cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18760002
title: IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data fusion and sensing technology_IEEESENSORSLETTERS2023_Data

description: The dataset has been collected on three touch modalities, i.e. slide a finger, brush a paintbrush, and rolling a washer. The dataset is made of 840 tensor data, 280 per class.

Seventy subjects performed each touch action two times on a 4 × 4 piezoelectric sensors matrix in two directions, i.e. horizontal and vertical starting from a random position. Each touch lasted 10 seconds and signals have been sampled at 3kHz. Each datum is a 3-mode tensor where the first two modes represent the 4 × 4 sensors geometry (i.e. a total of 16 channels) and the third dimension contains the time samples (10s × 3K Samples/s = 30000 samples per each channel)
DOI: 10.5281/zenodo.18760004
title: INTERNATIONAL EXPERIENCE IN MANAGING THE INTELLECTUALIZATION OF WOMEN'S LABOR

description: This article analyzes the reforms being implemented in the Republic of Uzbekistan to enhance the role and status of women in society, ensure their employment, improve working conditions, and strengthen their socio-economic activity. The study reveals the content of measures taken on the basis of regulatory legal acts aimed at ensuring gender equality in our country, in particular, resolutions and decrees of the President of the Republic of Uzbekistan. Issues of improving women's labor management, developing remote employment, vocational retraining, and supporting women's entrepreneurship are also highlighted. The article scientifically substantiates priority areas aimed at strengthening the social protection of women, increasing their employment, and ensuring their active participation in the life of society.
DOI: 10.5281/zenodo.18760003
title: INTERNATIONAL EXPERIENCE IN MANAGING THE INTELLECTUALIZATION OF WOMEN'S LABOR

description: This article analyzes the reforms being implemented in the Republic of Uzbekistan to enhance the role and status of women in society, ensure their employment, improve working conditions, and strengthen their socio-economic activity. The study reveals the content of measures taken on the basis of regulatory legal acts aimed at ensuring gender equality in our country, in particular, resolutions and decrees of the President of the Republic of Uzbekistan. Issues of improving women's labor management, developing remote employment, vocational retraining, and supporting women's entrepreneurship are also highlighted. The article scientifically substantiates priority areas aimed at strengthening the social protection of women, increasing their employment, and ensuring their active participation in the life of society.
DOI: 10.15468/dl.qvxjrh
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-74.50236 17.03969,-67.61023 17.03969,-67.61023 20.91759,-74.50236 20.91759,-74.50236 17.03969))",
 "HasGeospatialIssue is false",
 "TaxonKey is Libidibia monosperma"
 ]
}

 The dataset includes 21 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007466-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25380/iastate.28204850
title: PFOA Distribution in Pigs

description: The dataset includes one Excel spreadsheet containing PFOA (Perfluorooctanoic Acid) concentrations measured in plasma, follicular fluid, liver, and ovary of pigs administered 70 ng PFOA/kg body weight or controls, under thermal neutral or heat stress conditions. There are also 8 python scripts for analysis.
DOI: 10.15468/dl.85nzbc
title: Occurrence Download

description: A dataset containing 34683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "TaxonKey is Ammodramus savannarum"
 ]
}

 The dataset includes 34683 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007465-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25380/iastate.28204850.v1
title: PFOA Distribution in Pigs

description: The dataset includes one Excel spreadsheet containing PFOA (Perfluorooctanoic Acid) concentrations measured in plasma, follicular fluid, liver, and ovary of pigs administered 70 ng PFOA/kg body weight or controls, under thermal neutral or heat stress conditions. There are also 8 python scripts for analysis.
DOI: 10.6084/m9.figshare.31399899.v1
title: Supplementary data for integrated omics analysis of flavonoid biosynthesis in Ocimum gratissimum

description: This dataset contains the supplementary data associated with the study entitled “Unraveling Intraspecific Variation and Molecular Regulation of Flavonoid Biosynthesis in Ocimum gratissimum through Integrated Omics Approaches”. The files include an UpSet plot of flavonoid metabolites (Figure S1), correlation analysis between total flavonoid content and differential flavonoid metabolites (Table S1), and correlation analysis between candidate flavonoid metabolites and differentially expressed genes (Table S2). These data support the metabolomic and transcriptomic analyses presented in the article.
DOI: 10.6084/m9.figshare.31399899
title: Supplementary data for integrated omics analysis of flavonoid biosynthesis in Ocimum gratissimum

description: This dataset contains the supplementary data associated with the study entitled “Unraveling Intraspecific Variation and Molecular Regulation of Flavonoid Biosynthesis in Ocimum gratissimum through Integrated Omics Approaches”. The files include an UpSet plot of flavonoid metabolites (Figure S1), correlation analysis between total flavonoid content and differential flavonoid metabolites (Table S1), and correlation analysis between candidate flavonoid metabolites and differentially expressed genes (Table S2). These data support the metabolomic and transcriptomic analyses presented in the article.
DOI: 10.5281/zenodo.18628762
title: FuelFlex-LBOPred - Time-Series of Optical Sensor Signals for LBO Prediction in a Fuel-Flexible Jet Engine Nozzle

description: FuelFlex-LBOPred: Lean Blow-Out Prediction Dataset for Jet-A1–Hydrogen Fuel Blends

FuelFlex-LBOPred is a comprehensive experimental dataset for Lean Blow-Out (LBO) prediction in Jet-A1–hydrogen combustion systems. This dataset contains 50 experiments from a controlled combustor test rig, capturing optical sensor measurements during and before lean blow-out events.
DOI: 10.5281/zenodo.18628761
title: FuelFlex-LBOPred - Time-Series of Optical Sensor Signals for LBO Prediction in a Fuel-Flexible Jet Engine Nozzle

description: FuelFlex-LBOPred: Lean Blow-Out Prediction Dataset for Jet-A1–Hydrogen Fuel Blends

FuelFlex-LBOPred is a comprehensive experimental dataset for Lean Blow-Out (LBO) prediction in Jet-A1–hydrogen combustion systems. This dataset contains 50 experiments from a controlled combustor test rig, capturing optical sensor measurements during and before lean blow-out events.
DOI: 10.15468/dl.br7cdh
title: Occurrence Download

description: A dataset containing 2230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "TaxonKey is Pristipomoides filamentosus"
 ]
}

 The dataset includes 2230 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007469-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.djs4pz
title: Occurrence Download

description: A dataset containing 16010 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Persea americana"
 ]
}

 The dataset includes 16010 records from 688 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007468-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18745429
title: Dataset for "Impact of Clifford operations on non-stabilizing power and quantum chaos"

description: The uploaded folder contains multiple subfolders, each corresponding to a figure in the manuscript “Impact of Clifford operations on non-stabilizing power and quantum chaos” (arXiv:2505.14793). This work has been accepted for publication in Quantum. 

Each data subfolder is labeled Fig$k$, and, as the name indicates, contains the numerical data associated with Fig. $k$ of the manuscript. For example, the data in Fig2 correspond to Fig. 2 of the paper.

Figs. 1 and 5 contain schematic illustrations; therefore, no numerical data are associated with these figures.

Fig2 and Fig3

Thermalization of non-stabilizing power versus time steps





Data format: single-column NumPy arrays.




nonstab_N=2_k1(blue).npy: Non-stabilizing power for the system size (N=2), obtained from repeated applications of a single non-Clifford gate interspersed with random Clifford gates (blue markers in the figure).




SD_N=2_k1(blue).npy: Corresponding standard deviation for N=2 under the same protocol.



Fig4

Operator-space non-stabilizing power for Ising models





Data format: single-column NumPy arrays.




Example: OSNP_N=8_chaotic_ising.npy denotes the operator-space non-stabilizing power (OSNP) for N=8 in the chaotic Ising chain.




The Hamiltonian parameters are specified in the manuscript. 



Fig6 and Fig7

Average level-spacing ratio of Floquet circuits

These folders contain the numerical data corresponding to the average level-spacing ratio for two-qubit interactions along the edges of the tetrahedron geometry, for various system sizes.





Data format: two-column NumPy arrays.





Column 1: interaction parameter being varied.




Column 2: mean level-spacing ratio.






File names explicitly include:





The edge name (e.g., ID_CNOT)




The system size (e.g., N=12)


For instance: ID_SWAP_N12.npy indicates the mean-level sapcing ratio along the edge ID --- SWAP for the system size N=12. 




Fig8

Verification of analytical expressions for non-stabilizing power

This subfolder contains numerical data validating the analytical expressions for the non-stabilizing power of two-qubit gates along various tetrahedral edges.





Data format: two-column NumPy arrays:     




Column 1: parameter being varied, and  Column 2: corresponding non-stabilizing power.



Fig9

The data corresponds to the scatter plot, illustrating the entangling power and the non-stabilizing powers of the random two-qubit gates. The data file is a two-column array. The first column corresponds to the entangling power, and the second column corresponds to the non-stabilizing power.  

Fig10

Non-stabilizing power of two-qubit gates embedded in a four-qubit Hilbert space





Data format: identical to Fig8.




Column 1: varied parameter, and Column 2: corresponding non-stabilizing power.
DOI: 10.5281/zenodo.18745428
title: Dataset for "Impact of Clifford operations on non-stabilizing power and quantum chaos"

description: The uploaded folder contains multiple subfolders, each corresponding to a figure in the manuscript “Impact of Clifford operations on non-stabilizing power and quantum chaos” (arXiv:2505.14793). This work has been accepted for publication in Quantum. 

Each data subfolder is labeled Fig$k$, and, as the name indicates, contains the numerical data associated with Fig. $k$ of the manuscript. For example, the data in Fig2 correspond to Fig. 2 of the paper.

Figs. 1 and 5 contain schematic illustrations; therefore, no numerical data are associated with these figures.

Fig2 and Fig3

Thermalization of non-stabilizing power versus time steps





Data format: single-column NumPy arrays.




nonstab_N=2_k1(blue).npy: Non-stabilizing power for the system size (N=2), obtained from repeated applications of a single non-Clifford gate interspersed with random Clifford gates (blue markers in the figure).




SD_N=2_k1(blue).npy: Corresponding standard deviation for N=2 under the same protocol.



Fig4

Operator-space non-stabilizing power for Ising models





Data format: single-column NumPy arrays.




Example: OSNP_N=8_chaotic_ising.npy denotes the operator-space non-stabilizing power (OSNP) for N=8 in the chaotic Ising chain.




The Hamiltonian parameters are specified in the manuscript. 



Fig6 and Fig7

Average level-spacing ratio of Floquet circuits

These folders contain the numerical data corresponding to the average level-spacing ratio for two-qubit interactions along the edges of the tetrahedron geometry, for various system sizes.





Data format: two-column NumPy arrays.





Column 1: interaction parameter being varied.




Column 2: mean level-spacing ratio.






File names explicitly include:





The edge name (e.g., ID_CNOT)




The system size (e.g., N=12)


For instance: ID_SWAP_N12.npy indicates the mean-level sapcing ratio along the edge ID --- SWAP for the system size N=12. 




Fig8

Verification of analytical expressions for non-stabilizing power

This subfolder contains numerical data validating the analytical expressions for the non-stabilizing power of two-qubit gates along various tetrahedral edges.





Data format: two-column NumPy arrays:     




Column 1: parameter being varied, and  Column 2: corresponding non-stabilizing power.



Fig9

The data corresponds to the scatter plot, illustrating the entangling power and the non-stabilizing powers of the random two-qubit gates. The data file is a two-column array. The first column corresponds to the entangling power, and the second column corresponds to the non-stabilizing power.  

Fig10

Non-stabilizing power of two-qubit gates embedded in a four-qubit Hilbert space





Data format: identical to Fig8.




Column 1: varied parameter, and Column 2: corresponding non-stabilizing power.
DOI: 10.5284/1139415
title: Digital Archive from a Magnetometry Survey at a Proposed Battery Storage Facility, West Kingsmill Farm, Landulph, Cornwall, May 2024

description: This collection consists of a vector graphic, GIS and geophysics data from a detailed magnetometry survey by Archaeological Surveys Ltd. Work occurred ahead of a proposed battery storage facility at West Kingsmill Farm, Landulph, Cornwall in May 2024. The results indicate the presence of a rectilinear enclosure within the north western part of the site. A number of former field boundaries have also been located, the majority of which are mapped in the 19th and 20th centuries. A number of weakly positive linear anomalies can be seen, but it is not clear if these relate to cut features with an anthropogenic origin or natural features within the underlying slate geology.
DOI: 10.57702/c6af6stw
title: Wind Turbine SCADA Data For Early Fault Detection

description: This dataset is published together with the [paper](https://doi.org/10.3390/data9120138) "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section. 

The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines
from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features. 

The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.

The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), 
and consists of 5 wind turbines of an onshore wind farm in Portugal. 
It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. 
From this data 22 datasets were selected to be included in this data collection. 
The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.
Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files

More detailed information can be found in the included README-file.

**Notes**

In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data.
DOI: 10.5281/zenodo.18760032
title: Calculating animal distribution evenness metrics from GNSS tracking data.

description: This dataset includes data from GNSS tracking data from 39 adult limousine cows (.csv file) from a dehesa farm in Pedroche, Cordoba (Spain). GNSS fixations were collected at 30-minute intervals using commercial tracking devices (Digitanimal Ltd., Madrid, Spain). Besides the tracking data, a descriptive table of individual cows (.csv file) is also available. The perimeters of the farm are available in a vectorial spatial layer (.shp).

 

The objective of this dataset is provide a reproducible example dataset for the methodology described by Maroto-Molina et al. (2023). A commented R script (.html) describing the methodology is also included in this dataset.
DOI: 10.5281/zenodo.13384187
title: A Consumer Study on the Acceptance of Digitized Packaging for Food Quality Assessment

description: Supplementary diagrams and pre-processed dataset to reproduce the findings of the publication "A Consumer Study on the Acceptance of Digitized Packaging for Food Quality Assessment" by Elia Henrichs, Julia Marie Senge, and Christian Krupitzer (University of Hohenheim, Department of Food Informatics and Computational Science Hub, Fruwirthstr. 21, D-70599 Stuttgart, Germany).
DOI: 10.5281/zenodo.13384186
title: A Consumer Study on the Acceptance of Digitized Packaging for Food Quality Assessment

description: Supplementary diagrams and pre-processed dataset to reproduce the findings of the publication "A Consumer Study on the Acceptance of Digitized Packaging for Food Quality Assessment" by Elia Henrichs, Julia Marie Senge, and Christian Krupitzer (University of Hohenheim, Department of Food Informatics and Computational Science Hub, Fruwirthstr. 21, D-70599 Stuttgart, Germany).
DOI: 10.5281/zenodo.18760026
title: IMPROVING THE FLEXIBILITY OF YOUNG GYMNASTS AT THE BEGINNING INDOOR STAGE

description: This research sports in gymnastics elementary preparation in the phase young in gymnasts flexibility quality develop and improvement to the issues dedicated to. In the study flexibility sports in gymnastics place, its to age typical development features and training in the process scientific based methodical from approaches of use importance illuminating given. Scientific and methodological literature analysis, pedagogical observation, testing and experience methods based on young gymnasts flexibility level studied. Research during working issued and to practice current done improved methodology flexibility indicators noticeable at the level to increase service what did determined. Obtained results elementary in stages training process effective organization technical elements to master relief and young athletes health protection to do point from the point of view important practical importance has.
DOI: 10.5281/zenodo.18760027
title: IMPROVING THE FLEXIBILITY OF YOUNG GYMNASTS AT THE BEGINNING INDOOR STAGE

description: This research sports in gymnastics elementary preparation in the phase young in gymnasts flexibility quality develop and improvement to the issues dedicated to. In the study flexibility sports in gymnastics place, its to age typical development features and training in the process scientific based methodical from approaches of use importance illuminating given. Scientific and methodological literature analysis, pedagogical observation, testing and experience methods based on young gymnasts flexibility level studied. Research during working issued and to practice current done improved methodology flexibility indicators noticeable at the level to increase service what did determined. Obtained results elementary in stages training process effective organization technical elements to master relief and young athletes health protection to do point from the point of view important practical importance has.
DOI: 10.5281/zenodo.15130000
title: Temperature and Humidity Time Series of Cold Storage Room Monitoring

description:  

 

The datasets contain the raw data and preprocessed data (following the steps in the Jupyter Notebook) of 9 DHT22 sensors in a cold storage room. Details on how the data was gathered can be found in the publication "Towards Self-Adaptive Monitoring of Storage Environments with Distributed Sensor Systems" by Elia Henrichs, Florian Stoll, and Christian Krupitzer (in Proceedings of the 2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), 2025, pp. 85-90, https://doi.org/10.1109/ACSOS-C66519.2025.00033).

This dataset consists of the following files:



Raw.zip - The raw data CSV files of the nine Arduino-based data loggers, containing the semicolon-separated columns date (formatted as dd.mm.yyyy), time (formatted as HH:MM:SS), temperature, and humidity. These files can contain multiple headers.

Preprocessed.zip - The preprocessed data CSV files of the nine Arduino-based data loggers, containing the semicolon-separated columns date (formatted as dd.mm.yyyy), time (formatted as HH:MM:SS), temperature, and humidity. Multiple headers were removed, and the length of the datasets was aligned to equal length by filling missing values with NaN.

DataPreprocessing.ipynb - Jupyter Notebook containing the code to preprocess the data and create the overview file, which summarizes key characteristics of the dataset.

DataPreliminaryAnalysis.ipynb - Jupyter Notebook containing the code to perform the preliminary data analysis (general statistics, peaks, and matrix profiles).

experiment_actions.csv - CSV file logging performed actions (door openings and sensor movements).

overview.csv - CSV file summarizing key characteristics of the dataset and preliminary data analysis.

temphum_logger.ino - Source code to run the Arduino-based data logger with a sampling rate of 5 sec.

Arduino_setup_sketch_v1.png - Circuit diagram of the Arduino-based data logger.