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3,684 results for “extract”
Scripts and data from the study 'Characterisation of magnetic atomic and molecular beamlines for the extraction of empirical scattering-matrices' (PCCP manuscript number CP-ART-04-2024-001785)
<p>This repository contains the data used for the article "Characterisation of magnetic atomic and molecular beamlines for the extraction of empirical scattering-matrices" and includes the code for generating the figures in the publication.</p>
Dataset of miRNA-Disease Relations Extracted from Textual Data using Transformer-based Neural Networks
<p>Supplementary Data.</p>
Host Finding Disruption of Diaphorina Citri by Guava Shoot Extract and Menochilus Sexmaculatus
<div> <p>This material has presented on 2nd International Conference on Advance Research in Agriculture and Food 2023 in October 25, 2023.</p> </div>
The Impact of Feed-Solvent Ratio on Contella Asiatica Extraction Using Microwave-Assisted Extraction (MAE)
<div>This material has presented on 2nd International Conference on Advance Research in Agriculture and Food 2023 in October 25, 2023.</div>
Exploring the potential effects of Periplaneta americana (L.) extract on Alzheimer's disease based on network pharmacology combined with cellular experiments
Open the record for dataset details and reuse information.
Dataset: Probabilistic work extraction on a classical oscillator beyond the second law
<p>Data set for the article "Probabilistic work extraction on a classical oscillator beyond the second law" , currently published as a preprint on arXiv, and accepted in Physical Review Letters.</p> <p>The preprint and SM have been uploaded for convenience.</p> <p>Inside the compressed file, you will find several folders</p> <p>It contains:</p> <p> </p> <ol> <li><strong>/Exemple trajectories/</strong> <ul> <li> 10 folders named Protocol 0 , Protocol 1 .... each containing the raw data of the 100 first experiments for each protocol. Each protocol was repeated 3000 time to increase the statistics for the article. We provide a representative subset.</li> <li>The Python analysis code "readexperimentalfile.py" . It reads the data in the aforementioned files, and computes the initial potential, the final potential, and the work associated to each protocols. Optionnaly, it saves trajectories used create Fig3 and Fig 4.</li> </ul> </li> <li><strong>/Figures/</strong> <ul> <li>Fig 1 : The pdf file for Fig1. (stochastic2.pdf) as presented in the article. Other formats can be provided on demand.</li> <li>Fig 2: The pdf file for Fig2, Setup.pdf</li> <li>Fig 3: The python code used to create Fig3 , named "plotpotentiel.py". To use it, one need the parameters tuning the initial potential used in the fifth protocol 'fitdepart5.npy', the final potential 'fitfin5.npy' as well as the set of trajectories "zfin5.npy" and "zinit5.npy". All those files were created using the provided python code "readexperimentalfile.py" , and are already provided in the same folder.</li> <li>Fig4: The python code used to create Fig4, named "plottrajex.py". "ztotsubset5.npy" and "lambdasubset5.npy" are provided to directly plot z(t) and lambda(t) during the protocol. "fitdepart5.npy" and 'fitfin5.npy", also created using "readexperimentalfile.py", are needed. </li> <li>Fig5: a) Everything needed to plot the first part of Fig 5. using the python file "workdpdf.py". All the datas of the other experiments are provided, and one can also choose to plot the work probability density function for other protocols (by changing the 5 t o another number. b) "mainfig.py" creates the bottom part of Fig5.</li> </ul> </li> </ol>
Target Speech Extraction Dataset for Knowledge Boosting (Part 1)
<p><strong>Part 1 of the Target Speech Extraction Dataset</strong> as described in <em>Knowledge boosting during low-latency inference</em> (Interspeech 2024)</p> <p><strong>Abstract:</strong> Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time requirements and does not guarantee that both models will operate on the same data at the same time. We propose knowledge boosting, a novel technique that allows a large model to operate on time-delayed input during inference, while still boosting small model performance. Using a streaming neural network that processes 8 ms chunks, we evaluate different speech separation and enhancement tasks with communication delays of up to six chunks or 48 ms. Our results show larger gains where the performance gap between the small and large models is wide, demonstrating a promising method for large-small model collaboration for low-latency applications. </p>
Target Speech Extraction Dataset for Knowledge Boosting (Part 2)
<p><strong>Part 2 of the Target Speech Extraction Dataset</strong> as described in <em>Knowledge boosting during low-latency inference</em> (Interspeech 2024)</p> <p><strong>Abstract:</strong> Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time requirements and does not guarantee that both models will operate on the same data at the same time. We propose knowledge boosting, a novel technique that allows a large model to operate on time-delayed input during inference, while still boosting small model performance. Using a streaming neural network that processes 8 ms chunks, we evaluate different speech separation and enhancement tasks with communication delays of up to six chunks or 48 ms. Our results show larger gains where the performance gap between the small and large models is wide, demonstrating a promising method for large-small model collaboration for low-latency applications. </p>
Assisted Data Annotation for Business Process Information Extraction from Textual Documents
Open the record for dataset details and reuse information.
Chemical Name Extraction Based on Automatic Training Data Generation
<p>The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.</p>
ORCHSET: a dataset for melody extraction in symphonic music recordings
<p>Orchset is intended to be used as a dataset for the development and evaluation of melody extraction algorithms. This collection contains 64 audio excerpts focused on symphonic music. with their corresponding annotation of the melody.</p> <p>Melody is here defined as “the single (monophonic) pitch sequence that a listener might reproduce if asked to whistle or hum a piece of polyphonic music”.</p> <p>The dataset creation comprised several tasks: excerpts selection, recording sessions of people singing along with the excerpts, analysis of the recordings and melody annotation. A complete description of the dataset and the creation methodology is presented in this paper:</p> <blockquote> <p>Bosch, J., Marxer, R., Gomez, E., “Evaluation and Combination of Pitch Estimation Methods for Melody Extraction in Symphonic Classical Music”, Journal of New Music Research (2016)</p> </blockquote> <p>Please Acknowledge Orchset in Academic Research</p> <p><strong>Using this dataset</strong></p> <p>When Orchset is used for academic research, we would highly appreciate if scientific publications of works partly based on the Orchset dataset quote the above publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/orchset">https://www.upf.edu/web/mtg/orchset</a></p>
Raw data of the manuscript: Heterogeneous Freezing of Liquid Suspensions Including Juices and Extracts from Berries and Leaves from perennial Plants
<p>Freezing data</p>
Automated Extraction of Artificial Intelligence Model Metadata from Repositories
<p>Replication datasets for MSR submission: "Automated Extraction of Artificial Intelligence Model Metadata from Repositories"</p> <p>This package contains the following:</p> <p>1. The model zoo dataset of 277 repository URLs.<br> 2. The arXiv dataset of 1,398 repository URLs extracted from arXiv papers.<br> 3. The annotated evaluation samples for both datasets with incorrect and missing properties marked per model.<br> </p>
Alveolar ridge alterations in the maxillary anterior region after tooth extraction through orthodontic forced eruption for implant site development: A prospective CBCT case series
<p>Dataset for all analyses done in the study</p>
Organic aerosol source apportionment in Zurich using extractive electrospray ionization time-of-flight mass spectrometry (EESI-TOF): Part I, biogenic influences and day/night chemistry in summer
<p>Ambient measurement campaign took place during summer 2016 in Zurich. The sources of organic aerosol were disclosed. The novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) could provide direct chemical evidence linking ambient SOA to its precursor emissions including a strong influence of biogenic emissions. Additionally provided some insight into the day/night reaction environment and high-detailed chemical composition.</p>
Cytoscape session for the potato knowledge graph extracted with IBM Watson's supervised NLP model
<p>2 cytoscape session files (.cys) representing the genotypic-phenotypic knowledge networks retrieved from scientific literature using IBM Watson. Input to these files were the following: </p> <ul> <li>cytoscapeSession_trainingSet: A training set of 34 full-test articles about potato flesh color</li> <li>cytoscapesession_testSet: A testing set of a 4023 abstracts from PubMed.</li> </ul> <p> </p>
VILE: Vial Imaging and Label Extraction example image set
<p>Example images from 100 vials within the Gillies collection of Ephemeroptera at the Natural History Museum, London. Vials were imaged on a rotating stage.</p>
Supplementary material 2 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S2. Effects of water extracts of leaves of various plants on the numbers of egg pods laid when presented to adult female desert locusts mixed with sand.
Supplementary material 1 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S1. Effects of water extracts of desert locust frass after feeding on various plants on the numbers of egg pods laid when adult female desert locusts were presented with extracts mixed with sand.
Supplementary material 3 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S3. Effects of water extracts of rescue grass-fed desert locust frass on antennal lengths of embryos (A, mean ± SD; n = 6–10).
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.