Training, test data and model parameters. The last 3 columns show the MinORG, LT and HT parameters used to create the pathogenicity families and build the model for each of the 10 models. Zthr is a threshold value, calculated for each model at the cross validation phase, which is used, given the final prediction score, to decide if the input organisms will be predicted as pathogenic or non-pathogenic. The parameters for each model are chosen after 5-fold cross-validation tests.

This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 200 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: Marmousi model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

Dataset, splits, models, and scripts from the manuscript "When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?" are provided. The curated dataset includes 37 QM descriptors for 64,921 unique molecules across six levels of theory: wB97XD, B3LYP, M06-2X, PBE0, TPSS, and BP86. This dataset is stored in the data.tar.gz file, which also contains a file for multitask constraints applied to various atomic and bond properties. The data splits (training, validation, and test splits) for both random and scaffold-based divisions are saved as separate index files in splits.tar.gz. The trained D-MPNN models for predicting QM descriptors are saved in the models.tar.gz file. The scripts.tar.gz file contains ready-to-use scripts for training machine learning models to predict QM descriptors, as well as scripts for predicting QM descriptors using our trained models on unseen molecules and for applying radial basis function (RBF) expansion to QM atom and bond features.

Below are descriptions of the available scripts:

1. atom_bond_descriptors.sh: Trains atom/bond targets.
2. atom_bond_descriptors_predict.sh: Predicts atom/bond targets from pre-trained model.
3. dipole_quadrupole_moments.sh: Trains dipole and quadrupole moments.
4. dipole_quadrupole_moments_predict.sh: Predicts dipole and quadrupole moments from pre-trained model.
5. energy_gaps_IP_EA.sh: Trains energy gaps, ionization potential (IP), and electron affinity (EA).
6. energy_gaps_IP_EA_predict.sh: Predicts energy gaps, IP, and EA from pre-trained model.
7. get_constraints.py: Generates constraints file for testing dataset. This generated file needs to be provided before using our trained models to predict the atom/bond QM descriptors of your testing data.
8. csv2pkl.py: Converts QM atom and bond features to .pkl files using RBF expansion for use with Chemprop software.

Below is the procedure for running the ml-QM-GNN on your own dataset:

1. Use get_constraints.py to generate a constraint file required for predicting atom/bond QM descriptors with the trained ML models.
2. Execute atom_bond_descriptors_predict.sh to predict atom and bond properties. Run dipole_quadrupole_moments_predict.sh and energy_gaps_IP_EA_predict.sh to calculate molecular QM descriptors.
3. Utilize csv2pkl.py to convert the data from predicted atom/bond descriptors .csv file into separate atom and bond feature files (which are saved as .pkl files here).
4. Run Chemprop to train your models using the additional predicted features supported here.

Annotated test and train data sets. Both images and annotations are provided separately.

Validation data set for Hi5, Sf9 and HEK cells.

Confusion matrices for the determination of performance parameters

This training data was generated using GPT-4o as part of the 'Drawing with LLM' competition (https://www.kaggle.com/competitions/drawing-with-llms). It can be used to fine-tune small language models for the competition or serve as an augmentation dataset alongside other data sources.

The dataset is generated in two steps using the GPT-4o model. - In the first step, topic descriptions relevant to the competition are generated using a specific prompt. By running this prompt multiple times, over 3,000 descriptions were collected.

 
prompt=f""" I am participating in an SVG code generation competition.
  
   The competition involves generating SVG images based on short textual descriptions of everyday objects and scenes, spanning a wide range of categories. The key guidelines are as follows:
  
   - Descriptions are generic and do not contain brand names, trademarks, or personal names.
   - No descriptions include people, even in generic terms.
   - Descriptions are concise—each is no more than 200 characters, with an average length of about 50 characters.
   - Categories cover various domains, with some overlap between public and private test sets.
  
   To train a small LLM model, I am preparing a synthetic dataset. Could you generate 100 unique topics aligned with the competition style?
  
   Requirements:
   - Each topic should range between **20 and 200 characters**, with an **average around 60 characters**.
   - Ensure **diversity and creativity** across topics.
   - **50% of the topics** should come from the categories of **landscapes**, **abstract art**, and **fashion**.
   - Avoid duplication or overly similar phrasing.
  
   Example topics:
                 a purple forest at dusk, gray wool coat with a faux fur collar, a lighthouse overlooking the ocean, burgundy corduroy, pants with patch pockets and silver buttons, orange corduroy overalls, a purple silk scarf with tassel trim, a green lagoon under a cloudy sky, crimson rectangles forming a chaotic grid,  purple pyramids spiraling around a bronze cone, magenta trapezoids layered on a translucent silver sheet,  a snowy plain, black and white checkered pants,  a starlit night over snow-covered peaks, khaki triangles and azure crescents,  a maroon dodecahedron interwoven with teal threads.
  
   Please return the 100 topics in csv format.
   """
 

• In the second step, SVG code is generated by prompting the GPT-4o model. The following prompt is used to query the model to generate svg.

 
  prompt = f"""
      Generate SVG code to visually represent the following text description, while respecting the given constraints.
      
      Allowed Elements: `svg`, `path`, `circle`, `rect`, `ellipse`, `line`, `polyline`, `polygon`, `g`, `linearGradient`, `radialGradient`, `stop`, `defs`
      Allowed Attributes: `viewBox`, `width`, `height`, `fill`, `stroke`, `stroke-width`, `d`, `cx`, `cy`, `r`, `x`, `y`, `rx`, `ry`, `x1`, `y1`, `x2`, `y2`, `points`, `transform`, `opacity`
      

      Please ensure that the generated SVG code is well-formed, valid, and strictly adheres to these constraints. 
      Focus on a clear and concise representation of the input description within the given limitations. 
      Always give the complete SVG code with nothing omitted. Never use an ellipsis.

      The code is scored based on similarity to the description, Visual question anwering and aesthetic components.
      Please generate a detailed svg code accordingly.

      input description: {text}
      """
 

The raw SVG output is then cleaned and sanitized using a competition-specific sanitization class. After that, the cleaned SVG is scored using the SigLIP model to evaluate text-to-SVG similarity. Only SVGs with a score above 0.5 are included in the dataset. On average, out of three SVG generations, only one meets the quality threshold after the cleaning, sanitization, and scoring process.

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to assess status and trends of bat populations while developing innovative and community-driven conservation solutions using its unique data and technology infrastructure. To support scalability and transparency in the NABat acoustic data pipeline, we developed a fully-automated machine-learning algorithm. This dataset includes audio files of bat echolocation calls that were considered to develop V1.0 of the NABat machine-learning algorithm, however the test set (i.e., holdout dataset) has been excluded from this release. These recordings were collected by various bat monitoring partners across North America using ultrasonic acoustic recorders for stationary acoustic and mobile acoustic surveys. For more information on how these surveys may be conducted, see Chapters 4 and 5 of “A Plan for the North American Bat Monitoring Program” (https://doi.org/10.2737/SRS-GTR-208). These data were then post-processed by bat monitoring partners to remove noise files (or those that do not contain recognizable bat calls) and apply a species label to each file. There is undoubtedly variation in the steps that monitoring partners take to apply a species label, but the steps documented in “A Guide to Processing Bat Acoustic Data for the North American Bat Monitoring Program” (https://doi.org/10.3133/ofr20181068) include first processing with an automated classifier and then manually reviewing to confirm or downgrade the suggested species label. Once a manual ID label was applied, audio files of bat acoustic recordings were submitted to the NABat database in Waveform Audio File format. From these available files in the NABat database, we considered files from 35 classes (34 species and a noise class). Files for 4 species were excluded due to low sample size (Corynorhinus rafinesquii, N=3; Eumops floridanus, N =3; Lasiurus xanthinus, N = 4; Nyctinomops femorosaccus, N =11). From this pool, files were randomly selected until files for each species/grid cell combination were exhausted or the number of recordings reach 1250. The dataset was then randomly split into training, validation, and test sets (i.e., holdout dataset). This data release includes all files considered for training and validation, including files that had been excluded from model development and testing due to low sample size for a given species or because the threshold for species/grid cell combinations had been met. The test set (i.e., holdout dataset) is not included. Audio files are grouped by species, as indicated by the four-letter species code in the name of each folder. Definitions for each four-letter code, including Family, Genus, Species, and Common name, are also included as a dataset in this release.

This dataset contains the SQL tables of the training and test datasets used in our experimentation. These tables contain the preprocessed textual data (in a form of tokens) extracted from each training and test project. Besides the preprocessed textual data, this dataset also contains meta-data about the projects, GitHub topics, and GitHub collections. The GitHub projects are identified by the tuple “Owner” and “Name”. The descriptions of the table fields are attached to their respective data descriptions.

The dataset includes spectral correlation function (SCF) estimations by FFT accumulation method (FAM) for totally 4500 signals with 20000 I/Q samples. The signals belong to three different cellular communication standards: GSM, WCDMA, and LTE. The signals have been received from the different channels with multipath, fading, and noise. The dataset can be used to validate the designed classifier model aiming to identify cellular communication signals. For each signal, the dimension of SCF estimate is 8193*16. There are two train sets which must be used together (concatenate train_data_wo_mapping1 and train_data_wo_mapping2 ). Two train sets have 3000 signals totally, and the test set has 1500. The label of the cellular communication standards are given in dataset as follows: WCDMA -> 0 LTE -> 1 GSM -> 2 The dataset includes: 1. SCFDatatrain1.mat 2. SCFDatatrain2.mat 3. SCFDatatest.mat The contents of .mat files: train_class : denotes class labels of the train set, its dimension is 3000*1 double train_data_wo_mapping1 : includes the first half of the training data, its dimension 1500*1 cell train_data_wo_mapping2 : includes the second half of the training data, its dimension 1500*1 cell *Note, concatenate two cells given above (ie [train_data_wo_mapping1; train_data_wo_mapping2]) test_class : denotes class labels of the train set, its dimension is 1500*1 double test_data_without_mapping : includes the test data, its dimension 1500*1 cell Each cell contains 1500 SCF estimates (8193*16) . The dataset has been used for the paper "Real-World Considerations for Deep Learning in Wireless Signal Identification Based on Spectral Correlation Function" submitted for possible publication in IEEE Wireless Communication Letters. Please cite this paper, if you use the dataset.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant depression (TRD) via partition of the data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) cohort into a training and a testing dataset. We also included data from a small yet completely independent cohort RIS-INT-93 as an external test dataset. We used features from enrollment and level 1 treatment (up to week 2 response only) of STAR*D to explore the feature space comprehensively and applied machine learning methods to model TRD outcome at level 2. For TRD defined using QIDS-C16 remission criteria, multiple machine learning models were internally cross-validated in the STAR*D training dataset and externally validated in both the STAR*D testing dataset and RIS-INT-93 independent dataset with an area under the receiver operating characteristic curve (AUC) of 0.70–0.78 and 0.72–0.77, respectively. The upper bound for the AUC achievable with the full set of features could be as high as 0.78 in the STAR*D testing dataset. Model developed using top 30 features identified using feature selection technique (k-means clustering followed by χ2 test) achieved an AUC of 0.77 in the STAR*D testing dataset. In addition, the model developed using overlapping features between STAR*D and RIS-INT-93, achieved an AUC of > 0.70 in both the STAR*D testing and RIS-INT-93 datasets. Among all the features explored in STAR*D and RIS-INT-93 datasets, the most important feature was early or initial treatment response or symptom severity at week 2. These results indicate that prediction of TRD prior to undergoing a second round of antidepressant treatment could be feasible even in the absence of biomarker data.

Prediction Data of Base Models from AutoGluon on 71 classification datasets from the AutoML Benchmark for Balanced Accuracy and ROC AUC.

The files of this figshare item include data that was collected for the paper: CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure, Lennart Purucker, Joeran Beel, Second International Conference on Automated Machine Learning, 2023.

The data was stored and used with the assembled framework: https://github.com/ISG-Siegen/assembled.

In detail, the data contains the predictions of base models on validation and test as produced by running AutoGluon for 4 hours. Such prediction data is included for each model produced by AutoGluon on each fold of 10-fold cross-validation on the 71 classification datasets from the AutoML Benchmark. The data exists for two metrics (ROC AUC and Balanced Accuracy). More details can be found in the paper.

The data was collected by code created for the paper and is available in its reproducibility repository: https://doi.org/10.6084/m9.figshare.23609226.

Its usage is intended for but not limited to using assembled to evaluate post hoc ensembling methods for AutoML.

Details The link above points to a hosted server that facilitates the download. We opted for a hosted server, as we found no other suitable solution to share these large files (due to file size or storage limits) for a reasonable price. If you want to obtain the data in another way or know of a more suitable alternative, please contact Lennart Purucker.

The link resolves to a directory containing the following:

example_metatasks: contains an example metatask for test purposes before committing to downloading all files.
metatasks_roc_auc.zip: The Metatasks obtained by running AutoGluon for ROC AUC. metatasks_bacc.zip: The Metatasks obtained by running AutoGluon for Balanced Accuracy.

The size after unzipping is:

metatasks_roc_auc.zip: ~85GB metatasks_bacc.zip: ~100GB

The metatask .zip files contain 2 files per metatask. One .json file with metadata information and a .hdf file containing the prediction data. The details on how this should be read and used as a Metatask can be found in the assembled framework and the reproducibility repository. To obtain the data without Metataks, we advise looking at the file content and metadata individually or parsing them by using Metatasks first.

DeepVL training dataset

  Introduction

This dataset repository contains the training and testing datasets used in the paper: "DeepVL: Dynamics and Inertial Measurements-based Deep Velocity Learning for Underwater Odometry". The dataset was collected by manually pilotting an underwater robot in a pool and in the Trondhiem fjord.

  Dataset details

The training data is located in the train_full directory and the test data in test directory respectively. The training… See the full description on the dataset page: https://huggingface.co/datasets/ntnu-arl/deepvl-training-data.

Challenge 2 Image Sets. Training data is accompanied by interpolated steering values. Test data only has center image frames.

Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:

Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"

Contains recordings and manual annotations of ultrasonic vocalizations (USVs) of female and male residents towards an intruder mouse. Manual annotations were created for training and testing DAS. Original data source for the recordings:https://data.donders.ru.nl/collections/di/dcn/DSC_620840_0003_891?0 Original reference: Ivanenko A, Watkins P, Gerven MAJ van, Hammerschmidt K, Englitz B. 2020. Classifying sex and strain from mouse ultrasonic vocalizations using deep learning. PLOS Computational Biology 16:e1007918. USVs from a female resident towards a female intruder used for training and testing DAS: Rfem_Afem01_annotations.csv, Rfem_Afem01.npz Rfem_Afem02_annotations.csv, Rfem_Afem02.npz USVs from a male resident towards a female intruder used for testing generalization of the female-trained DAS model: Rmale_Afem01_annotations.csv, Rmale_Afem01.npz

Training and test-related data to accompany "Keyphrase Extraction for Technical Language Processing" by Alden Dima and Aaron Massey (in press). The subdirectories "keyphrase-extraction-jct-train" and "keyphrase-extraction-jct-test" contain a total of 1153 ThermoML files which are each associated with a corresponding Journal of Chemical Thermodynamics (JCT) article. These ThermoML files contain information about these papers in extensible markup language (XML) format including the title, authors, abstract, digital object identifier (DOI) and keywords. They also contain thermophysical property data unrelated to the keyphrase extraction study. These files were obtained from the National Institute of Standard and Technology (NIST) Thermodynamics Research Center (TRC) in Boulder, Colorado (https://trc.nist.gov/). Readers wishing to replicate this work will also need to obtain the original JCT articles which can be obtained from https://www.sciencedirect.com/journal/the-journal-of-chemical-thermodynamics.

The dataset consists of several columns that provide essential information for each entry. These columns include:

instruction: This column denotes the specific instruction given to the model for generating a response. responses: The model-generated responses to the given instruction are stored in this column. next_response: Following each previous response, this column indicates the subsequent response generated by the model. answer: The correct answer to the question asked in the instruction is provided in this column. is_human_response: This boolean column indicates whether a particular response was generated by a human or by an AI model. By analyzing this rich and diverse dataset, researchers and practitioners can gain valuable insights into various aspects of question answering tasks using AI models. It offers an opportunity for developers to train their models effectively while also facilitating rigorous evaluation methodologies.

Please note that specific dates are not included within this dataset description, focusing solely on providing accurate, informative, descriptive details about its content and purpose

How to use the dataset Understanding the Columns: This dataset contains several columns that provide important information for each entry:

instruction: The instruction given to the model for generating a response. responses: The model-generated responses to the given instruction. next_response: The next response generated by the model after the previous response. answer: The correct answer to the question asked in the instruction. is_human_response: Indicates whether a response is generated by a human or the model. Training Data (train.csv): Use train.csv file in this dataset as training data. It contains a large number of examples that you can use to train your question-answering models or algorithms.

Testing Data (test.csv): Use test.csv file in this dataset as testing data. It allows you to evaluate how well your models or algorithms perform on unseen questions and instructions.

Create Machine Learning Models: You can utilize this dataset's instructional components, including instructions, responses, next_responses, and human-generated answers, along with their respective labels like is_human_response (True/False) for training machine learning models specifically designed for question-answering tasks.

Evaluate Model Performance: After training your model using the provided training data, you can then test its performance on unseen questions from test.csv file by comparing its predicted responses with actual human-generated answers.

Data Augmentation: You can also augment this existing data in various ways such as paraphrasing existing instructions or generating alternative responses based on similar contexts within each example.

Build Conversational Agents: This dataset can be useful for training conversational agents or chatbots by leveraging the instruction-response pairs.

Remember, this dataset provides a valuable resource for building and evaluating question-answering models. Have fun exploring the data and discovering new insights!

Research Ideas Language Understanding: This dataset can be used to train models for question-answering tasks. Models can learn to understand and generate responses based on given instructions and previous responses.

Chatbot Development: With this dataset, developers can create chatbots that provide accurate and relevant answers to user questions. The models can be trained on various topics and domains, allowing the chatbot to answer a wide range of questions.

Educational Materials: This dataset can be used to develop educational materials, such as interactive quizzes or study guides. The models trained on this dataset can provide instant feedback and answers to students' questions, enhancing their learning experience.

Information Retrieval Systems: By training models on this dataset, information retrieval systems can be developed that help users find specific answers or information from large datasets or knowledge bases.

Customer Support: This dataset can be used in training customer support chatbots or virtual assistants that can provide quick and accurate responses to customer inquiries.

Language Generation Research: Researchers studying natural language generation (NLG) techniques could use this dataset for developing novel algorithms for generating coherent and contextually appropriate responses in question-answering scenarios.

Automatic Summarization Systems: Using the instruction-response pairs, automatic summarization systems could be trained that generate concise summaries of lengthy texts by understanding the main content of the text through answering questions.

Dialogue Systems Evaluation: The instruction-response pairs in this dataset could serve as a benchmark for evaluating the performance of dialogue systems in terms of response quality, relevance, coherence, etc.

9 . Machine Learning Training Data Augmentation : One clever ide

Dataset Description

Context and Methodology

• Research Domain/Project:
  This dataset was created for a machine learning experiment aimed at developing a classification model to predict outcomes based on a set of features. The primary research domain is disease prediction in patients. The dataset was used in the context of training, validating, and testing.

• Purpose of the Dataset:
  The purpose of this dataset is to provide training, validation, and testing data for the development of machine learning models. It includes labeled examples that help train classifiers to recognize patterns in the data and make predictions.

• Dataset Creation:
  Data preprocessing steps involved cleaning, normalization, and splitting the data into training, validation, and test sets. The data was carefully curated to ensure its quality and relevance to the problem at hand. For any missing values or outliers, appropriate handling techniques were applied (e.g., imputation, removal, etc.).

Technical Details

• Structure of the Dataset:
  The dataset consists of several files organized into folders by data type:

  • Training Data: Contains the training dataset used to train the machine learning model.

  • Validation Data: Used for hyperparameter tuning and model selection.

  • Test Data: Reserved for final model evaluation.

  Each folder contains files with consistent naming conventions for easy navigation, such as train_data.csv, validation_data.csv, and test_data.csv. Each file follows a tabular format with columns representing features and rows representing individual data points.

• Software Requirements:
  To open and work with this dataset, you need VS Code or Jupyter, which could include tools like:

  • Python (with libraries such as pandas, numpy, scikit-learn, matplotlib, etc.)

Further Details

• Reusability:
  Users of this dataset should be aware that it is designed for machine learning experiments involving classification tasks. The dataset is already split into training, validation, and test subsets. Any model trained with this dataset should be evaluated using the test set to ensure proper validation.

• Limitations:
  The dataset may not cover all edge cases, and it might have biases depending on the selection of data sources. It's important to consider these limitations when generalizing model results to real-world applications.