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834 results for “split”
Dataset of "Cobalt and nickel doped WSe2 as efficient electrocatalysts for water splitting and as cathodes in hydrogen evolution reaction PEM water electrolysis"
<p>Efficient electrocatalysts are crucial for water splitting and fuel cells. Using cheap alternatives that can improve reaction kinetics is essntial for advancing fuel cell technology. Although, tungsten diselinide (WSe2) is promising for electrocatalysis is not fully explored, especially in oxygen evolution and in applications such as polymer electrolyte membrane water electrolyzer.<br>In this work, we used a simple approach to dope WSe2 with cobalt and/or nickel atoms. The doped material was subsequently tested for hydrogen evolution reaction and oxygen evolution reaction. Accordingly, the two electrocatalysts are highly active and stable, affording low overpotentials comparable to those of noble metals. The effective introduction of heteroatoms causes the retention of coordination vacancies, furnishing active catalytic sites that enhanced electrocatalytic performance both in activity and charge transfer. Moreover, both doped materials show excellent performance and stability as cathode electrocatalysts in the polymer electrolyte membrane water electrolyzer with great promise for real-world applications.</p>
Full-length and split homologs of human proteins in the gut microbiome
<p>These files were generated as part of the manuscript "Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome" (submitted).</p> <p>The .tar file contains .ipc files that are tables of full-length (full_humcover3.ipc) and split homologs (part_humcover3.ipc) of human proteins in the gut microbiome, organized by alignment coverage threshold. For example, the directory `HumanUPR_0.67_src_20000_70` contains results obtained at a 67% alignment coverage threshold for the bacterial protein, and 70% for the human protein. Note that our pipeline collapses full-length alignments to the same UHGP-90 protein family into a single entry per species, with the number of genomes reported in the column nGenomes. Split homologs are not collapsed because genomic context is used to define them, and this context may differ across individual genomes.</p> <p>These files are in Arrow <a href="https://arrow.apache.org/docs/python/ipc.html#ipc">IPC</a> format, which provides compression and fast I/O for large tables. We recommend reading them using <a href="https://pola.rs/">pola.rs</a> or the <a href="https://arrow.apache.org/docs/r/">R Arrow</a> package. In particular, because the full-length homolog table is large, you may wish to work with it without loading it into memory, which can be accomplished using <a href="https://docs.pola.rs/api/python/dev/reference/api/polars.scan_ipc.html">scan_ipc</a> in pola.rs or <a href="https://arrow.apache.org/docs/r/reference/open_dataset.html">open_dataset</a> in R Arrow.</p> <p>We also provide gzipped .csv format datasets of full-length (pgkb_FH_drugs.csv.gz) and split (pgkb_SH_drugs.csv.gz) homologs, at the default 67% alignment coverage threshold for bacterial and 70% for human proteins, organized by their <a href="https://www.pharmgkb.org/">PharmGKB</a> annotations. For each drug annotated in PharmGKB as being metabolized by a human protein with full-length or split homologs, we provide the human protein(s) responsible, its xenobiotic enzyme class, the bacterial protein homolog(s), length and percent identity of the alignment, and either the specific genome (g, split homologs only) or the number of genomes (nGenomes, full homologs only). Xenobiotic enzyme classes are defined as in Figure 4 of the manuscript, with the additional classes "nucl" (nucleobase-containing metabolic proteins not annotated to any other class), "redox" (oxidoreductases not annotated to any other class), and "other" (all remaining proteins).</p>
Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
Split-beam echosounder data from keel-mounted EK60 during PolarFront 2023-08 cruise
<p><strong>PolarFront 2023-08 EK60</strong></p><p>The keel-mounted Simrad EK60® split-beam echosounder continuously recorded hydroacoustic data at 18, 38, and 120 kHz. The ping rate was set to 1.5 seconds and pulse length to 1,024 µs. </p>
Test splits for CREMA-D, emoDB, IEMOCAP, MELD, RAVDESS
<p>Test splits for the categorical emotion datasets CREMA-D, emoDB, IEMOCAP, MELD, RAVDESS used inside audEERING. </p><p>For each dataset, a CSV file is provided listing the file names included in the test split.</p><p>The test splits were designed trying to balance gender and emotional categories as good as possible.</p>
Multifunctional blazed gratings for multiband spatial filtering, retroreflection, splitting, and demultiplexing based on C2 symmetric photonic crystals
<p>These datafiles were generated by MATLAB to create a part of the figures in the paper.</p> <p>The work supported partially by the Narodowe Centrum Nauki (projects nos UMO-2015/17/B/ST3/00118 and UMO-2020/39/I/ST3/02413), TUBITAK (Program No. 2221), and projects UBACyT 20020150100028BA, UBACyT 20020190100108BA and CONICET PIP 11220170100633CO.</p> <p> </p>
Raw Data - Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance
<p>The dataset contains raw data that complements the article:</p> <p>Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance</p> <p>Christian Iffelsberger, Daniel Rojas, and Martin Pumera<strong>*</strong></p> <p>https://doi.org/10.1021/acs.jpcc.1c10686</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Normal mode splitting function predictions for mantle anisotropy
<p>Predictions for normal mode splitting functions for 6 models of mantle anisotropy, accompanying the paper published in Geophysical Journal International by Restelli, Koelemeijer & Ferreira (2023). This is version 2 related to the revised manuscript. </p> <p>More details can be found in the README. </p>
SPACCC_SPLIT
<p>[PlanTL/medicine/annotated corpus/guidelines/sentence splitting] First version of the sentence splitting annotations in the Spanish Clinical Case Corpus that have been carried out by means of the Spanish Clinical Case Corpus Part-of-Specch Tagger based on FreeLing3.1 (SPACCC_POS-TAGGER, <a href="https://github.com/PlanTL/SPACCC_POS-TAGGER">https://github.com/PlanTL/SPACCC_POS-TAGGER</a>).</p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
Connectivity networks for Acropora corals on the GBR to investigate split spawning
<p>Connectivity networks for Acropora corals on the GBR to investigate split spawning.</p> <p>If using these outputs please cite the article:</p> <p>Hock K, Doropoulos C, Gorton R, Condie SA, Mumby PJ. (2019). <strong>Split spawning increases robustness of coral larval supply and inter-reef connectivity</strong>. Nature Communications <strong>10</strong>, 3463.</p> <p>Link to the paper:</p> <p>https://rdcu.be/bOW1x</p> <p> </p> <p> </p> <p> </p>
Shear Wave Splitting and Mantle Flow beneath Alaska Data Set
<p>Entire data set for the (under review) publication "Shear Wave Splitting in Alaska."</p> <p>McPherson_S1_Station_Info is a table that contains the following columns (with header row): Station Name, Network, Latitude (Deg), Longitude (Deg). This is a table of all the seismic stations in Alaska and western Canada that we downloaded data from. Only stations that were active from Jan 1, 2010, to Aug 18, 2017 are included.</p> <p>McPherson_S2_Event_Info is a table that contains the following columns (with header row): Julian Date, Origin Time, Latitude (Deg), Longitude (Deg), Depth (km), Magnitude (Mw). This is a table of all the seismic events that occurred between Jan 1, 2010, to Aug 18, 2017 within the distance range 80 to 140 degrees from a station, over moment magnitude 5.</p> <p>McPherson_S3_Results_Info is a table that contains the following columns (with header row): Station Name, Back Azimuth (Deg), Distance (Deg), Fast Direction (Deg), Lower Bound (Deg), Upper Bound (Deg), Time Difference (sec), Lower Bound (sec), Upper Bound (sec), Julian Date, Origin Time. This table contains all of the minimum energy method (Silver & Chan, 1991) results that are displayed in Figures 4, 6-12 of the paper under review.</p> <p>McPherson_S4_Nulls_Info is a table that contains the following columns (with header row): Station Name, Back Azimuth (Deg), Distance (Deg), Julian Date, Origin Time. This tables contains all the null results displayed in Figure 5 of the paper under review.</p>
SoundDesc: Cleaned and Group-Filtered Splits
<p>This upload contains dataset splits of <em>SoundDesc</em> [1] and other supporting material for our paper:</p> <p><strong>Data leakage in cross-modal retrieval training: A case study </strong><a href="https://arxiv.org/abs/2302.12258">[arXiv]</a> <a href="https://doi.org/10.1109/ICASSP49357.2023.10094617">[ieeexplore]</a></p> <p>In our paper, we demonstrated that a data leakage problem in the previously published splits of SoundDesc leads to overly optimistic retrieval results.<br> Using an off-the-shelf audio fingerprinting software, we identified that the data leakage stems from duplicates in the dataset.<br> We define two new splits for the dataset: a cleaned split to remove the leakage and a group-filtered to avoid other kinds of weak contamination of the test data.</p> <p>SoundDesc is a dataset which was automatically sourced from the BBC Sound Effects web page [2]. The results from our paper can be reproduced using <em>clean_split01</em> and <em>group_filtered_split01</em>.</p> <p><em>If you use the splits, please cite our work:</em></p> <p>Benno Weck, Xavier Serra, "Data Leakage in Cross-Modal Retrieval Training: A Case Study," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094617.</p> <pre><code>@INPROCEEDINGS{10094617, author={Weck, Benno and Serra, Xavier}, booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Data Leakage in Cross-Modal Retrieval Training: A Case Study}, year={2023}, volume={}, number={}, pages={1-5}, doi={10.1109/ICASSP49357.2023.10094617}} </code></pre> <p>References:</p> <p>[1] A. S. Koepke, A. -M. Oncescu, J. Henriques, Z. Akata and S. Albanie, "Audio Retrieval with Natural Language Queries: A Benchmark Study," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3149712.</p> <p>[2] https://sound-effects.bbcrewind.co.uk/</p>
DUDE competition train - validation - test splits ground truth
<p>This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/).</p> <p> </p> <p><strong>V1.0.7 release</strong>: 41454 annotations for 4974 documents (train-validation-test)</p> <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23728 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6315 }) test: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 11402 }) }) ++update on answer_type +++formatting change to answers_variants ++++stricter check on answer_variants & rename annotations file <strong>+ blind test set (no ground truth answers provided) </strong>++ removed duplicates from test set: </pre> <blockquote> <p> "92bd5c758bda9bdceb5f67c17009207b_ac6964cbdf483e765b6668e27b3d0bc4",</p> <p> "6ee71a16d4e4d1dbd7c1f569a92d4e08_549f2a163f8ff3e9f0293cf59fdd98bc",</p> <p> "e6f3855472231a7ca6aada2f8e85fe5a_827c03a72f2552c722f2c872fd7f74c3",</p> <p> "e3eecd7cca5de11f1d17cd94ae6a8d77_6300df64e4cf6ba0600ac81278f68de2",</p> <p> "107b4037df8127a92ee4b6ae9b5df8fb_d7a60e7a9fc0b27487ea39cd7f56f98e",</p> <p> "300cc3900080064d308983f958141232_6a7cf1aad908d58a75ab8e02ddc856f4",</p> <p> "fdd3308efacddb88d4aa6e2073f481d4_138cb868ecc804a63cc7a4502c0009b2",</p> <p> "1f7de256ff1743d329a8402ba0d132e7_95b6e8758533a9817b9f20a958e7b776",</p> <p> "4f399b8c526ffb6a2fd585a18d4ed5ec_51097231bc327c26c59a4fd8d3ff3069",</p> </blockquote> <p> </p>
Data Repository Accompanying "Controllable single Cooper pair splitting in hybrid quantum dot systems"
<p>Code and datasets associated with the manuscript " Controllable single Cooper pair splitting in hybrid quantum dot systems". With the code and data included here, all necessary fits and analysis can be conducted to produce the figures given in the manuscript and its supplementary material. The only exception is that we include the results of the quantum dot stability diagram simulation, however this simulation involves no new physics and the procedure is described in detail in the manuscript's supplementary information.</p>
Audio captioning DCASE 2020 evaluation (testing) split
<p>This is the <strong>evaluation split for Task 6, Automated Audio Captioning, in DCASE 2020 Challenge</strong>. </p> <p>This evaluation split is the Clotho testing split, which is thoroughly described in the corresponding paper: </p> <p><em>K. Drossos, S. Lipping and T. Virtanen, "Clotho: an Audio Captioning Dataset," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736-740, doi: 10.1109/ICASSP40776.2020.9052990.</em></p> <p>available online at: <a href="https://arxiv.org/abs/1910.09387">https://arxiv.org/abs/1910.09387</a> and at: <a href="https://ieeexplore.ieee.org/document/9052990 ">https://ieeexplore.ieee.org/document/9052990 </a></p> <p>This evaluation split is meant to be used for the purposes of the Task 6 at the scientific challenge DCASE 2020. This split it is not meant to be used for developing audio captioning methods. For developing audio captioning methods, you should use the development and evaluation splits of Clotho. </p> <p>If you want the development and evaluation splits of Clotho dataset, you can find them also in Zenodo, at: <a href="https://zenodo.org/record/3490684">https://zenodo.org/record/3490684</a></p> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== License ==</strong></p> <p>The audio files in the archives:</p> <ul> <li>clotho_audio_test.7z </li> </ul> <p>and the associated meta-data in the CSV file:</p> <ul> <li>clotho_metadata_test.csv</li> </ul> <p>are under the corresponding licences (mostly CreativeCommons with attribution) of Freesound [1] platform, mentioned explicitly in the CSV file for each of the audio files. That is, each audio file in the 7z archive is listed in the CSV file with the meta-data. The meta-data for each file are: </p> <ul> <li>File name</li> <li>Start and ending samples for the excerpt that is used in the Clotho dataset</li> <li>Uploader/user in the Freesound platform (manufacturer)</li> <li>Link to the licence of the file</li> </ul> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== References ==</strong><br> [1] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245</p>
Phylogeography of lionfishes (Pterois) indicate taxonomic over splitting and hybrid origin of the invasive Pterois volitans
The evolutionary consequences of hybridization are poorly understood, especially in the marine realm where hybridization was once thought to be a rare occurrence. Previous research indicated that the lionfishes Pterois volitans and P. miles are sister species, both of which have been detected in the recent invasion of the Atlantic. Anecdotal data from the invasive range indicates they may hybridize, but previous studies have not examined the potential for these species to hybridize in the native range, or how such hybridization affects the distribution of genetic diversity. Here we address evolutionary divergence and population structure using mtDNA COI and two nuclear introns from 214 lionfish including four putative sister species (36 P. miles, 90 P. volitans, 32 P. lunulata, and 56 P. russelii) collected at 10 locations. Genetic data are supplemented with a re-examination of key morphological characters: dorsal, anal and pectoral fin ray counts. These data reveal two lineages (d = 0.041 in COI) among the four putative species: an Indian Ocean lineage, represented by P. miles and a Pacific Ocean lineage represented by P. lunulata and P. russelii. Lionfish identified as P. volitans appear to be hybrids between the sister linages of P. miles and P. lunulata/russelii, a conclusion supported by both the genetic data and morphology. The degree and geographic extent of introgression indicates widespread hybridization, or the absence of valid species distinctions between all four species. These findings also indicate that the lionfish invading tropical Atlantic Ocean, usually labeled P. volitans, is a hybrid.
Figs 25–28 in ON SPLITTING OF THE GENUS NOTOCUPES (COLEOPTERA: ARCHOSTEMATA): NEW DATA ON MORPHOLOGY AND TAXONOMY
Figs 25–28. Elytron venation types of five investigated genera of Archostemata. 25, 26 – Zygadenia alexrasnitsyni Strelnikova et Yan, 2021: 25 – photograph; 26 – interpretative
Figs 14–23 in ON SPLITTING OF THE GENUS NOTOCUPES (COLEOPTERA: ARCHOSTEMATA): NEW DATA ON MORPHOLOGY AND TAXONOMY
Figs 14–23. Integuments of extant Archostemata. 14, 16, 19, 20, 22 – Omma stanleyi Newman, 1839: 14 – abdominal sternite, tomography image; 16 – SEM–micrography of
Fig. 13 in ON SPLITTING OF THE GENUS NOTOCUPES (COLEOPTERA: ARCHOSTEMATA): NEW DATA ON MORPHOLOGY AND TAXONOMY
Fig. 13. Size and density of cuticular tubercles in species studied of ancient Rhabdocupes, Conеxicoxa, Notocupes, Brachilatus, Odontomma, Zygadenia and some extant Omma and Distocupes. Tubercles of Odontomma sulcatum were examined from photographs in Kirejtshuk (2020). Yellow – Triassic species; Red – Early Jurassic; Blue – Middle to Late Jurassic; Green – Cretaceous. Here and further orange columns represent density of small
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.