cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/x9hk67h4g6
title: Sleep and Awake

description: Research Hypothesis
The study hypothesizes that the distribution of data across training, validation, and test sets, categorized by awake and sleep states, will be balanced to support robust model performance across physiological conditions. It assumes the training set will dominate, with proportional allocations to validation and test sets, and that awake and sleep categories will reflect their natural or experimental occurrence.

Data Description
The dataset totals 2522 samples, split into training (2090 samples), validation (216 samples), and test (216 samples). It is categorized into awake and sleep states, visualized with the following percentages:

Train - Awake: 46.2%
Train - Sleep: 36.6%
Validation - Sleep: 4.6%
Validation - Awake: 3.97%
Test - Sleep: 4.6%
Test - Awake: 3.97%
Data Collection
The data likely originates from universe.roboflow.com to classify awake and sleep states. The dataset was divided with the training set at 83% (2090/2522), and validation and test sets each at 8.6% (216/2522), following a typical 80-10-10 split adjusted to the sample size.

Notable Findings
Training Set Dominance: The training set, with 46.2% awake and 36.6% sleep, comprises 82.9% of the data, emphasizing model training. The higher awake proportion suggests potential overrepresentation.
Balanced Validation and Test Sets: Both sets are nearly identical (4.6% sleep, 3.97% awake each), ensuring consistent performance evaluation.
Sleep vs. Awake Imbalance: Awake states total 54.14% (46.2% + 3.97% + 3.97%), while sleep states total 45.86% (36.6% + 4.6% + 4.6%), indicating a natural or intentional bias toward awake data.
Interpretation and Use
Model Training: The large training set, with more awake data, suggests optimization for awake-state predictions. Researchers should address potential overfitting by augmenting sleep data or adjusting weights.
Validation and Testing: The equal validation and test set distributions support reliable model tuning and testing, though their small size (8.6% each) may limit edge-case detection.
Implications: The awake-sleep imbalance may necessitate additional sleep data collection for fairness, unless it reflects real-world conditions.
Practical Use: This distribution suits training machine learning models (e.g., sleep stage classification) with the training set, validating hyperparameters with the validation set, and testing with the test set. Rebalancing techniques may be needed if sleep accuracy is critical.
This analysis highlights the dataset’s structure, guiding its use in predictive modeling while addressing potential biases.
DOI: 10.15468/dl.s954s2
title: Occurrence Download

description: A dataset containing 655551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bombus pascuorum"
 ]
}

 The dataset includes 655551 records from 891 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015068-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/x9hk67h4g6.1
title: Sleep and Awake

description: Research Hypothesis
The study hypothesizes that the distribution of data across training, validation, and test sets, categorized by awake and sleep states, will be balanced to support robust model performance across physiological conditions. It assumes the training set will dominate, with proportional allocations to validation and test sets, and that awake and sleep categories will reflect their natural or experimental occurrence.

Data Description
The dataset totals 2522 samples, split into training (2090 samples), validation (216 samples), and test (216 samples). It is categorized into awake and sleep states, visualized with the following percentages:

Train - Awake: 46.2%
Train - Sleep: 36.6%
Validation - Sleep: 4.6%
Validation - Awake: 3.97%
Test - Sleep: 4.6%
Test - Awake: 3.97%
Data Collection
The data likely originates from universe.roboflow.com to classify awake and sleep states. The dataset was divided with the training set at 83% (2090/2522), and validation and test sets each at 8.6% (216/2522), following a typical 80-10-10 split adjusted to the sample size.

Notable Findings
Training Set Dominance: The training set, with 46.2% awake and 36.6% sleep, comprises 82.9% of the data, emphasizing model training. The higher awake proportion suggests potential overrepresentation.
Balanced Validation and Test Sets: Both sets are nearly identical (4.6% sleep, 3.97% awake each), ensuring consistent performance evaluation.
Sleep vs. Awake Imbalance: Awake states total 54.14% (46.2% + 3.97% + 3.97%), while sleep states total 45.86% (36.6% + 4.6% + 4.6%), indicating a natural or intentional bias toward awake data.
Interpretation and Use
Model Training: The large training set, with more awake data, suggests optimization for awake-state predictions. Researchers should address potential overfitting by augmenting sleep data or adjusting weights.
Validation and Testing: The equal validation and test set distributions support reliable model tuning and testing, though their small size (8.6% each) may limit edge-case detection.
Implications: The awake-sleep imbalance may necessitate additional sleep data collection for fairness, unless it reflects real-world conditions.
Practical Use: This distribution suits training machine learning models (e.g., sleep stage classification) with the training set, validating hyperparameters with the validation set, and testing with the test set. Rebalancing techniques may be needed if sleep accuracy is critical.
This analysis highlights the dataset’s structure, guiding its use in predictive modeling while addressing potential biases.
DOI: 10.15468/dl.u5q958
title: Occurrence Download

description: A dataset containing 6925 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-58.85545 -34.26228,-58.85202 -34.26312,-58.8464 -34.25688,-58.83235 -34.26229,-58.82268 -34.26801,-58.82736 -34.27405,-58.8225 -34.27236,-58.82138 -34.27379,-58.82049 -34.27228,-58.81675 -34.27467,-58.81352 -34.26936,-58.80946 -34.26749,-58.80988 -34.26437,-58.80603 -34.26822,-58.80281 -34.26416,-58.79989 -34.26988,-58.79011 -34.27228,-58.78064 -34.27706,-58.78949 -34.26395,-58.79812 -34.2651,-58.79844 -34.25563,-58.80104 -34.25397,-58.81154 -34.25407,-58.8207 -34.24595,-58.81519 -34.24595,-58.82008 -34.23524,-58.81383 -34.23149,-58.82809 -34.22494,-58.82669 -34.22277,-58.82365 -34.22222,-58.82556 -34.22008,-58.82381 -34.21701,-58.82142 -34.21634,-58.81204 -34.22208,-58.8126 -34.21913,-58.82487 -34.21023,-58.83339 -34.21669,-58.85441 -34.25333,-58.87016 -34.22716,-58.86466 -34.18759,-58.85787 -34.18235,-58.87368 -34.18035,-58.88543 -34.18208,-58.89341 -34.18527,-58.90488 -34.2063,-58.91183 -34.19631,-58.92001 -34.1926,-58.90285 -34.21654,-58.89934 -34.21474,-58.89584 -34.22256,-58.89726 -34.22618,-58.89964 -34.2228,-58.90409 -34.22421,-58.89669 -34.23475,-58.90802 -34.24008,-58.90746 -34.24848,-58.89389 -34.27912,-58.89025 -34.27703,-58.88959 -34.28405,-58.88682 -34.28944,-58.88135 -34.29352,-58.87856 -34.29226,-58.87956 -34.29048,-58.8814 -34.2912,-58.88298 -34.28676,-58.88164 -34.28633,-58.88136 -34.28831,-58.87979 -34.28865,-58.87842 -34.28691,-58.88025 -34.28586,-58.87698 -34.28451,-58.88113 -34.28336,-58.87928 -34.28025,-58.87738 -34.28051,-58.87898 -34.27728,-58.87624 -34.27689,-58.87797 -34.27457,-58.8746 -34.27567,-58.87581 -34.2735,-58.87337 -34.27441,-58.87081 -34.27267,-58.86686 -34.27681,-58.86391 -34.27723,-58.86327 -34.2744,-58.86032 -34.27313,-58.863 -34.27054,-58.86423 -34.26575,-58.85839 -34.26198,-58.85545 -34.26228))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, February, March, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2020-2025"
 ]
}

 The dataset includes 6925 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015103-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9rgx2z4s9b
title: Data for: "Wild bats hunt insects faster under lit conditions by integrating acoustic and visual information"

description: We examined whether bats use vision to enhance sensory redundancy when orienting and hunting small insects. 

We equipped 21 wild common noctule bats with high-resolution sensor loggers to measure their echolocation and movements when commuting and foraging in dark and lit environments. 
The experiments took place in Germany during summers of 2022 and 2023.

While commuting, the bats maintained consistent echolocation sampling across light levels. 
When tracking prey in illuminated environments, bats emitted calls less frequently and with higher call levels compared with dark conditions. 

We have uploaded the data in tables and code to plot the main figures of the manuscript.
Commuting flights:
Repository_table 1+3: Wingbeat amplitude (m/s^2) and frequency (Hz) (Table 1) and call interval (seconds) (table 3) per 1 second commuting flights along with categorical explanatory variables Bat ID (21 individuals) and LightorDark (0 = dark, 1 = light).

Repository_table 2: Wingbeat amplitude and frequency per 1 second foraging flights along with categorical explanatory variables Bat ID and LightorDark
Repository_table 4: Call interval (ms), source levels (dB re 20µPa^2s) according to time before prey capture (s) along with categorical explanatory variables Bat ID and LightorDark
Repository_table 5: Wingbeat frequency, amplitude and approach speed of the bat towards to the prey (m/s) along with categorical explanatory variables "Bat ID" and "LightorDark" and "Cluster": kmeans clustering (with 3 clusters) of call data according to the call intervals.

Repository_table 6: 10 % darkest and 10 % lightest prey capture data with call intervals, call levels and time before prey captures according to "LightorDark", "BatID" and "Cluster". 

%% WavfilesExamples:
Folder with examples of prey captures from the 10 % darkest and brightest environments.
DOI: 10.17632/9rgx2z4s9b.5
title: Data for: "Wild bats hunt insects faster under lit conditions by integrating acoustic and visual information"

description: We examined whether bats use vision to enhance sensory redundancy when orienting and hunting small insects. 

We equipped 21 wild common noctule bats with high-resolution sensor loggers to measure their echolocation and movements when commuting and foraging in dark and lit environments. 
The experiments took place in Germany during summers of 2022 and 2023.

While commuting, the bats maintained consistent echolocation sampling across light levels. 
When tracking prey in illuminated environments, bats emitted calls less frequently and with higher call levels compared with dark conditions. 

We have uploaded the data in tables and code to plot the main figures of the manuscript.
Commuting flights:
Repository_table 1+3: Wingbeat amplitude (m/s^2) and frequency (Hz) (Table 1) and call interval (seconds) (table 3) per 1 second commuting flights along with categorical explanatory variables Bat ID (21 individuals) and LightorDark (0 = dark, 1 = light).

Repository_table 2: Wingbeat amplitude and frequency per 1 second foraging flights along with categorical explanatory variables Bat ID and LightorDark
Repository_table 4: Call interval (ms), source levels (dB re 20µPa^2s) according to time before prey capture (s) along with categorical explanatory variables Bat ID and LightorDark
Repository_table 5: Wingbeat frequency, amplitude and approach speed of the bat towards to the prey (m/s) along with categorical explanatory variables "Bat ID" and "LightorDark" and "Cluster": kmeans clustering (with 3 clusters) of call data according to the call intervals.

Repository_table 6: 10 % darkest and 10 % lightest prey capture data with call intervals, call levels and time before prey captures according to "LightorDark", "BatID" and "Cluster". 

%% WavfilesExamples:
Folder with examples of prey captures from the 10 % darkest and brightest environments.
DOI: 10.17632/7p2s7kmxt6.1
title: Toxic effects of trichlorfon on the hepatopancreas of Chinese mitten crab (Eriocheir sinensis): tissue damage, oxidative stress, and Ferroptosis

description: This study investigated the toxic effects and mechanisms of trichlorfon (TCF) on the hepatopancreas of Chinese mitten crabs (Eriocheir sinensis). The acute toxicity test results showed that the 96 h-LC50 of TCF on E. sinensis was 4.853 mg/L. We conducted an 8-day short-term exposure experiment with TCF concentrations of 0, 0.061, 0.121, and 0.243 mg/L. The results showed that TCF exposure induces oxidative stress and causes tissue damage and lesions to the hepatopancreas, including hepatic tubules exhibiting disruption, swelling, necrosis, and an ill-defined structure. Transcriptomics revealed differentially expressed genes (DEGs) at the different concentrations, KEGG, and GO enrichment analysis showed that the DEGs were mainly involved in the bile secretion pathway and iron ion binding terms. GSEA was used for single-function enrichment analysis of cell growth and death. We found that DEGs at TCF concentrations of 0.121 and 0.243 mg/L were significantly enriched in the Ferroptosis pathway. Moreover, the synaptic vesicle cycle, GABAergic synapse, and serotonergic synapse were significantly enriched in DEGs at 0.243 mg/L, which indicated that TCF may pose a potential threat to the nervous system.. The qPCR detection of Ferroptosis-related genes demonstrated that TCF induces hepatopancreatic Ferroptosis in E. sinensis. We hypothesized that lysosomal autophagy, mainly induced through FTH1 and NCOA4 interactions, released ferric ions, thereby indirectly contributing to Ferroptosis. In addition, WGCNA identified two key modules associated with the phenotype, and further identified the core hub genes were MEX3, RBM46, SLC7A5, and TYR. The results of this study can provide insights into the toxic effects of TCF on aquatic crustaceans.
DOI: 10.17632/p6pfts9xd9
title: (Market) power is (political) power! The pressure of declining competition on democracy

description: This file includes supplementary data and analysis code for the replication of the main results presented in the study.
DOI: 10.17632/p6pfts9xd9.1
title: (Market) power is (political) power! The pressure of declining competition on democracy

description: This file includes supplementary data and analysis code for the replication of the main results presented in the study.
DOI: 10.17632/4r4sp2vzn9
title: Effect of Flunitrazepam on Decomposition and Forensically Important Insects Colonization of Pig Carcasses

description: The research investigated the effects of Rohypnol on insect colonization and decomposition patterns in forensic entomology. The hypothesis was that increasing concentrations of Rohypnol would reduce insect abundance and prolong decomposition stages while maintaining consistent insect succession patterns. The study used four domestic pigs (Sus scrofa Linnaeus), with one serving as a control and three receiving different doses of Rohypnol (1 mg/250 ml, 2 mg/250 ml, and 3 mg/250 ml) dissolved in vodka before euthanasia. Carcasses were placed in individual metal cages at the University of Nairobi’s Veterinary Farm, Upper Kabete, and monitored under natural environmental conditions for 120 days.
Data collection focused on insect colonization patterns across five decomposition stages: fresh, bloated, active decay, advanced decay, and dry remains. Insects were sampled daily for the first eight days, twice daily for the next eight days, and once daily until the dry remains stage. Sampling methods included aerial nets, pitfall traps, and forceps from natural body openings and wounds. Maggot samples were preserved in 75% ethanol or reared to adulthood for species identification. Specimens were classified using standard entomological keys, and species abundance was recorded at different time points.
The first dataset includes insect species identification, abundance, and temporal distribution across decomposition stages. Calliphoridae was the most abundant family across all groups, followed by Muscidae and Sarcophagidae. Insect abundance decreased with increasing Rohypnol concentration, but species composition and succession patterns remained consistent. The decomposition process was prolonged in Rohypnol-exposed carcasses, suggesting a potential inhibitory effect of the drug on insect attraction or development.
The second dataset contains meteorological data from the study site, including temperature, humidity, and other environmental variables recorded throughout the study. These data provide context for understanding decomposition rates and insect activity under specific climatic conditions.
This dataset is valuable for forensic entomologists studying drug-induced variations in insect activity and post-mortem interval estimation. Researchers can use it to analyze species-specific decomposition timelines, compare insect succession across environmental conditions, and refine forensic methodologies for estimating time since death in drug-related cases.
DOI: 10.17632/4r4sp2vzn9.2
title: Effect of Flunitrazepam on Decomposition and Forensically Important Insects Colonization of Pig Carcasses

description: The research investigated the effects of Rohypnol on insect colonization and decomposition patterns in forensic entomology. The hypothesis was that increasing concentrations of Rohypnol would reduce insect abundance and prolong decomposition stages while maintaining consistent insect succession patterns. The study used four domestic pigs (Sus scrofa Linnaeus), with one serving as a control and three receiving different doses of Rohypnol (1 mg/250 ml, 2 mg/250 ml, and 3 mg/250 ml) dissolved in vodka before euthanasia. Carcasses were placed in individual metal cages at the University of Nairobi’s Veterinary Farm, Upper Kabete, and monitored under natural environmental conditions for 120 days.
Data collection focused on insect colonization patterns across five decomposition stages: fresh, bloated, active decay, advanced decay, and dry remains. Insects were sampled daily for the first eight days, twice daily for the next eight days, and once daily until the dry remains stage. Sampling methods included aerial nets, pitfall traps, and forceps from natural body openings and wounds. Maggot samples were preserved in 75% ethanol or reared to adulthood for species identification. Specimens were classified using standard entomological keys, and species abundance was recorded at different time points.
The first dataset includes insect species identification, abundance, and temporal distribution across decomposition stages. Calliphoridae was the most abundant family across all groups, followed by Muscidae and Sarcophagidae. Insect abundance decreased with increasing Rohypnol concentration, but species composition and succession patterns remained consistent. The decomposition process was prolonged in Rohypnol-exposed carcasses, suggesting a potential inhibitory effect of the drug on insect attraction or development.
The second dataset contains meteorological data from the study site, including temperature, humidity, and other environmental variables recorded throughout the study. These data provide context for understanding decomposition rates and insect activity under specific climatic conditions.
This dataset is valuable for forensic entomologists studying drug-induced variations in insect activity and post-mortem interval estimation. Researchers can use it to analyze species-specific decomposition timelines, compare insect succession across environmental conditions, and refine forensic methodologies for estimating time since death in drug-related cases.
DOI: 10.15468/dl.5bergm
title: Occurrence Download

description: A dataset listing the 209 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Zimbabwe",
 "TaxonKey is Apidae"
 ]
}

 The dataset's 209 records were derived from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015092-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jumqzx
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myrmelastes saturatus)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 120 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015104-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/s2br2wgfhj
title: Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring During Climbing and Walking Activities with Extracted Features

description: The Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring includes four distinct files, each serving a specific role in analyzing knee function during movement. The Walking Data file holds raw sensor readings collected during regular and brisk walking, helping to assess gait characteristics and joint mobility. The Climbing Data captures sensor output from stair-climbing tasks, offering valuable insights into knee performance under increased physical effort.Data is collected from 150 persons for both the activities for the duration of 2 minutes. To improve model training, the Augmentation Data contains artificially varied versions of the original signals created through methods like time shifting and noise injection. Lastly, the Feature Extraction Data provides a structured collection of calculated metrics such as minimum value, maximum value,mean,variance,skewness and kurtosis, which are useful for evaluating movement patterns and supporting further analysis in rehabilitation contexts. Together, these files form a robust foundation for research and development in wearable health monitoring systems.
DOI: 10.17632/s2br2wgfhj.1
title: Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring During Climbing and Walking Activities with Extracted Features

description: The Sensor-Based Dataset for Knee Joint Mobility and Rehabilitation Monitoring includes four distinct files, each serving a specific role in analyzing knee function during movement. The Walking Data file holds raw sensor readings collected during regular and brisk walking, helping to assess gait characteristics and joint mobility. The Climbing Data captures sensor output from stair-climbing tasks, offering valuable insights into knee performance under increased physical effort.Data is collected from 150 persons for both the activities for the duration of 2 minutes. To improve model training, the Augmentation Data contains artificially varied versions of the original signals created through methods like time shifting and noise injection. Lastly, the Feature Extraction Data provides a structured collection of calculated metrics such as minimum value, maximum value,mean,variance,skewness and kurtosis, which are useful for evaluating movement patterns and supporting further analysis in rehabilitation contexts. Together, these files form a robust foundation for research and development in wearable health monitoring systems.
DOI: 10.15468/dl.62khn5
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.9_1",
 "OccurrenceStatus is Present",
 "PublishingOrg is c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f",
 "TaxonKey is one of (Luronium natans natans, Luronium natans submersus, Luronium natans)"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015108-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.