</td> <td>calcification temperature calculated from Mg/Ca using Hollstein et al. (2017) multispecies equation</td> </tr> <tr> <td>T_Hollstein_spec</td> <td>calcification temperature calculated from Mg/Ca using species-specific Hollstein et al. (2017) equations</td> </tr> <tr> <td>T_Cleroux</td> <td>calcification temperature calculated from Mg/Ca using species-specific Cléroux et al. (2008) equations</td> </tr> <tr> <td>depth_anand</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_anand</td> </tr> <tr> <td>depth_Hollstein_multispec</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Hollstein_multispec</td> </tr> <tr> <td>depth_Hollstein_spec</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Hollstein_spec</td> </tr> <tr> <td>depth_Cleroux</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Cleroux</td> </tr> <tr> <td>ICa</td> <td>I/Ca (µmol/mol)</td> </tr> <tr> <td>ICa_corr</td> <td>I/Ca corrected for effect of reductive cleaning, as necessary (µmol/mol)</td> </tr> <tr> <td>O2av_0-500m</td> <td>average oxygen concentration in the top 500 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_0-500m</td> <td>minimum oxygen concentration in the top 500 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_alldepths</td> <td>minimum oxygen concentration at any depth in the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2av_0-100m</td> <td>average oxygen concentration in the top 100 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_0-100m</td> <td>minimum oxygen concentration in the top 100 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> </tbody> </table>
Data for "Density deficit of the Earth's core revealed by a multi-megabar primary pressure scale"
<p>This file is the dataset used in the article "Density deficit of the Earth's core revealed by a multi-megabar primary pressure scale", Sci. Adv. 9, eadh8706 (2023). https://doi.org/10.1126/sciadv.adh8706</p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
micro-PET data from core injection experiment
<p>Ascii file containing the micro-PET normalized radioactivity data.</p> <p>The format is x(cm), y(cm), z(cm), time(seconds), normalized radioactivity value</p> <p>.</p> <p>.</p> <p>.</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.