cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.mrbhc3
title: Occurrence Download

description: A dataset containing 350 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anthurium caucavallense",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 350 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004407-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rff3zpbcbx.4
title: Norm Elicitation and Field Experiment Data on Zero-Price Effects Across Contexts

description: The Role of Social Norms in Zero Price effects
DOI: 10.17632/xss38c24bp
title: Replication Package for "Does Restricting Access to Credit Affect Learning Outcomes? Evidence from a Regulatory Shock to Microfinance in India" (Kalliyil and Sahoo, 2026)

description: This replication package is for the paper “Does Restricting Access to Credit Affect Learning Outcomes? Evidence from a Regulatory Shock to Microfinance in India” by Muneer Kalliyil and Soham Sahoo.
DOI: 10.17632/xss38c24bp.1
title: Replication Package for "Does Restricting Access to Credit Affect Learning Outcomes? Evidence from a Regulatory Shock to Microfinance in India" (Kalliyil and Sahoo, 2026)

description: This replication package is for the paper “Does Restricting Access to Credit Affect Learning Outcomes? Evidence from a Regulatory Shock to Microfinance in India” by Muneer Kalliyil and Soham Sahoo.
DOI: 10.17632/dztvybypd4
title: Replication Folder for "Impact of pension insurance and health insurance on pension savings"

description: This replication folder contains the necessary codes to replicate the quantitative results of the paper.
DOI: 10.17632/dztvybypd4.1
title: Replication Folder for "Impact of pension insurance and health insurance on pension savings"

description: This replication folder contains the necessary codes to replicate the quantitative results of the paper.
DOI: 10.17632/c7zc46g3gy
title: Replication Data for: Analyzing and Quantifying Key Sources of Uncertainty in Intermittent Water Supply Simulation: Supply Characteristics, Household Tank Size and Time Series of Water Consumption From the Household Tanks

description: All data used and analysed in this study are provided. As outlined in the methodology presented in Figure 1 of the associated paper, the data were divided into three steps: (1) Definition, (2) Associate a uniform distribution, and (3) Analysis. For each step, the case study, data used, Python scripts have been made available. 
This study characterizes uncertainty in supply characteristics using probability distribution functions (PDFs), which were derived from the water supply schedules for Delhi dated May 12, 2020, as provided by Meyer (2023).
Dataset Reference:
Meyer, David, 2023, “Replication Data for: Learning from Intermittent Water Supply Schedules: Visualizing Equality, Equity, and Hydraulic Capacity in Bengaluru and Delhi, India”, https://doi.org/10.5683/SP3/QA0YQM Borealis, V1, UNF:6/qJHOdyuJtg==%3CfileUNF%3E.
The case study data were sourced from:
Campisano, A., Gullotta, A., & Modica, C. (2019). Using EPA-SWMM to simulate intermittent water distribution systems. Urban Water Journal, 15(10), 925–933. https://doi.org/10.1080/1573062X.2019.1597379.
DOI: 10.17632/c7zc46g3gy.1
title: Replication Data for: Analyzing and Quantifying Key Sources of Uncertainty in Intermittent Water Supply Simulation: Supply Characteristics, Household Tank Size and Time Series of Water Consumption From the Household Tanks

description: All data used and analysed in this study are provided. As outlined in the methodology presented in Figure 1 of the associated paper, the data were divided into three steps: (1) Definition, (2) Associate a uniform distribution, and (3) Analysis. For each step, the case study, data used, Python scripts have been made available. 
This study characterizes uncertainty in supply characteristics using probability distribution functions (PDFs), which were derived from the water supply schedules for Delhi dated May 12, 2020, as provided by Meyer (2023).
Dataset Reference:
Meyer, David, 2023, “Replication Data for: Learning from Intermittent Water Supply Schedules: Visualizing Equality, Equity, and Hydraulic Capacity in Bengaluru and Delhi, India”, https://doi.org/10.5683/SP3/QA0YQM Borealis, V1, UNF:6/qJHOdyuJtg==%3CfileUNF%3E.
The case study data were sourced from:
Campisano, A., Gullotta, A., & Modica, C. (2019). Using EPA-SWMM to simulate intermittent water distribution systems. Urban Water Journal, 15(10), 925–933. https://doi.org/10.1080/1573062X.2019.1597379.
DOI: 10.17632/gg88rc79vv
title: EpiEEG-60: A Balanced Epileptic Seizure and Normal EEG Dataset

description: This dataset contains raw Electroencephalogram (EEG) recordings collected from 60 distinct individuals. The cohort is equally divided into two groups: 30 neurologically normal subjects and 30 abnormal subjects clinically diagnosed with epilepsy. The dataset is intended to support research in automated seizure detection, neurological signal processing, and machine learning classification tasks.

#Participants and Demographics#

 Total Subjects: 60
 Groups: 30 Normal, 30 Epilepsy 
 Demographics: The participants include both male and female subjects with an age range of 17 to 70 years.
 Validation: All recordings were manually labeled and verified by an expert neurologist from the National Institute of Neuroscience and Hospital (NINS).

#Data Acquisition Details#

 Instrumentation: Signals were acquired using a Nihon Kohden EEG system.
 Sampling Rate: 500 Hz.
 Recording Duration: Approximately 30 ± 2 minutes per subject.
 Channel Configuration: Each recording comprises 43 channels, including standard EEG (electroencephalogram), EOG (electro-oculogram), MISC (miscellaneous reference), and BIO (physiological) channels.

 Filtering: Default acquisition settings included a 0–250 Hz bandwidth filter.

#File Formats and Structure#
The dataset is organized using a structured directory hierarchy. The main directory, Ready_data, contains two subdirectories: Normal and Abnormal. Inside these, subject-specific folders (e.g., Patient_01, Patient_02) contain the recording sessions.
Each recording session includes seven proprietary Nihon Kohden files necessary for full decoding and interpretation:

 .EEG: Contains the continuous, digitized raw EEG voltage waveforms.
 .21E: Header file providing essential acquisition parameters (channel indexing, sensitivity, sampling frequency) required for parsing the .EEG file.
 .EVT: Event annotation file storing time-stamped markers for clinical events (e.g., "Eyes Open," "Hyperventilation," "Seizure Onset").
 .CMT: Electrode configuration file defining channel labels and 10–20 system positions.
 .CN2: Channel-specific settings (amplifier configuration, calibration factors).
 .LOG: System log recording session history and hardware status.
 .TRD: Trend data files containing compressed signal views.

#Usage Notes#
These files are binary and typically require specialized libraries for processing. Python users can utilize MNE-Python (using the read_raw_nihon function) to load the data, as the .21E and .EEG files function together to reconstruct the signal. The .EVT files are critical for extracting epochs related to specific stimuli or clinical states.
DOI: 10.17632/gg88rc79vv.4
title: EpiEEG-60: A Balanced Epileptic Seizure and Normal EEG Dataset

description: This dataset contains raw Electroencephalogram (EEG) recordings collected from 60 distinct individuals. The cohort is equally divided into two groups: 30 neurologically normal subjects and 30 abnormal subjects clinically diagnosed with epilepsy. The dataset is intended to support research in automated seizure detection, neurological signal processing, and machine learning classification tasks.

#Participants and Demographics#

 Total Subjects: 60
 Groups: 30 Normal, 30 Epilepsy 
 Demographics: The participants include both male and female subjects with an age range of 17 to 70 years.
 Validation: All recordings were manually labeled and verified by an expert neurologist from the National Institute of Neuroscience and Hospital (NINS).

#Data Acquisition Details#

 Instrumentation: Signals were acquired using a Nihon Kohden EEG system.
 Sampling Rate: 500 Hz.
 Recording Duration: Approximately 30 ± 2 minutes per subject.
 Channel Configuration: Each recording comprises 43 channels, including standard EEG (electroencephalogram), EOG (electro-oculogram), MISC (miscellaneous reference), and BIO (physiological) channels.

 Filtering: Default acquisition settings included a 0–250 Hz bandwidth filter.

#File Formats and Structure#
The dataset is organized using a structured directory hierarchy. The main directory, Ready_data, contains two subdirectories: Normal and Abnormal. Inside these, subject-specific folders (e.g., Patient_01, Patient_02) contain the recording sessions.
Each recording session includes seven proprietary Nihon Kohden files necessary for full decoding and interpretation:

 .EEG: Contains the continuous, digitized raw EEG voltage waveforms.
 .21E: Header file providing essential acquisition parameters (channel indexing, sensitivity, sampling frequency) required for parsing the .EEG file.
 .EVT: Event annotation file storing time-stamped markers for clinical events (e.g., "Eyes Open," "Hyperventilation," "Seizure Onset").
 .CMT: Electrode configuration file defining channel labels and 10–20 system positions.
 .CN2: Channel-specific settings (amplifier configuration, calibration factors).
 .LOG: System log recording session history and hardware status.
 .TRD: Trend data files containing compressed signal views.

#Usage Notes#
These files are binary and typically require specialized libraries for processing. Python users can utilize MNE-Python (using the read_raw_nihon function) to load the data, as the .21E and .EEG files function together to reconstruct the signal. The .EVT files are critical for extracting epochs related to specific stimuli or clinical states.
DOI: 10.17632/37y95mtdff
title: Neodymium Isotope (ƐNd) data from the Atlantic Iberian margin
DOI: 10.17632/37y95mtdff.1
title: Neodymium Isotope (ƐNd) data from the Atlantic Iberian margin
DOI: 10.17632/2jt7dkhm8h
title: The Role of Non-Formal Training in an International Youth Mobility on Young Adult's Perception of Appearance and Dental Aesthetics

description: Raw Dataset
DOI: 10.17632/2jt7dkhm8h.1
title: The Role of Non-Formal Training in an International Youth Mobility on Young Adult's Perception of Appearance and Dental Aesthetics

description: Raw Dataset
DOI: 10.15468/dl.jqh435
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "StateProvince is Tamaulipas",
 "TaxonKey is Mucuna pruriens"
 ]
}

 The dataset includes 11 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004409-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wdb9p2ztb2
title: Lithium-ion battery cycling and aging measurements collected under tropical environmental conditions in Cameroon

description: This dataset was generated to characterize lithium-ion battery degradation in the context of electric vehicle operation under tropical environmental conditions. The working hypothesis underlying the data generation process is that sustained exposure to elevated ambient temperature and humidity, combined with repeated charge–discharge cycling typical of electric mobility usage profiles, accelerates electrochemical aging mechanisms and modifies the dynamic electrical and thermal response of the battery system.

The batteries were operated following controlled cycling protocols representative of electric vehicle usage, including repeated charge–discharge sequences at defined current rates. Data acquisition was performed using calibrated battery testing systems and integrated monitoring sensors. Continuous time-series measurements were recorded for voltage, current, and surface temperature. Cycle-resolved variables include discharge capacity, internal resistance estimation, cycle index, depth of discharge, and health-related indicators derived from capacity evolution.

Environmental parameters reflecting tropical operating conditions were monitored during the acquisition period to account for thermal stress exposure. The dataset includes both raw time-series signals and processed cycle-level summaries to enable multi-scale analysis.

The data exhibit progressive capacity fade, increasing internal resistance trends, and evolving voltage response profiles across cycles. Thermal behavior during high-current operation phases shows measurable variations consistent with aging progression. These patterns allow quantification of degradation trajectories under electric vehicle operational constraints.

The dataset is structured to support advanced modeling tasks including time-series forecasting, degradation curve fitting, multivariate regression, dimensionality reduction, regularization techniques addressing correlated predictors, and deep learning architectures for health estimation. It can also be used for benchmarking predictive maintenance algorithms, probabilistic graphical modeling, reliability analysis, and algorithm validation under high-temperature electric vehicle operating conditions.

The data are organized in structured spreadsheet format with clearly defined units and variable descriptions to facilitate reuse in electrochemical modeling, data-driven health estimation, and energy storage system reliability research.
DOI: 10.17632/wdb9p2ztb2.1
title: Lithium-ion battery cycling and aging measurements collected under tropical environmental conditions in Cameroon

description: This dataset was generated to characterize lithium-ion battery degradation in the context of electric vehicle operation under tropical environmental conditions. The working hypothesis underlying the data generation process is that sustained exposure to elevated ambient temperature and humidity, combined with repeated charge–discharge cycling typical of electric mobility usage profiles, accelerates electrochemical aging mechanisms and modifies the dynamic electrical and thermal response of the battery system.

The batteries were operated following controlled cycling protocols representative of electric vehicle usage, including repeated charge–discharge sequences at defined current rates. Data acquisition was performed using calibrated battery testing systems and integrated monitoring sensors. Continuous time-series measurements were recorded for voltage, current, and surface temperature. Cycle-resolved variables include discharge capacity, internal resistance estimation, cycle index, depth of discharge, and health-related indicators derived from capacity evolution.

Environmental parameters reflecting tropical operating conditions were monitored during the acquisition period to account for thermal stress exposure. The dataset includes both raw time-series signals and processed cycle-level summaries to enable multi-scale analysis.

The data exhibit progressive capacity fade, increasing internal resistance trends, and evolving voltage response profiles across cycles. Thermal behavior during high-current operation phases shows measurable variations consistent with aging progression. These patterns allow quantification of degradation trajectories under electric vehicle operational constraints.

The dataset is structured to support advanced modeling tasks including time-series forecasting, degradation curve fitting, multivariate regression, dimensionality reduction, regularization techniques addressing correlated predictors, and deep learning architectures for health estimation. It can also be used for benchmarking predictive maintenance algorithms, probabilistic graphical modeling, reliability analysis, and algorithm validation under high-temperature electric vehicle operating conditions.

The data are organized in structured spreadsheet format with clearly defined units and variable descriptions to facilitate reuse in electrochemical modeling, data-driven health estimation, and energy storage system reliability research.
DOI: 10.17632/55jsn2r724
title: Impact of digital economy on innovation and entrepreneurship: Evidence from China

description: This folder contains the replication materials for the empirical analyses presented in the paper. It includes the dataset and Stata code used to reproduce the main results.
DOI: 10.17632/55jsn2r724.1
title: Impact of digital economy on innovation and entrepreneurship: Evidence from China

description: This folder contains the replication materials for the empirical analyses presented in the paper. It includes the dataset and Stata code used to reproduce the main results.
DOI: 10.17632/tp5vd9k53s
title: DBAD

description: For questions pertaining to this dataset contact the corresponding investigator at roberts-brian-w@cooperhealth.edu.
DOI: 10.17632/tp5vd9k53s.1
title: DBAD

description: For questions pertaining to this dataset contact the corresponding investigator at roberts-brian-w@cooperhealth.edu.
DOI: 10.17632/37zfmxvdty
title: Lorenz System — Simulated Indirect Measurement Dataset

description: This dataset contains simulated time series generated from the Lorenz dynamical system together with explicitly modeled sensor measurements. The data provide full access to ground-truth state variables while reproducing realistic indirect observation processes, enabling benchmarking of system identification, state reconstruction, and nonlinear time-series analysis methods under controlled chaotic dynamics.
DOI: 10.17632/37zfmxvdty.1
title: Lorenz System — Simulated Indirect Measurement Dataset

description: This dataset contains simulated time series generated from the Lorenz dynamical system together with explicitly modeled sensor measurements. The data provide full access to ground-truth state variables while reproducing realistic indirect observation processes, enabling benchmarking of system identification, state reconstruction, and nonlinear time-series analysis methods under controlled chaotic dynamics.
DOI: 10.17632/b4jv3pxvz5
title: Coupled Van der Pol Oscillators — Realistic Mechanical Oscillator Dataset

description: This dataset includes numerical simulations of coupled Van der Pol oscillators designed to emulate nonlinear oscillatory behavior in natural and mechanical systems. Alongside complete ground-truth dynamics, the dataset provides synthetic sensor measurements that replicate realistic observation constraints, supporting evaluation of synchronization analysis, model discovery, and data-driven dynamics reconstruction approaches.
DOI: 10.17632/b4jv3pxvz5.1
title: Coupled Van der Pol Oscillators — Realistic Mechanical Oscillator Dataset

description: This dataset includes numerical simulations of coupled Van der Pol oscillators designed to emulate nonlinear oscillatory behavior in natural and mechanical systems. Alongside complete ground-truth dynamics, the dataset provides synthetic sensor measurements that replicate realistic observation constraints, supporting evaluation of synchronization analysis, model discovery, and data-driven dynamics reconstruction approaches.
DOI: 10.5281/zenodo.15098223
title: Recursive InPainting with Stable Difussion 2 Inpainting

description: Recursive inpainting with Stable Difussion

The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting, used in the paper How Stable is Stable Diffusion under Recursive InPainting (RIP)? Presented at the GenAI Evaluation KDD2024: KDD workshop on Evaluation and Trustworthiness of Generative AI Models



 




The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files:



original_imgs_grid.png* file with all the base images in an ordered grid or dataset folder with the original images.

mask_64x64/[number_figure]_64x64.png* with the inpainting version of [number_figure] and 64x64 mask

mask_128x128/[number_figure]_128x128.png* with the inpainting version of [number_figure] and 128x128 mask

mask_256x256/[number_figure]_256x256.png* with the inpainting version of [number_figure] and 256x256 mask

lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image.


* Only images that license allow to share

There are three different experiments:



lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting.

lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image.

lpisp_distances_styles: contains recursive inpaintings of 80 images taken from seven pictorial styles and from sketches of an architect in subsets to 10 images.



Styles:

Mods folder: contains the same inpaintings but for modifications of the base image:



Black_White

Blue_Green: version with no red components.

Red_Blue: version with no green components.

Red_Green: version with no blue components.






The folder code includes the a README.md with the instructions and the scripts to run the experimentsCite our work in:

Conde, J., Gonzalez, M., Martínez, G. et al. Recursive InPainting (RIP): how much information is lost under recursive inferences?. AI & Soc 40, 6309–6325 (2025). https://doi.org/10.1007/s00146-025-02351-5