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834 results for “split”
Figs 5–8 in ON SPLITTING OF THE GENUS NOTOCUPES (COLEOPTERA: ARCHOSTEMATA): NEW DATA ON MORPHOLOGY AND TAXONOMY
Figs 5–8. Representatives of the four investigated genera. 5 – Rhabdocupes protensus
Split-beam echosounder data from keel-mounted EK60 during ARCTOS Polar Front 2021-05 cruise
<p><strong>PolarFront 2021-05 ship EK60</strong></p><p>Hydroacoustic data collected from 2021-05-14T10:43:37Z to 2021-05-21T08:18:33Z along the cruise transects from the three available frequencies installed on Helmer Hanssen (Simrad EK60, 18 kHz, 38 kHz and 120 kHz).</p><p>For further details, see <a href="https://doi.org/10.5281/zenodo.7384075">cruise report</a> section 5.2.</p>
Upper-mantle anisotropy in the southeastern margin of the Tibetan Plateau revealed by fullwave SKS splitting intensity tomography
<p>This dataset contains the raw 3-component 100s SKS waveforms, measured splitting intensities and our final inverted anisotropic model for SE Tibet.</p>
Dataset, splits, models, and scripts for the QM descriptors prediction
<p>Dataset, splits, models, and scripts from the manuscript "When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?" are provided. The curated dataset includes 37 QM descriptors for 64,921 unique molecules across six levels of theory: wB97XD, B3LYP, M06-2X, PBE0, TPSS, and BP86. This dataset is stored in the data.tar.gz file, which also contains a file for multitask constraints applied to various atomic and bond properties. The data splits (training, validation, and test splits) for both random and scaffold-based divisions are saved as separate index files in splits.tar.gz. The trained D-MPNN models for predicting QM descriptors are saved in the models.tar.gz file. The scripts.tar.gz file contains ready-to-use scripts for training machine learning models to predict QM descriptors, as well as scripts for predicting QM descriptors using our trained models on unseen molecules and for applying radial basis function (RBF) expansion to QM atom and bond features.</p> <p>Below are descriptions of the available scripts:</p> <ol> <li><code>atom_bond_descriptors.sh</code>: Trains atom/bond targets.</li> <li><code>atom_bond_descriptors_predict.sh</code>: Predicts atom/bond targets from pre-trained model.</li> <li><code>dipole_quadrupole_moments.sh</code>: Trains dipole and quadrupole moments.</li> <li><code>dipole_quadrupole_moments_predict.sh</code>: Predicts dipole and quadrupole moments from pre-trained model.</li> <li><code>energy_gaps_IP_EA.sh</code>: Trains energy gaps, ionization potential (IP), and electron affinity (EA).</li> <li><code>energy_gaps_IP_EA_predict.sh</code>: Predicts energy gaps, IP, and EA from pre-trained model.</li> <li><code>get_constraints.py</code>: Generates constraints file for testing dataset. This generated file needs to be provided before using our trained models to predict the atom/bond QM descriptors of your testing data.</li> <li><code>csv2pkl.py</code>: Converts QM atom and bond features to .pkl files using RBF expansion for use with Chemprop software.</li> </ol> <p>Below is the procedure for running the ml-QM-GNN on your own dataset:</p> <ol> <li>Use <code>get_constraints.py</code> to generate a constraint file required for predicting atom/bond QM descriptors with the trained ML models.</li> <li>Execute <code>atom_bond_descriptors_predict.sh</code> to predict atom and bond properties. Run <code>dipole_quadrupole_moments_predict.sh</code> and <code>energy_gaps_IP_EA_predict.sh</code> to calculate molecular QM descriptors.</li> <li>Utilize <code>csv2pkl.py</code> to convert the data from predicted atom/bond descriptors .csv file into separate atom and bond feature files (which are saved as .pkl files here).</li> <li>Run <a href="https://github.com/chemprop/chemprop">Chemprop</a> to train your models using the additional predicted features supported here.</li> </ol>
Split_and_Destroy_Data
<p>Times in sec for the the checkpoint and restore of connections and their data in a Docker environment ("exp_4_check" and "exp_4_check"), as well as the results from the simulation part for the connections partitioning.</p> <p> </p> <p>The final paper that includes this data has a reference of: Kampourakis, V.; Makrakis, G.M.; Kolias, C. From Seek-and-Destroy to Split-and-Destroy: Connection Partitioning as an Effective Tool against Low-Rate DoS Attacks. <em>Future Internet</em> <strong>2024</strong>, <em>16</em>, 137. https://doi.org/10.3390/fi16040137</p>
Exposed area dependent PEC performance of BiVO4 photoanodes for solar water splitting
<p>#Data set of "Exposed area dependent PEC performance of BiVO4 photoanodes for solar water splitting"</p> <p>---</p> <p>## GENERAL INFORMATION</p> <p>1. Data set title: "Exposed area dependent PEC performance of BiVO4 photoanodes for solar water splitting"</p> <p>2.Authorship: <br> Name: Christian Robles<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0003-1166-2950</p> <p> Name: Laura Montañés<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-1076-013X <br> <br> Name: Camilo A. Mesa<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-8450-2563</p> <p> Name: Ana Gutiérrez-Blanco<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0001-9412-2321</p> <p> Name: Francisco Fabregat-Santiago<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-7503-1245</p> <p> Name: Agustín O Alvarez<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID:0000-0002-0920-5390</p> <p> Name: Sixto Giménez<br> Institution: Institude of Advanced Materials (INAM), Universitat Jaume I, 12006, Castelló, Spain<br> ORCID: 0000-0002-4522-3174<br> Email: <sjulia@uji.es></p> <p>##FILE DESCRIPTION<br>---------------</p> <p>### Figure 1<br>-Fig1a.txt : Average photocurrent–voltage (J–V) plots measured by back illumination in 0.1 M KPi with 0.1 M Na2SO3 for the different exposed areas. Dark currents are represented as dashed lines.<br>-Fig1b.txt : Chronoamperometry plot measured by back illumination in 0.1 M KPi with 0.1 M Na2SO3 for the three exposed areas. Inset: BiVO4 photoanodes synthetized by electrochemical deposition and thermal evaporation. The area was delimited using an epoxy resin.<br>-Fig1c.txt : Average of current–voltage (J–V) plots measured by back illumination in 0.1 M KPi with 0.1 M Na2SO3 for an equal to exposed area of 1 cm2 (black), 0.2 cm2 (green) and a photoanode of 1 cm2 with a reduced exposed area of 0.2 cm2 (blue). </p> <p>### Figure 2<br>-Fig2a.txt : Charge-transfer resistance (Rct) as a function of applied potential extracted from IS.<br>-Fig2b.txt : Capacitance as a function of applied potential extracted from IS.<br>-Fig2c.txt : Series Resistance (Rc) as a function of the applied potential extracted from IS.<br>-Fig2d.txt : Density of surface states (DOS) as a function of applied potential extracted from IS.<br>-Fig2e.txt : IR-chopped chronoamperometry measurements at 1.2 V vs RHE.<br>-Fig2f.txt : Mobility of electrons (blue), diffusion resistance (green) and recombination resistance (red) as function of the exposed area extracted from the fitting of IMPS, IMVS and IMVS.</p> <p>### Figure S3<br>-FigS3a.txt : Current–voltage (J–V) plots measured in 0.1 M KPi (red) and 0.1 M KPi with 0.1 M Na2SO3 (blue).<br>-FigS3b.txt : Current–voltage (J–V) plots measured by front illumination (light blue) and back illumination (dark blue) in 0.1 M KPi with 0.1 M Na2SO3.</p> <p>### Figure S4<br>-FigS4a.txt : Incident-photon to current efficiency (IPCE) under back illumination measured and 0.1 M KPi with 0.1 M Na2SO3.<br>-FigS4b.txt : optical absorbance of the BiVO4 photoanode and its obtained bandgap (2.47 eV) extracted from the Tauc plot for a indirect transition.</p> <p>###Figure S5<br>-FigS5a.txt : Raman spectra for the 0.2 cm2 exposed area before measuring. Colours represent different spots measured in the same sample.<br>-FigS5b.txt : Raman spectra for the 0.4 cm2 exposed area before measuring. Colours represent different spots measured in the same sample.<br>-FigS5c.txt : Raman spectra for the 1 cm2 exposed area before measuring. Colours represent different spots measured in the same sample.<br>-FigS5d.txt : Raman spectra for the 0.2 cm2 exposed area after measuring. Colours represent different spots measured in the same sample.<br>-FigS5e.txt : Raman spectra for the 0.4 cm2 exposed area after measuring. Colours represent different spots measured in the same sample.<br>-FigS5f.txt : Raman spectra for the 1 cm2 exposed area after measuring. Colours represent different spots measured in the same sample.</p> <p> </p>
Major population splits coincide with episodes of rapid climate change in a forest-dependent bird
<p>Climate change influences population demography by altering patterns of gene flow and reproductive isolation. Direct mutation rates offer the possibility for accurate dating on the within-species level but are currently only available for a handful of vertebrate species. Here, we use the first directly estimated mutation rate in birds to study the evolutionary history of pied flycatchers (Ficedula hypoleuca). Using a combination of demographic inference and environmental niche modelling, we show that all major population splits in this forest-dependent system occurred during periods of increased climate instability and rapid global temperature change. We show that the divergent Spanish subspecies originated during the Eemian-Weichselian transition 115 – 104 thousand years ago (kya), and not during the last glacial maximum (26.5 - 19 kya), as previously suggested. The magnitude and rates of climate change during the glacial-interglacial transitions that preceded population splits in pied flycatchers were similar to, or exceeded, those predicted to occur in the course of the current, human-induced climate crisis. As such, our results provide a timely reminder of the strong impact that episodes of climate instability and rapid temperature changes can have on species' evolutionary trajectories, with important implications for the natural world in the Anthropocene.</p>
Data for: An implicit split-operator algorithm for the nonlinear time-dependent Schrödinger equation
<p>Data for publication: J. Roulet, J. Vanicek, An implicit split-operator algorithm for the nonlinear time-dependent Schrödinger equation, J. Chem. Phys. <strong>155</strong>, 204109 (2021).</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Datasets for the paper "ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells"
<p>In this archive, you can find all the data used in the paper "ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells".</p> <p><strong>sklearn_full_cells.csv</strong> is the dataset from the paper of Pimentel et al. filtered with only Data Science notebooks.<br> <strong>complete.csv</strong> is the dataset obtained after the full run of ReSplit on the dataset: both merging and splitting.<br> <strong>split.csv</strong> is the dataset obtained after running only the splitting part of our dataset.<br> <strong>merged.csv</strong> is the dataset obtained after running only the merging part of our dataset.<br> <strong>duplicates_id.csv</strong> contains the IDs of the duplicate notebooks for deduplication.<br> <strong>changes.csv</strong> contains the IDs of the datasets, as well as their length before and after running ReSplit.<br> <strong>survey.csv</strong> is the table with the results of the survey.</p> <p>In the dataset CSVs, each line is a cell that has a unique identifier and an identifier of the corresonding notebook.</p>
(D2.1_dataset01) Parameter values used in the optimal loads split algorithm to find the baseload
<p>In Task 2.1 of hybridGEOTABS project, an <em>optimal load splitting algorithm (OLSA) </em>was developed and validated. The OLSA splits the building thermal loads (calculated in T2.2 of the project) to the baseload and residual loads. The baseload is defined as the maximum share of GEOTABS that allows to minimise the energy use of the entire (primary + secondary) heating and cooling system, while maintaining thermal comfort in the building and by using and taking into account the thermal storage in the TABS. The algorithm is documented in detail in D2.1 of the hybridGEOTABS project. This document reports all the parameter values used in the OLSA.</p>
Beatings of ratchet current magneto-oscillations in GaN-based grating gate structures: Manifestation of spin-orbit band splitting
<p>OPEN DATA related to the research publication:</p> <p>Sai, P., Potashin, S. O., Szoła, M., Yavorskiy, D., Cywiński, G., Prystawko, P., ... & Kachorovskii, V. Y. (2021). <strong>Beatings of ratchet current magneto-oscillations in GaN-based grating gate structures: Manifestation of spin-orbit band splitting.</strong> <em>Physical Review B</em>, <em>104</em>(4), 045301 [arXiv:2102.12791].</p> <p><em>Abstract</em>: We report on the study of the magnetic ratchet effect in AlGaN/GaN heterostructures superimposed with a lateral superlattice formed by a dual-grating gate structure. We demonstrate that irradiation of the superlattice with a terahertz beam results in the <em>direct</em> ratchet current, which shows giant magneto-oscillations in the regime of Shubnikov–de Haas oscillations. The oscillations have the same period and are in phase with the resistivity oscillations. Remarkably, their amplitude is greatly enhanced as compared with the ratchet current at zero magnetic field, and the envelope of these oscillations exhibits large beatings as a function of the magnetic field. We demonstrate that the beatings are caused by the spin-orbit (SO) splitting of the conduction band. We develop a theory which gives a good qualitative explanation of all experimental observations and allows us to extract the SO splitting constant α<sub>SO</sub>=7.5±1.5 meVÅ. We also discuss how our results are modified by plasmonic effects and show that these effects become more pronounced with decreasing the period of the grating gate structures down to submicrons.</p>
THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING
<p>Code and data for the paper THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING - B. Beaucamp, T. Leduc, V. Tourre, M. Servières</p>
Planar Graph Classifier - Set Split
<p>Set with class information (Planar Graph Classifier - Class Information - 10.5281/zenodo.6510113) is split into training and test set for model fitting and evaluation.</p>
Salmon SPLiT-seq tutorial data
<p>This is the data required to run the salmon and alevin-fry tutorial on SPLiT-seq data. </p>
Updating splits, lumps, and shuffles: Reconciling GenBank names with standardized avian taxonomies
Abstract Biodiversity research has advanced by testing expectations of ecological and evolutionary hypotheses through the linking of large-scale genetic, distributional, and trait datasets. The rise of molecular systematics over the past 30 years has resulted in a wealth of DNA sequences from around the globe. Yet, advances in molecular systematics also have created taxonomic instability, as new estimates of evolutionary relationships and interpretations of species limits have required widespread scientific name changes. Taxonomic instability, colloquially "splits, lumps, and shuffles," presents logistical challenges to large-scale biodiversity research because (1) the same species or sets of populations may be listed under different names in different data sources, or (2) the same name may apply to different sets of populations representing different taxonomic concepts. Consequently, distributional and trait data are often difficult to link directly to primary DNA sequence data without extensive and time-consuming curation. Here, we present RANT: Reconciliation of Avian NCBI Taxonomy. RANT applies taxonomic reconciliation to standardize avian taxon names in use in NCBI GenBank, a primary source of genetic data, to a widely used and regularly updated avian taxonomy: eBird/Clements. Of 14,341 avian species/subspecies names in GenBank, 11,031 directly matched an eBird/Clements; these link to more than 6 million nucleotide sequences. For the remaining unmatched avian names in GenBank, we used Avibase's system of taxonomic concepts, taxonomic descriptions in Cornell's Birds of the World, and DNA sequence metadata to identify corresponding eBird/Clements names. Reconciled names linked to more than 600,000 nucleotide sequences, ~9% of all avian sequences on GenBank. Nearly 10% of eBird/Clements names had nucleotide sequences listed under 2 or more GenBank names. Our taxonomic reconciliation is a first step towards rigorous and open-source curation of avian GenBank sequences and is available at GitHub, where it can be updated to correspond to future annual eBird/Clements taxonomic updates.
Mode Splitting Spectrogram and Analysis Plots, 11 May 2022
<p>Spectrogram collected in Spectrum Lab by Steve WA5FRF. Digitized manually using MATLAB. Digitization and computation code at <a href="https://github.com/KCollins/wa5frf-plots">https://github.com/KCollins/wa5frf-plots</a>.</p>
High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH
<p>Dataset for the article "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"</p>
Fig. 6 in The origin and diversification of the Entorrhizales: deep evolutionary roots but recent speciation with a phylogenetic and phenotypic split between associates of the Cyperaceae and Juncaceae
Fig. 6 Spores of Entorrhiza fuirenae (holotype) seen by light microscopy. Scale bar = 10 μm
Transparent TiO2 nanotubes supporting silver sulfide for photoelectrochemical water splitting
<p>The following dataset contains research data that is the basis of the research article:</p> <p>"Transparent TiO2 nanotubes supporting silver sulfide for photoelectrochemical water splitting"</p> <p>Contents of the package are the following:</p> <p>a) Experimental results of impedance spectra recorded for fTiNT and gTiNT</p> <p>b) Experimental results of UV-vis absorbance for Ag2S-coated and non-coated fTiNT, and gTiNT </p> <p>c) Cyclic voltammetry carried out in 0.5 M Na2SO4 and Raman spectra for 25-Ag2S/gTiNT electrode </p> <p>d) Cyclic voltammetry carried out in 0.1 M NaOH and Raman spectra for 45-Ag2S/gTiNT electrode </p> <p> </p>
RDF2Vec DBpedia Page Rank split embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a Page Rank split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</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.