cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14298278
title: Normal and Anomalous Audio Data Processed with the Wavelet Scattering Transform, Collected During the Operation of FastBlade, a Site for Regenerative Fatigue Testing

description: Dataset Information

The uploaded dataset is the output of the wavelet scattering transform (WST) applied to audio data collected during the operation of FastBlade, a site for regenerative fatigue testing: https://www.fastblade.eng.ed.ac.uk/. 

WST is applied to the audio data to extract features that can be used to detect anomalous operation of the set-up during a fatigue test of a tidal turbine blade. The machine learning model used for the classification task is trained on normal operation data. Subsequently, feature maps from the hidden layers of the deep neural network trained are examined to identify specific features that:



are common to all training samples,

do not appear in the anomalous samples.  


The following files can be found in the dataset:



Normal_Data_Training.npy - normal operation data used for training. 

Normal_Data_Validation.npy - normal operation data used for feature selection. 

Anomalous_Data_Validation.npy - anomalous operation data used for feature selection. 

Normal_Data_Test.npy - normal operation data used for model evaluation. 

Anomalous_Data_Test.npy - anomalous operation data used for model evaluation. 


The full details of the experiment, the machine learning model trained, and the results are presented in the paper published in the "Engineering Applications of Artificial Intelligence" journal, titled: "An audio-based framework for anomaly detection in large-scale structural testing", https://doi.org/10.1016/j.engappai.2024.109889. 

Loading the Dataset

The best way to access the dataset is using Python's numpy library. Example use case: 

Training_Data = np.load('Path_to_file/Normal_Data_Training.npy')

Array Dimensions

Considering array dimensions in the following format - (X, Y, Z):



X is the number of samples,

Y is the height of each sample, 

Z is the width of each sample. 


The data has the following sizes: 

Normal_Data_Training: (1347, 221, 375)Normal_Data_Validation: (1347, 221, 375)Anomalous_Data_Validation: (80, 221, 375)Normal_Data_Test: (1347, 221, 375)Anomalous_Data_Test: (79, 221, 375)
DOI: 10.5281/zenodo.14298279
title: Normal and Anomalous Audio Data Processed with the Wavelet Scattering Transform, Collected During the Operation of FastBlade, a Site for Regenerative Fatigue Testing

description: Dataset Information

The uploaded dataset is the output of the wavelet scattering transform (WST) applied to audio data collected during the operation of FastBlade, a site for regenerative fatigue testing: https://www.fastblade.eng.ed.ac.uk/. 

WST is applied to the audio data to extract features that can be used to detect anomalous operation of the set-up during a fatigue test of a tidal turbine blade. The machine learning model used for the classification task is trained on normal operation data. Subsequently, feature maps from the hidden layers of the deep neural network trained are examined to identify specific features that:



are common to all training samples,

do not appear in the anomalous samples.  


The following files can be found in the dataset:



Normal_Data_Training.npy - normal operation data used for training. 

Normal_Data_Validation.npy - normal operation data used for feature selection. 

Anomalous_Data_Validation.npy - anomalous operation data used for feature selection. 

Normal_Data_Test.npy - normal operation data used for model evaluation. 

Anomalous_Data_Test.npy - anomalous operation data used for model evaluation. 


The full details of the experiment, the machine learning model trained, and the results are presented in the paper published in the "Engineering Applications of Artificial Intelligence" journal, titled: "An audio-based framework for anomaly detection in large-scale structural testing", https://doi.org/10.1016/j.engappai.2024.109889. 

Loading the Dataset

The best way to access the dataset is using Python's numpy library. Example use case: 

Training_Data = np.load('Path_to_file/Normal_Data_Training.npy')

Array Dimensions

Considering array dimensions in the following format - (X, Y, Z):



X is the number of samples,

Y is the height of each sample, 

Z is the width of each sample. 


The data has the following sizes: 

Normal_Data_Training: (1347, 221, 375)Normal_Data_Validation: (1347, 221, 375)Anomalous_Data_Validation: (80, 221, 375)Normal_Data_Test: (1347, 221, 375)Anomalous_Data_Test: (79, 221, 375)
DOI: 10.15468/dl.zp4svn
title: Occurrence Download

description: A dataset containing 555 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chelidonichthys spinosus (McClelland, 1844)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 555 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002038-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60656/556d97de6e6342e9
title: Custom collection of lidar data from Hyytiälä, Kenttärova, and Lindenberg between 1 Aug and 30 Sep 2024
DOI: 10.15468/dl.pp9shz
title: Occurrence Download

description: A dataset containing 305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 66522820-055c-11d8-b84e-b8a03c50a862",
 "TaxonKey is Laudakia caucasia (Eichwald, 1831)"
 ]
}

 The dataset includes 305 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002039-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zb328g68zj.1
title: Replication files - The Leisure Gains from International Trade

description: These files presents the codes and data to replicate the findings of Velasquez, Agustin. 2025. The Leisure Gains from International Trade - Journal of International Economics.
DOI: 10.17632/ycpfwgf2dx.1
title: Whatever it takes to understand a central banker

description: Replication Code for Whatever it takes to undestand a Central Banker

Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes **embeddings**—a language model trained using machine learning techniques—to locate words and documents in a multidimensional vector space. 

To accomplish this, we gather a text corpus that is unparalleled in size and diversity within the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. 

The combination of both allows us to provide high-quality, central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. 

Our findings indicate that these deviations in communication significantly affect market expectations and impact monetary policy actions, substantially reducing the inflation response parameter in an estimated Taylor Rule.
DOI: 10.15468/dl.wpb9u2
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chelidonichthys kumu (Cuvier, 1829)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 42 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002040-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14755179
title: PRO dataset
DOI: 10.5281/zenodo.14755180
title: PRO dataset
DOI: 10.17632/bcn8tgpvwr.1
title: The effect of digital detox using the MinimalistPhone app on the behavior of young users and their emotional experience.

description: Digital detox is a relatively new and rapidly evolving concept that encompasses practices and activities varying in intensity, restriction, duration, and level of disconnection. Given the complexity of comprehensively addressing this topic, our study focused on the concept of minimalistic detox through the use of the MinimalistPhone app. Using a pretest-posttest design with an experimental and control group, we examined the effects of an intervention implemented via the MinimalistPhone application. Our primary goal was to observe specific changes in behavior and smartphone usage, particularly in the areas of habitual behavior, process usage, and social usage, within the context of reducing context-specific stimuli that trigger sequences of undesirable habitual behavior. By assessing levels of experienced positive and negative affect related to smartphone usage, we evaluated the intervention's effect on affective well-being. Additionally, we incorporated objective data on total smartphone screen time as a supplementary measure of smartphone usage change.
The study included 57 participants, divided into an experimental group (N = 29; mean age = 22.8 years; SD = 5.01) and a control group (N = 28; mean age = 25 years; SD = 8.08). Participants in the experimental group used the MinimalistPhone app for 14 days. The app encourages intentional smartphone use by disrupting habits and removing visual cues that trigger unconscious behavior. It increases cognitive effort through additional steps, such as searching an alphabetical list and tracking usage time, making it less intuitive for users reliant on icons.
Data were collected on habitual smartphone usage (van Deursen et al., 2015), process and social smartphone usage (Limayem et al., 2003; van Deursen et al., 2015), affective states (via PANAS; Watson et al., 1988), and objectively measured screen time before and after the intervention. Our findings supported the hypothesis that the MinimalistPhone application reduces habitual behavior and total screen time. However, these results should be interpreted cautiously due to notable pretest differences between the groups. In terms of procedural smartphone use, the experimental group exhibited greater variability in pretest-to-posttest change compared to the control group. Conversely, social smartphone use remained relatively stable in the experimental group, with only small differences between pretest and posttest. The intervention did not significantly affect affective states.
The dataset includes pretest and posttest data on habitual behavior, procedural and social smartphone use, screen time, and positive and negative affect related to smartphone usage, along with a detailed data description.
DOI: 10.48597/6yde-tbdg
title: Measurement of Inorganics in precipitation at Hallahus

description: Measurement of Inorganics in precipitation at Hallahus
DOI: 10.48597/3gqa-ah2k
title: Measurement of PAHs (polycyclic aromatic hydrocarbons) at Hallahus

description: Measurement of PAHs (polycyclic aromatic hydrocarbons) at Hallahus
DOI: 10.17632/zgybt9cgd2.1
title: Design of High Voltage Power Supply System for Application of Electrohydrodynamic Drying

description: The dataset comprising folders #1 to #15 contains the electrical measurements (voltage (V), frequency (f), current (I), & power (P)) procured using Yokogawa oscilloscope unless specified as 'Vmt' or 'DS' that correspond to data procured using in-built-voltmeter and in-built-DSO138, taken at various points (P1 to P16) in the electrical circuit of the high voltage power supply for steady (SS) or transient (TS) states during 'partialON' or 'ON' stages:

#1-P1-P4-V-SS-partialON
#2-P2-P5-V-TS-partialON
#3-P5-P6-V-TS-partialON
#4-P1,P7-P9-V-SS&TS-partialON
#5-P7-P10-V-SS&TS-partialON
#6-P10-P12-V-TS-partialON
#7-P13-P14-V-SS&TS-partialON
#8-P15-P16-V,I-SS-ON
#9-(Vmt&DS)-P6-V,f-SS-partialON&ON
#10-P-SS-ON

The multiple data points, if any, for different settings of the two input parameters, namely input voltage and switching frequency, can be found in sub-folders inside these folders.
DOI: 10.15468/dl.gb7tuf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trigla spinosa McClelland, 1844",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002042-250127130748423/datasets/export for details.
DOI: 10.5281/zenodo.14755190
title: Dataset for Oral Monosodium Glutamate Administration Causes Early Onset of Alzheimer's Disease-Like Pathophysiology in APP/PS1 Mice

description: LTP measures for the electrophysiological measures of the 2019 paper: Fuchsberger T, Yuste R, Martinez-Bellver S, Blanco-Gandia MC, Torres-Cuevas I, Blasco-Serra A, Arango R, Miñarro J, Rodríguez-Arias M, Teruel-Marti V, Lloret A, Viña J. Oral Monosodium Glutamate Administration Causes Early Onset of Alzheimer's Disease-Like Pathophysiology in APP/PS1 Mice. J Alzheimers Dis. 2019;72(3):957-975. doi: 10.3233/JAD-190274. PMID: 31658055.

 

Format:
DOI: 10.5281/zenodo.14755191
title: Dataset for Oral Monosodium Glutamate Administration Causes Early Onset of Alzheimer's Disease-Like Pathophysiology in APP/PS1 Mice

description: LTP measures for the electrophysiological measures of the 2019 paper: Fuchsberger T, Yuste R, Martinez-Bellver S, Blanco-Gandia MC, Torres-Cuevas I, Blasco-Serra A, Arango R, Miñarro J, Rodríguez-Arias M, Teruel-Marti V, Lloret A, Viña J. Oral Monosodium Glutamate Administration Causes Early Onset of Alzheimer's Disease-Like Pathophysiology in APP/PS1 Mice. J Alzheimers Dis. 2019;72(3):957-975. doi: 10.3233/JAD-190274. PMID: 31658055.

 

Format:
DOI: 10.15468/dl.n8zh58
title: Occurrence Download

description: A dataset containing 763 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Survey and Conservation Strategy of the Freshwater Fish Resources in Rivers, Lakes and Ponds of Taiwan"
 ]
}

 The dataset includes 763 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002043-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.bpr.28114241.v1
title: Supplementary information

description: Supplementary information
DOI: 10.60893/figshare.bpr.28114241
title: Supplementary information

description: Supplementary information
DOI: 10.15468/dl.wyfwwm
title: Occurrence Download

description: A dataset containing 818238 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Material sample)",
 "Continent is one of (North America, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Arecaceae"
 ]
}

 The dataset includes 818238 records from 1044 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002027-250127130748423/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/9765318
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5517/ccdc.csd.cc2ft6m3
title: CCDC 2258555: Experimental Crystal Structure Determination

description: Related Article: Xueli Cui, Chunming Chen, Mei Xie, Taotao Zhao, Jianfeng Yi, Weiqiang Sun, Zhuang Xiong, Jinhui Hu, Wing-Leung Wong, Jia-Qiang Wu|2024|Org.Biomol.Chem.|22|7965|doi:10.1039/D4OB01158A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hf245
title: CCDC 2306466: Experimental Crystal Structure Determination

description: Related Article: Qingqing Fan, Qingshuang Li, Hongjian Sun, Xiaoyan Li|2024|Dalton Trans.|53|16261|doi:10.1039/D4DT02057J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hf1cc
title: CCDC 2306442: Experimental Crystal Structure Determination

description: Related Article: Qingqing Fan, Qingshuang Li, Hongjian Sun, Xiaoyan Li|2024|Dalton Trans.|53|16261|doi:10.1039/D4DT02057J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hmsgd
title: CCDC 2312924: Experimental Crystal Structure Determination

description: Related Article: Qingqing Fan, Qingshuang Li, Hongjian Sun, Xiaoyan Li|2024|Dalton Trans.|53|16261|doi:10.1039/D4DT02057J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hjxkj
title: CCDC 2310168: Experimental Crystal Structure Determination

description: Related Article: Qingqing Fan, Qingshuang Li, Hongjian Sun, Xiaoyan Li|2024|Dalton Trans.|53|16261|doi:10.1039/D4DT02057J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kk85l
title: CCDC 2370079: Experimental Crystal Structure Determination

description: Related Article: Debashis Basu, Connly Yan, Neal P. Mankad|2024|Inorg.Chem.|63|19738|doi:10.1021/acs.inorgchem.4c02922
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kk86m
title: CCDC 2370080: Experimental Crystal Structure Determination

description: Related Article: Debashis Basu, Connly Yan, Neal P. Mankad|2024|Inorg.Chem.|63|19738|doi:10.1021/acs.inorgchem.4c02922
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kk87n
title: CCDC 2370081: Experimental Crystal Structure Determination

description: Related Article: Debashis Basu, Connly Yan, Neal P. Mankad|2024|Inorg.Chem.|63|19738|doi:10.1021/acs.inorgchem.4c02922
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kk88p
title: CCDC 2370082: Experimental Crystal Structure Determination

description: Related Article: Debashis Basu, Connly Yan, Neal P. Mankad|2024|Inorg.Chem.|63|19738|doi:10.1021/acs.inorgchem.4c02922
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kk89q
title: CCDC 2370083: Experimental Crystal Structure Determination

description: Related Article: Debashis Basu, Connly Yan, Neal P. Mankad|2024|Inorg.Chem.|63|19738|doi:10.1021/acs.inorgchem.4c02922
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kk8br
title: CCDC 2370084: Experimental Crystal Structure Determination

description: Related Article: Debashis Basu, Connly Yan, Neal P. Mankad|2024|Inorg.Chem.|63|19738|doi:10.1021/acs.inorgchem.4c02922
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kkcfy
title: CCDC 2370180: Experimental Crystal Structure Determination

description: Related Article: Wen-Li Yang, Xin Yan, Miao Wang, Hao Yuan, Yuan-Yuan Tang, Yan Qin, Xian-Jiang Song|2024|Inorg.Chem.Front.|11|6874|doi:10.1039/D4QI01780C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kky69
title: CCDC 2370731: Experimental Crystal Structure Determination

description: Related Article: Antonis Anastassiades, Dimitris I. Alexandropoulos, Christian D. Buch, Stergios Piligkos, Anastasios J. Tasiopoulos|2024|Dalton Trans.|53|16202|doi:10.1039/D4DT02054E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2kky7b
title: CCDC 2370732: Experimental Crystal Structure Determination

description: Related Article: Antonis Anastassiades, Dimitris I. Alexandropoulos, Christian D. Buch, Stergios Piligkos, Anastasios J. Tasiopoulos|2024|Dalton Trans.|53|16202|doi:10.1039/D4DT02054E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.