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2,885 results for “transferability”
Environmental Subsidies and Similar Transfers from Europe to the Rest of the World
<p>Environmental subsidies and similar transfers (current, capital, tax abatement, subsidy) for all environmental protection and resource management activities from EU countries to the rest of the world.</p> <p>The original dataset is of Eurostat is plagued with missing data. Our version on the <a href="https://zenodo.org/communities/greendeal_observatory/">Green Deal Data Observatory</a>, though could be further improved, offers a 167% larger congruent data matrix for supervised or unsupervised learning (machine learning, regression analysis) than the <a href="https://ec.europa.eu/eurostat/databrowser/view/ENV_ESST_GG/default/table?lang=en">original dataset</a>: Environmental subsidies and similar transfers from general government, by environmental activity, sector of recipient and ESA category of transfer.</p> <p> </p>
Dataset for Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics"</strong></p><p>published in ChemElectroChem</p><p>Specifically, the following measurements are provided:</p><ul><li>Scanning electrochemical cell microscopy (SECCM). Cyclic voltammetry (E, i) data for each location across the sample. 5 cycles.</li><li>Chronoamperometry (i, t) for the anodization process.</li><li>Atomic Force Microscopy (AFM) topography.</li><li>Raman microscopy</li><li>X-ray photoelectron spectroscopy (XPS)</li><li>Scanning electron microscopy (SEM)</li></ul>
Trophic transfer of Everglades marsh consumer biomass to Everglades Estuaries (FCE LTER), Everglades National Park, South Florida, USA, December 2010 to July 2013
We measured the trophic transfer of secondary consumer biomass from the Everglades marshes to the oligohaline reaches of the Shark River by sampling the diets of four common large bodied piscivorous fishes occurring at the marsh-estuary oligohaline ecotone. The four species sampled were Florida bass (Micropterus floridanus), bowfin (Amia calva), common snook (Centropomus undecimalis), and red drum (Sciaenops ocellatus). We sampled diets via pulsed gastric lavage, a relatively non-lethal and effective sampling technique used to measure trophic interactions. We quantified trophic transfer of marsh biomass to the estuary when a focal piscivore consumed a prey species that was likely a migrant from adjacent marshes. A more detailed description of these methods can be found in citation #28. In the presented data, we combined estimates of relative abundance of piscivores from standardized electrofishing techniques (# of piscivores/ 100 meters of sampled shoreline) with biomass of marsh species consumed in the estuary to calculate the biomass (g) transferred to the estuary per 100 meters of shoreline. These values serve as our index of how much biomass is being exported off of the marsh to the estuary through consumer mediated habitat linkages. An important key finding from this work is that disturbance, in particular drought, can sever this biomass linkage, and conserve biomass export off of karstic wetlands to estuaries through of marsh secondary consumer trophic pathways.
Undinarchaeota illuminate DPANN phylogeny and the impact of gene transfer on archaeal evolution
<p><strong>General Description </strong></p> <p>Repository with all analyses described our paper: <a href="https://www.nature.com/articles/s41467-020-17408-w">Undinarchaeota illuminate DPANN phylogeny and the impact of gene transfer on archaeal evolution</a>.</p> <p>If you find this work useful for your own analyses, please cite this work.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The evolution and diversification of Archaea is central to the history of life on Earth. Cultivation-independent approaches have revealed the existence of the DPANN archaea: a radiation of organisms with small cell and genome sizes. Currently, the placement of the various DPANN lineages and in turn the early evolution of metabolism and symbiosis are debated. Here, we reconstructed genomes of a thus far uncharacterized archaeal phylum-level lineage UAP2 (<em>Candidatus</em> Undinarchaeota). Comparative genomics revealed that members of the Undinarchaeota have small estimated genome sizes and, while potentially being able to conserve energy through fermentation, likely depend on partner organisms for the acquisition of vitamins, amino acids and other metabolites. In contrast to previous indications, our phylogenomic analyses robustly placed the Undinarchaeota as independent lineage between two major and highly supported clans of ‘DPANN’. Furthermore, our work suggests that DPANN archaea have exchanged core genes with their hosts by horizontal gene transfer, adding to the difficulty of placing DPANN in the tree of life (ToL). In several cases, this pattern is sufficiently dominant that known symbiont-host clades can be identified by inferring routes of HGT across the ToL. Together, our findings provide crucial insights into the origins and evolution of DPANN archaea and their hosts.</p> <p><strong>The annotation workflow for archaeal/bacterial genomes that was used for this paper is also available on github (<a href="https://github.com/ndombrowski/Genome_annotations">here</a>) and an updated version that includes the COG search is available on: </strong><a href="https://github.com/ndombrowski/Annotation_worfklow">https://github.com/ndombrowski/Annotation_workflow</a></p> <p> </p> <p><strong>Repository Contents</strong></p> <p><strong>1_Genome_files.tar.gz</strong> includes all Undinarchaeota (original name UAP2) metagenome-assembled genomes (MAGs). This includes: </p> <ol> <li>The original contigs for each UAP2 MAG (fna files)</li> <li>The prokka output for each UAP2 MAG (faa files)</li> <li>A concatenated file of all proteins from each UAP2 MAG and all archaeal reference genomes (364 genomes in total). This folder also includes a list of archaeal genomes investigated.</li> </ol> <p><strong>2_Phylogenies.tar.gz</strong> includes all files for the phylogenetic analyses. This includes the following folders:</p> <p>1. Files for the concatenated species trees for different taxa sets. These files are related to the following parts of the manuscript: Supplementary Table 6; Figure 1 and Supplementary Figures S8-S58. The folder includes the following:</p> <ul> <li>Folder '1_unaligned_sequences' includes individual protein sequences extract from the different taxa sets.</li> <li>Folder '2_alignments' includes the alignment files generated by MAFFT.</li> <li>Folder '3_alignments_trimmed' includes the alignments trimmed with BMGE.</li> <li>Folder '4_phylogenies' includes the IQ-TREE output for all phylogenies as well as color-annotation file for figtree. Additionally files rooted with minimal ancestor deviation (MAD) rooting (*.rooted) are provided. Note, that for the final figures the *treefile_renamed (i.e. the iqtree file with the full taxa string) were artificially rooted using the DPANN archaea. The numbering corresponds to Supplementary Table S6 of the main manuscript.</li> <li>Folder ' 5_pdfs' includes the PDFs for each tree</li> </ul> <p>2. Files for single gene trees that includes:</p> <ul> <li>The folder '1_arcogs' includes the unaligned proteins, alignments, trimmed alignments, trees and pdfs for the single gene trees based on the arCOG identifiers. The arCOGs were extract from 12 UAP2 MAGs + 352 archaeal + 3020 bacterial + 100 eukaryotic genomes. ArCOGs were only considered if they occurred in at least 3 UAP2 genomes. Notice, these files were used to investigate UAP2 for HGT events and correspond to the following parts of the manuscript: Figure 4 and Supplementary Tables 4, 5, 20-22. Additionally, the folder 0_parsing includes some information on how to generate count tables for each marker gene.</li> <li>The folder '151_markers' including the proteins, alignments, trimmed alignments, trees and pdfs for evaluating the 151 marker set used for the concatenated species tree. Files were provided for the 127 and 364 taxa set. These files were used as a basis for the concatenated species trees that were used to generated Supplementary Figures S8-S58. Additionally, the trees were used for ranking marker proteins and generating Supplementary Tables 4-5. For the 364 taxa set, the folder also included a subfolder 0_parsing that provides scripts to investigate some statistics for each marker protein, including the average protein length, average alignment length and average bootstrap support.</li> <li>The folder '3_other_individual_trees' includes the proteins, alignments and phylogenies for the 16S_23S, RubisCO and primase analyses. The data was used to generate the following parts of the manuscript: Supplementary Table 11, Supplementary Figures 3-5, 57 and 59.</li> </ul> <p><strong>3_Scripts.tar.gz</strong> includes all files for the phylogenetic analyses. This includes the following folders:</p> <p>1. The files for the main workflow for the annotations and phylogenies.</p> <ul> <li>This folder includes the workflow to generate annotations for archaeal genomes as well as an example script that was used to generate phylogenies. These analyses were typically run on a in-house bioinformatics cluster with 4x Xeon Gold 6140 2.3 GHz processors using bash, python and perl. The used system runs a Linux operating system, Red Hat Enterprise 7.5.</li> </ul> <p>2. A folder providing any required dependencies that include:</p> <ul> <li>any python or perl scripts that were used during this study and/or that are mentioned in the methods section</li> <li>Databases used for the annotations, esp. if these were slightly modified. Notice, changes typically include parsing of the mapping files or modifications of the sequence headers for easier parsing.</li> <li>mapping files needed to link the genome accession ids to the taxonomy string as well as lists of protein IDs used for different phylogenies (i.e. 14 + 48 arCOGs used for protein phylogenies)</li> </ul> <p>3. R scripts (including all needed input files) used to: </p> <ul> <li>generate tables and figures for the annotations, i.e. Figure 2 and 3 and Supplementary Tables 7, 8, 9, 12, 13-15 and Supplementary Figures 60, 62-64 . The input folder includes the raw output from the annotation workflow and includes annotations for the 12 UAP2 MAGs as well as 352 archaeal reference genomes.</li> <li>generate tables and figures for the HGT analyses, i.e. Figure 4 and Supplementary Tables S20-22 Here, proteins based on arCOGs were extracted from 364 archaeal, 3020 bacterial and 98 eukaryotic genomes and used to generate single protein phylogenies. The resulting trees were used to investigate horizontal gene transfer events and the necessary scripts are provided in this folder.</li> <li>generate tables and figures for the amino acid identify (AAI) comparisons, i.e. Supplementary Table S3 and Supplementary Figure S2. </li> <li>rank the marker genes for concatenated species trees for the 127 and 364 taxa set. These were used to generate Supplementary Tables S4 and S5.</li> </ul> <p><strong>General comment:</strong></p> <p>In contrast to the previous version, this datasets includes some small additional scripts generated during the revision process of the corresponding manuscript.</p> <p> </p>
ATLAS Rucio Transfers Dataset
<p>This dataset is released to encourage the study of ATLAS file transfers in the Worldwide LHC Computing Grid environment, to better understand the transfer processes in this particularly heterogeneous environment.</p> <p> </p> <p>Joaquin Bogado (UNLP)</p> <p>Mario Lassnig (CERN)</p> <p>Fernando Monticelli (UNLP)</p> <p>Thomas Beermann (University of Wuppertal)</p> <p>Javier Díaz (UNLP)</p> <p>2020-11-27</p> <p>jbogado @ linti.unlp.edu.ar</p> <p>Motivation</p> <p>This dataset is released to encourage the study of ATLAS file transfers in the Worldwide LHC Computing Grid[1] environment, to better understand the transfer processes in this particularly heterogeneous environment.</p> <p>Rucio[2] is a Distributed Data Management system. Data from the Rucio ATLAS instance from June and July 2019 was retrieved and summarized in the present dataset. The Rucio ATLAS instance is responsible to keep track of the files of the ATLAS Experiment[3] at CERN. These files are stored all around the world in 100+ data centers. In order to work with the files, physicists around the world need to move them across sites. Rucio delegates the file transfer to another subsystem called FTS[4]. The rules in Rucio are groups of file transfers that are done as a unit, i.e.: a physicist may need a set of files to do an analysis, then they create a rule specifying which files need to be moved to where, and when the rule is done the analysis can start.</p> <p>If the Rule Time To Complete (RTTC) can be predicted with certain accuracy, this will allow the Rucio system and the ATLAS Experiment to schedule the transfers in a smarter way, eventually helping to optimize the resources the experiment has to do more and faster science.</p> <p>State of the art</p> <p>The metric used to calculate the accuracy is the Fraction of Good Predictions (FoGP). Formally, the FoGP is defined as in the equation that follows</p> <p>FoGP(y, y, τ) = 1/n i = 1ng(yi, yi, τ)</p> <p>Where y is the vector of observations, y is the vector of predictions, g is a function that returns 1 if the relative error | yi - yi | / yi < τ , and 0 otherwise. For a group of predictions we have that FoGP(y, y, τ = 0.1) = 0.5. This means that 50% of the predictions have less than 10% of relative error. This easy to understand metric allows to compare models directly, independently from their implementation, and only focus on the predictions the model made.</p> <p>We estimate a FoGP(τ = 0.1) > 0.95 for a model to be useful. However, there are no known models that can predict the RTTC at the rule creation time with such a high accuracy. Models with FoGP(τ = 0.1) ~= 0.5 could be useful to give feedback to the users about how much time the transfers will take. Best models known have a FoGP(τ = 0.1) = 0.14.</p> <p>Fields description</p> <p>account</p> <p>The hashed account name from the user that issued the transfer. This data has been anonymized and does not represent the real name of the user in the system. </p> <p>state</p> <p>The final state of the transfer. 'D' means the transfer is done, 'F' means the transfer has failed. Other states represent internal states from Rucio and are not important. Very few transfers showed other states than D or F.</p> <p>activity</p> <p>The activity of the transfer. It's related to the priority of the transfers inside the system. Priorities are based on shares and related to the 'share' field. As transfers requests are queued in Rucio and in FTS, transfers are picked to be served with a probability equal to its share among all the transfers that are in the queue at that time.</p> <p>SIZE</p> <p>The size in bytes of the file to be transferred.</p> <p>src_rse/dst_rse</p> <p>The source/destination Rucio Storage Element (RSE). An RSE is a logical unit inside Rucio that represents a dedicated storage location of a data center. Usually there are more than one physical machine. Rucio doesn't know how many storage nodes compose an RSE, so this is the minimum logical unit of storage for the system. Both fields have been anonymized.</p> <p>id</p> <p>The unique identifier of a transfer request. If a transfer needs to be retried, the next attempt will have a different id. </p> <p>previous_attempt_id</p> <p>If the transfer request is a retry, the id of the previous attempt is filled in. Otherwise, this field is empty.</p> <p>retry_count</p> <p>This is the number of times a transfer has been retried. If it is the first attempt, the field is 0. </p> <p>rule_id</p> <p>This is the id of the rule the transfer belongs to. All the transfers in the same rule share the same rule_id.</p> <p>external_host</p> <p>This is the hash of the FTS server that will trigger the actual transfer of files between the files. There are several FTS servers and some are shared with other Experiments outside ATLAS. It is known that the server with hash fe1d4db902b6271 is used by ATLAS Experiment exclusively, so this can be a good place to start.</p> <p>RTIME</p> <p>This is the time in seconds the transfer spends in the Rucio System, since it is created at the created timestamp, till the transfer is submitted to the FTS system, at the submitted timestamp. This can be calculated as submitted - created. This value is not available to the system until the transfer is submitted, that is the submitted timestamp.</p> <p>QTIME</p> <p>This is the time in seconds the transfer spends in the FTS System, since it is submitted by Rucio the submitted timestamp, till the transfer starts its network time at the started timestamp. This can be calculated as started - submitted. This value is not available to the system until the transfer ends, that is until the ended timestamp, because FTS does not propagate the started time of a transfer immediately, but only once the transfer ends or fails.</p> <p>NTIME</p> <p>This is the actual time in seconds the file is being transferred, using the network, since the transfer is started by FTS at the started timestamp, till the transfer ends at the ended timestamp. This can be calculated as ended - started. The value is not available to the system until the transfer ends, that is the ended timestamp.</p> <p>RATE</p> <p>This is the average rate in bytes per second of each transfer. It is calculated as SIZE/NTIME and is not available till the transfer ends.</p> <p>link</p> <p>This is the hash that represents a source/destination RSE pair. Links have peculiarities that make them unique, and likely affect the RTTC, e.g., some links have higher bandwidth, or the disks of the associated storages in the respective source and destination RSEs are faster than the ones on other links. </p> <p>created</p> <p>This is the time at which a transfer request is created in Rucio. For all the transfers that share the same rule_id, the minimum created timestamp is also the rule creation time, at which we want to know the RTTC. All date timestamps have a resolution of 1 second.</p> <p>submitted</p> <p>This is the time at which the transfer request is submitted from Rucio to FTS. </p> <p>started</p> <p>This is the time at which the transfer request starts the actual transfer, using the network. This data will not be known until the transfer ends because FTS doesn't publish this data immediately but only once the transfer ends.</p> <p>ended</p> <p>This is the time at which the transfer ends. For all the transfers that share the same rule_id, the maximum ended timestamp is also the ending time of the rule. </p> <p>share</p> <p>This is a number between 0 and 1 that represents the weighted probability of a transfer of being picked to be served given its activity.</p> <p>Target</p> <p>The target of the study is to know the Rule Time To Complete (RTTC) at the creation time of the rule. The creation time of the rule is the minimum created timestamp of those transfers that share the same rule_id. The RTTC can be computed as the ending time of the rule minus the starting time of the rule, being the ending time of the rule, the maximum ended timestamp of all the transfers that share the same rule_id.</p> <p> </p> <p> </p> <p>References</p> <p> </p> <ol> <li> <p>Worldwide LHC Computing Grid. <a href="https://wlcg.web.cern.ch/">https://wlcg.web.cern.ch/</a> Retrieved 23/11/2020</p> </li> <li> <p>Rucio Scientific Data Management. <a href="https://rucio.cern.ch/">https://rucio.cern.ch/</a> Retrieved 23/11/2020</p> </li> <li> <p>The ATLAS Experiment. <a href="https://atlas.cern/">https://atlas.cern/</a> Retrieved 23/11/2020</p> </li> </ol> <p>File Transfer Service. <a href="https://fts.web.cern.ch/fts/">https://fts.web.cern.ch/fts/</a> Retrieved 23/11/2020</p>
Single column 1D radiative transfer simulations during PS106 including low-level-stratus clouds in the central Arctic
<p>The collection of datasets published contain the input parameters and output simulations from a single column 1D radiative transfer simulations using the <strong>R</strong>apid <strong>R</strong>adiative <strong>T</strong>ransfer <strong>M</strong>odel for <strong>G</strong>eneral Circulation Model (GCM) applications (RRTMG).</p><p>The data set contains simulations for the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing data which were processed with the Cloudnet algorithm to derive cloud macro - and microphyiscal products. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.</p>
System transferability of a Raman-based oesophageal tissue classification
<p>This dataset contains 560819 Raman spectra (Uncorrected_MedianFiltered_SizeMatched_SMART_x) taken from 61 oesophageal samples (sampleID) representing 51 patients (patientID). The data was acquired across three independent sites (centre) using the same make of spectrometer - Renishaw RA816 Biological Analyser (Renishaw plc, Wotton-under-edge, UK). Ostensibly the same sample was collected by all three sites (three adjacent FFPE tissue slices were obtained and regions of interest were identified by a histopathologist on one of the centres). The samples belong to one of 5 pathology classes: NSQ (0), IM(1), LGD(2), HGD(3) and AC(4). </p> <p>Included in this version is the protocol used to collect this data, particularly focused on the aquisition of Raman spectra.</p>
Figure 2: Direct and indirect paths of knowledge transfer to New Zealand to manage sand drifting in the nineteenth and twentieth centuries.
<p>Figure 2 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
On the Effectiveness of Transfer Learning for Code Search - Replication Package
<p>This repository represents the replication package for the paper <em>On the Effectiveness of Transfer Learning for Code Search</em>.</p> <p>The paper is published in the journal <em>IEEE Transactions on Software Engineering (TSE)</em>.</p> <p>In this replication package, we provide all the data and scripts we used in our study.</p>
Viral Pneumonia Classification Using Machine and Transfer Learning Techniques
<p>Pneumonia is considered a deadly and harmful disease throughout the world. Pneumonia can be lethal if not treated promptly with antibiotics. As a result, early detection of pneumonia increases the likelihood of recovery and lowers mortality. X-rays are one of the most important diagnostic tools for pneumonia. Because of its lower diagnostic costs, the chest X-ray is routinely used to diagnose various lung illnesses. Indeed, diagnosis can be subjective for various reasons, including disease presentation, which might be confusing in chest X-ray images or misdiagnosed as another condition. As a result, the employment of chest X-rays for the diagnoses of pneumonia disease is considered a way forward to fight the challenges being faced with during the examination process and expert readings of results. The dataset comprises 1,067 Pneumonia Chest X-ray images that were curated from the Hopskin Diagnostic Center Nigeria for Research Purposes. This was used to classify Pneumonia disease for pneumonia class encoding. The result yield Pneumonia Disease with High Accuracy, precision and Recall. </p>
Transfer of sensorimotor learning reveals phoneme representations in preliterate children - Dataset
<p>This file provides formants values in each speaker and for each trial of the experiment described in the article : Transfer of sensorimotor learning reveals phoneme representations in preliterate children.</p> <p> </p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Dataset: A database of near-field head-related transfer functions based on measurements with a laser spark source
<p>This is a database of near-field head-related transfer functions (HRTFs) of an artificial head, measured at four distances (0.2, 0.3, 0.4 and 0.5 m), with 49 positions recorded at each distance, for a total of 196 measurement points. The HRTFs were recorded using an acoustic pulse created by a laser-induced breakdown of air (LIB), which realizes a close to ideal, massless, monopole sound source. The repository contains the original measurement data (raw_data.zip), the derived HRTFs both with (NF_LIB_HRTF_LFE.sofa) and without (NF_LIB_HRTF_measured.sofa) a low-frequency extension (LFE) applied, as well as the MATLAB code used to process the measurement data and to apply the LFE (LIB_HRTF_DB.zip). The database is made publicly available to support future research into nearby sound localization, and virtual/augmented reality applications.</p> <p>Please see the accompanying paper for further details: Marschall et al. (2023), <a href="https://doi.org/10.1016/j.apacoust.2022.109173">A database of near-field head-related transfer functions based on measurements with a laser spark source</a>, Applied Acoustics. </p>
Soil and meteorological data, and finite element simulation framework for heat transfer through shrubs in winter near Lautaret pass, French Alps
<p>The data allow the calculation using finite element modeling of heat transfer through shrub branches and snow between the atmosphere and the soil. The shrubs are green alders (Alnus viridis). The site where they are found is called Alnus-Nivus (45.034750°N, 6.413630°E, 2034 m asl) near Col du Lautaret, French Alps. The soil data consist in temperature and volumetric liquid water content at 5 and 15 cm depths. One spot is near the alder collar (ALNUS), the other spot is 6 m away, under grass (GRASS).</p> <p>The meteorological data were obtained from the FR-Clt station, 750 m away (45.041278°N, 6.410611°E, 2046 m asl). See (Gupta et al., 2023) for details. Only the data relevant for heat transfer simulations are given.</p> <p>The simulation framework gives the alder mesh used in the heat transfer simulations. Typical simulations use a wood thermal conductivity of 1 W m<sup>-1</sup> K<sup>-1</sup> and a snow thermal conductivity of 0.1 W m<sup>-1</sup> K<sup>-1</sup>. Based on observations, the snow height at Alnus-Nivus is likely to be at least twice the value at FR-Clt. Forcing uses the snow surface temperature, derived from upwelling longwave radiation using an emissivity of 1. The data allow testing thermal bridging through shrub branches. These data are used in a publication in preparation: Domine, Fourteau, Choler, Exploration of Thermal Bridging Through Shrub Branches in Alpine Snow.</p> <p>Reference</p> <p>Gupta, A., Reverdy, A., Cohard, J. M., Hector, B., Descloitres, M., Vandervaere, J. P., Coulaud, C., Biron, R., Liger, L., Maxwell, R., Valay, J. G., and Voisin, D.: Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment, Hydrol. Earth Syst. Sci., 27, 191-212, 2023.</p>
Systematic investigation of mitochondrial transfer between cancer and T cells at singlecell level
<p>The benmark datasets about MT transfer, including mtSNV profile, coverage information, cell information and expression information.</p>
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN
<p>This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' <em>(DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>).</em></p> <p>We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.</p> <p>The final models and code are made publicly available via Github: <a href="https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout">https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout</a>.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>) when using the data in this repository.</p> <p>The csv-file <em>FRCNN_Zoobot_SDSS_GZCS_detections.csv</em> has the following columns. Alternatively, the file <em>FRCNN_Zoobot_SDSS_GZCS_detections.gzip</em> contains the same data but stored as a parquet-file.</p> <table> <tbody><tr> <th>Column name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>specobjid</td> <td>SDSS spec object ID</td> </tr> <tr> <td>dr7objid</td> <td>SDSS DR7 object ID</td> </tr> <tr> <td>clump_id</td> <td>Clump index</td> </tr> <tr> <td>clump_label_id</td> <td>Clump label ID (1 or 2)</td> </tr> <tr> <td>clump_label_name</td> <td>Clump label name</td> </tr> <tr> <td>clump_score</td> <td>Detection score for the clump</td> </tr> <tr> <td>clump_centre_ra</td> <td>Clump centroid RA in degrees</td> </tr> <tr> <td>clump_centre_dec</td> <td>Clump centroid dec in degrees</td> </tr> <tr> <td>clump_flux_u</td> <td>Clump u-band flux in Jy</td> </tr> <tr> <td>clump_flux_g</td> <td>Clump g-band flux in Jy</td> </tr> <tr> <td>clump_flux_r</td> <td>Clump r-band flux in Jy</td> </tr> <tr> <td>clump_flux_i</td> <td>Clump i-band flux in Jy</td> </tr> <tr> <td>clump_flux_z</td> <td>Clump z-band flux in Jy</td> </tr> <tr> <td>clump_flux_err_u</td> <td>Clump u-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_g</td> <td>Clump g-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_r</td> <td>Clump r-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_i</td> <td>Clump i-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_z</td> <td>Clump z-band flux error in Jy</td> </tr> <tr> <td>clump_mag_u</td> <td>Clump u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_g</td> <td>Clump g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_r</td> <td>Clump r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_i</td> <td>Clump i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_z</td> <td>Clump z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_ext_mag_u</td> <td>Clump u-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_g</td> <td>Clump g-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_r</td> <td>Clump r-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_i</td> <td>Clump i-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_z</td> <td>Clump z-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u</td> <td>Clump corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_g</td> <td>Clump corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_r</td> <td>Clump corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_i</td> <td>Clump corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_z</td> <td>Clump corrected z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u_g</td> <td>Clump colour (u-g)</td> </tr> <tr> <td>clump_mag_corr_g_r</td> <td>Clump colour (g-r)</td> </tr> <tr> <td>clump_mag_corr_r_i</td> <td>Clump colour (r-i)</td> </tr> <tr> <td>clump_mag_corr_i_z</td> <td>Clump colour (i-z)</td> </tr> <tr> <td>clump_flux_ratio</td> <td>Est. clump/galaxy near-UV flux ratio (u-band)</td> </tr> <tr> <td>is_clump_3pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >3%</td> </tr> <tr> <td>is_clump_8pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >8%</td> </tr> <tr> <td>galaxy_ra</td> <td>Host galaxy RA in degrees</td> </tr> <tr> <td>galaxy_dec</td> <td>Host galaxy dec in degrees</td> </tr> <tr> <td>galaxy_z</td> <td>Host galaxy redshift</td> </tr> <tr> <td>galaxy_mag_u</td> <td>Host galaxy u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_g</td> <td>Host galaxy g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_r</td> <td>Host galaxy r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_i</td> <td>Host galaxy i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_z</td> <td>Host galaxy z-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_u</td> <td>Host galaxy u-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_g</td> <td>Host galaxy g-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_r</td> <td>Host galaxy r-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_i</td> <td>Host galaxy i-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_z</td> <td>Host galaxy z-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_flux_u</td> <td>Host galaxy u-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_g</td> <td>Host galaxy g-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_r</td> <td>Host galaxy r-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_i</td> <td>Host galaxy i-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_z</td> <td>Host galaxy z-band flux in Jy</td> </tr> <tr> <td>galaxy_expAB_r</td> <td>Host galaxy axis ratio from SDSS</td> </tr> <tr> <td>galaxy_expRad_r</td> <td>Host galaxy exponential fit scale radius from SDSS</td> </tr> <tr> <td>galaxy_lmass</td> <td>Host galaxy log mass in MSun</td> </tr> <tr> <td>galaxy_lssfr</td> <td>Host galaxy log specific SFR</td> </tr> <tr> <td>galaxy_mag_corr_u</td> <td>Host galaxy corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_g</td> <td>Host galaxy corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_r</td> <td>Host galaxy corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_i</td> <td>Host galaxy corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_z</td> <td>Host galaxy corrected z-band magnitude (AB-mag)</td> </tr> </tbody> </table> <p> </p>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database A
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.2 and contains 429,316 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database F
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 557,395 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</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.