Cross-validation is a common method to validate a QSAR model. In cross-validation, some compounds are held out as a test set, while the remaining compounds form a training set. A model is built from the training set, and the test set compounds are predicted on that model. The agreement of the predicted and observed activity values of the test set (measured by, say, R2) is an estimate of the self-consistency of the model and is sometimes taken as an indication of the predictivity of the model. This estimate of predictivity can be optimistic or pessimistic compared to true prospective prediction, depending how compounds in the test set are selected. Here, we show that time-split selection gives an R2 that is more like that of true prospective prediction than the R2 from random selection (too optimistic) or from our analog of leave-class-out selection (too pessimistic). Time-split selection should be used in addition to random selection as a standard for cross-validation in QSAR model building.

Feature preparation Preprocessing was applied to the data, such as creating dummy variables and performing transformations (centering, scaling, YeoJohnson) using the preProcess() function from the “caret” package in R. The correlation among the variables was examined and no serious multicollinearity problems were found. A stepwise variable selection was performed using a logistic regression model. The final set of variables included: Demographic: age, body mass index, sex, ethnicity, smoking History of disease: heart disease, migraine, insomnia, gastrointestinal disease, COVID-19 history: covid vaccination, rashes, conjunctivitis, shortness of breath, chest pain, cough, runny nose, dysgeusia, muscle and joint pain, fatigue, fever ,COVID-19 reinfection, and ICU admission. These variables were used to train and test various machine-learning models Model selection and training The data was randomly split into 80% training and 20% testing subsets. The “h2o” package in R version 4.3.1 was employed to implement different algorithms. AutoML was first used, which automatically explored a range of models with different configurations. Gradient Boosting Machines (GBM), Random Forest (RF), and Regularized Generalized Linear Model (GLM) were identified as the best-performing models on our data and their parameters were fine-tuned. An ensemble method that stacked different models together was also used, as it could sometimes improve the accuracy. The models were evaluated using the area under the curve (AUC) and C-statistics as diagnostic measures. The model with the highest AUC was selected for further analysis using the confusion matrix, accuracy, sensitivity, specificity, and F1 and F2 scores. The optimal prediction threshold was determined by plotting the sensitivity, specificity, and accuracy and choosing the point of intersection as it balanced the trade-off between the three metrics. The model’s predictions were also plotted, and the quantile ranges were used to classify the model’s prediction as follows: > 1st quantile, > 2nd quantile, > 3rd quartile and < 3rd quartile (very low, low, moderate, high) respectively. Metric Formula C-statistics (TPR + TNR - 1) / 2 Sensitivity/Recall TP / (TP + FN) Specificity TN / (TN + FP) Accuracy (TP + TN) / (TP + TN + FP + FN) F1 score 2 * (precision * recall) / (precision + recall) Model interpretation We used the variable importance plot, which is a measure of how much each variable contributes to the prediction power of a machine learning model. In H2O package, variable importance for GBM and RF is calculated by measuring the decrease in the model's error when a variable is split on. The more a variable's split decreases the error, the more important that variable is considered to be. The error is calculated using the following formula: 𝑆𝐸=𝑀𝑆𝐸∗𝑁=𝑉𝐴𝑅∗𝑁 and then it is scaled between 0 and 1 and plotted. Also, we used The SHAP summary plot which is a graphical tool to visualize the impact of input features on the prediction of a machine learning model. SHAP stands for SHapley Additive exPlanations, a method to calculate the contribution of each feature to the prediction by averaging over all possible subsets of features [28]. SHAP summary plot shows the distribution of the SHAP values for each feature across the data instances. We use the h2o.shap_summary_plot() function in R to generate the SHAP summary plot for our GBM model. We pass the model object and the test data as arguments, and optionally specify the columns (features) we want to include in the plot. The plot shows the SHAP values for each feature on the x-axis, and the features on the y-axis. The color indicates whether the feature value is low (blue) or high (red). The plot also shows the distribution of the feature values as a density plot on the right.

Processed data and code for "Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage," Naqvi et al 2024.

Directory is organized into 4 subfolders, each tar'ed and gzipped:

data_analysis.tar.gz - Processed data for modulation of TWIST1 levels and calculation of RE responsiveness to TWIST1 dosage

• atac_design.txt - design matrix for ATAC-seq TWIST1 titration samples
• all.sub.150bpclust.greater2.500bp.merge.TWIST1.titr.ATAC.counts.txt - ATAC-seq counts from all samples over all reproducible ATAC-seq peak regions, as defined in Naqvi et al 2023
• atac_deseq_fitmodels_moded50.R - R code for calculating new version of ED50 and response to full depletion from TWIST1 titration data (note, uses drm.R function from 10.5281/zenodo.7689948, install drc() with this version to avoid errors)

baseline_models.tar.gz - Code and data for training baseline models to predict RE responsiveness to SOX9/TWIST1 dosage

• {sox9|twist1}.{0v100|ed50}.{train|valid|test}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds
• HOCOMOCOv11_core_HUMAN_mono_jaspar_format.all.sub.150bpclust.greater2.500bp.merge.minus300bp.p01.maxscore.mat.cpg.gc.basemean.txt.gz - matrix of predictors for all REs. Quantitative encoding of PWM match for all HOCOMOCO motifs + CpG + GC content, plus unperturbed ATAC-seq signal
• train_baseline.R - R code to train baseline (LASSO regression or random forest) models using predictor matrix and the provided training data.
  • Note: training the random forest to predict full TF depletion is computationally intensive because it is across all REs, if doing this run on CPU for ~6 hrs.

chrombpnet_models.tar.gz - Remainder of code, data, and models for fine-tuning and interpreting ChromBPNet mdoels to predict RE responsiveness to SOX9/TWIST1 dosage

• Fine-tuning code, data, models
  • {all|sox9.direct|twist1.bound.down}.{train|valid|test}.{ed50|0v100.log2fc}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds
  • pretrained.unperturbed.chrombpnet.h5 - Pretrained model of unperturbed ATAC-seq signal in CNCCs, obtained by running ChromBPNet (https://github.com/kundajelab/chrombpnet) on DMSO-treated SOX9/TWIST1-tagged ATAC-seq data
  • finetune_chrombpnet.py - code for fine-tuning the pretrained model for any of the relevant prediction tasks (ED50/ effect of full TF depletion for SOX9/TWIST1)
  • best.model.chrombpnet.{0v100|ed50}.{sox9|twist1}.h5 - output of finetune_chrombpnet.py, best model after 10 training epochs for the indicated task
  • chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.{h5|bw} - contribution scores for the indicated predictive model, obtained by running chrombpnet contribs_bw on the corresponding model h5 file.
  • chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.modisco.{h5|bw} - TF-MoDIsCo output from the corresponding contribution score file
• Interpretation code, data, models
  • contrib_h5_to_projshap_npy.py - code to convert contrib .h5 files into .npy files containing projected SHAP scores (required because the CWM matching code takes this format of contribution scores)
  • sox9.direct.10col.bed, twist1.bound.down.10col.uniq.bed - regions over which CWMs will be matched (likely direct targets of each TF)
  • match_cwms.py - Python code to match individual CWM instances. Takes as input: modisco .h5 file, SHAP .npy file, bed file of regions to be matched. Output is a bed file of all CWM matches (not pruned, contains many redundant matches).
  • chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.bed - output of match_cwms.py
  • take_max_overlap.py - code to merge output of match_cwms.py into clusters, and then take the maximum (length-normalized) match score in each cluster as the representative CWM match of that cluster. Requires upstream bedtools commands to be piped in, see example usage in file.
  • chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.maxoverlap.bed - output of take_max_overlap.py. These CWM instances are the ones used throughout the paper.

modisco_reports.zip - TF-MoDIsCo reports from running on the fine-tuned ChromBPNet models

• modisco_report_{sox9|twist1}_{0v100|ed50}: folders containing images of discovered CWMs and HTMLs/PDFs of summarized reports from running TF-MoDisCo on the indicated fine-tuned ChromBPNet model

mirny_model.tar.gz - Code and data for analyzing and fitting Mirny model of TF-nucleosome competition to observed RE dosage response curves

• twist1.strong.multi.only.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper.
• twist1.strong.weak{1|2|3}.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and the indicated number of sensitizing (weak) Coordinators and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper.
• MirnyModelAnalysis.py - Python code for analysis of Mirny model of TF-nucleosome competition. Contains implementations of analytic solutions, as well as code to fit model to observed ED50 and hill coefficients in the provided data files.

This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners.DescriptionThe SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet.Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page.

To listen to audio samples of the dataset, please see our Github page.

The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction.

This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios.

Terms of use

The dataset may be used for research purposes only, for non-commercial purposes only, and may be distributed with the same terms.

Every time you produce research that has used this dataset, please cite the dataset appropriately.

Cite as:

@inproceedings{maniati22_interspeech, author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis}, title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}}, year=2022, booktitle={Proc. Interspeech 2022}, pages={2388--2392}, doi={10.21437/Interspeech.2022-10922} }

References of resources & models used

Voice & synthesized texts:K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017.

Vocoder:J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019.R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020.

Acoustic models:N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020.Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017.J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018.J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020.M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015.M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019.Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021.K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021.T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020.

Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016:M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007.V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008.A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009.S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010.S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011.S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012.S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013.S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016.

Contact

Alexandra Vioni - a.vioni@samsung.com

If you have any questions or comments about the dataset, please feel free to write to us.

We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.

A fully annotated subset of the SO242/2_171-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation:

• anemone
• coral
• crustacean
• ipnops fish
• litter
• ophiuroid
• other fauna
• sea cucumber
• sponge
• stalked crinoid

For a definition of the classes see [1].

Related datasets:

• S083: https://doi.org/10.5281/zenodo.3600132

• S155: https://doi.org/10.5281/zenodo.3603803

• S233: https://doi.org/10.5281/zenodo.3603815

This dataset contains the following files:

• annotations/test.csv: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model.
• annotations/train.csv: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model.
• images/: Directory that contains all the original image files.
• dataset.json: JSON file that contains information about the dataset.
  • name: The name of the dataset.
  • images_dir: Name of the directory that contains the original image files.
  • metadata_file: Path to the CSV file that contains image metadata.
  • test_annotations_file: Path to the CSV file that contains the test annotations.
  • train_annotations_file: Path to the CSV file that contains the train annotations.
  • annotation_patches_dir: Name of the directory that should contain the scale- and style-transferred annotation patches.
  • crop_dimension: Edge length of an annotation or style patch in pixels.
• metadata.csv: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.

Dataset contains light curves of 6 rocket body types from Mini Mega Tortora database (MMT)¹. The dataset was created to be used as a benchmark for rocket body light curve classification. For more informations follow the original paper: RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification²

Class labels: - ARIANE 5 R/B - ATLAS 5 CENTAUR R/B - CZ-3B R/B - DELTA 4 R/B - FALCON 9 R/B - H-2A R/B

Dataset description Usage ```python

from datasets import load_dataset

dataset = load_dataset("kyselica/RoBo6", data_files={"train": "train.csv", "test": "test.csv"}) dataset DatasetDict({ train: Dataset({ features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'], num_rows: 5676 }) test: Dataset({ features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'], num_rows: 1404 }) }) ```

label - class name id - unique identifier of the light curve from MMT part - part number of the light curve period - rotational period of the object mag - relative path to the magnitude values file phase - relative path to the phase values file time - relative path to the time values file

Mean and standard deviation of magnitudes are stored in mean_std.csv file.

File structure

data directory contains 5 subdirectories, one for each class. Light curves are stored in file triplets in the following format:

where

MMT Rocket Bodies ├── README.md ├── train.csv ├── test.csv ├── mean_std.csv ├── data │ ├── ARIANE 5 R_B │ │ ├──

Data preprocessing To create data sutable for both CNN and RNN based models, the light curves were preprocessed in the following way:

Split the light curves if the gap between two consecutive measurements is larger than object's rotational period. Split the light curves to have maximum span 1_000 seconds. Filter out light curves which folded form divided into 100 bins has more than 25% of bins empty. Resample the light curves to 10_000 points with step 0.1 seconds. Filter out light curves with less than 100 measurements.

Citation @article{kyselica2024robo6, title={RoBo6: Standardized MMT Light Curve Dataset for Rocket Body Classification}, author={Kyselica, Daniel and {\v{S}}uppa, Marek and {\v{S}}ilha, Ji{\v{r}}{\'\i} and {\v{D}}urikovi{\v{c}}, Roman}, journal={arXiv preprint arXiv:2412.00544}, year={2024} }

References

Description:

Downsized (256x256) camera trap images used for the analyses in "Can CNN-based species classification generalise across variation in habitat within a camera trap survey?", and the dataset composition for each analysis. Note that images tagged as 'human' have been removed from this dataset. Full-size images for the BorneoCam dataset will be made available at LILA.science. The full SAFE camera trap dataset metadata is available at DOI: 10.5281/zenodo.6627707.

Project: This dataset was collected as part of the following SAFE research project: Machine learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions

Funding: These data were collected as part of research funded by:

• NERC (NERC QMEE CDT Studentship, NE/P012345/1, http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A)

This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This dataset consists of 3 files: CT_image_data_info2.xlsx, DN_256x256_image_files.zip, DN_generalisability_code.zip

CT_image_data_info2.xlsx

This file contains dataset metadata and 1 data tables:

1. Dataset Images (described in worksheet Dataset_images)

   Description: This worksheet details the composition of each dataset used in the analyses

   Number of fields: 69

   Number of data rows: 270287

   Fields:

   • filename: Root ID (Field type: id)
   • camera_trap_site: Site ID for the camera trap location (Field type: location)
   • taxon: Taxon recorded by camera trap (Field type: taxa)
   • dist_level: Level of disturbance at site (Field type: ordered categorical)
   • baseline: Label as to whether image is included in the baseline training, validation (val) or test set, or not included (NA) (Field type: categorical)
   • increased_cap: Label as to whether image is included in the 'increased cap' training, validation (val) or test set, or not included (NA) (Field type: categorical)
   • dist_individ_event_level: Label as to whether image is included in the 'individual disturbance level datasets split at event level' training, validation (val) or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_1: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_2: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_2: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_2_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_2_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_2_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_2_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_2_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_2_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_2_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_2_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_2_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_3_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_quad_1_2_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_quad_1_2_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_quad_1_2_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type:

Introduction\r

Training.gov.au (TGA) is the National Register of Vocational Education and Training in Australia and contains authoritative information about Registered Training Organisations (RTOs), Nationally Recognised Training (NRT) and the approved scope of each RTO to deliver NRT as required in national and jurisdictional legislation.\r \r

TGA web-services overview\r

TGA has a web service available to allow external systems to access and utilise information stored in TGA through an external system. The TGA web service is exposed through a single interface and web service users are assigned a data reader role which will apply to all data stored in the TGA.\r \r The web service can be broadly split into three categories:\r \r 1. RTOs and other organisation types;\r \r 2. Training components including Accredited courses, Accredited course Modules Training Packages, Qualifications, Skill Sets and Units of Competency;\r \r 3. System metadata including static data and statistical classifications.\r \r Users will gain access to the TGA web service by first passing a user name and password through to the web server. The web server will then authenticate the user against the TGA security provider before passing the request to the application that supplies the web services.\r \r There are two web services environments:\r \r 1. Production - ws.training.gov.au – National Register production web services\r \r 2. Sandbox - ws.sandbox.training.gov.au – National Register sandbox web services. \r \r The National Register sandbox web service is used to test against the current version of the web services where the functionality will be identical to the current production release. The web service definition and schema of the National Register sandbox database will also be identical to that of production release at any given point in time. The National Register sandbox database will be cleared down at regular intervals and realigned with the National Register production environment.\r \r Each environment has three configured services:\r \r 1. Organisation Service;\r \r 2. Training Component Service; and\r \r 3. Classification Service.\r \r

Sandbox environment access\r

To access the download area for web services, navigate to http://tga.hsd.com.au and use the below name and password:\r \r Username: WebService.Read (case sensitive)\r \r Password: Asdf098 (case sensitive)\r \r This download area contains various versions of the following artefacts that you may find useful\r \r • Training.gov.au web service specification document;\r \r • Training.gov.au logical data model and definitions document;\r \r • .NET web service SDK sample app (with source code);\r \r • Java sample client (with source code);\r \r • How to setup web service client in VS 2010 video; and\r \r • Web services WSDL's and XSD's.\r \r For the business areas, the specification/definition documents and the sample application is a good place to start while the IT areas will find the sample source code and the video useful to start developing against the TGA web services.\r \r The web services Sandbox end point is: https://ws.sandbox.training.gov.au/Deewr.Tga.Webservices \r \r

Production web service access\r

Once you are ready to access the production web service, please email the TGA team at tgaproject@education.gov.au to obtain a unique user name and password.\r

4096 classes of retinal waves, 2000 images per class.

4096_split.tar.gz: Images split into train/test/val sets (80%/10%/10%).

4096_info.zip: Retinal Wave Simulator Parameters per Class.

Generated using Retinal Wave Simulator https://github.com/BennyCa/Retinal-Wave-Simulator adapted from https://swindale.ecc.ubc.ca/home-page/software/retinal-wave-models/

Used in project Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development: https://github.com/BennyCa/ReWaRD

A fully annotated subset of the SO242/1_83-1_AUV10 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation:

• anemone
• coral
• crustacean
• ipnops fish
• litter
• ophiuroid
• other fauna
• sea cucumber
• sponge
• stalked crinoid

For a definition of the classes see [1].

Related datasets:

• S155: https://doi.org/10.5281/zenodo.3603803

• S171: https://doi.org/10.5281/zenodo.3603809

• S233: https://doi.org/10.5281/zenodo.3603815

This dataset contains the following files:

• annotations/test.csv: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model.
• annotations/train.csv: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model.
• images/: Directory that contains all the original image files.
• dataset.json: JSON file that contains information about the dataset.
  • name: The name of the dataset.
  • images_dir: Name of the directory that contains the original image files.
  • metadata_file: Path to the CSV file that contains image metadata.
  • test_annotations_file: Path to the CSV file that contains the test annotations.
  • train_annotations_file: Path to the CSV file that contains the train annotations.
  • annotation_patches_dir: Name of the directory that should contain the scale- and style-transferred annotation patches.
  • crop_dimension: Edge length of an annotation or style patch in pixels.
• metadata.csv: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.

RRegrs study for Growth Yield for original and corrected/filterred datasets: inputs training and test files, R scripts to split the datasets, plot for outlier removal.

Dataset

Camper dataset form https://stats.idre.ucla.edu/r/dae/zip/. The dataset contains data on 250 groups that went to a park. Each group was questioned about how many fish they caught (count), how many children were in the group (child), how many people were in the group (persons), if they used a live bait and whether or not they brought a camper to the park (camper). You split the data into train and test dataset.

Acknowledgements

University of California, Los Angeles (UCLA) Dataset.

The MERGE dataset is a collection of audio, lyrics, and bimodal datasets for conducting research on Music Emotion Recognition. A complete version is provided for each modality. The audio datasets provide 30-second excerpts for each sample, while full lyrics are provided in the relevant datasets. The amount of available samples in each dataset is the following:

• MERGE Audio Complete: 3554
• MERGE Audio Balanced: 3232
• MERGE Lyrics Complete: 2568
• MERGE Lyrics Balanced: 2400
• MERGE Bimodal Complete: 2216
• MERGE Bimodal Balanced: 2000

Additional Contents

Each dataset contains the following additional files:

• av_values: File containing the arousal and valence values for each sample sorted by their identifier;
• tvt_dataframes: Train, validate, and test splits for each dataset. Both a 70-15-15 and a 40-30-30 split are provided.

Metadata

A metadata spreadsheet is provided for each dataset with the following information for each sample, if available:

• Song (Audio and Lyrics datasets) - Song identifiers. Identifiers starting with MT were extracted from the AllMusic platform, while those starting with A or L were collected from private collections;
• Quadrant - Label corresponding to one of the four quadrants from Russell's Circumplex Model;
• AllMusic Id - For samples starting with A or L, the matching AllMusic identifier is also provided. This was used to complement the available information for the samples originally obtained from the platform;
• Artist - First performing artist or band;
• Title - Song title;
• Relevance - AllMusic metric representing the relevance of the song in relation to the query used;
• Duration - Song length in seconds;
• Moods - User-generated mood tags extracted from the AllMusic platform and available in Warriner's affective dictionary;
• MoodsAll - User-generated mood tags extracted from the AllMusic platform;
• Genres - User-generated genre tags extracted from the AllMusic platform;
• Themes - User-generated theme tags extracted from the AllMusic platform;
• Styles - User-generated style tags extracted from the AllMusic platform;
• AppearancesTrackIDs - All AllMusic identifiers related with a sample;
• Sample - Availability of the sample in the AllMusic platform;
• SampleURL - URL to the 30-second excerpt in AllMusic;
• ActualYear - Year of song release.

Citation

If you use some part of the MERGE dataset in your research, please cite the following article:

Louro, P. L. and Redinho, H. and Santos, R. and Malheiro, R. and Panda, R. and Paiva, R. P. (2024). MERGE - A Bimodal Dataset For Static Music Emotion Recognition. arxiv. URL: https://arxiv.org/abs/2407.06060.

BibTeX:

@misc{louro2024mergebimodaldataset,
title={MERGE -- A Bimodal Dataset for Static Music Emotion Recognition},
author={Pedro Lima Louro and Hugo Redinho and Ricardo Santos and Ricardo Malheiro and Renato Panda and Rui Pedro Paiva},
year={2024},
eprint={2407.06060},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2407.06060},
}

Acknowledgements

This work is funded by FCT - Foundation for Science and Technology, I.P., within the scope of the projects: MERGE - DOI: 10.54499/PTDC/CCI-COM/3171/2021 financed with national funds (PIDDAC) via the Portuguese State Budget; and project CISUC - UID/CEC/00326/2020 with funds from the European Social Fund, through the Regional Operational Program Centro 2020.

Renato Panda was supported by Ci2 - FCT UIDP/05567/2020.