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Data from: Ligusticopsis muliensis, a new combination separated from Peucedanum sensu lato (Apiaceae)
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Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes
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DNA-stimulated liquid-liquid phase separation by eukaryotic topoisomerase II modulates catalytic function
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A harvest was conducted to determine productivity of rare species not found in at least 4 quadrats per site in a separate small quadrat aboveground biomass harvest, Arctic LTER 1997.
A harvest was conducted to determine productivity of rare species not found in at least 4 quadrats per site in a separate small quadrat aboveground biomass harvest (see 97lg3sbm.txt). Harvests occurred in a tussock, watertrack, and snowbed community at 3 sites - acidic tundra near Toolik (site of acidic LTER plots), nonacidic tundra near Toolik Lake(site of non-acidic LTER plots), and acidic tundra near Sagwon. Moss and lichen data are presented by species elsewhere (see 97lgmosslichen).
WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement
<p>This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER <a href="https://ieeexplore.ieee.org/abstract/document/8110202">Kiselev2018</a>. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). </p> <p>If you use this dataset please cite:</p> <ul> <li><strong>E. Ceolini, I. Kiselev and S. Liu, "Evaluating multi-channel multi-device speech separation algorithms in the wild: a hardware-software solution," in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>.</strong></li> </ul> <p>===</p> <p>Each sample is a 16-channel wav file in which the order of the channel follows the following logic:</p> <p>0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4</p> <p>Refer to the <a href="https://github.com/SensorsAudioINI/WHISPER_SET_1/blob/master/WHISPER4_floor_annotated.png">floor plan</a> for a visual illustration of the microphone arrangement.</p> <p>The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation.</p> <ul> <li>In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4)</li> <li>In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB)</li> </ul> <p>Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav).</p> <p>== </p> <p>A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on <a href="https://github.com/Enny1991/beamformers">this beamforming library</a> and <a href="https://github.com/Enny1991/sep_eval">this tool</a> to evaluate the quality of the separation.</p> <p>==</p> <p>Refer to the README.md in the dataset for more information.</p> <p>For any question please contact enea.ceolini@gmail.com</p>
Model data for "Flow Separation and Increased Drag Coefficient in Estuarine Channels with Curvature"
<p>These are the model data we generated using ROMS and analyzed for the journal article "Increased Drag Coefficient in Estuarine Channels with Curvature". Files include 8 sinuous channel models and 2 straight channels. sinuous_channel_1.nc and straight_channel_1.nc are the pair of models analyzed in section 3. sinuous_channel_1_avg.nc and straight_channel_1_avg.nc are the one-hour average result. sinuous_channel_2_avg.nc and straight_channel_2_avg.nc are the pair of deep channel models. sinuous_channel_3_avg.nc and others are the other different sinuous channel models.</p>
Figure 67. Character 31 separated the 8 in DELTA for Beginners. An introduction into the taxonomy software package DELTA
Figure 67. Character 31 separated the 8 Remaining Taxa into halves by eliminating 4 taxa.
EUREC4A: HALO flight phase separation: Awesome Albatross
<p>Awesome Albatross is the first version of the flight segmentation for HALO flights during the EUREC4A field campaign.</p>
Divergent microbiota of echinoid eggs separated by the Isthmus of Panama
<p>Relationships between animals and their associated microbiota are dependent on the evolutionary history of the host and on the environment. The majority of studies tend to focus on one of these factors and rarely consider how both determine the community composition of the associated bacteria. One "natural experiment" to test how evolutionary history, shared environments, and the interaction between these factors drive community composition is to compare geminate species pairs. Echinoids separated by the Isthmus of Panama are suitable for this comparison due to the known evolutionary history and differences in oceanographic characteristics of the Caribbean Sea and Pacific Ocean. By comparing the egg-associated microbiota of the <i>Echinometra</i> and <i>Diadema</i> geminate species pairs, we show that each pair of geminate species associates with a distinct bacterial community in a pattern consistent with phylosymbiosis, and that the interaction between the evolutionary history of the host and the environment best explain differences in these communities. Moreover, we found that particular microbial taxa differed considerably between, but not within, oceans and that the microbiota of the two Caribbean <i>Echinometra</i> species were dominated by the phototrophic Oxyphotobacteria. Taken together, data presented here support the hypothesis that the microbiota associated with geminate species are another characteristic of these taxa that diverged in ~2.8 million years of isolation.</p>
Free Universal Sound Separation Dataset
<p>The Free Universal Sound Separation (FUSS) Dataset is a database of arbitrary sound mixtures and source-level references, for use in experiments on arbitrary sound separation. </p> <p>This is the official sound separation data for the DCASE2020 Challenge Task 4: Sound Event Detection and Separation in Domestic Environments.</p> <p><strong>Citation: </strong>If you use the FUSS dataset or part of it, please cite our paper describing the dataset and baseline [1]. FUSS is based on <a href="https://annotator.freesound.org/fsd/">FSD data</a> so please also cite [2]:</p> <p><strong>Overview: </strong>FUSS audio data is sourced from a pre-release of <a href="https://annotator.freesound.org/fsd/">Freesound dataset</a> known as (FSD50k), a sound event dataset composed of Freesound content annotated with labels from the AudioSet Ontology. Using the FSD50K labels, these source files have been screened such that they likely only contain a single type of sound. Labels are not provided for these source files, and are not considered part of the challenge. For the purpose of the DCASE Task4 Sound Separation and Event Detection challenge, systems should not use FSD50K labels, even though they may become available upon FSD50K release.</p> <p>To create mixtures, 10 second clips of sources are convolved with simulated room impulse responses and added together. Each 10 second mixture contains between 1 and 4 sources. Source files longer than 10 seconds are considered "background" sources. Every mixture contains one background source, which is active for the entire duration. We provide: a software recipe to create the dataset, the room impulse responses, and the original source audio.</p> <p><strong>Motivation for use in DCASE2020 Challenge Task 4: </strong> This dataset provides a platform to investigate how source separation may help with event detection and vice versa. Previous work has shown that universal sound separation (separation of arbitrary sounds) is possible [3], and that event detection can help with universal sound separation [4]. It remains to be seen whether sound separation can help with event detection. Event detection is more difficult in noisy environments, and so separation could be a useful pre-processing step. Data with strong labels for event detection are relatively scarce, especially when restricted to specific classes within a domain. In contrast, source separation data needs no event labels for training, and may be more plentiful. In this setting, the idea is to utilize larger unlabeled separation data to train separation systems, which can serve as a front-end to event-detection systems trained on more limited data.</p> <p><strong>Room simulation: </strong>Room impulse responses are simulated using the image method with frequency-dependent walls. Each impulse corresponds to a rectangular room of random size with random wall materials, where a single microphone and up to 4 sources are placed at random spatial locations.</p> <p><strong>Recipe for data creation: </strong>The data creation recipe starts with scripts, based on<a href="https://github.com/justinsalamon/scaper"> scaper</a> [5], to generate mixtures of events with random timing of source events, along with a background source that spans the duration of the mixture clip. The scipts for this are at<a href="https://github.com/google-research/sound-separation/tree/master/datasets/fuss"> this GitHub repo</a>.</p> <p>The data are reverberated using a different room simulation for each mixture. In this simulation each source has its own reverberation corresponding to a different spatial location. The reverberated mixtures are created by summing over the reverberated sources. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired.</p> <p>The constituent source files for each mixture are also generated for use as references for training and evaluation. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired.</p> <p>Note: no attempt was made to remove digital silence from the freesound source data, so some reference sources may include digital silence, and there are a few mixtures where the background reference is all digital silence. Digital silence can also be observed in the event recognition public evaluation data, so it is important to be able to handle this in practice. Our evaluation scripts handle it by ignoring any reference sources that are silent. </p> <p><strong>Format: </strong>All audio clips are provided as uncompressed PCM 16 bit, 16 kHz, mono audio files.</p> <p><strong>Data split: </strong> The FUSS dataset is partitioned into "train", "validation", and "eval" sets, following the same splits used in FSD data. Specifically, the train and validation sets are sourced from the FSD50K dev set, and we have ensured that clips in train come from different uploaders than the clips in validation. The eval set is sourced from the FSD50K eval split.</p> <p><strong>Baseline System: </strong>A baseline system for the FUSS dataset is available at <a href="https://github.com/google-research/sound-separation/tree/master/datasets/fuss">dcase2020_fuss_baseline</a>.</p> <p><strong>License: </strong>All audio clips (i.e., in FUSS_fsd_data.tar.gz) used in the preparation of Free Universal Source Separation (FUSS) dataset are designated Creative Commons (CC0) and were obtained from<a href="http://freesound.org"> freesound.org</a>. The source data in FUSS_fsd_data.tar.gz were selected using labels from the<a href="https://annotator.freesound.org/fsd/"> FSD50K corpus</a>, which is licensed as Creative Commons Attribution 4.0 International (CC BY 4.0) License.</p> <p>The FUSS dataset as a whole, is a curated, reverberated, mixed, and partitioned preparation, and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This license is specified in the `LICENSE-DATASET` file downloaded with the `FUSS_license_doc.tar.gz` file.</p> <p><strong>Notes:</strong></p> <p>Added in v1.2: </p> <ul> <li>FUSS_baseline_dry_model.tar.gz: baseline separation model trained on non-reverberated (dry) data. </li> <li>FUSS_DESED_baseline_dry_2_model.tar.gz:: baseline separation model for the DESED task, trained on a mixture of DESED in-domain data and FUSS data</li> </ul> <p>Added in v1.3:</p> <ul> <li>FUSS_DESED_baseline_dry_1_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED mixtures from dry FUSS mixtures (DmFm)</li> <li>FUSS_DESED_baseline_dry_4_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, dry FUSS mixture, and 5 DESED foreground sources with PIT (PIT)</li> <li>FUSS_DESED_baseline_dry_4np_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, 10 DESED classes, and dry FUSS mixture without PIT (Classwise)</li> <li>FUSS_DESED_baseline_dry_6_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, 5 DESED foreground sources, 4 dry FUSS sources, with groupwise PIT (GroupPIT)</li> </ul> <p>The names in parentheses are the task names from Table 3 of the following paper: <a href="https://arxiv.org/pdf/2007.03932.pdf">Nicolas Turpault, Scott Wisdom, Hakan Erdogan, John R. Hershey, Romain Serizel, Eduardo Fonseca, Prem Seetharaman, and Justin Salamon, "Improving Sound Event Detection in Domestic Environments using Sound Separation", DCASE 2020.</a></p>
NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR- 0,25bar
<p>As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three</p>
Data from: Separating differential allocation by females from direct effects of male condition in a beetle
<p>Differential allocation is the adjustment of reproductive allocation, typically by a female, in response to the quality of her male partner. A recent theoretical model suggests that differential allocation may influence trade-offs between reproductive traits within a breeding attempt. Furthermore, it is often difficult to distinguish differential allocation from direct effects of male condition. We address these gaps using a novel cross-fostering design to exclude direct effects of male condition and to test whether differential allocation affects trade-offs between and within breeding attempts. This design detects differential allocation as effects of a female's mating partner and direct effects of male condition as effects of the larvae's sire. We used the burying beetle Nicrophorus vespilloides, a species which adjusts reproductive allocation by culling some larvae after hatching. We used food-deprivation to manipulate the nutritional condition of both the female's mating partner and the larvae's sire. We find clear evidence for differential allocation as females mating with food-deprived males had fewer larvae than females mating with control males. There was a trade-off between number and size of larvae when females mated with control males, but a positive relationship when females mated with food-deprived males. Thus, differential allocation influenced relationships between reproductive traits within a breeding attempt, but not necessarily through trade-offs. Instead, we suggest that there may be cryptic heterogeneity in quality among females or their mating partners that was only exposed when females mated with a male in poor condition.</p>
The utilization of micro-mesoporous carbon-based filler in the P84 hollow fiber membrane for gas separation
<p>This research carried out a unique micro-mesoporous carbon particle incorporation into P84 co-polyimide membrane for improved gas separation performance. The carbon filler was prepared using a hard template method from zeolite and called as zeolite templated carbon (ZTC). This research aims to study the loading amount of ZTC into P84 co-polyimide toward the gas separation performance. The ZTC was prepared using simple impregnation method of sucrose into hard template of zeolite Y. The SEM result showing a dispersed ZTC particle on the membrane surface and cross-section. The pore size distribution of ZTC revealed that the particle consists of two characteristics of micro and mesoporous region. It was noted that with only 0.5 wt% of ZTC addition, the permeability was boosted up from 4.68 to 7.06 and from 8.95 to 13.15 Barrer, for CO2 and H2 respectively as compared to the neat membrane. On the other hand, the optimum loading was at 1 wt%, where the membrane received thermal stability boost of 10% along with the 62.4 and 35% of selectivity boost of CO2/CH4 and H2/CH4, respectively. It was noticed that the position of the filler on the membrane surface was significantly affecting the gas transport mechanism of the membrane. Overall, te results demonstrated that the addition of ZTC with proper filler position has potential candidate to be applicable in the gas separation involving CO2 and H2.</p>
Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
<p><span>Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely <a name="_Hlk56106607">distinguishable</a>. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is <a name="_Hlk48748756">based on the state-of-the-art U-Net</a>, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. </span></p> <p><span>Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.</span></p>
Data from: Is repeatability of metabolic rate influenced by social separation? a test with a teleost fish
<p>Metabolic rates are typically thought to have important influences on fitness and more broadly be relevant to the ecology and evolution of animals. Previous studies demonstrate that metabolic rates are repeatable to a certain extent under constant conditions, but how social conditions influence the repeatability of metabolic rate remains largely unknown. In this study we investigated the repeatability of resting metabolic rate (RMR) in the highly-social crucian carp<i> </i>(<i>Carassius auratus</i>)<i> </i>after being socially separated for different time periods relative to control fish that were not socially separated. We found that RMR was repeatable in fish in the control group, while the repeatability of RMR disappeared quickly (even within 7 d) when fish were exposed to social separation. This study is the first to examine the role of social separation at different time periods on the repeatability of intra-individual physiological variation in fish. <span>We highlight that inter-individual repeatability of metabolic rate can be substantial</span> <span>over time but was eliminated by </span>social separation<span>. </span>The findings <span>indicate that the repeatability of metabolic rate in fish is condition-dependent, and that the change in repeatability of metabolic rate should not be overlooked when considering the ecological and evolutionary effects of </span>environmental change<span>.</span></p>
Three geographically separate domestications of Asian rice
<p>Data sets reported in Civan, P., Craig, H., Cox, C.J. & Brown, T. Three geographically separate domestications of Asian rice. Nature Plants 1, 15164 (2015).</p> <p>SNP matrices extracted from the whole genome SNP dataset [Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–503 (2012)].</p> <p>Data formats:<br> - PED format (.ped, .pedind, .pedsnp)<br> - PHYLIP format (.phy pseudomolecules)</p>
HHDS - Spanish HipHop Dataset for Music Source Separation
<p><strong>What is HHDS?</strong></p> <p>HHDS is a reduced compilation of Hip Hop songs, used to train a Convolutional Neural Network (CNN) for audio source separation in [1], built on top of the DeepConvSep framework [2] developed at the Music Technology Group (MTG), Universitat Pompeu Fabra.</p> <p>The structure of HHDS follows the convention of DSD100 [3] (Demixing Secrets Dataset). HHDS contains the separated tracks for the categories of bass, drums, vocals and others in monophonic WAV les with a sampling rate of 44100Hz. The mixture is calculated by normalizing the sum of the tracks. The main difference with respect to DSD100 is that in HHDS there are HipHop songs only, instead of many different genres. The total number of songs is 18, from which 13 are used for training and 5 are used for evaluation.</p> <p>A detailed list of the songs included in the dataset can be found inside the .zip file provided. The reader can also find the code for this dataset in the DeepConvSep repository in the path examples/hiphopss.</p> <p> </p> <p><strong>References</strong></p> <p>[1] "A Deep Learning Approach to Source Separation and Remixing of HipHop music", Héctor Martel, Undergraduate Thesis, Universitat Pompeu Fabra 2016-2017. </p> <p>[2] DeepConvSep repository on GitHub: https://github.com/MTG/DeepConvSep</p> <p>[3] Demixing Secrets Dataset (DSD100), SiSEC2016: http://liutkus.net/DSD100.zip</p>
Experimental procedure for the paper Perceptual Evaluation of Source Separation for Remixing Music
<p>All the files you need to rerun the experiment described in the paper:</p> <p>H. Wierstorf, D. Ward, R. Mason, E. M. Grais, C. Hummersone, M. D. Plumbley, "Perceptual Evaluation of Source Separation for Remixing Music," in 143rd Convention of the Audio Engineering Society, 2017.</p> <p>The actual stimuli are not part of this publication, but can be regenerated or be downloaded from https://doi.org/10.5281/zenodo.835182<br> </p>
Figures and data for the paper Perceptual Evaluation of Source Separation for Remixing Music
<p>Listening test results and figures for the paper:</p> <p>H. Wierstorf, D. Ward, R. Mason, E. M. Grais, C. Hummersone, M. D. Plumbley,<br> "Perceptual Evaluation of Source Separation for Remixing Music," in 143rd<br> Convention of the Audio Engineering Society, 2017.</p> <p>`fig02/data/` contains the results from single listeners and median results across<br> listeners.<br> `fig02/fig02.plt` is the code to regenerate `fig02/fig02.pdf`.</p> <p>`fig03/data/` contains average medians across all songs.<br> `fig03/fig03.plt` is the code to regenerate `fig03/fig03.pdf`.</p> <p>The listening test results were extracted from the raw data stored together with<br> the experimental procedure in https://doi.org/10.5281/zenodo.835191.<br> There the file `experiment/saves/ratings/analyze_results.py` can be run in order<br> to regenerate the result files provided with this publication.<br> </p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
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.