cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.q9qfrr
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Selago thomsonii",
 "HasCoordinate is true",
 "Geometry POLYGON ((-17.66889 -35.11691, 51.34702 -35.11691, 51.34702 37.62, -17.66889 37.62, -17.66889 -35.11691))"
 ]
}

 The dataset includes 64 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033448-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsprvx
title: Occurrence Download

description: A dataset containing 969 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trifolium repens",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 969 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033449-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9bfu9b
title: Occurrence Download

description: A dataset containing 62 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mirabilis jalapa",
 "Country is Uganda",
 "HasCoordinate is true"
 ]
}

 The dataset includes 62 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033450-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16361639
title: Responsible Innovation Roles - survey responses
DOI: 10.5281/zenodo.16361640
title: Responsible Innovation Roles - survey responses
DOI: 10.15468/dl.yfzxcp
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paspalum dilatatum",
 "Country is Guyana",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033451-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ht3r7d
title: Occurrence Download

description: A dataset containing 9533 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Eurycea bislineata)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 9533 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033453-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12554311
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.duu2ge
title: Occurrence Download

description: A dataset containing 27043 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lonicera involucrata"
 ]
}

 The dataset includes 27043 records from 324 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033452-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgaqdd
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Holocheilus illustris",
 "Year 1986-2026"
 ]
}

 The dataset includes 41 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006405-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18600529
title: Data and Code to Reproduce Figures and Model Evaluation in "Using Transfer and Active Learning to Improve BirdNET Detection of Rare Nocturnal Birds"

description: This repository complements the paper titled “Using transfer and active learning to improve BirdNET detection of rare nocturnal birds from Passive Acoustic Monitoring in a Neotropical forest.” It contains the data used to train and evaluate models, the .tflite models themselves, and all the code and data necessary to reproduce the figures in the paper.

For details on the workflow and methods, please refer to the companion repository: https://doi.org/10.5281/zenodo.18564530.

🗂️ Contents (as ZIPs)

1. BN_evaluation-and_retraining.zip

Includes:





📁 train_set_0/:Initial training set used for transfer learning, with subfolders per class and the original training set tracker.




📁 model_test/:208 WAV files used for testing, plus 208 TXT annotation files used to evaluate model performance.




📁 models/:All models trained during the initial retraining (model 0) and through the active learning loop (models 1–9), saved as .tflite. Also includes:





A .csv file with the number of training samples per model.




A .csv with training parameters.




A .txt with class labels used during model training.





2. to_reproduce_figures.zip

Includes:









📁 data/:CSV files used for generating figures.




📁 code/:Jupyter Notebooks and one RMarkdown (.Rmd) file for figure generation.




📁 html/:HTML-rendered versions of the notebooks for easy viewing and reproducibility.
DOI: 10.5281/zenodo.18600530
title: Data and Code to Reproduce Figures and Model Evaluation in "Using Transfer and Active Learning to Improve BirdNET Detection of Rare Nocturnal Birds"

description: This repository complements the paper titled “Using transfer and active learning to improve BirdNET detection of rare nocturnal birds from Passive Acoustic Monitoring in a Neotropical forest.” It contains the data used to train and evaluate models, the .tflite models themselves, and all the code and data necessary to reproduce the figures in the paper.

For details on the workflow and methods, please refer to the companion repository: https://doi.org/10.5281/zenodo.18564530.

🗂️ Contents (as ZIPs)

1. BN_evaluation-and_retraining.zip

Includes:





📁 train_set_0/:Initial training set used for transfer learning, with subfolders per class and the original training set tracker.




📁 model_test/:208 WAV files used for testing, plus 208 TXT annotation files used to evaluate model performance.




📁 models/:All models trained during the initial retraining (model 0) and through the active learning loop (models 1–9), saved as .tflite. Also includes:





A .csv file with the number of training samples per model.




A .csv with training parameters.




A .txt with class labels used during model training.





2. to_reproduce_figures.zip

Includes:









📁 data/:CSV files used for generating figures.




📁 code/:Jupyter Notebooks and one RMarkdown (.Rmd) file for figure generation.




📁 html/:HTML-rendered versions of the notebooks for easy viewing and reproducibility.
DOI: 10.15468/dl.676kpu
title: Occurrence Download

description: A dataset containing 23343 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ara militaris"
 ]
}

 The dataset includes 23343 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006406-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18601496
title: ГУМАНИТАРНОЕ ИЗМЕРЕНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ В ОБРАЗОВАНИИ
DOI: 10.5281/zenodo.18601495
title: ГУМАНИТАРНОЕ ИЗМЕРЕНИЕ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ В ОБРАЗОВАНИИ
DOI: 10.15468/dl.hus8xk
title: Occurrence Download

description: A dataset containing 14356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Conolophus marthae, Leucophaeus fuliginosus)"
 ]
}

 The dataset includes 14356 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006407-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u29z5g
title: Occurrence Download

description: A dataset containing 3712 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Campomanesia adamantium"
 ]
}

 The dataset includes 3712 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006409-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30165705
title: An Adjacency-Adaptive Gaussian Process Method for Sample Efficient Response Surface Modeling and Test-Point Acquisition

description: Accurate response surface modeling of complex systems is often hindered by the need for a considerable number of costly evaluations. In this work, we propose a novel configuration for semi-supervised and active learning in Gaussian processes, which incorporates manifold information in the form of adjacency vectors to enrich with additional data-driven features. We also introduce an analytical formulation for the base parameters to enable adaptive feature extraction. Additionally, we adopt the composite likelihood function for training, offering an efficient alternative to the marginal likelihood reliance on expensive matrix inversion. Through an extensive simulation study as well as a case study, we evaluate the effectiveness of the proposed method and compare its performance against some of the common methods in the literature.
DOI: 10.6084/m9.figshare.30165705.v2
title: An Adjacency-Adaptive Gaussian Process Method for Sample Efficient Response Surface Modeling and Test-Point Acquisition

description: Accurate response surface modeling of complex systems is often hindered by the need for a considerable number of costly evaluations. In this work, we propose a novel configuration for semi-supervised and active learning in Gaussian processes, which incorporates manifold information in the form of adjacency vectors to enrich with additional data-driven features. We also introduce an analytical formulation for the base parameters to enable adaptive feature extraction. Additionally, we adopt the composite likelihood function for training, offering an efficient alternative to the marginal likelihood reliance on expensive matrix inversion. Through an extensive simulation study as well as a case study, we evaluate the effectiveness of the proposed method and compare its performance against some of the common methods in the literature.
DOI: 10.6084/m9.figshare.29492786
title: Orthogonal Calibration via Posterior Projections with Applications to the Schwarzschild Model

description: The orbital superposition method originally developed by <i>Schwarzschild</i> is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new relationships between the galaxy’s global characteristics. Scientists are specifically interested in finding optimal parameter choices for this model that best match physical measurements along with quantifying the uncertainty of such procedures. This renders a statistical calibration problem with multivariate outcomes. In this article, we develop a Bayesian method for calibration with <i>multivariate outcomes</i> using orthogonal bias functions thus ensuring parameter identifiability. Our approach is based on projecting the posterior to an appropriate space which allows the user to choose any nonparametric prior on the bias function(s) instead of having to model it (them) with Gaussian processes. We develop a functional projection approach using the theory of Hilbert spaces. A finite-dimensional analogue of the projection problem is also considered. We illustrate the proposed approach using a BART prior and apply it to calibrate the Schwarzschild model illustrating how a multivariate approach may resolve discrepancies resulting from a univariate calibration.
DOI: 10.6084/m9.figshare.29492786.v1
title: Orthogonal calibration via posterior projections with applications to the Schwarzschild model

description: The orbital superposition method originally developed by Schwarzschild (1979) is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new relationships between the galaxy’s global characteristics. Scientists are specifically interested in finding optimal parameter choices for this model that best match physical measurements along with quantifying the uncertainty of such procedures. This renders a statistical calibration problem with multivariate outcomes. In this article, we develop a Bayesian method for calibration with <i>multivariate outcomes</i> using orthogonal bias functions thus ensuring parameter identifiability. Our approach is based on projecting the posterior to an appropriate space which allows the user to choose any nonparametric prior on the bias function(s) instead of having to model it (them) with Gaussian processes. We develop a functional projection approach using the theory of Hilbert spaces. A finite-dimensional analogue of the projection problem is also considered. We illustrate the proposed approach using a BART prior and apply it to calibrate the Schwarzschild model illustrating how a multivariate approach may resolve discrepancies resulting from a univariate calibration.
DOI: 10.6084/m9.figshare.29492786.v2
title: Orthogonal Calibration via Posterior Projections with Applications to the Schwarzschild Model

description: The orbital superposition method originally developed by <i>Schwarzschild</i> is used to study the dynamics of growth of a black hole and its host galaxy, and has uncovered new relationships between the galaxy’s global characteristics. Scientists are specifically interested in finding optimal parameter choices for this model that best match physical measurements along with quantifying the uncertainty of such procedures. This renders a statistical calibration problem with multivariate outcomes. In this article, we develop a Bayesian method for calibration with <i>multivariate outcomes</i> using orthogonal bias functions thus ensuring parameter identifiability. Our approach is based on projecting the posterior to an appropriate space which allows the user to choose any nonparametric prior on the bias function(s) instead of having to model it (them) with Gaussian processes. We develop a functional projection approach using the theory of Hilbert spaces. A finite-dimensional analogue of the projection problem is also considered. We illustrate the proposed approach using a BART prior and apply it to calibrate the Schwarzschild model illustrating how a multivariate approach may resolve discrepancies resulting from a univariate calibration.
DOI: 10.6084/m9.figshare.29821430.v1
title: Information Sharing for Robust and Stable Cross-Validation

description: Robust estimators for linear regression require non-convex objective functions to shield against adverse effects of contamination, including outliers. This non-convexity brings challenges, particularly when combined with penalization in high-dimensional settings. A crucial challenge is selecting hyperparameters for the penalty based on a finite sample. In practice, cross-validation (CV) is the prevalent strategy with good performance for convex estimators. Applied with robust estimators, however, CV often gives subpar results due to the interplay between multiple local minima and the penalty. The best local minimum attained on the full training data may not be the minimum with the desired statistical properties. Furthermore, there may be a mismatch between this minimum and the minima attained in the CV folds which are used for evaluating the prediction error. This article introduces a novel adaptive CV strategy that tracks multiple minima for each combination of hyperparameters and subsets of the data. A matching scheme is presented for correctly evaluating minima computed on the full training data using the best-matching minima from the CV folds. We show that the proposed strategy reduces the variability of the estimated performance metric, leads to smoother CV curves, and therefore substantially increases the reliability and utility of robust penalized estimators. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30158296
title: Stream-Based Active Learning for Process Monitoring

description: Statistical process monitoring (SPM) methods are essential tools in quality management to assess the stability of industrial processes, that is, to dynamically classify the process state as in control, under normal operating conditions, or out of control, otherwise. Although traditional SPM methods are based on unsupervised approaches, supervised methods leverage process data with labels revealing the true process state. However, labeling procedures are often expensive, making it impractical to annotate all data points. To address this challenge, we propose a novel stream-based active learning strategy for SPM that selects the most informative data points for labeling under a limited budget. While traditional active learning methods assume independently distributed data, we explicitly account for temporal dependencies in data streams, leveraging partially hidden Markov models to integrate labeled and unlabeled observations. The proposed method balances exploration, to detect unseen process states, and exploitation, to refine classification accuracy within known states, and extends pool-based active learning to real-time settings by providing labeling decisions for each incoming data point. The proposed method’s performance in classifying the process state is assessed through a simulation and a case study on monitoring a resistance spot welding process in the automotive industry, which motivated this research.
DOI: 10.6084/m9.figshare.30158296.v1
title: Stream-Based Active Learning for Process Monitoring

description: Statistical process monitoring (SPM) methods are essential tools in quality management to assess the stability of industrial processes, that is, to dynamically classify the process state as in control, under normal operating conditions, or out of control, otherwise. Although traditional SPM methods are based on unsupervised approaches, supervised methods leverage process data with labels revealing the true process state. However, labeling procedures are often expensive, making it impractical to annotate all data points. To address this challenge, we propose a novel stream-based active learning strategy for SPM that selects the most informative data points for labeling under a limited budget. While traditional active learning methods assume independently distributed data, we explicitly account for temporal dependencies in data streams, leveraging partially hidden Markov models to integrate labeled and unlabeled observations. The proposed method balances exploration, to detect unseen process states, and exploitation, to refine classification accuracy within known states, and extends pool-based active learning to real-time settings by providing labeling decisions for each incoming data point. The proposed method’s performance in classifying the process state is assessed through a simulation and a case study on monitoring a resistance spot welding process in the automotive industry, which motivated this research.
DOI: 10.6084/m9.figshare.30165705.v1
title: An Adjacency-Adaptive Gaussian Process Method for Sample Efficient Response Surface Modeling and Test-Point Acquisition

description: Accurate response surface modeling of complex systems is often hindered by the need for a considerable number of costly evaluations. In this work, we propose a novel configuration for semi-supervised and active learning in Gaussian processes, which incorporates manifold information in the form of adjacency vectors to enrich with additional data-driven features. We also introduce an analytical formulation for the base parameters to enable adaptive feature extraction. Additionally, we adopt the composite likelihood function for training, offering an efficient alternative to the marginal likelihood reliance on expensive matrix inversion. Through an extensive simulation study as well as a case study, we evaluate the effectiveness of the proposed method and compare its performance against some of the common methods in the literature.
DOI: 10.6084/m9.figshare.29821430
title: Information Sharing for Robust and Stable Cross-Validation

description: Robust estimators for linear regression require non-convex objective functions to shield against adverse effects of contamination, including outliers. This non-convexity brings challenges, particularly when combined with penalization in high-dimensional settings. A crucial challenge is selecting hyperparameters for the penalty based on a finite sample. In practice, cross-validation (CV) is the prevalent strategy with good performance for convex estimators. Applied with robust estimators, however, CV often gives subpar results due to the interplay between multiple local minima and the penalty. The best local minimum attained on the full training data may not be the minimum with the desired statistical properties. Furthermore, there may be a mismatch between this minimum and the minima attained in the CV folds which are used for evaluating the prediction error. This article introduces a novel adaptive CV strategy that tracks multiple minima for each combination of hyperparameters and subsets of the data. A matching scheme is presented for correctly evaluating minima computed on the full training data using the best-matching minima from the CV folds. We show that the proposed strategy reduces the variability of the estimated performance metric, leads to smoother CV curves, and therefore substantially increases the reliability and utility of robust penalized estimators. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30158298
title: Active Learning of Piecewise Gaussian Process Surrogates

description: Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this article, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.