Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
99
datasets available to search
ShareScore release 0.9.0
Dataset results
99 results for “Transfer learning”
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>
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>
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>
Automated MESSENGER Plasma Region Classifications via Unsupervised Transfer Learning
<p>This file contains the 1-minute resolution dataset (“labeled_sunside_data_3labels.csv”) for Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, features that go into clustering and post-cleaning methods, spacecraft positions (in MSO), total magnetic field, raw and cleaned clustering labels, and raw and cleaned transition name.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning (DOI: 10.3389/fspas.2025.1608091).</p> <p>This work was supported by NASA grants 80NSSC19K0789 and 80NSSC22K0993.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data_3labels.csv description</strong></p> <table style="width: 100.063%; height: 851.2px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p><strong>Column Name</strong></p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p><strong>Description</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> Epoch</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Epoch in datetime (YYYY-MM-DD HH:MM:SS)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> x_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>x position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> y_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>y position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> z_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>z position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> btot_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Total magnetic field [nT]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> norm_Btot</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Magnitude of the total magnetic field normalized to 150nT. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_max_width</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_high_low</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> high_intensity</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Boolean if there is a peak with a higher minimum intensity threshold. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> spectra_counts</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>A ratio of spectra bins with non-zero counts to all possible spectra bins (i.e. way to determine if too much missing spectra data). See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> raw_named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind)</p> </td> </tr> <tr> <td style="width: 17.3792%;"> <p>intermediate_named_label</p> </td> <td style="width: 78.9512%;"> <p>Cleaned cluster assigned plasma region label with only relabeling rules applied. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned cluster assigned plasma region label with relabeling rules and post-processing applied (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> raw_transition_name</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Raw transition names (e.g. bow shock, magnetopause) based on "raw_named_label" cluster labels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> transition_name</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned transition names (e.g. bow shock, magnetopause) after removing likely transient transitions based on "named_label" cluster labels. See paper for more information</p> </td> </tr> </tbody> </table> <p> </p>
Spatial Span and Matrix Reasoning data from the UW-Madison Learning and Transfer Lab
<p><strong>Matrices_SpatialSpan.csv</strong> includes one row for every mouse click for every trial for each participant's spatial span performance (for similar spatial span methods see Cochrane, Simmering, & Green, 2019, PLOS One). Participant IDs, trial numbers, the presence [f] or absence [n] of feedback, and task order (spatial span first or spatial span second) are included alongside by-click accuracy. Also included are each participants' average scores on a subset of items from the UCMRT (Pahor et al., 2019, Beh. Res. Meth) and from the matrices developed at Sandia National Laboratories (Matzen et al., 2010, Beh. Res. Meth.).</p> <p><strong>robustCor.R </strong>is R code implementing a test of bivariate correlation. Univariate Yeo-Johnson transformations are applied, then bootstrapped correlations coefficients are calculated. Point estimates, CI, and Bayes Factors are each returned.</p> <p>Data were collected and code was developed as part of A. Cochrane's dissertation work at the University of Wisconsin - Madison under the supervision of C. Shawn Green.</p>
Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning (Datasets)
<p>This repository contains the datasets used in the article "Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning" (Coutinho & Schuller, 2017). </p> <p>In that article four different data sets were used: SEMAINE, RECOLA, ME14 and MP (acronyms and datasets described below). The SEMAINE (speech) and ME14 (music) corpora were used for the unsupervised training of the Denoising Auto-encoders (domain adaptation stage) - only the audio features extracted from the audio files in these corpora were used and are provided in this repository. The RECOLA (speech) and MP (music) corpora were used for the supervised training phase - both the audio features extracted from the audio files and the Arousal and Valence annotations were used. In this repository, we provide the audio features extracted from the audio files for both corpora, and Arousal and Valence annotations for some of the music datasets (those that the author of this repository is the data curator).</p> <p>Below, you can find description of the various corpora, the details about the data stored in this repository and information on how to obtain the rest of the data used by Coutinho and Schuller (2017).</p> <p><strong>SEMAINE (speech)</strong></p> <p>The SEMAINE corpus (McKeown, Valstar, Cowie, Pantic & Schroder, 2012) was developed specifically to address the task of achieving emotion-rich interactions, and it is adequate for this task as it comprises a wide range of emotional speech. It includes video and speech recordings of spontaneous interactions between human and emotionally stereotyped `characters'. Coutinho & Schuller (2017) used a subset of this database (called <em>Solid-SAL</em>). The <em>Solid-SAL</em> dataset is freely available for scientific research purposes (see http://semaine-db.eu). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/SEMAINE).</p> <p><strong>RECOLA (speech)</strong></p> <p>The RECOLA database (Ringeval, Sonderegger, Sauer & Lalanne, 2013) consists of multimodal recordings (audio, video, and peripheral physiological activity) of spontaneous dyadic interactions between French adults. Coutinho & Schuller (2017) used the RECOLA-Audio module which consists of the audio recordings of each participant in the dyadic phase of the task. In particular, they used the non-segmented high-quality audio signals (WAV format, 44.1kHz, 16bits), obtained through unidirectional headset microphones, of the first five minutes of each interaction. Annotations consist of time-continuous ratings of the level of Arousal and Valence dimensions of emotion perceived by each rater while seeing and listening the audio-visual recordings of each participant task. The publicly available annotated dataset includes only part of the data which amounts to a total number of 23 instances. The time frame length used by Coutinho & Schuller (2017) is 1s (the original annotations were downsampled). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/RECOLA). To obtain the annotations you should contact the author of the original study (see https://diuf.unifr.ch/diva/recola/download.html for further details).</p> <p><strong>ME14 (music)</strong></p> <p>The MediaEval ``Emotion in Music'' task is dedicated to the estimation of Arousal and Valence scores continuously in time and value for song excerpts from the Free Music Archive. Coutinho and Schuller (2017) used the whole corpus (development and test sets for the 2014 challenge) which includes 1,744 songs belonging to 11 musical styles -- Soul, Blues, Electronic, Rock, Classical, Hip-Hop, International, Folk, Jazz, Country, and Pop (maximum of five songs per artist). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/ME14). The full dataset (including annotations) can be obtained from http://www.multimediaeval.org/mediaeval2014/emotion2014/.</p> <p><strong>MP (music)</strong></p> <p>This is a corpus compiled specifically for this work described in Coutinho & Schuller (2017) using data collected in four previous studies. It consists of emotionally diverse full music pieces from a variety of musical styles (Classical and contemporary Western Art, Baroque, Bossa Nova, Rock, Pop, Heavy Metal, and Film Music). Annotations were obtained in controlled laboratory experiments whereby the emotional character of each piece was evaluated time-continuously in terms of levels of Arousal and Valence perceived by listeners (ranging between 35 to 52 in the four studies). In what follows, some details about the various studies are described.</p> <ul> <li>MP<sub>DB1</sub>: This subset of the MP corpus consists of the data reported by Korhonen (2004), and gently made available by the author. This dataset includes six full (or long excerpts) music pieces ranging from 151s to 315s in length (only classical music). Each piece was annotated by 35 participants (14 females). The time series correspondents to each music piece were collected at 1Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features used in Coutinho & Schuller (2017) (under features/MP/DB1). To obtain the labels please contact the author of the original study.</li> <li>MP<sub>DB2</sub>: The dataset by Coutinho & Cangelosi (2011) includes 9 full pieces (43s to 240s long) of classical music (romantic repertoire) annotated by 39 subjects (19 females). Values were recorded every time the mouse was moved with a precision of 1 ms. The resultant timeseries were then resampled (moving average) to a synchronous rate of 1 Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features (under features/MP/DB2) and labels (under annotations/MP/DB2) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB3</sub>: This dataset was collected by Coutinho & Dibben (2012) and it consists of 8 pieces of film music (84s to 130s long) taken from the late 20th century Hollywood film repertoire. Emotion ratings were given by 52 participants (26 females). The annotation procedure, data processing, and golden standard calculations were identical to MP<sub>DB2</sub>. This repository includes the audio features (under features/MP/DB3) and labels (under annotations/MP/DB3) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB4</sub>: This dataset was collected by Grewe, Nagel, Kopiez and Altenmüller (2007), and gently made available by the authors. It includes seven music pieces (127s to 502s in length) of heterogeneous styles (e.g., Rock, Pop, Heavy Metal, Classical). Each music piece was annotated by 38 participants (29 females) using an identical methodology to MP<sub>DB2</sub> and MP<sub>DB3</sub>. Data processing and golden standard calculations were also identical. This repository includes the audio features (under features/MP/DB4) used in Coutinho & Schuller (2017). To obtain the labels contact the authors of the original study</li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. <em>Emotion</em>, <em>11</em>(4), 921.</p> <p>Coutinho, E., & Dibben, N. (2013). Psychoacoustic cues to emotion in speech prosody and music. <em>Cognition & Emotion</em>, <em>27</em>(4), 658-684.</p> <p>Coutinho E, Schuller B (2017) Shared acoustic codes underlie emotional communication in music and speech—Evidence from deep transfer learning. PLoS ONE 12(6): e0179289. https://doi. org/10.1371/journal.pone.0179289.</p> <p>Grewe, O., Nagel, F., Kopiez, R., Altenmüller, E. (2007). Emotions over time: synchronicity and development of subjective, physiological, and facial affective reactions to music. <em>Emotion, 7</em>(4), pp. 774-788. DOI: 10.1037/1528-3542.7.4.774.</p> <p>Korhonen, M. (2004). Modeling Continuous Emotional Appraisals of Music Using System Identification. Available from: http://hdl.handle.net/10012/879.</p> <p>McKeown, G., Valstar, M., Cowie, R., Pantic, M., Schroder, M. (2012). The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. <em>IEEE Transactions on Affective Computing</em>, 3, pp. 5-17. DOI: http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2011.20.</p> <p>Ringeval, F., Sonderegger, A., Sauer, J. & Lalanne, D. (2013). Introducing the RECOLA Multimodal Corpus of Remote Collaborative and Affective Interactions. In <em>Proceedings of the 2nd International Workshop on Emotion Representation, Analysis and Synthesis in Continuous Time and Space (EmoSPACE 2013)</em>, Shanghai, China. IEEE</p>
Data and code used in analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity
<p>Evaluation datasets, Python scripts, and computation environments used to conduct analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity. <br><br>transfer_learning_project.zip also contains a vignette describing the use of a generalized script for adapting these methods to novel acoustic classification tasks. </p> <p> </p>
Nanofluid heat transfer and machine learning
<p>Table 1. Machine learning application for nanofluids in porous media.</p> <p>Table 2. Summary of machine learning application: Nanofluids in heat exchangers</p>
Data for Universal prediction of cell cycle position using transfer learning
<p>The repo contains the data for Universal prediction of cell cycle position using transfer learning (https://www.biorxiv.org/content/10.1101/2021.04.06.438463v2).</p> <p>The scripts to analyze and generate all figures could be found at https://github.com/hansenlab/tricycle_paper_figs</p> <p>v1.1 update: add neurosphere_scvelo.qs - an R SingleCellExperiment object saved as qs file that has spliced counts, unspliced counts, and all outputs from scvelo.</p>
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
<p>This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182</p>
Subset of 300 out of 3000 Prepared Sentinel 2 Scenes for Transfer Learning and Super-sampling.
<p>This dataset contains a random subset of 300 out of 3000 Sentinel 2 scenes prepared for Transfer Learning and Super-sampling. It comes in the form of zipped NumPy arrays in the npz format. The files have are named in the following fashion:</p> <p>latutide+latitude_decimals_longitude+longitude_decimals_month_of_the_year_for_mosaic. </p> <p>Each file contains:</p> <p>bands.npy: The Sentinel 2 bands in 10m resolution uint10: B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12. The 20m bands have been resampled using bilinear resampling.</p> <p>nir.npy: B08 Resampled to 20m using average resampling and then resampled to 10m using bilinear. Useful for training super-sampling models.</p> <p>scl.npy: The Sentinel 2 Scene Classification file. Contains information on cloud cover and land cover.</p> <p>sincos.npy: Contains the latitude, longitude, and time of capture for each pixel encoded to sine and cosine waves in the [0,1] interval. The is useful when training a model to predict where on the globe an image was captured.</p> <p>The images can be processed to patches using the buteo toolbox: </p> <p>`pip install buteo --upgrade</p> <p>`import buteo as beo`</p> <p>`beo.get_patches(beo.raster_to_array("path_to_bands"))`</p> <p> </p>
Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors
<p>The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors. <br> The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40 with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1.</p> <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> <p>To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch. <br> The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor: <br> sensorA_train<br> sensorA_test<br> sensorB_train<br> sensorB_test<br> sensorC_train<br> sensorC_test</p> <p> Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440).<br> The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID.</p> <p>Although this is a mat file, it can be opened as an hdf5 file.</p>
Transfer learning with generative models for object detection on limited datasets
<p>The provided datasets are used for the analysis in the work "Transfer learning with generative models for object detection on limited datasets" (https://doi.org/10.1088/2632-2153/ad65b5). The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine. The provided datasets are built with the help of Gligen and the already existing NuImages, Ozfish and Deepfish datasets. The file "CarGenerated.zip" contains images generated with Gligen and with provided bounding boxes around cars in an urban environment. The file "fishes_on_bkg.zip" provides fish images generated with fishes from Deepfish inpainted with Gligen on generated backgrounds. The file "fish_text.zip" contains images completely generated with Gligen containing fishes with annotated bounding boxes. Finally, the file "oz_masked_512.zip" contains a simpler dataset of copy paste images of Deepfish fishes on Ozfish backrounds. All the files contains the images saved in different folders for training and validation, plus an index file called gt_fish.csv for the bounding boxes.</p>
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Surface wave tomography with transfer learning and Moho constraints: Method and application to China mainland
<p>The content of this study is the inversion of surface waves based on deep learning. The dataset includes the trained deep learning model, dispersion data, synthetic data test results, real data application results, error analysis, and more detailes.</p>
Transfer learning and DNA language models enhance transcription factor binding predictions
<p>This is the dataset for replicating the results of the paper called "Transfer learning and DNA language models enhance transcription factor binding predictions" by Ekin Deniz Aksu and Martin Vingron.</p> <p>See https://github.com/ekinda/tfbs_prediction_paper</p>
Data for "Transferring Chemical and Energetic Knowledge Between Molecular Systems With Machine Learning"
<p>Data used in the paper "Transferring Chemical and Energetic Knowledge Between Molecular Systems With Machine Learning."</p> <p>The following is a description of each file:</p> <p>- ala_dipep_full.zip contains the JSON files for alanine dipeptide</p> <p>- ala_dipep_full.txt contains the free energy values for alanine dipeptide</p> <p>- trialanine.zip contains the JSON files for trialanine</p> <p>- trialanine.txt contains the free energy values for trialanine</p> <p>- decaalanine.zip contains the JSON files for decaalanine, broken into groups</p> <p> </p> <p>Each JSON file contains the following properties:</p> <p>- atom_types: describing the short strings used for various types of atoms via their mass and radius.</p> <p>- atoms: describing each individual atom, with their type via the short string in atom_types, their partial charge, and coordinates.</p> <p>- angles: describing the angles formed between three atoms, their atom indices, as well as their angular value.</p> <p>- dihedrals: describing the dihedrals formed between four atoms, their atom indices, as well as their dihedral value.</p> <p>- bonds: describing the existence of pairwise bonds between atoms via a binary number.</p> <p>- van_der_waals: describing the van der Waals forces between pairs of atoms.</p> <p>- coulomb: describing the Coulomb forces between pairs of atoms.</p>
Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
<p>Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.</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.